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Psychoradiology
Psychoradiology
psyrad
Psychoradiology
2634-4416
Oxford University Press

10.1093/psyrad/kkae013
kkae013
Review
AcademicSubjects/MED00385
AcademicSubjects/MED00800
AcademicSubjects/MED00870
AcademicSubjects/SCI01870
AcademicSubjects/SCI02100
Advancements in MR hardware systems and magnetic field control: B0 shimming, RF coils, and gradient techniques for enhancing magnetic resonance imaging and spectroscopy
https://orcid.org/0000-0003-3429-7730
Shang Yun Conceptualization Formal analysis Investigation Methodology Resources Writing - original draft Department of Radiology, Weill Medical College of Cornell University, New York, NY 10065, United States

Simegn Gizeaddis Lamesgin Investigation Methodology Writing - review & editing Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, United States

Gillen Kelly Writing - review & editing Department of Radiology, Weill Medical College of Cornell University, New York, NY 10065, United States

Yang Hsin-Jung Writing - review & editing Department of Biomedical Sciences, Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA 90048, United States

Han Hui Conceptualization Supervision Writing - review & editing Department of Radiology, Weill Medical College of Cornell University, New York, NY 10065, United States

Correspondence: Hui Han, huh4006@med.cornell.edu
Yun Shang and Gizeaddis Lamesgin Simegn contributed equally to this work.

2024
14 8 2024
14 8 2024
4 kkae01317 3 2024
02 7 2024
12 8 2024
10 9 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of West China School of Medicine/West China Hospital (WCSM/WCH) of Sichuan University.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

High magnetic field homogeneity is critical for magnetic resonance imaging (MRI), functional MRI, and magnetic resonance spectroscopy (MRS) applications. B0 inhomogeneity during MR scans is a long-standing problem resulting from magnet imperfections and site conditions, with the main issue being the inhomogeneity across the human body caused by differences in magnetic susceptibilities between tissues, resulting in signal loss, image distortion, and poor spectral resolution. Through a combination of passive and active shim techniques, as well as technological advances employing multi-coil techniques, optimal coil design, motion tracking, and real-time modifications, improved field homogeneity and image quality have been achieved in MRI/MRS. The integration of RF and shim coils brings a high shim efficiency due to the proximity of participants. This technique will potentially be applied to high-density RF coils with a high-density shim array for improved B0 homogeneity. Simultaneous shimming and image encoding can be achieved using multi-coil array, which also enables the development of novel encoding methods using advanced magnetic field control. Field monitoring enables the capture and real-time compensation for dynamic field perturbance beyond the static background inhomogeneity. These advancements have the potential to better use the scanner performance to enhance diagnostic capabilities and broaden applications of MRI/MRS in a variety of clinical and research settings. The purpose of this paper is to provide an overview of the latest advances in B0 magnetic field shimming and magnetic field control techniques as well as MR hardware, and to emphasize their significance and potential impact on improving the data quality of MRI/MRS.

B0 shim
fMRI
passive shim
spherical harmonic shim
multi-coil shim
iPRES
shim-RF coil
gradient
National Institute of Neurological Disorders and Stroke 10.13039/100000065 National Institutes of Health 10.13039/100000002 R01NS121544 R01HL156818 AG032306 SBIR 10.13039/100006370 R43NS120795
==== Body
pmcIntroduction

Magnetic resonance imaging (MRI) is a powerful diagnostic tool, offering non-invasive visualization of anatomical structures and providing valuable insights into physiological processes throughout the entire body (Bogaert et al., 2012; Choi and Jezzard, 2021; Major and Anderson, 2019; Roth and Deshmukh, 2016). Diffusion tensor imaging (DTI), functional MRI (fMRI), and magnetic resonance spectroscopy (MRS) techniques play crucial roles in the research of psychiatry by providing details of the brain's structure, function, and metabolism.

DTI is commonly used to look into the neuro tissue microstructure inside the brain. It measures the direction and magnitude of water molecule diffusion, which can reveal the orientation and paths of nerve fibers to understand the integrity of the white matter tracts in the brain (Alexander et al., 2007). The potential difference in diffusivity between healthy participants and patients with psychiatric disorders can provide critical information and clues from structure changes for disease diagnosis, subtyping, and assessment after treatment (Assaf and Pasternak, 2008) as well as identify the causes of mental diseases and disorders (Podwalski et al., 2021).

fMRI is widely used in the study of psychiatric disorders for determining neuro activation patterns and effective brain connectivity with resting-state (Van Den Heuvel and Pol, 2010) or task-based stimulation by obtaining the blood-oxygen-level-dependent (BOLD) signal associated with neural activity (Glover, 2011). Besides the BOLD signal, researchers of fMRI studies measure cerebral blood flow with the use of arterial spin labeling technique to investigate, for example, vascular remodeling in brain disorders (Buxton, 2005) as well as measure cerebral blood volume using vascular-space occupancy to indicate microvascular abnormalities and cortical layer-dependent activity (Beckett et al., 2020; Hua et al., 2017, 2011; Lu and van Zijl, 2012). Furthermore, fMRI can be used to assess depression in clinical trials both before and after the treatment (Kotoula et al., 2023) as well as the functional changes in regional brain for schizophrenia patients (Huang et al., 2010).

MRS is often used to quantitatively measure the concentration of metabolites in a specific region of the brain or other tissues throughout the body (Cox, 1996; De Graaf, 2019; Tognarelli et al., 2015). The change in metabolite concentration associated with psychiatric disorders can help us understand the biochemical change along the neurotransmission pathways (Rothman et al., 2011) as well as the pathological features and causes (Steel et al., 2001). MRS can be used to diagnose brain tumors (Howe et al., 2003; Preul et al., 1996) and to understand neurodegenerative diseases such as Alzheimer's disease (Mandal, 2007) and multiple sclerosis (Swanberg et al., 2019).

