A technique to generate synthetic CT from MRI for abdominal radiotherapy

Hsu, Shu-Hui; DuPre, Pamela; Peng, Qi

doi:10.1002/acm2.12816

Cited by 16 publications

(6 citation statements)

References 22 publications

(46 reference statements)

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“…CT is also essential to achieve accurate radiation dose calculation for radiotherapy [12,18,19]. Several approaches have recently been proposed to generate synthetic CT (sCT) from MR images [20][21][22][23]. However, MR-derived sCT is challenged by the variety of tissue types and bowel gas present in the pelvic region.…”

Section: Introductionmentioning

confidence: 99%

Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients

et al. 2021

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Background: Radiotherapy (RT) planning for cervical cancer patients entails the acquisition of both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Further, molecular imaging by Positron Emission Tomography (PET) could contribute to target volume delineation as well as treatment response monitoring. The objective of this study was to investigate the feasibility of a PET/MRIonly RT planning workflow of patients with cervical cancer. This includes attenuation correction (AC) of MRI hardware and dedicated positioning equipment as well as evaluating MRI-derived synthetic CT (sCT) of the pelvic region for positioning verification and dose calculation to enable a PET/MRIonly setup. Material and methods: 16 patients underwent PET/MRI using a dedicated RT setup after the routine CT (or PET/CT), including eight pilot patients and eight cervical cancer patients who were subsequently referred for RT. Data from 18 patients with gynecological cancer were added for training a deep convolutional neural network to generate sCT from Dixon MRI. The mean absolute difference between the dose distributions calculated on sCT and a reference CT was measured in the RT target volume and organs at risk. PET AC by sCT and a reference CT were compared in the tumor volume. Results: All patients completed the examination. sCT was inferred for each patient in less than 5 s. The dosimetric analysis of the sCT-based dose planning showed a mean absolute error (MAE) of 0.17 ± 0.12 Gy inside the planning target volumes (PTV). PET images reconstructed with sCT and CT had no significant difference in quantification for all patients. Conclusions: These results suggest that multiparametric PET/MRI can be successfully integrated as a one-stop-shop in the RT workflow of patients with cervical cancer.

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Section: Introductionmentioning

confidence: 99%

Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients

et al. 2021

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“…With the advent of MRI-linac devices, in which an MRI is merged with a linear accelerator, the MRI is also the only available imaging modality. Fuzzy c-means clustering has been used to classify tissues head and neck and abdominal MRIs, which are then mapped based on attenuation properties into a synthetic CT representing most probably Hounsfield units [136,137]. CNNs and GANs have proven capable of generating high accuracy synthetic CTs from brain, head and neck and liver MRIs for the purposes of photon and proton treatment planning, as shown in Fig.…”

Section: Treatment Planningmentioning

confidence: 99%

Machine learning applications in radiation oncology

Field

Hardcastle

Jameson

et al. 2021

Physics and Imaging in Radiation Oncology

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Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.

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“…Because of the lack of tissue density information needed for dose calculation in MRgRT, an ED map was generated from MRI to allow for adaptive planning based on daily MRI. To address this issue, several approaches have been developed to generate ED maps also called synthetic computed tomography (syCT) from MRI (MRI-based syCT): i) the bulk density assignment (12)(13)(14)(15)(16)(17)(18)(19) consisting in a direct density assignment methods that determine volumes of interest (tissues or organs) on the patient's MRI and assign them a given density; ii) atlas-based method (20)(21)(22) based on atlas registration that spatially map an image (for example, CT) to the patient's MRI, merging them to generate a pseudo CT scan; and iii) voxel-by-voxel conversion (23-27) and deep learning (28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) are based on statistical learning model relationships between CT and MRI intensities and then apply the resulting model to the patient's MRI. As an emerging method, deep learning indeed shows promising results, reflecting in high accuracy, automation, and efficiency in the generation of syCT from MRI.…”

Section: Introductionmentioning

confidence: 99%

Improving the clinical workflow of a MR-Linac by dosimetric evaluation of synthetic CT

et al. 2022

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Adaptive radiotherapy performed on the daily magnetic resonance imaging (MRI) is an option to improve the treatment quality. In the adapt-to-shape workflow of 1.5-T MR-Linac, the contours of structures are adjusted on the basis of patient daily MRI, and the adapted plan is recalculated on the MRI-based synthetic computed tomography (syCT) generated by bulk density assignment. Because dosimetric accuracy of this strategy is a priority and requires evaluation, this study aims to explore the usefulness of adding an assessment of dosimetric errors associated with recalculation on syCT to the clinical workflow. Sixty-one patients, with various tumor sites, treated using a 1.5-T MR-Linac were included in this study. In Monaco V5.4, the target and organs at risk (OARs) were contoured, and a reference CT plan that contains information about the outlined contours, their average electron density (ED), and the priority of ED assignment was generated. To evaluate the dosimetric error of syCT caused by the inherent approximation within bulk density assignment, the reference CT plan was recalculated on the syCT obtained from the reference CT by forcing all contoured structures to their mean ED defined on the reference plan. The dose–volume histogram (DVH) and dose distribution of the CT and syCT plan were compared. The causes of dosimetric discrepancies were investigated, and the reference plan was reworked to minimize errors if needed. For 54 patients, gamma analysis of the dose distribution on syCT and CT show a median pass rate of 99.7% and 98.5% with the criteria of 3%/3 mm and 2%/2 mm, respectively. DVH difference of targets and OARs remained less than 1.5% or 1 Gy. For the remaining patients, factors (i.e., inappropriate ED assignments) influenced the dosimetric agreement of the syCT vs. CT reference DVH by up to 21%. The causes of the errors were promptly identified, and the DVH dosimetry was realigned except for two lung treatments for which a significant discrepancy remained. The recalculation on the syCT obtained from the planning CT is a powerful tool to assess and decrease the minimal error committed during the adaptive plan on the MRI-based syCT.

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A technique to generate synthetic CT from MRI for abdominal radiotherapy

Cited by 16 publications

References 22 publications

Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients

Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients

Machine learning applications in radiation oncology

Improving the clinical workflow of a MR-Linac by dosimetric evaluation of synthetic CT

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