
==== Front
Z Med Phys
Z Med Phys
Zeitschrift für Medizinische Physik
0939-3889
1876-4436
Elsevier

S0939-3889(22)00134-9
10.1016/j.zemedi.2022.12.001
Short Communication
A body mass index-based method for “MR-only” abdominal MR-guided adaptive radiotherapy
Rippke Carolin carolin.rippke@med.uni-heidelberg.de
abc⁎
Renkamp C. Katharina ab
Stahl-Arnsberger Christiane d
Miltner Annette d
Buchele Carolin abc
Hörner-Rieber Juliane abcdeg
Ristau Jonas ab
Debus Jürgen abcdefg
Alber Markus abc
Klüter Sebastian Sebastian.klueter@med.uni-heidelberg.de
ab⁎
a Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
b Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany
c Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
d Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
e National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
f Heidelberg Ion-Beam Therapy Center (HIT), Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
g German Cancer Consortium (DKTK), Core-center Heidelberg, Heidelberg, Germany
⁎ Corresponding authors: Carolin Rippke and Sebastian Klüter, Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany. carolin.rippke@med.uni-heidelberg.deSebastian.klueter@med.uni-heidelberg.de
08 2 2023
8 2024
08 2 2023
34 3 456467
9 8 2022
9 12 2022
© 2022 The Author(s)
2022
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Purpose

Dose calculation for MR-guided radiotherapy (MRgRT) at the 0.35 T MR-Linac is currently based on deformation of planning CTs (defCT) acquired for each patient. We present a simple and robust bulk density overwrite synthetic CT (sCT) method for abdominal treatments in order to streamline clinical workflows.

Method

Fifty-six abdominal patient treatment plans were retrospectively evaluated. All patients had been treated at the MR-Linac using MR datasets for treatment planning and plan adaption and defCT for dose calculation. Bulk density CTs (4M-sCT) were generated from MR images with four material compartments (bone, lung, air, soft tissue). The relative electron densities (RED) for bone and lung were extracted from contoured CT structure average REDs. For soft tissue, a correlation between BMI and RED was evaluated. Dose was recalculated on 4M-sCT and compared to dose distributions on defCTs assessing dose differences in the PTV and organs at risk (OAR).

Results

Mean RED of bone was 1.17 ± 0.02, mean RED of lung 0.17 ± 0.05. The correlation between BMI and RED for soft tissue was statistically significant (p < 0.01). PTV dose differences between 4M-sCT and defCT were Dmean: −0.4 ± 1.0%, D1%: −0.3 ± 1.1% and D95%: −0.5 ± 1.0%. OARs showed D2%: −0.3 ± 1.9% and Dmean: −0.1 ± 1.4% differences. Local 3D gamma index pass rates (2%/2mm) between dose calculated using 4M-sCT and defCT were 96.8 ± 2.6% (range 89.9–99.6%).

Conclusion

The presented method for sCT generation enables precise dose calculation for MR-only abdominal MRgRT.

Keywords

Adaptive radiotherapy
Image guided radiotherapy
Synthetic CT
Bulk density
MR-only
MR guided radiotherapy
==== Body
pmc1 Introduction

In adaptive MR-guided radiotherapy (MRgRT), a daily MR image is acquired in order to represent the current anatomy of the patient. Based on this information, the plan is reoptimized but relative electron density (RED) information for dose calculation is not directly available from the MR image [1].

In current clinical workflows at the MRIdian 0.35T MR-Linac (ViewRay Inc., Mountain View, CA, USA), it is possible to perform a rigid or deformable registration of a planning CT to the current MR image set [2]. In this workflow, while the patient is on the table, the CT registration to the daily acquired MR image dataset has to be evaluated and often manually corrected through contouring of the external skin contour or manual air, lung or water density overwrites. The treatment planning system (TPS) suggests RED values from ICRU report 46 for these density assignments [3]. The Unity (Elekta AB., Stockholm, Sweden) MR-Linac workflow uses either rigid planning CT image registration or patient specific density overwrites for each organ extracted from the planning CT images for bulk density overwrites [4].

