bibtext: @InProceedings{ Zor_EnhancedquickDWI_MICCAI2024,
                   author = { Zormpas-Petridis, Konstantinos and Candito, Antonio and Messiou, Christina and Koh, Dow-Mu and Blackledge, Matthew D. },
                   title = { { Enhanced-quickDWI: Achieving equivalent clinical quality by denoising heavily sub-sampled diffusion-weighted imaging data } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: Whole-body diffusion-weighted imaging (DWI) is a sensitive tool for assessing the spread of metastatic bone malignancies. It offers voxel-wise calculation of apparent diffusion coefficient (ADC) which correlates with tissue cellularity, providing a potential imaging biomarker for tumour re-sponse assessment. However, DWI is an inherently noisy technique requiring many signal aver-ages over multiple b-values, leading to times of up to 30 minutes for a whole-body exam. We present a novel neural network implicitly designed to provide high-quality images from heavily sub-sampled diffusion data (only 1 signal average) which allow whole-body acquisitions of ~5 minutes. We demonstrate that our network can achieve equivalent quality to the clinical b-value and ADC images in a radiological multi-reader study of 100 patients for whole-body and abdo-men-pelvis data. We also achieved good agreement to the quantitative values of clinical images within multi-lesion segmentations in 16 patients compared to a previous approach.
title: Enhanced-quickDWI: Achieving equivalent clinical quality by denoising heavily sub-sampled diffusion-weighted imaging data
authors: Zormpas-Petridis, Konstantinos Candito, Antonio Messiou, Christina Koh, Dow-Mu Blackledge, Matthew D.