Although MR has been proven to be a powerful tool in these applications, the demand for a more powerful magnetic field control system is desirable for more detailed microstructure and functional sensitivity in brain studies (Feinberg et al., 2023). For instance, a higher gradient strength and slew rate are desirable in DTI and fMRI to increase spatial resolution by achieving a higher bandwidth within the same field of view and a shorter echo spacing (Huang et al., 2021). Such superior gradient performance can be achieved using a novel gradient coil design (Davids et al., 2023). The gradient field is used when spatial encoding is required, while the main magnetic field (B0) provides the fields for magnetization and the off-resonance information of each proton, which affects all aspects of imaging. The success of MRI critically depends on the ability to achieve high magnetic field homogeneity within the imaging volume due to the reasons for their sensitivities to adversely affect data quality. The presence of inhomogeneities in the B0 magnetic field can result in distorted images, signal loss, blurring, and limited spatial resolution, which could compromise the accuracy and reliability of MRI-based clinical and research exams (Atalay et al., 2001; Ferreira et al., 2013; Jezzard and Balaban, 1995; Jezzard and Clare, 1999; Le Bihan et al., 2006; Serai, 2022; Tournier et al., 2011; Wieben et al., 2008), especially with the adoption of echo planar imaging (EPI) sequences in DTI and fMRI scans. Specifically, having a poor B0 homogeneity in MRS can result in a wider linewidth, a lower signal-to-noise ratio (SNR), and overlapped peaks of metabolites, resulting in a lower degree of accuracy in concentration quantification (De Graaf, 2019; Juchem et al., 2021; Juchem and de Graaf, 2017). The best remedy to mitigate those issues is through the physical homogenization procedure, referred to as B0 shimming. By doing so, it is important to understand different types of field inhomogeneity found in MR scans, as well as the associated magnetic field control techniques used to compensate for them. This review aims to provide an overview of recent advances in B0 magnetic field shimming techniques, emphasizing their significance and potential impact on MRI/MRS, as well as introduce other advanced B0 magnetic field control techniques.

The main B0 magnetic field is generated by a magnet, which is typically shimmed to create an approximately homogeneous imaging volume of about one part per million (ppm) peak to peak for acceptable fat suppression in clinical applications (Foo et al., 2018). Meanwhile, human tissues can be magnetized into different strengths by the main B0, generating local fields that are superimposed over the magnet imperfections (Schenck, 1996). In addition to participant-specific anatomical structure, local fields are most severe and present critical challenges in areas where the susceptibility difference is high, such as regions close to tissue–air interfaces, leading to signal loss and geometric distortions. These challenging regions are spread throughout the whole body, including the inferior frontal cortex and temporal brain regions (Smith et al., 2013), the spinal cord (Vannesjo et al., 2018), the heart (Shang et al., 2022; Wieben et al., 2008), the liver (Roberts et al., 2021), the breast (Boer et al., 2014), the kidneys (Gilani et al., 2023), and the prostate (Caglic et al., 2017). At 3 T, these local B0 offsets can reach hundreds of Hertz, which are much higher than those magnet imperfections. Therefore, they dominate the B0 field imperfection during scanning. In cardiovascular MRI, the workhorse balanced steady-state free procession sequence has the highest sensitivity to the B0 off-resonance, leading to signal loss of dark band artifacts (Schär et al., 2004; Wieben et al., 2008). This sequence can show more dark bands and signal variation artifacts with blood flow in the presence of B0 inhomogeneity (Ferreira et al., 2013). B0 inhomogeneity leads to a faster T2* decay so that the gradient echo sequence can have a significant signal attenuation in myocardium at ultra-high field 7 T (Meloni et al., 2014), as well as failure in fat suppression for musculoskeletal scans (Del Grande et al., 2014). There are severe susceptibility artifacts that can be caused by air pockets near the imaging organ (Caglic et al., 2017; Ferreira et al., 2013). Other patient-specific factors can further worsen local field distortions, such as cardiac implants (Ferreira et al., 2013; Sasaki et al., 2011) and orthopedic metal implants (Boschheidgen et al., 2021; Koch et al., 2018). Enhancing the B0 inhomogeneity can mitigate or reduce these artifacts for improved data quality. It may be beneficial for precision diagnosis and personalized treatment (Lambin et al., 2017).

The goal of B0 shim methods is to spatially improve the uniformity of the imaging volume by creating and superimposing a secondary B0 correction field over the original field distortion. Generally, magnetic B0 shim technologies involve passive shim and active shim. As one of the earliest MRI technologies, passive shim compensates for static field perturbations by placing fixed iron pieces strategically on the magnet (Belov et al., 1995; Dorri et al., 1993; Hoult and Lee, 1985). The iron pieces, often in the form of small plates, are magnetized inside the magnet bore, creating additional magnetic fields that counteract deviations in the main magnetic field. The distribution of iron pieces is typically calculated uniquely for each specific scanner and it is stationary without the possibility to change during the scan. Owing to variations in the field distribution associated with participant-specific anatomies, in vivo shimming relies on the active shim technology that uses additional shim coils that can be independently controlled to actively counteract magnetic field perturbations (Gruetter, 1993; Gruetter and Boesch, 1992; Roméo and Hoult, 1984; Wen-Tao et al., 2010). This approach enables participant-specific compensation for both static and dynamic sources of field variations, providing enhanced field homogeneity throughout the imaging session.

Spherical harmonic (SH) based B0 shimming is the most popular active shim method configured in the scanner. This shim technique uses SHs as basis functions to mathematically represent magnetic field distributions. Each SH field distribution is generated by a specific SH shim coil installed in the scanner, and the field distortions are minimized by manipulating their shim currents corresponding to harmonic coefficients (Gruetter, 1993). However, shim coils are limited in space and cost and, therefore, are typically limited to the second order so that strong local field distortions cannot be corrected. The limitation of SH shim capability has motivated the development of multi-coil shim systems, which consist of multiple independent shim coils strategically situated around the volume of interest (Aghaeifar, Zhou, et al., 2020; Juchem, Nixon, et al., 2010; Meneses et al., 2022). Individual multi-coil channels can be controlled independently allowing for fine-tuning the magnetic field homogeneity, leading to superior shim results compared to low-order SH shim coils (Juchem, Brown, et al., 2011). The field shaping using multiple coil loops has also been applied to gradient coil design whereby coil elements were arranged in a regular grid pattern or not to provide flexibility in generating encoding fields with simultaneous spatially localized shim capabilities in MRI (Juchem et al., 2020; Littin et al., 2018; Theilenberg et al., 2023).

In previous proof-of-concept studies, multi-coil shim loops have been demonstrated to offer superior shim performance, particularly in the orbitofrontal cortex, which exhibits strong local field distortions (Juchem, Rudrapatna, et al., 2015). Given that the shim coils and radiofrequency (RF) coils are separated for decoupling, with shim coils either inside or outside RF coils, it is unlikely that both will achieve the highest efficiency (i.e. close to the participant). To maximize the sensitivities for both of them, the integrated parallel reception, excitation, and shimming (iPRES) was introduced to integrate both RF and shim coils into one single array (Han et al., 2013; Stockmann, Witzel, et al., 2016; Truong et al., 2014). The demand for a higher SNR and imaging acceleration motivates the design of high-density coil arrays (Gruber et al., 2023; Uğurbil et al., 2019; Wiggins et al., 2009), from which the increased number of RF loops can be utilized to potentially increase the efficiency and precision of multi-coil shim approaches.