Especially in the abdomen, these methods can lead to suboptimal results as abdominal organs are very mobile and the diaphragm as well as fillings and air pockets in hollow organs are highly variable [5], [6]. This results in density and consequently dose calculation errors in clinical routine [7], [8]. Tissue inhomogeneities, especially bone and air, may have non-negligible effects on the dose distribution and have to be assessed in detail [9], [10], [11]. Bone visibility may be limited on MR images that are used for treatment planning and adaption. Air cavities are among the largest density changes in the human body density map. The electron return effect in the magnetic field causes additional dose changes [12].

To overcome these challenges, a large amount of research is dedicated to generating “synthetic CTs” (sCT) from MR images [13], [14]. SCTs assign densities to either structures or each voxel on the MR image. One method that has been widely explored in research relies on neural networks that learn to create sCTs from MRs with artificial intelligence [15], [16]. Neural networks are ideally trained with the dedicated MR sequence which they are later used on, therefore commercial products have to be specific and there are few releases for certain sites such as the brain and pelvis until now [17], [18]. In the abdomen, it is additionally difficult to find MR and CT image pairs that match well enough to serve as training database of the network. First neural networks have been presented for sCT generation with 0.35 T images also in the abdomen [19], [20], [21]. However, an important problem of this research especially related to artificial intelligence is the translation to clinical routine. It is currently not possible to import external sCTs in the adaptive workflow as implemented by ViewRay and neural networks can be difficult to understand and control. Therefore, it appears at this point that structure-based bulk density overwrite approaches are useful and easier to implement in clinical routine once suitable density values are found and structures have been delineated.

SCTs can be created with bulk densities assigned to relatively few and less complex structures (possibly bone, fat, soft tissue, air and lung). There is no consensus on the number of necessary compartments and suitable density values yet. Cusumano et al. suggested the use of mean patient specific RED values for abdominal MRgRT as compared to ICRU suggested RED values for fat, soft tissue, lung and bone and obtained accurate clinical dose calculation results with slightly better results for patient specific REDs [22]. In their workflow, fat and soft tissue were always contoured separately and a patient planning CT was always necessary to obtain patient specific RED values.

We suggest to simplify this method even further by obtaining a correlation between body mass index (BMI) and patient specific soft tissue (including fat) RED. Our method is a real MR-only workflow without the need to acquire future patient planning CT images. Fat contouring becomes redundant, streamlining the workflow. We performed analysis of uncertainties and aimed to implement a robust and clinically feasible method for sCT bulk density overwrites.

2 Material and methods

2.1 Patient imaging and treatment planning

Fifty-six patient plans with abdominal tumor location (liver (31), lymph node (9) and adrenal metastasis (16)) which had been treated with intensity-modulated radiotherapy (IMRT) on an MR-Linac were retrospectively included in this study. Retrospective data analysis was approved by the ethics committee of the University Hospital (S-543/2018). Simulation MR and CT image sets had been acquired at the same day within two to three hours for each patient. CT imaging had been performed following clinical protocols with a voxel size of 1 × 1 × 3 mm3. Hounsfield unit (HU) values had been converted to relative electron density with the HU-density table of the CT based on institutional standards for electron density calibration of the used CT scanners, including phantom density insert measurements and regular quality assurance for each reconstruction protocol. All MR images had been acquired at the 0.35 T MR-Linac with a T2/T1 weighted true fast imaging with steady state precession sequence (TrueFISP) [23], [24], a voxel size of 1.5 × 1.5 × 3 mm3 and fields of view of 40–50 × 40–45 × 43 cm3. Patients had been placed head first supine on flat-bed MR and CT table using the same positioning aids, with arms up, covered by (dummy) coils and had been given breathing commands so that all images had been acquired in inspiration breath hold.

The CT had been automatically deformed to the MR and checked as part of the clinical treatment procedure. Consequently, each patient plan initially had three image sets: CT, MR and deformed CT (defCT), with structures required for clinical treatment contoured on the MR image set. Clinical treatment plans with 6 to 21 beams (average 12.3) had been calculated in the Viewray TPS using the density distribution from the defCT and a Monte Carlo algorithm with a dose grid size of 0.2 or 0.3 cm, a magnetic field of 0.35 T and a statistical uncertainty of 0.5% in the PTV [25].

