Pseudo-CT Generation for MRI-Only Radiation Therapy Treatment Planning: Comparison Among Patch-Based, Atlas-Based, and Bulk Density Methods

Largent, A.; Barateau, A.; Nunes, Jean‐Claude; Lafond, C.; Greer, Peter; Dowling, Jason; Saint‐Jalmes, Hervé; Acosta, Oscar; Crevoisier, R. de

doi:10.1016/j.ijrobp.2018.10.002

Cited by 37 publications

(24 citation statements)

References 38 publications

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“…Kim et al [18] reported no significant dosimetric difference between continuous HU or bulk-HU sCTs but obtained higher gamma-index passing-rate of 97.2% vs. 94.0% for continuous sCT evaluated with 1%/1 mm gamma-criteria. Largent et al [17] found that the mean gamma-index pass-rate for continuous HU values (99.5%) was significantly higher than that of bulk density method (96.1%). Both Kim et al and Largent et al used only two bulk densities: one for bone and one for soft-tissue that could explain worse dosimetric agreement in comparison to our results.…”

Section: Discussionmentioning

confidence: 95%

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Assessment of dosimetric and positioning accuracy of a magnetic resonance imaging-only solution for external beam radiotherapy of pelvic anatomy

Kemppainen

Suilamo

Ranta

et al. 2019

Physics and Imaging in Radiation Oncology

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Background and purpose: The clinical feasibility of synthetic computed tomography (sCT) images derived from magnetic resonance imaging (MRI) images for external beam radiation therapy (EBRT) planning have been studied and adopted into clinical use recently. This paper evaluates the dosimetric and positioning performance of a sCT approach for different pelvic cancers. Materials and methods: Seventy-five patients receiving EBRT at Turku University Hospital (Turku, Finland) were enrolled in the study. The sCT images were generated as part of a clinical MRI-simulation procedure. Dose calculation accuracy was assessed by comparing the sCT-based calculation with a CT-based calculation. In addition, we evaluated the patient position verification accuracy for both digitally reconstructed radiograph (DRR) and cone beam computed tomography (CBCT)-based image guidance using a subset of the cohort. Furthermore, the relevance of using continuous Hounsfield unit values was assessed. Results: The mean (standard deviation) relative dose difference in the planning target volume mean dose computed over various cancer groups was less than 0.2 (0.4)% between sCT and CT. Among all groups, the average minimum gamma-index pass-rates were better than 95% with a 2%/2mm gamma-criteria. The difference between sCT-and CT-DRR-based patient positioning was less than 0.3 (1.4) mm in all directions. The registrations of sCT to CBCT produced similar results as compared with CT to CBCT registrations. Conclusions: The use of sCT for clinical EBRT dose calculation and patient positioning in the investigated types of pelvic cancers was dosimetrically and geometrically accurate for clinical use.

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Section: Discussionmentioning

confidence: 95%

“…Several groups have assessed the dosimetric and positioning for prostate cancer with comparison of continuous HU and bulk HU assignment (see e.g. [16][17][18][19]). Two recent studies have evaluated the feasibility of MRI-only workflow for rectal cancers [20,21].…”

Section: Introductionmentioning

confidence: 99%

Assessment of dosimetric and positioning accuracy of a magnetic resonance imaging-only solution for external beam radiotherapy of pelvic anatomy

Kemppainen

Suilamo

Ranta

et al. 2019

Physics and Imaging in Radiation Oncology

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“…Application of automated bone segmentation on MRI may cause dosimetric inaccuracy for the BAD MR maps. More accurate segmentation can potentially be achieved via manual contouring however this is time consuming and the level of accuracy can vary depending on the level of expertise and experience of individuals (10, 30).…”

Section: Discussionmentioning

confidence: 99%

“…Different methods have been introduced to create sCT scans for prostate radiotherapy planning. Bulk-density planning was initially investigated as a method for sCT generation (5–10). These studies applied a density of water to the body with an additional separate density for bone.…”

Section: Introductionmentioning

confidence: 99%

“…Atlas-based methods involve pair-wise image registration of CT and MRI scans based on anatomical structures to form the atlas, registration of atlas MRI scans to target MRI scan, and mapping the estimated Hounsfield unit (HU) values based on the atlas CT data (11–14). Patch based methods involve feature extraction and patch partitioning from interpatient group-wise affine registration (10, 15). The target feature patches are selected using the approximate nearest neighbor search from the training cohort and sCT patches are generated using the multipoint-wise aggregation scheme.…”

Section: Introductionmentioning

confidence: 99%

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Bulk Anatomical Density Based Dose Calculation for Patient-Specific Quality Assurance of MRI-Only Prostate Radiotherapy