Shim results are influenced not only by the hardware shim capabilities and the shim algorithm, but also by the target field distribution used to calculate the optimized shim currents. Obtaining a map of magnetic field inhomogeneities relies on field mapping, which involves acquiring a series of images at different echo times, usually based on a multi-echo gradient echo sequence. An accurate in vivo B0 map and selection of the desired shimming region are crucial to optimal field correction (Boer et al., 2020). Respiratory motions can cause artifacts and phase errors in B0 maps, which typically require breath-hold acquisition to minimize these effects, particularly in the organs of the heart (Shang et al., 2023) and liver (Matakos et al., 2017). Cardiac triggering is also required for cardiac phase-specific B0 acquisition. The static and dynamic factors responsible for B0 variations in MRI experiments have led to the development of methods for B0 magnetic field shimming to address the imaging issues posed by such field inhomogeneities.

In the scanner, B0 shimming is most commonly performed by using a shim box covering the imaging region (Hezel et al., 2012). The field inhomogeneity is optimized over the entire covered region, referred to as global shimming. A shim box might include the B0 information of the region we are not interested in, which can affect the calculation of the optimal shim currents. It is preferable to have an advanced segmentation algorithm (Wang, Chen, et al., 2022) to mask the region(s) of interest (ROI) for improved global B0 shimming (Li et al., 2024). Because of cases in MR acquisition using multiple slices, field distribution within smaller ROI tends to be dominated by low-order SH terms, a method known as spatially dynamic shimming that optimizes field homogeneity within the ROI of each individual slice, resulting in better overall field homogeneity (Juchem, Nixon, Diduch, et al., 2010). Shim currents are dynamically updated when a k-space line acquisition is switched from one slice to another.

Physiological motions, such as respiration and cardiac pulsation (Boer et al., 2012; Peters et al., 2009; Sloots et al., 2020; Van de Moortele et al., 2002), induce temporal variations in the magnetic field in the brain. Additionally, respiratory and cardiac motion-induced anatomical changes cause B0 variation in the heart (Kubach et al., 2009; Shang et al., 2023). Respiratory motion has been observed to cause greater field variations in the heart compared to those caused by cardiac motion (Huang et al., 2023). Meanwhile, respiration could also lead to B0 variation in the brain (Van de Moortele et al., 2002; van Gelderen et al., 2007) and spinal cord (Vannesjo et al., 2018), resulting in additional frequency shifts in MRS (Juchem et al., 2021). To resolve these issues, fast field mapping techniques used for navigators (Simegn et al., 2019) and training a field variation model (van Gelderen et al., 2007) have enabled the update of shim currents during different respiratory phases to improve the stability of MR echoes and minimize the resultant artifacts. More advanced shimming approaches would require additional hardware and software development.

Both spatially and temporally dynamic shimming require updating of the shim currents during the scan, which can induce eddy currents in conductors adjacent to the shim coils, including the surface of the magnet's warm bore and other conductor surfaces such as RF shielding, leading to temporally varying gradient fields in addition to the encoding fields. The use of SH coils for dynamic shimming can result in significant eddy currents due to their proximity to those conductors (Juchem, Nixon, Diduch, et al., 2010), causing temporally varying distortions in the background field. Even though scanners typically have compensation techniques such as pre-emphasis to minimize eddy currents (Jehenson et al., 1990), residual eddy currents still exist, especially when strong gradient pulses in diffusion or EPI sequences are employed (Wilm et al., 2015). Since the localized shim coils are positioned at a far distance from the magnet bore and other large conductor surfaces, the eddy current issue will be minimized when using this type of hardware component on both spatially dynamic and real-time shimming.

Besides the existing high-order eddy currents and residual eddy currents after pre-emphasis, there are other unpredictable sources of field disturbances to B0, such as the instability of field generation components caused by system warm-up and external sources, such as elevators located near the magnet room. Owing to the temporal variation and unpredictability of those fields, real-time field monitoring techniques, referred to as field cameras (Dietrich et al., 2016; Wilm et al., 2015), have been developed to capture such fields and correct them with advanced reconstructions.

Advanced B0 magnetic field control and shimming techniques are essential for achieving high-quality MR-acquired data. The ability to minimize magnetic field inhomogeneities and variations has an impact on image quality, spatial resolution, and spectral fidelity. If researchers and practitioners are aware of the latest developments, they can apply these techniques to improve data quality and advance medical imaging for improved clinical outcomes.

Passive shim

Passive shim is an established technique used in MRI to address static magnetic field inhomogeneities by employing a fixed shim structure strategically positioned around the magnet (Dorri et al., 1993). Passive shim involves the use of various ferromagnetic materials (e.g. silicon steels) to minimize field distortion and compensate for the field inhomogeneity caused by the imperfect manufacture of the magnet and site conditions such as steel beams in the building structure. Iron pieces are strategically positioned around the MRI magnet to target specific ROI and correct field distortions within imaging volume (Belov et al., 1995; Kong et al., 2016; Wang, Qu, et al., 2023). The placement of these shim pieces is determined based on a combination of field measurements, modeling, and optimization techniques to achieve the best possible field homogeneity (Dorri et al., 1993; Noguchi et al., 2014; Sanchez et al., 2006; Ungersma et al., 2004). The passive shim materials have the disadvantage of being temperature-sensitive, so when those shim pieces heat up due to eddy currents and warm up on the bore, the B0 field drift may occur (Jezzard, 2006). Studies have demonstrated the effectiveness of passive shimming in compensating for the field inhomogeneity in the human body, such as the use of diamagnetic intra-oral shims to reduce field artifacts in the prefrontal cortex (Cusack et al., 2005; Wilson, Jenkinson, and Jezzard, 2002). Similarly, external diamagnetic and paramagnetic passive shims have been utilized for whole-brain shimming in mice (Koch et al., 2006). Another form of passive shim is achieved by matching the magnetic properties of a human head holder, typically made of pyrolytic graphite foam, to the diamagnetic properties of the head and neck that it supports (Lee et al., 2015). Additionally, ferromagnetic substances have been utilized for passive shim as a cost-effective method of generating strong magnetic fields. These materials were applied in a participant-specific manner to enhance magnetic field homogeneity (Jesmanowicz et al., 2001). Figure 1 illustrates the overview of the passive shim technique and representative applications.