2.2 Density overwrites

For the patient plans, bone, lung and air gaps of clinical impact were contoured additionally to the existing clinical contours in RayStation 10A (RaySearch Laboratories AB, Stockholm, Sweden) by experienced RTTs on MR and CT datasets. Additionally, bones were contoured on defCTs for a set of 18 patients. The average HU values of bone, lung and soft tissue (soft tissue = external skin contour minus bone, lung and air) ±5 cm cranio-caudal of the PTV were extracted in RayStation for each patient and converted to RED using the HU-density table as described above. The limited region was chosen in order to stay in the high contrast image region with reliable contouring. Furthermore, a correlation between soft tissue density and body mass index (BMI) was evaluated. The BMI was obtained from patient records. Pearson correlation (Pearson's r) and significance testing (p-value) were performed on the density – BMI dataset.

For all patients, density overwrites in the four material categories (4M) bone, soft tissue, lung and air were performed, dose distributions were recalculated on these data sets and compared to the initial dose distribution on the defCT. Dose differences in the PTV (Dmean, D1%, D95%) were calculated to represent the DVH shape as well as in OARs which were considered in clinical goals (D2%, Dmean). Relative uncertainty increases in regions with less dose as there are less photon histories in Monte Carlo dose calculations [27]. Therefore, OARs with a mean dose <1 Gy were excluded from analysis. Dose differences were assessed asΔD=100D4M-sCT-DdefCTDdefCT.

A correlation between dose difference parameters and PTV distance to lung was examined. For plan comparison, the dose distributions of 4M-sCT and defCT were compared with a local 3D gamma index (2%/2mm and 3%/3mm) [28] calculated in Matlab R2021a (The MathWorks, Inc., Natick, Massachusetts, United States) using “CalcGamma” public license code with a 10% dose threshold.

To assess the impact of the BMI correlated soft tissue density overwrite, plans were recalculated for all patients on an “average sCT” (a-sCT). Here, the soft tissue structure was overwritten with the population average soft tissue density from CT. Then, dose differences on a-sCT were compared to defCT as before. Further, the 10% of patients showing the largest difference between a-sCT soft tissue and BMI correlated 4M-sCT soft tissue density were identified. For those cases, dose was recalculated on a new “patient-specific sCT” (p-sCT) with the soft tissue structure overwritten with their patient-specific density from the CT and compared to defCT.

2.3 Robustness checks

In order to assess the robustness of the suggested method, the effects of air and bone contouring were evaluated.

Since manual contour checks (and recontouring, if necessary) during daily plan adaption usually focus on a 3 cm ring around the PTV, it was assumed all air gaps inside the 3 cm ring are contoured and the focus was outside of a 3 cm ring [29]. For this, a cubic virtual water phantom (30 × 30 × 20 cm) was created in the TPS and a 9.96 × 9.96 cm field was calculated (Dref) in a 0.35 T transversal magnetic field with a prescription of 4 Gy to isocenter, a dose grid of 0.3 cm and an uncertainty of 0.5%. The simplified PTV was a circle of 2 cm radius. Then, circular air gaps of varying diameter (1 cm–12 cm) were placed bordering to the 3 cm ring and the field dose was recalculated (Dair) and compared to the dose in the phantom containing no air cavity (see Fig. 1). This setup is similar to previous studies assessing dosimetric effects of air in a magnetic field [12], [30]. This one-beam arrangement poses a worst-case scenario as multiple-beam arrangements smear the effects. The maximum dose difference of ΔD% within the PTV was calculated asΔD%=100Dair-DrefDref

Figure 1 a) Schematic simulation setup of the water phantom with an air pocket. b) Dose deposition in the central plane of the phantom with air pocket of 2 cm diameter.

To find out whether the image set on which bones were contoured influences dose calculation results, the following analysis was performed: For the subgroup of patients with bone contoured on defCT, the bone structures contoured on the MRI datasets were compared to the bone structures contoured on the CT and the defCT. Each of the images were registered in RayStation 10A and the bone structures were copied to the MR image. Three simple sCTs were created from the MR image by overwriting the body with water (RED = 1.0) and the lung and1) the bone structure contoured on MR (sCT1)

2) the bone structure from defCT (sCT2) and

3) the bone structure from CT (sCT3)

with REDs from ICRU report 46 (lung = 0.26 and bone = 1.12) [3].

