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

Abstract

Purpose: Rigid image registration (RIR) accuracy is crucial for image guided radiotherapy (IGRT). However, existing clinical image registration assessment methods cannot separate and quantify RIR error sources. Herein, we develop an extension of the 'full circle method' for RIR consistency. Paired registration circuits are used to isolate sources of RIR error caused by reference dataset substitution, from those inherent to the underlying RIR. This approach was demonstrated in the context of MRI-only IGRT, assessing substitution of MRI-derived synthetic-CT (sCT) for conventional CT, in a cohort of rectal cancer patients. Materials and methods: Planning CT, MRI-derived sCT, and two CBCTs from seven rectal cancer patients were retrospectively registered with global and soft tissue clipbox based RIR. Paired registration circuits were constructed using two moving (cone beam CT) images and two reference images (CT and sCT), per patient. Differences between inconsistencies in registration circuits containing CT and sCT were used to determine changes in registration accuracy due to substitution of sCT for CT. Results: sCT was found to be equivalent to CT under global RIR, with median differences of 0.05 mm and 0.01°. Soft tissue clipbox based RIR with sCT exhibited gross misregistration (>5 mm or 3°) for 3 patients. Registration consistency was degraded compared to CT across the cohort, with median differences of 0.54 mm and 0.15°. Conclusion: A paired registration circuit methodology for assessing RIR accuracy without ground truth information was developed and demonstrated for MRI-only IGRT in rectal cancer. This highlighted a reduction in clipbox based RIR consistency when sCT was substituted for conventional CT. The developed method enabled separation of degraded registration accuracy, from other error sources within the overall registration inconsistency. This novel methodology is applicable to any RIR scenario and enables analysis of the change in RIR performance on modification of image data or process.

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

“…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.…”

“…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.…”

“…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.…”

An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology

“…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.…”

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

“…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.…”