Figure 1: Passive shim methods and their applications. (A) An exemplary passive shim system on a 9.4 T magnet (reprinted from Wang, Qu, et al., 2023, with the permission of AIP Publishing) with the shim structure installed at the inner side of the magnet bore. There are 24 shim trays with evenly distributed shim pockets that accommodate shim irons to compensate for field inhomogeneities. (B) The optimal target magnetic field method can be used to derive the distribution of shim irons within each shim tray (reprinted from Kong et al., 2016, with permission from Elsevier). (C) Photograph of the pyrolytic graphite and plastic mouth mold for mouth shim. The shallow end of the shim is placed near the front of the roof of the mouth and the generated fields are used to compensate for the inhomogeneity in the brain especially in the frontal cortex region (reprinted from Cusack et al., 2005, with permission from Elsevier). (D) Sample-specific passive shim assembly mounted onto the head coil composed of both diamagnetic (bismuth) and paramagnetic (zirconium) materials (reprinted from Koch et al., 2006, with permission from Elsevier). Experimental results on animals demonstrated improved shim results in the brain. (E) Design and implemental fabrication of local in vivo shimming using the passive shim technique with four blocks of Niobium in front of the brain. Those shim materials were assembled and mounted on top of the head coil using a customized structure (reprinted from Yang et al., 2011, with permission from Elsevier).

While passive shim is widely used in MRI systems, its primary purpose is to compensate for the static B0 inhomogeneity of the magnet. B0 inhomogeneities during a scan, however, can also be caused by magnetizations of different body parts. These are the primary cause of B0 inhomogeneities when participants are inside the magnet. The distributions of those B0 inhomogeneities within the imaging field of view are participant-specific due to various body sizes, shapes, anatomical structures, and physiological motions, whereas passive shim is limited in its ability to adjust the shim field strength and polarity. To perform a reliable shim for each participant, flexibility in shaping shim fields is essential, which is the major reason for the use of active shim in MR scanners.

Active shim

Active shim is an advanced technique used in MRI to actively compensate for both static and dynamic magnetic field inhomogeneities by employing adjustable shim coils (Gruetter, 1993; Gruetter and Boesch, 1992; Jezzard and Balaban, 1995; Schneider and Glover, 1991; Zaitsev et al., 2017). Roméo and Hoult proposed an active shim coil design method based on the field modeling of a single circular loop (Roméo and Hoult, 1984). Different locations of circular loops and angles of the arcs, as well as their current flow orientations, lead to cancellations of SH terms, leaving the primary term associated with the specific-term SH coil. In addition to field modeling, a numerical optimization method has been used to design superconducting shim coils in a high-field scanner (Qu et al., 2024). The target field-based stream function optimization (Peeren, 2003; Schenck et al., 1987; Turner, 1986, 1993) has been widely used in the design of gradient coils (Poole and Bowtell, 2007; Wang, Wang, et al., 2023; Winkler, Schmitt, et al., 2018) as well as in the design of high-order SH shim coils (Niu et al., 2022). Figure 2 illustrates examples of active shim coil designs. Active shim approaches include the use of SH coils and multi-coil array.

Figure 2: Active shim coils design and implementation. (A) SH coil design method using the field modeling a single loop. Different locations of circular loops and angles of the arcs as well as their current flow orientations lead to cancellations of SH terms, leaving the primary term associated with the specific-term SH coil according to (Roméo and Hoult, 1984). (B) Advanced SH coil design using stream function optimization and exemplary geometries of gradient coils and their fabrication for ultra-high field MRI (reprinted from Winkler, Schmitt, et al., 2018, with permission from Elsevier). Shielding coils (blue) are typically included outside of the primary coils (red) to minimize eddy currents. (C) Second-order and exemplary third-order SH shim coils designed by stream function optimization method using an open-source software (Mäkinen et al., 2020; Zetter et al., 2020). The red and blue colors of the wires represent the opposite directions of current flow.

Spherical harmonic shim

SH shim is based on the theory that magnetic field distribution is determined by infinite SH functions derived from the Laplace equation solutions (Jackson, 1999). Even though the space is no longer considered empty when studying objects with MRI and MRS, the SH framework continues to be widely used due to its practicality and widespread application in chemistry and physics. SH functions are orthogonal and organized into orders N, with each order consisting of 2N + 1 terms. The three first-order terms represent linear field gradients, which are typically the same coils used for spatial encoding. With higher-order terms, shapes have greater symmetry and complexity, allowing for the modeling of increasingly complex fields (Juchem and de Graaf, 2017). Generally, the magnetic field resulting from higher SH orders is more likely to resemble the spatial characteristics of the specific B0 distortions, thus facilitating their subsequent compensation through B0 shimming. The number of basis functions available for modeling the field distortion determines the degree of flexibility with which the magnetic field can be shaped. As a result, including higher orders improves the expected outcome of shimming by enhancing the quality of magnetic field adjustment. Figure 3 shows the SH field distributions from first to third order in a sphere geometry, as well as an example of a shim analysis on the B0 map of a human brain from first to ninth order. Using a higher-order SH shim, the inhomogeneity decreases with a smaller spot size of an inhomogeneous field in the inferior frontal cortex, indicating that the shim field generated is closer to the target field.

Figure 3: (A) Field distributions of SH functions in the human brain and their application in compensating for field inhomogeneity. All SH terms from first to third order with their field distributions in sphere geometry using 3D visualizations and three orthogonal 2D slices measured across the sphere center (i.e. zero point). Within the sphere, field distributions are symmetrical, and patterns become more complex as the order increases. (B) SH fields were applied to an example of the human brain's field distribution for the analysis of shims from the first to the ninth order (the original brain B0 map was obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative database). A strong distribution of local fields was observed in the orbitofrontal cortex. Using a higher order of SH shim, the spot size of an inhomogeneous field becomes smaller and smaller, indicating that the shim field generated is closer to the target field.

SH shim coils are the most common shim technique and the standard components in an MR scanner, which typically has shim coils up to second or third order due to practical and cost reasons. To achieve more localized shimming, researchers have utilized a shim insert for the brain with SH shim up to sixth order at 7 T (Pan et al., 2012) and at 9.4 T (Chang et al., 2018). Applying high-order SH shimming capability enhances the field homogeneity in the frontal cortex, leading to an improved overall homogeneity across the brain. Specifically, B0 inhomogeneity was reduced by 55% with the slice through the region of subcortical nuclei compared to the results from second-order scanner shim, as well as the fields within specific voxels for spectroscopy (Pan et al., 2012). Such a high-order SH shim system was also applied to the EPI sequence of fMRI, resulting in increased BOLD sensitivity (Kim et al., 2017) and reduced geometric distortion (Hetherington et al., 2021). Shimming ensures that fMRI data are clearer and more reliable, allowing researchers to detect subtle brain activity with greater accuracy and confidence.