It was evaluated if the image set on which bones were contoured makes a difference through recalculation of the plans (no optimization) on the simple sCTs on the basis of the PTV DVH parameters presented before.

3 Results

3.1 Density overwrites

Mean RED of the bones for all patients was 1.171 ± 0.023 (range 1.13–1.24) and mean RED of lung 0.172 ± 0.048 (range 0.10–0.27) (see Table 1). Bone and soft tissue densities were on average close to IRCU report 46 [3] values while lung contours of our patient cohort had significantly lower average densities than stated by the ICRU. A strong correlation (|r| > 0.6) between BMI and soft tissue RED was found, is statistically significant (p < 0.01) and is illustrated for men and women in Fig. 2. Abdominal soft tissue RED was calculated for women asRED=-0.0052∗BMI+1.1115

and for men asRED=-0.0052∗BMI+1.1185.

Table 1 Average patient specific HU and RED values for the evaluated patient cohort for bulk density overwrite structures as contoured on the treatment planning CT depicted separately for the three patient groups.

	Bone	Lung	Soft tissue	
[HU ± Std]	[RED ± Std]	[HU ± Std]	[RED ± Std]	[HU ± Std]	[RED ± Std]	
ICRU report 46[3]	185.8	1.2	−725.1	0.26	0	1	
Liver (this Study)	285.5 ± 53.3	1.17 ± 0.03	−812.1 ± 48.2	0.17 ± 0.05	1.8 ± 20.4	1.01 ± 0.03	
Lymph node (this Study)	295.2 ± 23.3	1.17 ± 0.02	−822.6 ± 31.1	0.16 ± 0.03	−29.8 ± 18.0	0.96 ± 0.02	
Adrenal (THIS STUDY)	285.5 ± 49.4	1.17 ± 0.02	−800.5 ± 43.8	0.18 ± 0.04	−18.4 ± 26.3	0.97 ± 0.04	
ALL PATIENTS (THIS STUDY)	287.0 ± 48.8	1.17 ± 0.03	−809.7 ± 45.8	0.17 ± 0.05	−8.9 ± 25.3	0.99 ± 0.04	

Figure 2 Correlation between BMI and RED for abdominal indications for men and women. Colors indicate the treatment site: purple – liver, yellow – adrenal, orange – lymph nodes.

Fig. 3 shows exemplary comparisons of 4M-sCT and defCT for each of the indications. Average dose differences in the PTV between 4M-sCT and defCT were (median [range]) −0.3 [−2.6, 2.8]% Dmean, −0.2 [−2.7, 2.8]% D1% and −0.5 [−2.7, 2.2]% D95% (see Fig. 4). The dose differences were normally distributed (Jarque-Bera test, p > 0.1). For liver patients, the 95% confidence interval as indicated by the dashed line in the boxplot was smaller than 2% dose difference (−0.0 ± 0.8% Dmean, −0.1 ± 0.9% D1% und −0.2 ± 0.8% D95%). For lymph node and adrenal patients, the dose differences were larger (lymph nodes: −0.7 ± 1.1% Dmean, −0.5 ± 1.2% D1% und −1.0 ± 1.1% D95%; adrenal: −0.8 ± 1.2% Dmean, −0.7 ± 1.4% D1% und −0.8 ± 1.1% D95%). There was no correlation between dose difference parameters and PTV distance to lung (|r| < 0.21, p > 0.01).Figure 3 Exemplary comparison of MR, defCT and 4M-sCT with dose distributions for the three indications. Dose distributions were recalculated on the image set that they are shown on. Gamma index maps show the difference between the dose on defCT and the dose on 4M-sCT (from PTW-VeriSoft V7.2).

Figure 4 Dose differences in the PTV between defCT and 4M-sCT calculated as (D4M-sCT – DdefCT)/DdefCT*100. The central bar indicates the median, bottom and top box edges indicate 25th/75th percentiles. The whiskers extend to the 95% confidence interval and the outliers are plotted as '+'.

