Identification and separation of rigid image registration error sources, demonstrated for MRI-only image guided radiotherapy

Tyyger, M.; Nix, Michael; Al-Qaisieh, B.; Teo, Mark; Speight, R.

doi:10.1088/2057-1976/ab81ad

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Our results are in line with those in the literature, whether comparing against the baseline findings from prostate studies [5][6][7][8][9][10] or the more relevant rectum study findings of Maspero [13] and Tyyger [14]. We found the systematic impact of sCT and MR on translations and rotations were <± 1 mm and <±0.5 • similarly to Maspero for sCT reference images.…”

Section: Tablesupporting

confidence: 90%

“…Their findings suggested that sCT could be utilised as a reference image for rectal CBCT registrations, with Maspero's [13] mean differences in translations and rotations when using sCT vs. CT <±1 mm and <±0.5 • respectively. However, Tyyger [14] also found in three patients' gross misregistration occurred when using the sCT and both studies had limited patient numbers, including only 3 female patients.…”

Section: Introductionmentioning

confidence: 97%

“…To the authors knowledge no studies have assessed CBCT patient positioning accuracy in a MR-only workflow for anal cancers, while two studies have assessed rectal cancer CBCT patient positioning using sCT, but not MR, as a reference image [13,14]. Maspero et al and Tyyger et al assessed the use of sCTs generated by commercially available sCT models using clinically available CBCT positioning software for ten (seven male, three female) and seven (all male) rectal cancer patients respectively.…”

Section: Introductionmentioning

confidence: 99%

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Patient position verification in magnetic-resonance imaging only radiotherapy of anal and rectal cancers

Bird

Beasley

Nix

et al. 2021

Physics and Imaging in Radiation Oncology

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Background and Purpose: Magnetic resonance (MR)-only treatment pathways require either the MR-simulation or synthetic-computed tomography (sCT) as an alternative reference image for cone beam computed tomography (CBCT) patient position verification. This study assessed whether using T2 MR or sCT as CBCT reference images introduces systematic registration errors as compared to CT for anal and rectal cancers. Materials and Methods: A total of 32 patients (18 rectum,14 anus) received pre-treatment CT-and T2 MRsimulation. Routine treatment CBCTs were acquired. sCTs were generated using a validated research model. The local clinical registration protocol, using a grey-scale registration algorithm, was performed for 216 CBCTs using CT, MR and sCT as the reference image. Linear mixed effects modelling identified systematic differences between modalities. Results: Systematic translation and rotation differences to CT for MR were − 0.3 to + 0.3 mm and − 0.1 to 0.4 • for anal cancers and − 0.4 to 0.0 mm and 0.0 to 0.1 • for rectal cancers, and for sCT were − 0.4 to + 0.8 mm, − 0.1 to 0.2 • for anal cancers and − 0.6 to + 0.2 mm, − 0.1 to + 0.1 • for rectal cancers. Conclusions: T2 MR or sCT can successfully be used as reference images for anal and rectal cancer CBCT position verification with systematic differences to CT <±1 mm and <±0.5 • . Clinical enabling of alternative modalities as reference images by vendors is required to reduce challenges associated with their use.

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Section: Tablesupporting

confidence: 90%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Patient position verification in magnetic-resonance imaging only radiotherapy of anal and rectal cancers

Bird

Beasley

Nix

et al. 2021

Physics and Imaging in Radiation Oncology

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“…As an effective tool, computer-aided diagnosis plays an increasingly important role in the actual diagnosis and treatment of orthopedic diseases [ 24 ]. In order to facilitate for doctors operating or showing the effect of algorithm realization, the realization of computer-aided diagnosis technology usually needs the help of software system.…”