et al. 2019

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Prostate cancer treatment planning can be performed using magnetic resonance imaging (MRI) only with sCT scans. However, sCT scans are computer generated from MRI data and therefore robust, efficient, and accurate patient-specific quality assurance methods for dosimetric verification are required. Bulk anatomical density (BAD) maps can be generated based on anatomical contours derived from the MRI image. This study investigates and optimizes the BAD map approach for sCT quality assurance with a large patient CT and MRI dataset. 3D T2-weighted MRI and full density CT images of 54 patients were used to create BAD maps with different tissue class combinations. Mean Hounsfield units (HU) of Fat (F: below −30 HU), the entire Tissue [T: excluding bone (B)], and Muscle (M: excluding bone and fat) were derived from the CT scans. CT based BAD maps (BADBT,CT and BADBMF,CT) and a conventional bone and water bulk-density method (BADBW,CT) were compared to full CT calculations with bone assignments to 366 HU (measured) and 288 HU (obtained from literature). Optimal bulk densities of Tissue for BADBT,CT and Bone for BADBMF,CT were derived to provide zero mean isocenter dose agreement to the CT plan. Using the optimal densities, the dose agreement of BADBT,CT and BADBMF,CT to CT was redetermined. These maps were then created for the MRI dataset using auto-generated contours and dose calculations compared to CT. The average mean density of Bone, Fat, Muscle, and Tissue were 365.5 ± 62.2, −109.5 ± 12.9, 23.3 ± 9.7, and −46.3 ± 15.2 HU, respectively. Comparing to other bulk-density maps, BADBMF,CT maps provided the closest dose to CT. Calculated optimal mean densities of Tissue and Bone were −32.7 and 323.7 HU, respectively. The isocenter dose agreement of the optimal density assigned BADBT,CT and BADBMF,CT to full density CT were 0.10 ± 0.65% and 0.01 ± 0.45%, respectively. The isocenter dose agreement of MRI generated BADBT,MR and BADBMF,MR to full density CT were −0.15 ± 0.90% and −0.16 ± 0.65%, respectively. The BAD method with optimal bulk densities can provide robust, accurate and efficient patient-specific quality assurance for dose calculations in MRI-only radiotherapy.

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A study of Bayesian deep network uncertainty and its application to synthetic CT generation for MR‐only radiotherapy treatment planning

Law,

Tse,

et al. 2023

Medical Physics

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BackgroundThe use of synthetic computed tomography (CT) for radiotherapy treatment planning has received considerable attention because of the absence of ionizing radiation and close spatial correspondence to source magnetic resonance (MR) images, which have excellent tissue contrast. However, in an MR‐only environment, little effort has been made to examine the quality of synthetic CT images without using the original CT images.PurposeTo estimate synthetic CT quality without referring to original CT images, this study established the relationship between synthetic CT uncertainty and Bayesian uncertainty, and proposed a new Bayesian deep network for generating synthetic CT images and estimating synthetic CT uncertainty for MR‐only radiotherapy treatment planning.Methods and MaterialsA novel deep Bayesian network was formulated using probabilistic network weights. Two mathematical expressions were proposed to quantify the Bayesian uncertainty of the network and synthetic CT uncertainty, which was closely related to the mean absolute error (MAE) in Hounsfield Unit (HU) of synthetic CT. These uncertainties were examined to demonstrate the accuracy of representing the synthetic CT uncertainty using a Bayesian counterpart. We developed a hybrid Bayesian architecture and a new data normalization scheme, enabling the Bayesian network to generate both accurate synthetic CT and reliable uncertainty information when probabilistic weights were applied. The proposed method was evaluated in 59 patients (13/12/32/2 for training/validation/testing/uncertainty visualization) diagnosed with prostate cancer, who underwent same‐day pelvic CT‐ and MR‐acquisitions. To assess the relationship between Bayesian and synthetic CT uncertainties, linear and non‐linear correlation coefficients were calculated on per‐voxel, per‐tissue, and per‐patient bases. For accessing the accuracy of the CT number and dosimetric accuracy, the proposed method was compared with a commercially available atlas‐based method (MRCAT) and a U‐Net conditional‐generative adversarial network (UcGAN).ResultsThe proposed model exhibited 44.33 MAE, outperforming UcGAN 52.51 and MRCAT 54.87. The gamma rate (2%/2 mm dose difference/distance to agreement) of the proposed model was 98.68%, comparable to that of UcGAN (98.60%) and MRCAT (98.56%). The per‐patient and per‐tissue linear correlation coefficients between the Bayesian and synthetic CT uncertainties ranged from 0.53 to 0.83, implying a moderate to strong linear correlation. Per‐voxel correlation coefficients varied from −0.13 to 0.67 depending on the regions‐of‐interest evaluated, indicating tissue‐dependent correlation. The R2 value for estimating MAE solely using Bayesian uncertainty was 0.98, suggesting that the uncertainty of the proposed model was an ideal candidate for predicting synthetic CT error, without referring to the original CT.ConclusionThis study established a relationship between the Bayesian model uncertainty and synthetic CT uncertainty. A novel Bayesian deep network was proposed to generate a synthetic CT and estimate its uncertainty. Various metrics were used to thoroughly examine the relationship between the uncertainties of the proposed Bayesian model and the generated synthetic CT. Compared with existing approaches, the proposed model showed comparable CT number and dosimetric accuracies. The experiments showed that the proposed Bayesian model was capable of producing accurate synthetic CT, and was an effective indicator of the uncertainty and error associated with synthetic CT in MR‐only workflows.

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Pseudo-CT Generation for MRI-Only Radiation Therapy Treatment Planning: Comparison Among Patch-Based, Atlas-Based, and Bulk Density Methods

Cited by 37 publications

References 38 publications

Assessment of dosimetric and positioning accuracy of a magnetic resonance imaging-only solution for external beam radiotherapy of pelvic anatomy

Assessment of dosimetric and positioning accuracy of a magnetic resonance imaging-only solution for external beam radiotherapy of pelvic anatomy

Bulk Anatomical Density Based Dose Calculation for Patient-Specific Quality Assurance of MRI-Only Prostate Radiotherapy

A study of Bayesian deep network uncertainty and its application to synthetic CT generation for MR‐only radiotherapy treatment planning

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