Dose differences for all clinically relevant OARs (in total 260) were evaluated. The comparison between 4M-sCT and defCT yielded median differences of −0.2 [−11.2, 3.7]% D2% and −0.1 [−5.2, 8.0]% Dmean (Table 2).Table 2 OAR dose deviations comparing 4M-sCT and defCT.

	OAR (NuMBER OF OARs evaluated)	Δ D2% [%]	ΔDmean [%]	
Median	Min	Max	Median	Min	Max	
Liver Patients	All (142)	0.0	−11.2	3.7	0.0	−5.2	3.9	
	Liver (25)	−0.1	−1.3	1.9	0.0	−1.1	1.4	
	Bowel (13)	−0.3	−11.2	−1.5	0.0	−1.5	2.6	
	Duodenum (12)	0.1	−1.7	1.9	−0.1	−1.3	0.9	
	Stomach (23)	0.0	−4.5	2.8	0.0	−1.8	2.7	
	Kidney left (7)	0.1	−1.4	2.3	0.0	−0.8	2.5	
	Kidney right (14)	−0.1	−2.5	0.8	0.0	−1.0	0.8	
	Heart (8)	0.2	−1.6	2.1	0.3	−0.5	1.8	
	Esophagus (17)	0.5	−4.1	3.0	0..6	−5.2	3.9	
	Spinal Cord (23)	−0.2	−2.0	3.7	0.3	−2.0	3.1	
Lymph node Patients	All (37)	0.2	−3.4	3.4	0.0	−1.9	1.6	
	Liver (2)	1.0	0.2	1.8	0.7	−0.1	1.8	
	Bowel (5)	−0.4	−2.0	0.9	0.0	−0.6	0.8	
	Duodenum (5)	−2.5	−3.4	3.4	−1.3	−1.9	1.1	
	Stomach (3)	1.2	0.2	1.4	0.0	−1.6	0.9	
	Kidney left (4)	−0.8	−2.2	0.8	−1.3	−1.7	1.1	
	Kidney right (4)	0.5	−1.8	2.7	0.7	−1.8	1.6	
	Esophagus (1)	1.8			1.7			
	Spinal Cord (4)	−0.1	−2.7	1.2	0.0	−1.8	0.8	
Adrenal patients	All (81)	−0.7	−9.9	3.4	−0.7	−5.2	8.0	
	Liver (11)	−0.5	−2.2	0.7	−0.4	−1.3	0.9	
	Bowel (10)	−1.1	−4.0	2.3	−0.7	−2.8	1.5	
	Duodenum (9)	−1.0	−3.9	3.4	−0.8	−3.6	3.9	
	Stomach (13)	−1.5	−3.4	1.1	−0.6	−2.9	8.0	
	Kidney left (15)	−0.8	−3.7	2.8	−0.9	−2.3	1.5	
	Kidney right (13)	−1.3	−2.9	1.8	−1.1	−2.7	1.7	
	Heart (1)	1.7			2.7			
	Esophagus (4)	−0.9	−9.9	0.7	−0.6	−5.2	0.8	
	Spinal Cord (14)	0.8	−3.2	1.8	−0.3	−2.2	2.3	
ALL	(260)	−0.3	−11.2	3.7	−0.1	−5.2	8.0	

Mean local 3D gamma index pass rate (2%/2mm) of 4M-sCT and defCT was 96.8% (range 89.9–99.6%) and 98.1% (range 90.0–99.9%) for a criterion of 3%/3mm.

The median dose differences between a-sCT and defCT were −0.1 [−4.2, 2.7]% Dmean, 0.0 [−4.8, 3.3]% D1% and −0.5 [−4.2, 2.9]% D95% for all patients.

