The clinical adaptation of 4D-MRI in respiratory motion management is limited by the low image quality and motion artifacts of 4D-MRI sequences.This study aims to develop a novel artifact Map-guided Nonlocal mean(AM-N...The clinical adaptation of 4D-MRI in respiratory motion management is limited by the low image quality and motion artifacts of 4D-MRI sequences.This study aims to develop a novel artifact Map-guided Nonlocal mean(AM-NLM)technique that can be integrated into the clinical 4D-MRI workflow to suppress motion artifacts and enhance image quality.The AM-NLM technique was developed and tested on 4D-MR images of 28 liver cancer patients.A multiphase motion field was computed on the frames with the minimum average localized gradient entropy for each phase to generate a full set of improved quality 4D-MR images.Artifact maps were calculated based on the local image sharpness to guide nonlocal averaging,and a set of denoised eight-phase 4D-MR images was finally generated.The 4D-MR images were evaluated for image quality and motion accuracy.Conventional 4D-MRI approaches were also evaluated for comparison.AM-NLM 4D-MR images have significant improvements in SNR and CNR compared to the original 4D-MR images.High motion accuracy was achieved for AM-NLM 4D-MR images because the average deviation in the diaphragm position from the mean value for each phase was at the subvoxel level.Both qualitative and quantitative results suggested that the 4D-MR images generated by the AM-NLM technique had high image quality while maintaining image sharpness and motion accuracy.The AM-NLM technique has shown capability of suppressing motion artifacts and enhancing image quality of clinically acquired 4D-MR images,making it a promising technique in applications of 4D-MRI in radiotherapy.展开更多
基金the MR Imaging Unit at The University of Hong Kong for providing the research MRI scanning services.This work was partly supported by the General Research Fund(GRF)[grant numbers 15102118,15102219,15104323,and 15104822]the Uni-versity Grants Committee,Health and Medical Research Fund(HMRF)[grant numbers 06173276 and 10211606]+1 种基金the Health Bureau,Innova-tion and TechnologySupport Programme[grant number ITS/049/22FP]the NSFC Young Scientist Fund[grant number 82202941]from the People's Republic of China.
文摘The clinical adaptation of 4D-MRI in respiratory motion management is limited by the low image quality and motion artifacts of 4D-MRI sequences.This study aims to develop a novel artifact Map-guided Nonlocal mean(AM-NLM)technique that can be integrated into the clinical 4D-MRI workflow to suppress motion artifacts and enhance image quality.The AM-NLM technique was developed and tested on 4D-MR images of 28 liver cancer patients.A multiphase motion field was computed on the frames with the minimum average localized gradient entropy for each phase to generate a full set of improved quality 4D-MR images.Artifact maps were calculated based on the local image sharpness to guide nonlocal averaging,and a set of denoised eight-phase 4D-MR images was finally generated.The 4D-MR images were evaluated for image quality and motion accuracy.Conventional 4D-MRI approaches were also evaluated for comparison.AM-NLM 4D-MR images have significant improvements in SNR and CNR compared to the original 4D-MR images.High motion accuracy was achieved for AM-NLM 4D-MR images because the average deviation in the diaphragm position from the mean value for each phase was at the subvoxel level.Both qualitative and quantitative results suggested that the 4D-MR images generated by the AM-NLM technique had high image quality while maintaining image sharpness and motion accuracy.The AM-NLM technique has shown capability of suppressing motion artifacts and enhancing image quality of clinically acquired 4D-MR images,making it a promising technique in applications of 4D-MRI in radiotherapy.