Section: Design Of Computer-aided Diagnosis System For Orthopedic Diseasesmentioning

confidence: 99%

An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology

Guo

Chen

2021

Journal of Healthcare Engineering

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There are many kinds of orthopedic diseases with complex professional background, and it is easy to miss diagnosis and misdiagnosis. The computer-aided diagnosis system of orthopedic diseases based on the key technology of medical image processing can locate and display the lesion location area by visualization, measuring and providing disease diagnosis indexes. It is of great significance to assist orthopedic doctors to diagnose orthopedic diseases from the perspective of visual vision and quantitative indicators, which can improve the diagnosis rate and accuracy of orthopedic diseases, reduce the pain of patients, and shorten the treatment time of diseases. To solve the problem of possible spatial inconsistency of medical images of orthopedic diseases, we propose an image registration method based on volume feature point selection and Powell. Through the linear search strategy of golden section method and Powell algorithm optimization, the best spatial transformation parameters are found, which maximizes the normalized mutual information between images to be registered, thus ensuring the consistency of two-dimensional spatial positions. According to the proposed algorithm, a computer-aided diagnosis system of orthopedic diseases is developed and designed independently. The system consists of five modules, which can complete many functions such as medical image input and output, algorithm processing, and effect display. The experimental results show that the system developed in this paper has good results in cartilage tissue segmentation, bone and urate agglomeration segmentation, urate agglomeration artifact removal, two-dimensional and three-dimensional image registration, and visualization. The system can be applied to clinical gout and cartilage defect diagnosis and evaluation, providing sufficient basis to assist doctors in making diagnosis decisions.

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“…In future, we will train PxCGAN with an increased number of our CT-MRI datasets. Another limitation is that the ground truth in image quality and geometric assessment for sMRI can have inaccuracies from a rigid image registration between CT and T2-weighted MRI as performed in the clinic (Hamdan et al 2017, Padgett et al 2018, Tyyger et al 2020. Even though the anatomical changes between the two scans are as small as possible, the bladder-filling difference might have confused the training of PxCGAN.…”

Section: Discussionmentioning

confidence: 99%

Prostate segmentation accuracy using synthetic MRI for high-dose-rate prostate brachytherapy treatment planning

et al. 2023

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Objective: Both CT and MRI images are acquired for HDR prostate brachytherapy patients at our institution. CT is used to identify catheters and MRI is used to segment the prostate. To address scenarios of limited MRI access, we developed a novel Generative Adversarial Network (GAN) to generate synthetic MRI (sMRI) from CT with sufficient soft-tissue contrast to provide accurate prostate segmentation without MRI (rMRI).Approach: Our hybrid GAN, PxCGAN was trained utilizing 58 paired CT-MRI datasets from our HDR prostate patients. Using 20 independent CT-MRI datasets, the image quality of sMRI was tested using MAE, MSE, PSNR, and SSIM. These metrics were compared with the metrics of sMRI generated using Pix2Pix and CycleGAN. The accuracy of prostate segmentation on sMRI was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD) on the prostate delineated by three radiation oncologists (RO) on sMRI vs. rMRI. To estimate inter-observer variability (IOV), these metrics between prostate contours delineated by each RO on rMRI and the prostate delineated by treating RO on rMRI ("gold standard") were calculated.Main results: Qualitatively, sMRI images show enhanced soft-tissue contrast at the prostate boundary compared with CT scans. For MAE and MSE, PxCGAN and CycleGAN have similar results, while the MAE of PxCGAN is smaller than that of Pix2Pix. PSNR and SSIM of PxCGAN are significantly higher than Pix2Pix and CycleGAN (p<0.01). The DSC for sMRI vs. rMRI is within the range of the IOV, while HD for sMRI vs. rMRI is smaller than the HD for IOV for all ROs (p≤0.03).Significance: PxCGAN generates sMRI images from the treatment planning CT scans which depicts enhanced soft-tissue contrast at the prostate boundary. The accuracy of prostate segmentation on sMRI compared to rMRI is within the segmentation variation on rMRI between different radiation oncologists.

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Identification and separation of rigid image registration error sources, demonstrated for MRI-only image guided radiotherapy

Cited by 6 publications

References 19 publications

Patient position verification in magnetic-resonance imaging only radiotherapy of anal and rectal cancers

Patient position verification in magnetic-resonance imaging only radiotherapy of anal and rectal cancers

An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology

Prostate segmentation accuracy using synthetic MRI for high-dose-rate prostate brachytherapy treatment planning

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