For the six evaluated patient cases with the largest difference between their CT soft tissue and BMI correlated density, the dose differences between p-sCT and defCT were −0.7 [−1.9, 0.2]% Dmean, −0.5 [−1.5, 1.4]% D1% and −1.1 [−2.2, −0.3]% D95%. The comparisons between p-sCT and defCT indicate that the DVH parameters deviate up to 2.5% due to inaccurate density assignments. However, the dose differences to defCT did not decrease on average with p-sCT as compared to 4M-sCT dose differences. For one patient case with 5% density difference (very small patient, high BMI but not much fat) that was overwritten with its CT soft tissue density, dose differences between p-sCT and defCT increased (D95% −1.4%, D1% −1.5%, Dmean −1.5% as compared to −1.1%, −1.0% and −0.9% for 4M-sCT).

3.2 Robustness checks

Dose was calculated in the simulation water phantom and compared to calculated dose in the water phantom with air gaps of variable sizes. Air pockets smaller than 1 cm diameter outside of the 3 cm ring around the PTV cause dose changes smaller than |ΔD%| = 1.1% in the PTV. Air pockets with 2 cm diameter cause dose changes that are larger than |ΔD%| = 5% for some points in the PTV. Therefore, all air gaps inside the ring and additionally all pockets that are outside the ring, within the beam path and larger than 1 cm diameter need to be contoured and assigned to air density.

Mean dose (Dmean) difference comparing defCT to sCT1 was −0.7 ± 1.2%, for sCT2 the difference was −0.8 ± 1.2% and for sCT3 was −0.8 ± 1.1%. No significant dose differences were detected for D1% and D95% for the patients depending on where the bones of the patient were contoured (Fig. 5). Also, whether bones were contoured on the defCT or on the CT did not significantly change PTV dose in the evaluated patient cohort.Figure 5 Liver dose differences in the PTV for bulk density overwrites with different bone structure sources. The dose differences arise because sCTs are overly simple compared to defCT, not considering air gaps and patient fat ratios. The central bar indicates the median, bottom and top box edges indicate 25th/75th percentiles. The whiskers extend to the 95% confidence interval and the outliers are plotted as '+'.

4 Discussion

4.1 Densities in 4M-sCT

4M-sCT enables precise dose calculation based on the MR image set in the abdomen especially for liver irradiation. The correlation between BMI and RED enables patient-specific density overwrites for soft tissue. Therefore, it becomes unnecessary to contour fat and non-adipose tissues separately. Soft tissue REDs were assessed for men and women separately due to expected differing body fat proportions, but our results showed that this is not necessary. A two-sample t-test with equal variances was performed with the null hypothesis being “densities of men and women are the same”. The two-tailed p-value was 0.7, therefore we cannot reject the null hypothesis and conclude that the BMI-correlated densities for men and women in the examined population are the same (p > 0.05). Thus, future studies may implement a single fit for the male and female population. With this method, patient specific densities can be chosen without the need of acquiring a CT and with minimal additional contouring.

Average lung densities differed from ICRU report 46 [3] values because the lung was only partly contoured for our abdominal patients. In inspiration, lower density regions of the lung expand towards the abdomen. In the inspiration breath hold state the lung is air filled, which lowers the density. The upper lung regions include more higher density structures, i.e. vessels [31]. Especially for liver tumors close to the lung, this is important to consider. Average bone densities were in good agreement with ICRU report 46 [3] values and other studies [32].

It is a limitation of our study that the BMI correlated density value is extracted as a single representative value of the patient cohort. This does not consider other patient specific parameters such as distribution of fat within the patient or tumor location within the abdomen. However, in inspiration breath hold, the lung expands to the abdomen and even the most cranial liver lesions are surrounded mostly by soft tissue (including fat). While we did not find a correlation between PTV distance to lung and DVH parameter differences, further studies with more patients might be needed to further validate this.

4.2 Workflow of 4M-sCT

The modality used for bone contouring did not significantly change PTV dose. Therefore, clinical implementation of MR-only MRgRT using 4M-sCT is straight-forward with typically used MR sequences and within the established MRgRT workflow [33], [34]. Using this workflow, it becomes feasible to simulate and treat abdominal patients with single fraction MR-only radiotherapy in one day [35], [36]. On the simulation MR image, air and bone have to be contoured additional to PTV and OAR structures. During the time-critical adaptive workflow, they can be deformed to fit the current image and manually corrected if necessary. Then, bulk density overwrites can be activated with chosen REDs and the workflow can be continued normally. Before clinical implementation, a risk assessment as outlined in recent guidelines should be performed for the process [37]. It is also important to streamline the workflow so that little time passes between density assignment and irradiation.

