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Obtaining New Contrasts From a Single T1-Weighted Image                                                

Y Wu1, Y Ma2, N Kovalchuk1, J Du2, L Xing1*, (1) Stanford University, Palo Alto, CA, (2) University of California San Diego, San Diego, CA

Presentations

TH-IePD-TRACK 2-4 (Thursday, 7/29/2021) 12:30 PM - 1:00 PM [Eastern Time (GMT-4)]

Purpose: While MRI contrast is predetermined by imaging protocol, it can be retrospectively tuned, which is potentially useful for normalizing MRI data for radiomics. In this study, we present a new paradigm to obtain various contrasts from a single T1-weighted image.

Methods: We propose a contrast tuning framework that combines deep learning-based quantitative MRI with Bloch equations. Using deep neural networks, quantitative tissue relaxation parametric maps (T1 map, proton density map) and field map (B1 map) are predicted from a single T1 weighted image. Here, ground truth T1 map was obtained from four variable-flip-angle T1-weighted images and a B1 map (measured using the actual flip angle method); proton density map was calculated from T1-weighted image and T1 map. In these tasks, self-attention convolutional neural network is employed with unique shortcuts equipped and attention mechanism integrated. A total of 1,344 knee images from 56 subjects are utilized for training and testing in six-fold cross validation. 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).

Results: 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.

Conclusion: A new data-driven strategy is proposed for retrospective MRI contrast tuning from a single T1-weighted image, which requires no additional data acquisition.

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    Keywords

    Quantitative Imaging

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    Not Applicable / None Entered.

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