Using the SH system, local B0 shimming in fMRI offers improved magnetic field homogeneity in specific regions to increase local BOLD sensitivity (Balteau et al., 2010; Wilson, Jenkinson, De Araujo, et al., 2002). Recent studies highlight the remarkable potential of advanced shimming techniques and demonstrated the effectiveness of dynamic shimming in spinal cord, from which the signal density of T2* weighted imaging in the spinal cord was increased by up to 20% (Finsterbusch et al., 2012; Kaptan et al., 2022). These findings underscore the valuable contributions of advanced shimming to fMRI research and diagnostics, with the promise of providing more accurate and impactful outcomes.

SH shim can improve spectral resolution and reduce image distortions caused by field inhomogeneities. By minimizing field variations, SH shimming enables better separation and identification of spectral peaks, thereby enhancing the quantification and characterization of tissue metabolites (Juchem et al., 2021). Despite this, SH shimming has many limitations including reduced effectiveness without higher-order shims, reduced sensitivity due to distance from the body, induced eddy currents with dynamic updating (Juchem, Nixon, Diduch, et al., 2010), and potential interaction with the gradient system via mechanical vibrations (Boulant et al., 2024). To address these challenges and better utilize the capabilities of MRI, researchers are exploring alternative shimming techniques.

Multi-coil shim

Multi-coil shim is an active shim technique that uses multiple independent shim coils strategically positioned around the imaging volume to improve the homogeneity of the magnetic field within the target ROI (Juchem, Nixon, et al., 2011, 2010). Optimizing the geometry design in multi-coil is typically performed using various advanced algorithms, such as averaging the optimization results (Juchem, Nixon, et al., 2010), a genetic algorithm (Stockmann, Guerin, et al., 2016), a stream-function based method (Jia et al., 2020; Meneses et al., 2022) and a customized nonlinear constrained optimization procedure (Aghaeifar, Zhou, et al., 2020). Based on the target structure surface and constraints on the number of channels, these algorithms were designed to achieve the best coil efficiency and shim results using a number of B0 maps across different participants. For participant-specific B0 shimming during the MR scan, it is typical to obtain B0 information within the imaging space to determine the shim currents for each multi-coil element for minimizing the field inhomogeneities. Optimization techniques such as least-square fitting or gradient-based methods are commonly employed to determine the optimal set of currents for achieving the best field homogeneity.

Multi-coil shimming can account for higher-order field distortions beyond the low-order terms typically addressed by SH shimming techniques (Juchem, Nixon, et al., 2011). This approach has been successfully used both statically and dynamically for B0 shimming in rodents (Juchem, Brown, et al., 2011; Juchem et al., 2014) and humans (Juchem, Nixon, et al., 2011), and is referred to as the dynamic multi-coil technique (DYNAMITE). Figure 4 shows the field modeling, exemplary implementation of a multi-coil array, and its application in the human brain. Superior shim capability in the multi-coil array can be achieved compared with the conventional SH shim up to third order at 7 T (Juchem and de Graaf, 2017; Juchem, Rudrapatna, et al., 2015) with significantly improved B0 inhomogeneity in the brain across participants, reduced geometric distortion, and signal loss in EPI sequence. Research has demonstrated the efficacy of multi-coil shimming for a better presentation of BOLD with a larger activated cluster (Aghaeifar, Bause, et al., 2020; Gao et al., 2020). The DYNAMITE technique has also been demonstrated to be capable of generating image encoding fields in both miniaturized and human-size setups (Juchem, Nahhass, et al., 2015; Juchem et al., 2020). Although the multi-coil shim approach shows its strong shim capability, the implementation of multi-coil shimming required intensive work on the fabrication and setup of the entire hardware and software. Meanwhile, the geometry design of the multi-coil array is limited by the available space within magnet bore for different applications.

Figure 4: Dynamic multi-coil shim technique (DYNAMITE) and its applications in the human brain for minimizing B0 inhomogeneity and reducing image distortions. (A) Magnetic field modeling of a single circular loop and the corresponding field distribution (reprinted from Juchem, Nixon, et al., 2010, with permission from John Wiley & Sons, Inc.). (B) Simulation and experimental setup of a multi-coil array for the human brain at 7 T (reprinted from Juchem, Nixon, et al., 2011, with permission from Elsevier). Strong local B0 inhomogeneity in the frontal cortex and temporal lobes are significantly reduced with the use of DYNAMITE in both (C) 3D visualizations and (D) coronal orientations at 7 T (reprinted from Juchem and de Graaf, 2017; Juchem, Rudrapatna, et al., 2015, with permission from Elsevier). Improved B0 inhomogeneity can significantly reduce the signal loss and image distortion in EPI sequence, suggesting potentially better outcomes in fMRI study.

Shim at ultra-high field strength

MRI scanners with ultra-high field strength (≥7 T) are being developed for human study to achieve a higher SNR and spatial resolution in the resulting images and spectrum. Inhomogeneities within ultra-high field magnets scale with increasing magnet field strength in terms of Hz. The passive shim is a common method of compensating for magnetic field variations. Nevertheless, using passive shims only in ultra-high field systems is not practical. According to the B-H curve of shim irons, those irons are usually saturated, so an increase in magnetic strength will not greatly affect of the shim field produced by each iron sheet. As a result, if only passive shimming is considered, more shimming irons will be required. The 1.5 T magnets typically require 10 kg, while 3 T magnets require 20–50 kg (Parizh and Stautner, 2022). The shimming irons are also sensitive to temperature, which can be affected by induced eddy currents on them and the heating of the gradient coil and magnet bore. The field drift can occur due to temperature fluctuation within shim irons (Wang, Wang, et al., 2022). Additionally, there will be a significant amount of irons, which will take up a lot of space and require an extra secure mechanical housing with increased Lorentz forces (Warner, 2016). Those challenges are similar for human scanners at 7 T (Wu et al., 2024), 9.4 T (Vaughan et al., 2006), 10.5 T (He et al., 2020), 11.7 T (Quettier et al., 2023), and potential 14 T (Budinger and Bird, 2018; Ladd et al., 2023).

Therefore, ultra-high field magnets are typically equipped with superconducting shim coils as well as passive shims. The superconducting shim coils are low-order SH coils wound with superconducting wires. The design of those shim coils includes first- and second-order SH terms, partial third SH orders (Qu et al., 2024), and some higher-order zonal terms (Zn) (Wu et al., 2024). By using those superconducting shim coils, the primary low-order SH terms can be eliminated, and passive shims can compensate for the remaining higher-order SH terms (Parizh and Stautner, 2022). Using both shimming techniques in a magnet can enhance field uniformity and stability and reduce the amount of shimming irons. However, including the superconducting shim coils makes the magnet more expensive and more complex to construct.