A disadvantage of the method is the time-consuming manual contouring of air and bones. However, defCT in the current clinical workflow also needs manual corrections regularly, especially on the diaphragm and for air gaps. For this, one has to switch between MR and defCT images and has to visibly detect differences. This time-consuming and error-prone process may lead to significant dose differences in the target volume and OARs as large density differences may remain undetected due to the process taking place under time pressure. For 4M-sCT as well as when using defCT, attention has to be paid to the skin structure, but 4M-sCT has the advantage that it is only necessary to check the skin in one image set.

4.3 Precision and limitations of 4M-sCT

Factors that contribute to sCT uncertainties within an MR-only workflow are manual contouring variability or imprecisions due to time restrictions. Further, the BMI-density correlation does not yield the perfect soft tissue RED for every patient, the difference between the RED estimated by the BMI-density correlation and real RED may be up to 5%. Bone visibility is limited on the MR, especially for the ribs. However, photon attenuation is relatively insensitive to the small density variations caused by these factors. Here, there was no significant impact of bone contouring source despite amplification of dose differences due to heterogeneities caused by the magnetic field [21].

Average RED values may need to be derived for each clinic separately due to site-specific contouring practices even when applicable guidelines are followed [38]. Additionally, the protocols regarding inspiration state or other relevant parameters might vary. It has been shown that the situation may change between contouring and treatment when performing adaptive treatment [39]. The time frame during which the intrafractional anatomical situation is stable remains to investigate outside of the scope of this work.

Comparable sCT generation methods achieve similar precision. While Fu et al. [19] report a gamma pass rate of >95% (2%/2mm) using a neural network generating sCTs from 0.35 T MR images for 12 abdominal patients, Cusumano et al. [22] report a 99.2% pass rate for patient specific bulk density overwrites and a 94.1% pass rate for ICRU report 46 [3] based bulk density overwrites for 16 abdominal patients. Our gamma pass rate of 96.8% indicates sufficient precision with the advantage that CT imaging is no longer required and the workflow is easy to implement in clinical routine.

For dose calculation on sCTs generated from MR, it has been suggested that the 95% confidence interval should be within a 2% dosimetric deviation to produce clinical acceptable results [40]. In the present study, this was the case considering PTV dose volume histogram (DVH) parameters with 4M-sCT for the liver patients, but not for lymph node and adrenal metastasis treatment plans when comparing dose calculation on defCT and 4M-sCT. This is possibly due to variable air pockets in the bowel that were not present or larger in the planning CT or that moved along the intestinal loops between MR and CT image acquisition. It has to be considered that these uncertainties persist for MRgRT with dose calculation on defCTs (Fig. 6). Because OARs lie in lower dose regions and small absolute dose differences pose a larger percentage change, standard deviations were found to be larger for the OARs than for PTV DVH metrics in general.Figure 6 Comparison of 4M-sCT (d) and defCT (b). The original dose distribution of the treatment plan is calculated on defCT. Dose difference between 4M-sCT and defCT is shown on the bottom right (f). Air pockets moved into the beam path between MR and CT image acquisition (arrows).

There is a small systematic under-dosage when using 4M-sCT, which can be explained by three reasons. First, often there is more air present within the patient contour in defCT than in the MR and sCT datasets. This is due to air gaps that tend to accumulate in the abdomen while the patient is lying on the table. Second, there are volume effects on defCT images at the edge between soft tissue and air especially at the outer patient contour that cannot be replicated by bulk density sCTs (half voxels within the soft tissue are represented by half density in defCT but full density in sCT). Third, the external patient dimension in the defCT can be only equal or smaller than the skin contour on the MR since defCT is cut at the outer MR contour during the vendor implemented workflow at the 0.35 T MR-Linac.

Comparison with an average population density override used for all patients (a-sCT) shows that while the median dose deviation does not change, the minimum and maximum deviations increase when using a single average density for soft tissue. The maximum dose deviations of up to 4.8% are considered unacceptable in our institution. It can be seen that especially for patients that have a density below average (more fat), the dose differences increase (figure not shown). This is due to the large volume of soft tissue that the beam passes through. The effect is smaller for thin patients. However, the deviations for patients that have near-average density are due to already discussed problems with air and external patient contours.

