PURPOSE AND BACKGROUND: The purpose was to validate the accuracy of motion models derived from deformable registration from four-dimensional computed tomography (4DCT) and breath-hold contrast enhanced computed tomography (BHCCT) scans for liver SBRT. Additionally, the image quality of the time averaged mid-position (MidP) CT constructed using the detected motion model was assessed. MATERIALS AND METHODS: 4DCT and BHCCT liver scans of 11 patients were acquired with 1 or 2 fiducial markers. Using parametric sampling the markers were digitally removed. Phase-based optical flow was used to register the 4D frames and the BHCCT, and create MidP data. We compared the deformable registration of the markerless scans with the actual displacement of the markers to assess registration accuracy. The noise levels of the MidP scans were compared to those of the 4DCT and BHCCT data. RESULTS: We found an average misregistration of 1.8mm (± 0.5mm). The constructed MidPCT scan contained around three times less noise than the original 4D scan. The residual error between the MidPCT and the BHCCT was 3.0mm (± 0.9 mm). CONCLUSIONS: High precision deformable image registration of 4DCT and BHCCT liver cancer patients was achieved and used to create motion compensated MidPCT scans, with increased contrast-to-noise (CNR) levels. This improved visualisation of tumours and anatomy, facilitates radiotherapy treatment planning.
PURPOSE AND BACKGROUND: The purpose was to validate the accuracy of motion models derived from deformable registration from four-dimensional computed tomography (4DCT) and breath-hold contrast enhanced computed tomography (BHCCT) scans for liver SBRT. Additionally, the image quality of the time averaged mid-position (MidP) CT constructed using the detected motion model was assessed. MATERIALS AND METHODS: 4DCT and BHCCT liver scans of 11 patients were acquired with 1 or 2 fiducial markers. Using parametric sampling the markers were digitally removed. Phase-based optical flow was used to register the 4D frames and the BHCCT, and create MidP data. We compared the deformable registration of the markerless scans with the actual displacement of the markers to assess registration accuracy. The noise levels of the MidP scans were compared to those of the 4DCT and BHCCT data. RESULTS: We found an average misregistration of 1.8mm (± 0.5mm). The constructed MidPCT scan contained around three times less noise than the original 4D scan. The residual error between the MidPCT and the BHCCT was 3.0mm (± 0.9 mm). CONCLUSIONS: High precision deformable image registration of 4DCT and BHCCT liver cancerpatients was achieved and used to create motion compensated MidPCT scans, with increased contrast-to-noise (CNR) levels. This improved visualisation of tumours and anatomy, facilitates radiotherapy treatment planning.
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