Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning

Guerreiro, Filipa; Burgos, Ninon; Dunlop, A.; Wong, Kevin; Petkar, Imran; Nutting, C.; Harrington, Kevin; Bhide, Shree; Newbold, Kate; Dearnaley, D.; deSouza, Nandita M.; Morgan, Veronica A.; McClelland, Jamie R.; Nill, Simeon; Cardoso, M. Jorge; Ourselin, Sébastien; Oelfke, Uwe; Knopf, Antje

doi:10.1016/j.ejmp.2017.02.017

Cited by 59 publications

(53 citation statements)

References 31 publications

(70 reference statements)

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“…Several methods have been proposed in literature to produce a synthetic computed tomography (sCT). The two main approaches are voxel‐based conversion and atlas‐based methods . With voxel‐based methods, often nonconventional sequences like ultrashort or zero echo time sequences are acquired to enhance bone visualization and aid the differentiation between air and bone on MRI .…”

Section: Introductionmentioning

confidence: 99%

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

et al. 2019

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Purpose To develop and evaluate a patch‐based convolutional neural network (CNN) to generate synthetic computed tomography (sCT) images for magnetic resonance (MR)‐only workflow for radiotherapy of head and neck tumors. A patch‐based deep learning method was chosen to improve robustness to abnormal anatomies caused by large tumors, surgical excisions, or dental artifacts. In this study, we evaluate whether the generated sCT images enable accurate MR‐based dose calculations in the head and neck region. Methods We conducted a retrospective study on 34 patients with head and neck cancer who underwent both CT and MR imaging for radiotherapy treatment planning. To generate the sCTs, a large field‐of‐view T2‐weighted Turbo Spin Echo MR sequence was used from the clinical protocol for multiple types of head and neck tumors. To align images as well as possible on a voxel‐wise level, CT scans were nonrigidly registered to the MR (CTreg). The CNN was based on a U‐net architecture and consisted of 14 layers with 3 × 3 × 3 filters. Patches of 48 × 48 × 48 were randomly extracted and fed into the training. sCTs were created for all patients using threefold cross validation. For each patient, the clinical CT‐based treatment plan was recalculated on sCT using Monaco TPS (Elekta). We evaluated mean absolute error (MAE) and mean error (ME) within the body contours and dice scores in air and bone mask. Also, dose differences and gamma pass rates between CT‐ and sCT‐based plans inside the body contours were calculated. Results sCT generation took 4 min per patient. The MAE over the patient population of the sCT within the intersection of body contours was 75 ± 9 Hounsfield Units (HU) (±1 SD), and the ME was 9 ± 11 HU. Dice scores of the air and bone masks (CTreg vs sCT) were 0.79 ± 0.08 and 0.70 ± 0.07, respectively. Dosimetric analysis showed mean deviations of −0.03% ± 0.05% for dose within the body contours and −0.07% ± 0.22% inside the >90% dose volume. Dental artifacts obscuring the CT could be circumvented in the sCT by the CNN‐based approach in combination with Turbo Spin Echo (TSE) magnetic resonance imaging (MRI) sequence that typically is less prone to susceptibility artifacts. Conclusions The presented CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate, and can therefore be used for MR‐only radiotherapy treatment planning of the head and neck.

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Section: Introductionmentioning

confidence: 99%

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

et al. 2019

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“…So far, many of the algorithms proposed in the literature and those implemented clinically have demonstrated promising performance and as such, they seem ready for use in clinical setting with acceptable errors . Aside from the brain, MRI‐guided radiation therapy for the prostate in the pelvic region has attracted much attention recently as some algorithms are becoming clinically available .…”

Section: Introductionmentioning

confidence: 99%

Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI‐guided radiation planning in the pelvic region

et al. 2018

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Overall, machine learning and advanced atlas-based methods exhibited promising performance by achieving reliable organ segmentation and synthetic CT generation. DCNN appears to have slightly better performance by achieving accurate automated organ segmentation and relatively small dosimetric errors (followed closely by advanced atlas-based methods, which in some cases achieved similar performance). However, the DCNN approach showed higher vulnerability to anatomical variation, where a greater number of outliers was observed with this method. Considering the dosimetric results obtained from the evaluated methods, the challenge of electron density estimation from MR images can be resolved with a clinically tolerable error.

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“…The estimation method utilized a synthetic CT in order to match DRRs to the on‐board projections in the data fidelity constraint. Different studies have been to done to investigate various ways of generating synthetic CT images, including use of bulk densities, voxel‐based intensity conversions, and deformable registration‐based methods . Each approach has its advantages and limitations.…”

Section: Discussionmentioning

confidence: 99%

“…In the discrepancy region, mean HU and noise values from a nearby region in the deformed CT are used to fill in realistic heterogeneous HU values. The internal textures of the liver including the tumor region were generated in the deformed CT directly based on the deformable registration, similar as the approach in other previous studies . The sCT prior is then used to optimize the deformation field map using the MMFD algorithm.…”

Section: Methodsmentioning

confidence: 99%

A Novel method to generate on‐board 4D MRI using prior 4D MRI and on‐board kV projections from a conventional LINAC for target localization in liver SBRT

et al. 2018

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Purpose: On-board MRI can provide superb soft tissue contrast for improving liver SBRT localization. However, the availability of on-board MRI in clinics is extremely limited. On the contrary, on-board kV imaging systems are widely available on radiotherapy machines, but its capability to localize tumors in soft tissue is limited due to its poor soft tissue contrast. This study aims to explore the feasibility of using an on-board kV imaging system and patient prior knowledge to generate on-board four-dimensional (4D)-MRI for target localization in liver SBRT. Methods: Prior 4D MRI volumes were separated into end of expiration (EOE) phase (MRI prior ) and all other phases. MRI prior was used to generate a synthetic CT at EOE phase (sCT prior ). On-board 4D MRI at each respiratory phase was considered a deformation of MRI prior . The deformation field map (DFM) was estimated by matching DRRs of the deformed sCT prior to on-board kV projections using a motion modeling and free-form deformation optimization algorithm. The on-board 4D MRI method was evaluated using both XCAT simulation and real patient data. The accuracy of the estimated on-board 4D MRI was quantitatively evaluated using Volume Percent Difference (VPD), Volume Dice Coefficient (VDC), and Center of Mass Shift (COMS). Effects of scan angle and number of projections were also evaluated. Results: In the XCAT study, VPD/VDC/COMS among all XCAT scenarios were 10.16 AE 1.31%/ 0.95 AE 0.01/0.88 AE 0.15 mm using orthogonal-view 30°scan angles with 102 projections. The onboard 4D MRI method was robust against the various scan angles and projection numbers evaluated. In the patient study, estimated on-board 4D MRI was generated successfully when compared to the "reference on-board 4D MRI" for the liver patient case. Conclusions: A method was developed to generate on-board 4D MRI using prior 4D MRI and onboard limited kV projections. Preliminary results demonstrated the potential for MRI-based image guidance for liver SBRT using only a kV imaging system on a conventional LINAC.

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Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning

Cited by 59 publications

References 31 publications

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI‐guided radiation planning in the pelvic region

A Novel method to generate on‐board 4D MRI using prior 4D MRI and on‐board kV projections from a conventional LINAC for target localization in liver SBRT

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