Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy

Palmér, Emilia; Karlsson, Anna; Nordström, Fredrik; Petruson, Karin; Siversson, Carl; Ljungberg, Maria; Sohlin, Maja

doi:10.1016/j.phro.2020.12.007

Cited by 30 publications

(32 citation statements)

References 34 publications

(33 reference statements)

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“…for brain, 65 pelvis 116 in I, and for H&N, thorax, and pelvis in II. 66 In the last month, Palmer et al 198 also reported using a prereleased version of a DL-based sCT generation approach for H&N in MR-only RT. Another essential aspect that needs to be satisfied is the compliance to the currently adopted regulations, 199 where vendors can offer vital support.…”

Section: Benefits and Challenges For Clinical Implementationsmentioning

confidence: 99%

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

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Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy, and III) derive attenuation maps for the correction of positron emission tomography (PET).Appropriate database searching was performed on journal articles published between January 2014 and December 2020.The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DLbased sCT generation was evaluated, assessing the clinical readiness of the presented methods.

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Section: Benefits and Challenges For Clinical Implementationsmentioning

confidence: 99%

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

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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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“…The sCT images were generated using the CE approved sCT generation software MRI Planner (v 2.2, Spectronic Medical, AB, Helsingborg, Sweden), previously validated for both brain and head and neck cancer ( 8 , 20 ). The software is deep learning-based and utilizes a 3D deep convolutional neural network to generate sCT images based on Dixon images (fat, water, in-phase and out-of-phase).…”

Section: Methodsmentioning

confidence: 99%

Prospective Clinical Feasibility Study for MRI-Only Brain Radiotherapy

et al. 2022

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ObjectivesMRI-only radiotherapy (RT) provides a workflow to decrease the geometric uncertainty introduced by the image registration process between MRI and CT data and to streamline the RT planning. Despite the recent availability of validated synthetic CT (sCT) methods for the head region, there are no clinical implementations reported for brain tumors. Based on a preceding validation study of sCT, this study aims to investigate MRI-only brain RT through a prospective clinical feasibility study with endpoints for dosimetry and patient setup.Material and MethodsTwenty-one glioma patients were included. MRI Dixon images were used to generate sCT images using a CE-marked deep learning-based software. RT treatment plans were generated based on MRI delineated anatomical structures and sCT for absorbed dose calculations. CT scans were acquired but strictly used for sCT quality assurance (QA). Prospective QA was performed prior to MRI-only treatment approval, comparing sCT and CT image characteristics and calculated dose distributions. Additional retrospective analysis of patient positioning and dose distribution gamma evaluation was performed.ResultsTwenty out of 21 patients were treated using the MRI-only workflow. A single patient was excluded due to an MRI artifact caused by a hemostatic substance injected near the target during surgery preceding radiotherapy. All other patients fulfilled the acceptance criteria. Dose deviations in target were within ±1% for all patients in the prospective analysis. Retrospective analysis yielded gamma pass rates (2%, 2 mm) above 99%. Patient positioning using CBCT images was within ± 1 mm for registrations with sCT compared to CT.ConclusionWe report a successful clinical study of MRI-only brain radiotherapy, conducted using both prospective and retrospective analysis. Synthetic CT images generated using the CE-marked deep learning-based software were clinically robust based on endpoints for dosimetry and patient positioning.

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Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy

Abstract: Highlights The geometry of the synthetic CT is comparable to the CT in the H&N region. Synthetic CT in the H&N region provides similar absorbed dose calculation as the CT. Absorbed dose calculations in the dental region could benefit from using synthetic CT.

Cited by 30 publications

References 34 publications

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

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

Prospective Clinical Feasibility Study for MRI-Only Brain Radiotherapy

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