Regarding the shimming for subject-induced field variations at ultra-high field scanners, resistive SH shim coils typically built into the gradient coil are used for active shimming for SH orders up to the third. As a result of increased susceptibility effects at ultra-high field strengths, B0 inhomogeneities at tissue–air interfaces also increase, resulting in insufficient low-order SH shimming. Multi-coil shim technique has shown significantly superior shim capability in studies of the human brain at 7 T (Juchem, Rudrapatna, et al., 2015), 9.4 T (Aghaeifar, Zhou, et al., 2020), and animal scans at 11.7 T (Juchem et al., 2014). It will be a potential option for participant-specific shim for whole-body scanners at 10.5 and 11.7 T. As a result of the increased inhomogeneities within participants at those B0 strengths, there is a need for more currents and wire turns, potentially requiring advanced thermal management. Previous research has demonstrated mechanical vibrations in the local coils, affecting the output current stability and requiring epoxy curing for the entire assembly (Aghaeifar, Zhou, et al., 2020).

Shim-RF coil

Multi-coil shim arrays offer additional benefits due to their inherent similarity to RF coil arrays used for receiving and transmitting signals. As such, research groups have been working on developing shim-RF coils with the integration of the RF and the shim loops into a single coil array (Darnell et al., 2017; Gao et al., 2020; Han et al., 2013; Stockmann, Witzel, et al., 2016; Truong et al., 2014; Zhou et al., 2020). The primary purpose of this combined device is to maximize the efficiency and capabilities of both RF and B0 shim coils, while minimizing negative effects on their performance (Winkler, Warr, et al., 2018). This technique has the potential for highly efficient packaging, as it allows for a tightly fitting coil array to save the utilization of space. This technique still utilizes the multi-coil shim technique with the characteristics of its superior shim capability. With the proximity of the shim coils to the participants, a higher shim field strength can be achieved to compensate for strong local B0 inhomogeneities located in the deep organs within human body, such as the heart and liver (Wang, Serry, et al., 2022; Yang et al., 2020).

One of the major techniques used in coil design is the shared conductor between RF and shim coils with the use of parallel inductors (Han et al. 2018,). It allows DC shim currents to flow through the RF loops while minimizing the impact on the RF performance, referred to as iPRES (Han et al., 2013; Truong et al., 2014) or AC/DC (Stockmann, Witzel, et al., 2016) coils. The efficacy of the iPRES shimming has demonstrated a significant reduction in distortion in EPI sequence (Han et al., 2013). In vivo imaging experiments have demonstrated that utilizing spatially dynamic shimming with iPRES can further reduce B0 inhomogeneity in the brain after second-order SH shimming. With whole-brain shimming, ~34% inhomogeneity can be further reduced, while slice-wise dynamic shimming can reduce inhomogeneity by ~60%, as well as reduced geometric distortion in EPI images (Truong et al., 2014). The iPRES approach has been used in the fabrication of shim-RF coils at 7 T for improved MRI in the human brain (Stockmann et al., 2022). The efficacy of this technology has also been tested in abdomen (Darnell et al., 2017) and spinal cord (Cuthbertson et al., 2022), in which the iPRES coil further includes wireless data transfer for improved coil setup efficiency (Darnell et al., 2019). One of the limitations of the iPRES approach is the coupling between the shim and RF loops, leading to the degradation of the Q-ratio and an average of 13% SNR loss (Stockmann, Witzel, et al., 2016). Additionally, the geometry of RF loops is optimized for imaging performance, but may not be optimized for shimming, resulting in a loss of shimming flexibility. Recent research has proposed to place the shim coils orthogonal to the RF coil plane to minimize their coupling and shows efficacy for brain shimming (Zhou et al., 2020), although this will use significantly more space to accommodate coils, which is challenging for body MRI with large patients. The inclusion of shim coils requires the use of more RF chokes for mitigation of common mode currents for both RF and shielded shim current wires, it will potentially affect the RF transmission due to the coupling with DC components, especially at 7 T with local transmission (Stockmann et al., 2022). Figure 5 illustrates the schematic circuit of a novel iPRES RF coil circuit as well as its application in B0 shimming for brain and body.

Figure 5: Schematic circuit of iPRES coil array and comparison of field maps with and without iPRES (Han et al., 2013; Han et al., 2018). (A) Concept of integrated RF and shim loops with reduced usage of total coil space. A 32-channel iPRES design on a geometry resembling the human head. (B) The B0 inhomogeneities in the human brain can be significantly reduced by the use of iPRES especially in the regions of the prefrontal cortex and temporal lobes. (C) iPRES coil element design. (D) Diagram of iPRES body coil and the corresponding control system for various clinical applications in torso, cardiovascular, and musculoskeletal MRI.

Real-time shimming and navigator

Beyond the novel RF coil designs, the development of advanced software techniques, such as real-time B0 field compensation, can also contribute to improved image quality by minimizing the B0 variations mainly caused by physiological motions and participant movements (Gilani et al., 2023; Van de Moortele et al., 2002; Vannesjo et al., 2018). The traditional B0 shimming involves acquiring field maps before the imaging sequence and applying static shim adjustments based on these pre-acquired maps. Real-time shimming, however, involves the acquisition of additional field maps during the imaging sequence, typically using fast pulse sequences designed for this purpose (Alhamud et al., 2016; Boer et al., 2012; Simegn et al., 2019; Wallace et al., 2021), to obtain real-time information about field variations. In addition to fast calculation and updating of shim currents, it enables the temporally dynamic adjustment of shim currents during image acquisition (Boer et al., 2012; Van der Kouwe et al., 2006; van Gelderen et al., 2007; Ward et al., 2002). The real-time shimming technique has gained attention in recent research and development efforts (Duerst et al., 2015; Wallace et al., 2022).

Navigator-based B0 shimming algorithms have been widely used for real-time compensation of B0 field inhomogeneities. Most of these algorithms include a navigator sequence or integrate it with the target sequence to estimate temporal B0 conditions. The duration of such a navigator sequence may range from a few to hundreds of milliseconds. The total scanning time could be significantly increased in the case of navigators with a long TR time. Recent advancements in navigator-based B0 shimming algorithms have focused on improving efficiency and clinical applicability. One area of progress lies in the acquisition of navigator signals. A previous study proposed a 4.2-ms cloverleaf navigator for estimating rigid body motion and linear shims (Van der Kouwe et al., 2006). Recent research adopted a FID-based navigator with similar TR time and utilized the spatial information of each RF coil element to estimate B0 conditions during scans (Wallace et al., 2022). These methods allow for more efficient and seamless integration of navigator-based B0 shimming algorithms into clinical MRI protocols. In the application of DTI, a navigator-based method has been instrumental in reducing susceptibility-induced distortions with greater spatial overlap with anatomical references (Alhamud et al., 2016). The benefits of real-time shimming are evident in fMRI of spinal cord with a higher temporal SNR and reduced distortion (Topfer et al., 2018). Accurate detection and tracking of patient motion are essential for precise and real-time adjustment of shim currents in various MRI and MRS applications for simultaneous correction of motion and corresponding B0 variation (Alhamud et al., 2016; Andronesi et al., 2021; Hess et al., 2012, 2011; Liu et al., 2021; Saleh et al., 2016; Simegn et al., 2019). Figure 6 illustrates exemplary techniques of real-time compensation of B0 variations.

