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• W3209644378 abstract "Purpose/Objective(s)While versatile soft tissue contrasts are achievable in MRI, contrast attainable from every MRI scan is predetermined by imaging protocol. Retrospective tuning of MRI contrast provides an opportunity to presumably change imaging parameter values without actual data acquisition. In this study, we present a new paradigm to obtain various contrasts from a single T1-weighted image. In this way, MRI data acquired at different medical centers can be normalized and used for large-scaled radiomics analysis.Materials/MethodsA novel framework is proposed for retrospective contrast tuning, which combines deep learning-based quantitative MRI with Bloch equations. Theoretically, retrospective change of MRI contrast is achievable by applying Bloch equations on tissue relaxation parametric maps. However, in practice, quantitative MRI is seldom acquired due to long scan time. Leveraging from the capability of deep learning, we propose a quantitative MRI approach to extract tissue relaxation parametric maps from a single MR image without extra data acquisition. Using deep neural networks, we predict quantitative tissue relaxation parametric maps (T1 map, proton density map) and radiofrequency field map (B1 map) from a single T1-weighted image. Subsequently, various MRI contrasts are obtained from estimated parametric maps with the application of Bloch equations. The principle is validated on knee MRI. A total of 1,344 slice images from 56 subjects were retrospectively collected. For every subject, T1 map was measured from four variable flip angles T1-weighted images and a B1 map (obtained using the actual flip angle method), and proton density map was calculated from T1-weighted image and T1 map. These maps are used as the ground truth. In our study, every quantitative parametric map is predicted from a single T1 weighted image using a self-attention convolutional neural network equipped with unique shortcuts and attention mechanism (to make efficient use of non-local information). All the images are utilized for training and testing with the six-fold cross validation strategy adopted. Given estimated parametric maps, MR images with different contrasts are generated with the application of Bloch equations. While a wide spectrum of contrasts can be obtained with various imaging parameter values, the result is only validated at certain contrasts (corresponding to variable flip angles).ResultsHigh accuracy has been achieved in quantitative parametric maps and qualitative images. From a T1-weighted image, quantitative T1 map, proton density map and B1 map are predicted with an averaged correlation coefficient of 0.95∼0.99 and L1 error of 0.02∼0.09; and T1-weighted images corresponding to alternative flip angles are obtained with an averaged correlation coefficient of 0.97∼0.99 and L1 error of 0.04∼0.09.ConclusionA new data-driven strategy is proposed for retrospective MRI contrast tuning from a single T1-weighted image, which requires no additional data acquisition. While versatile soft tissue contrasts are achievable in MRI, contrast attainable from every MRI scan is predetermined by imaging protocol. Retrospective tuning of MRI contrast provides an opportunity to presumably change imaging parameter values without actual data acquisition. In this study, we present a new paradigm to obtain various contrasts from a single T1-weighted image. In this way, MRI data acquired at different medical centers can be normalized and used for large-scaled radiomics analysis. A novel framework is proposed for retrospective contrast tuning, which combines deep learning-based quantitative MRI with Bloch equations. Theoretically, retrospective change of MRI contrast is achievable by applying Bloch equations on tissue relaxation parametric maps. However, in practice, quantitative MRI is seldom acquired due to long scan time. Leveraging from the capability of deep learning, we propose a quantitative MRI approach to extract tissue relaxation parametric maps from a single MR image without extra data acquisition. Using deep neural networks, we predict quantitative tissue relaxation parametric maps (T1 map, proton density map) and radiofrequency field map (B1 map) from a single T1-weighted image. Subsequently, various MRI contrasts are obtained from estimated parametric maps with the application of Bloch equations. The principle is validated on knee MRI. A total of 1,344 slice images from 56 subjects were retrospectively collected. For every subject, T1 map was measured from four variable flip angles T1-weighted images and a B1 map (obtained using the actual flip angle method), and proton density map was calculated from T1-weighted image and T1 map. These maps are used as the ground truth. In our study, every quantitative parametric map is predicted from a single T1 weighted image using a self-attention convolutional neural network equipped with unique shortcuts and attention mechanism (to make efficient use of non-local information). All the images are utilized for training and testing with the six-fold cross validation strategy adopted. Given estimated parametric maps, MR images with different contrasts are generated with the application of Bloch equations. While a wide spectrum of contrasts can be obtained with various imaging parameter values, the result is only validated at certain contrasts (corresponding to variable flip angles). High accuracy has been achieved in quantitative parametric maps and qualitative images. From a T1-weighted image, quantitative T1 map, proton density map and B1 map are predicted with an averaged correlation coefficient of 0.95∼0.99 and L1 error of 0.02∼0.09; and T1-weighted images corresponding to alternative flip angles are obtained with an averaged correlation coefficient of 0.97∼0.99 and L1 error of 0.04∼0.09. A new data-driven strategy is proposed for retrospective MRI contrast tuning from a single T1-weighted image, which requires no additional data acquisition." @default.
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• W3209644378 date "2021-11-01" @default.
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• W3209644378 title "Retrospective Tuning of MRI Contrast From a Single T1-Weighted Image" @default.
• W3209644378 doi "https://doi.org/10.1016/j.ijrobp.2021.07.493" @default.
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