The reason for p-sCT deviations for one patient case with 5% density difference between CT and BMI correlated soft tissue density were air gaps that appear in defCT but not in MR and sCT images. For another patient, we suspect that the weight was incorrectly documented or the BMI was too old. Therefore, it is very important to verify correct BMI detection and documentation and the corresponding density assignment when applying this method in clinical practice. Ideally, the BMI should be documented in the TPS.

Recent guidelines recommend an overall dose delivery accuracy of 5% in the PTV for stereotactic radiotherapy [41], [42]. This will be met with 4M-sCT including all dose calculation uncertainties if the dose delivery uncertainty is below 2% [43]. Quantification of total dose delivery accuracy was outside the scope of this work. The uncertainties of 4M-sCT should be considered in clinical goals and especially for critical OAR constraints when performing MR-only MRgRT.

Small variations in the gradient linearity of the MR scanner may lead to geometrical distortions that can lead to a deformed skin structure which may impact dose calculation [44]. The MR integrated in the MR-Linac is corrected for geometrical distortion and the spatial integrity is part of regular quality assurance procedures [45]. It remains to be assessed how residual errors influence dose calculation in sCT based dose calculation.

4.4 Outlook on MR-only MRgRT

4M-sCT can be used for single as well as multiple fraction treatments. In any case, the workload is reduced as no CT and neither manual deformation check nor density correction in the CT are necessary. For multiple fraction treatments, random errors such as air pockets that appear or disappear in the beam path are expected to average out. A single fraction MR-only workflow is attractive due to its efficiency, but it also comes with particular quality assurance needs. Relevant systematic or random errors have to be mitigated. This is difficult to achieve for defCT but can be accomplished with 4M-sCT by keeping track of the error sources (see Section 4.3). Random errors from lung, air and skin contouring can be minimized through a four-eyes principle and double checks. Possible systematic errors from differences between the suggested and real density values as well as possible bone contouring difficulties on the MR have been shown to result in small dose deviations. The remaining, probably most relevant error source is the time passing between contouring and irradiation and related density changes in the patient. Currently, the additional contouring of air and bone during the adaptive workflow might take up to 10 minutes for complex anatomical situations. However, this is at the moment the only option to account for variable abdominal air configurations and avoid deformable registration errors. Improved automatic MR contouring tools are desirable to minimize time and effort as well as observer variabilities and human error probabilities.

In conclusion, 4M-sCT enables abdominal MR-only MRgRT and omits the workload and patient dose of the planning CT. The presented methods may be adopted to derive patient specific densities for other body sites.

5 Conclusion

Using patient specific density overwrites for soft tissue based on BMI and population specific average REDs for lung and bone, clinically acceptable sCTs can be generated for abdominal MR guided radiotherapy without the need of CT acquisition. This might save departmental resources, patient dose and also avoids deformable registration errors.

Funding information

The installation of the MR-Linac in Heidelberg was kindly funded by the German Research Foundation DFG (funding reference DE 614/16-1).

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: SK has received speaker fees and travel reimbursement from ViewRay Inc. outside the submitted work. JHR received speaker fees and travel reimbursement from ViewRay Inc., travel reimbursement from IntraOP Medical and Elekta Instrument AB as well as grants from IntraOP Medical and Varian Medical Systems outside the submitted work. JD received grants from CRI – The Clinical Research Institute GmbH, ViewRay Inc., Accuray International, Accuray Incorporated, RaySearch Laboratories AB, Vision RT limited, Astellas Pharma GmbH, Merck Serono GmbH, Astra Zeneca GmbH, Solution Akademie GmbH, Ergomed PLC Surrey Research Park, Siemens Healthcare GmbH, Quintiles GmbH, Pharmaceutical Research Associates GmbH, Boehringer Ingelheim Pharma GmbH Co, PTW-Freiburg Dr. Pychlau GmbH, Nanobiotix A.A. as well as IntraOP Medical, all outside the submitted work.
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