Figure 6: Exemplary techniques of real-time compensation of B0 variations. (A) Introduction of the B0 compensation method (reprinted from van Gelderen et al., 2007, with permission from John Wiley & Sons, Inc.). Various B0 maps across the respiratory cycle were acquired for training to a function phase change based on respiratory phases. The training model was then applied to a clinical scan for calculating the shim currents based on respiration. (B) Single and double volumetric navigators for motion and shim correction in chemical exchange saturation transfer (CEST) MRI (reprinted from Simegn et al., 2019, with permission from John Wiley & Sons, Inc.). Individual partitions of single navigator (vNav) (only for motion correction) or pair of navigators (DvNavs) were inserted and were acquired in an interleaved fashion for both motion and shim corrections.

Despite significant progress, there are still challenges implementing navigator-based B0 shimming algorithms. These methods were largely developed for motion-tracking, and the accuracy is affected by physiological noise as well as the relatively long acquisition time and the resulting low-resolution tracking map (Boer, 2023). Obtaining the variation of the background field through real-time field monitoring is excellent for prospective and retrospective B0 compensation (Duerst et al., 2015). Although such a system provides the superior capability to obtain different kinds of field variation up to the third SH order and provide a rapid feedback control, it is applicable to detect the background B0, for example, respiration-induced field modulation in the brain, but not for those caused by self and surrounding motions within the organs such as heart and liver. Motion-resolved field mapping (Huang et al., 2023; Mackowiak et al., 2023), including the variations induced by physiological motions before the clinical scan, would be an alternative way to implement real-time shimming for MRI scans such as free-breathing cardiac imaging (Christodoulou et al., 2018; Feng et al., 2016; Larson et al., 2005; Liu et al., 2010). Such a strategy requires an accurate field estimation based on real-time electrocardiogram (ECG) and respiratory gating signals and flexible adjustments in dynamically updating shimming current using an advanced control system.

Advances for magnetic field control beyond B0 shimming

Beyond the common SH-based active shim method, a variety of advanced technologies have been developed to utilize multi-coil techniques for a better control and correction of the magnetic field beyond the induced B0 variations from anatomical structures. Besides, field cameras are capable of acquiring magnetic field variations caused by temporally varying and non-predictable sources outside the human body (Barmet et al., 2009; Dietrich et al., 2016). Figure 7 illustrates hardware developments in high-density coil arrays, matrix gradient array, and real-time field monitoring.

Figure 7: Recent technological advances for excellent MR performances. (A) High-density receive coil array (96-ch) and transmit coil (16-ch) were used in the NexGen 7 T scanner with a high-performance gradient system (Feinberg et al., 2023). (B) A 128-channel RF receive coil (64-ch posterior + 64-ch anterior) used for body MRI (reprinted from Hardy et al., 2008, with permission from John Wiley & Sons, Inc.). (C) Design and implementation of a 64-ch head coil with effective gains in SNR and reduction of g-factor noise at 7 T (reprinted from Uğurbil et al., 2019, with permission from John Wiley & Sons, Inc.). (D) The implementation of a completed matrix gradient coil system (reprinted from Littin et al., 2018, with permission from John Wiley & Sons, Inc.). (E) A field monitoring system for real-time acquisition of magnetic field variations (reprinted from Dietrich et al., 2016, with permission from John Wiley & Sons, Inc.).

High-density coil arrays

Advances in RF coil design require the ultra-high field (e.g. 7 T) imaging system to use the advantages of high magnetic fields to achieve higher SNR and spatial/spectral resolution in MRI/MRS (Clément et al., 2019; Hosseinnezhadian et al., 2018; Moser et al., 2012; Yan et al., 2014; Zhang et al., 2017). High-density coil arrays have emerged as a significant technological advancement in MRI (Hardy et al., 2008; Uğurbil, 2018). These coil arrays consist of a larger number of smaller coil elements arranged in close proximity, providing increased spatial coverage and sensitivity compared to traditional coil designs. In brain imaging, by increasing the number of coil elements, these arrays offer improved SNR in the cortex region of the brain at high or ultra-high field scanners (Feinberg et al., 2023; Gruber et al., 2023; Li et al., 2019; Uğurbil, 2018; Uğurbil et al., 2019; Wiggins et al., 2009).

The increased number of coil elements may also allow for including B0 shim features to facilitate localized shimming, where adjustments can be tailored to specific anatomical regions, such as the brain or heart, leading to improved field homogeneity and image quality. The benefits of high-density coil arrays in B0 shimming have been demonstrated in a recent study via B0 shim simulation (Stockmann and Wald, 2018). A high-density RF coil array will not only enhance the SNR, but also accelerate the image acquisition using parallel imaging techniques (Griswold et al., 2002; Pruessmann et al., 1999; Sodickson and Manning, 1997), leading to higher temporal resolution in fMRI. RF coils with high density would also aid in improving the spatial and temporal resolution for detecting and characterizing small lesions, particularly in contrast-enhanced abdomen imaging, which is prone to motion-induced artifacts (Runge et al., 2017). There are, however, engineering challenges associated with mitigating the common mode currents along RF cables, especially at higher frequencies (Wiggins et al., 2009). Adding more RF chokes will crowd the coil surface and may affect RF transmission. It makes the process of adding shim coils more difficult and impossible. In addition, the increased number of receive channels results in a greater flow of data with a longer reconstruction time.

Advanced gradient systems

Advances in conventional gradient systems, such as higher gradient strength and slow rate, contribute to notable improvements in MRI. In fMRI, advanced gradient systems enable more rapid acquisition of data, facilitating the examination of brain activation patterns with increased temporal resolution (Zaitsev et al., 2015). In diffusion-weighted imaging, faster switching times and enhanced gradient strengths of advanced systems allow for shorter diffusion encoding times and increased spatial resolution as well as a higher diffusion contrast (Bammer, 2003; Feinberg et al., 2023; Huang et al., 2021; Le Bihan et al., 2006; Tang and Zhou, 2019). Advanced gradient systems have also played a crucial role in the development of advanced imaging techniques. For example, the use of high-performance gradients have facilitated the implementation of advanced diffusion imaging techniques such as high-angular-resolution diffusion imaging (Descoteaux, 2015) and diffusion spectrum imaging (Wedeen et al., 2008).

