Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

Purpose: Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b -value images and associated bias in quantitative ADC values. Deep learning -based reconstruction and denoising may provide a solution to address this challenge. Methods: The effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C -SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning -based technique (C -SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation ( CV ) and non -parametric statistical tests. Results: The simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV increased from 0.037 to 0.508. At high acceleration and low flip angle, C -SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV ={0.508, 0.426 and 0.254} were obtained for {SENSE, C -SENSE, C -SENSE AI}, the improvement by C -SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C -SENSE AI vs. C -SENSE and p < 0.001 for C -SENSE AI vs. SENSE; CCC: non -overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV ={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C -SENSE AI as compared to C -SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non -overlapping CI). Conclusion: ADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep -learning based C -SENSE AI reconstruction.