Recent advancements in hardware design and imaging methodologies have led to the development of an innovative gradient system known as the matrix gradient system (Hennig et al., 2008; Jia et al., 2016; Littin et al., 2018). The concept of matrix gradients is intended to provide high levels of flexibility in the design and implementation of gradient encoding fields. In traditional gradient systems, linear encoding fields are provided such as X, Y, Z, and the gradient coil also includes low-order SH shim coils up to second or third order for B0 shimming. Linear gradient coils with imaging field of view compromise linearity and efficiency, thus limiting design flexibility and performance. The matrix gradient consists of several smaller coil elements that allow for the design of arbitrary field distributions, being capable of providing nonlinear/non-Cartesian encoding fields, which offers opportunities to develop new imaging techniques and the capability to encode specific regions such as the cortex of the brain.

The idea of coil array-based encoding is not only applicable to the common scanner with a large cylinder-shaped magnet, but also is suitable for an accessible head-only MR scanner (Garwood et al., 2020; Vaughan et al., 2016). One of the major limitations of such scanners is the downsized magnet, which generates a very inhomogeneous magnetic field distribution (e.g. hundreds of parts per million) within the imaging field of view. Therefore, new methodologies are needed to overcome the challenges posed by the large B0 inhomogeneity. Snyder et al. (2014) developed a method for encoding spins spatially and temporally with a frequency-sweeping RF pulse and an advanced nonlinear encoding trajectory known as STEREO, which relaxed the requirement for B0 inhomogeneity for imaging to hundreds of ppm. This type of encoding scheme can be implemented using a multi-coil array that was designed and implemented for an accessible head-only MR scanner (Froelich et al., 2024; Theilenberg et al., 2023). The multi-coil design also enables the use of simultaneous image encoding and B0 shimming for potentially better use of the scanner's performance.

Since each element in a multi-coil array is controlled independently, the same numbers of current sources and controllers are needed and they can be synchronized, which is beyond the scope of typical commercial MR products and requires customized development or integration. Commercial multi-coil amplifiers are typically designed for shimming, with limited output current and voltage, thereby limiting the output field strength and rising time (Juchem et al., 2020). When surrounding conductors, multi-coil-induced eddy currents will become more complex to manage due to the significant number of coil elements. Mechanical vibrations of the multi-coil array and the use of higher currents during image encoding require advanced mechanical design or epoxy curing with water cooling.

Real-time field monitoring

MRI is not only affected by static B0 inhomogeneity, but also is challenged by temporally variable field changes. The gradient pulse switching on and off can induce eddy currents in the conductive structures around the gradient coil. Those eddy current-induced field distributions could distort the desired encoding fields, resulting in geometric distortion and image artifacts. While MR scanners usually compensate for eddy currents at linear order terms, there exist high-order terms caused by asymmetric design within magnets and non-concentricity of gradient coils (Jehenson et al., 1990). Other dynamic field terms are responsible for field perturbations during the scan (Wilm et al., 2015), including temperature-dependent B0 field drift, changing gradient system behavior, and external sources such as floor vibrations, etc.

Variations in these fields are not constant, as they depend on the scan sequences and the surrounding environment. Their induced phase shifts intertwine with image signals and cannot be distinguished, thus causing apparent image artifacts. Navigator echoes can be used to measure these phase shifts (Jezzard et al., 1998), however, they require extra acquisitions and these fields may continue to change during navigator echoes and imaging echoes, suggesting that this method is not an ideal solution. Moreover, the additional acquisition would also result in perturbations in the field. Therefore, it is best to monitor the field directly and concurrently during the scan without causing new changes to the field. Here is a concise demonstration of several studies on this subject over years, including the proof of concept for field monitoring (Barmet et al., 2008), field probe design (De Zanche et al., 2008), the transmit/receive system (Barmet et al., 2009), and a reconstruction method for correction of field perturbations (Wilm et al., 2011). Using field monitoring and advanced image reconstruction algorithms, it has been demonstrated that the image quality of diffusion tensor images can be improved while eliminating most blurring and ghosting artifacts (Wilm et al., 2015). To use the field monitoring system, the entire hardware system and the associated software for control and reconstruction must be installed, which is an expensive endeavor.

Conclusion

This study provides an overview of the B0 magnetic field shimming and control techniques, as well as their applications. B0 shimming is one of the main factors that contribute to high-quality images and spectra from MR scanners. Hybrid use of passive and active shims with superconducting coils has significantly improved the uniformity and stability of ultra-high field magnets. Enhanced B0 magnetic field homogeneity is critical to compensate for inhomogeneities within the human body by using resistive SH shim coils and local multi-channel shim coils. These techniques are highly beneficial for inhomogeneity-sensitive sequences such as balanced steady-state free procession, EPI, T2*, spectroscopic sequences, etc. It has been demonstrated in various studies that enhanced shimming is beneficial for fMRI, DTI, and MRS in psychiatry and neuroscience, as well as for body MRI applications. Real-time shimming techniques with navigator and field monitoring techniques can be used to compensate for variations in the magnetic field caused by physiological motions. Advances in hardware, such as the development of high-density RF coils, will further promote the development of shim-RF coils with high shim efficiency and a high number of shim channels for better outcomes. Gradient coils, implemented using multi-coil techniques, allow simultaneous encoding and B0 shimming of images. Developing optimal shim algorithms will enable better outcomes by taking advantage of all the capabilities of the shim hardware. Shim systems will be further developed with the goal of optimizing shim adjustments and enhancing the accuracy and adaptability of field compensations. Standardization, validation, and collaboration among researchers, clinicians, and industry representatives are critical to ensuring consistent and reproducible results and enabling widespread clinical application of B0 field shimming techniques, which will ultimately benefit patients by reducing imaging artifacts, providing more accurate and reliable diagnostic information.

Acknowledgements and disclosure

This study was supported by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institutes of Health (NIH) grants R01NS121544 and R01HL156818. Additionally, support was provided by NIH SBIR grant R43NS120795. We acknowledge data collection and sharing from the project funded by the Frontotemporal Lobar Degeneration Neuroimaging Initiative (National Institutes of Health Grant R01 AG032306). Hsin-Jung Yang and Hui Han hold equity in Lucidity Medical LLC.

Author contributions

Yun Shang (conceptualization, formal analysis, investigation, methodology, resources, writing – original draft), Gizeaddis Lamesgin Simegn (investigation, methodology, writing – review and editing), Kelly Gillen (writing – review and editing), Hsin-Jung Yang (writing – review and editing), and Hui Han (conceptualization, supervision, writing – review and editing)

Conflict of interest

One of the authors, Hui Han, is also the editor board member of Psychoradiology. He was blinded from reviewing or making decisions on the manuscript.
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