2 research outputs found
Learning Apparent Diffusion Coefficient Maps from Accelerated Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Model
Purpose: To accelerate radially sampled diffusion weighted spin-echo
(Rad-DW-SE) acquisition method for generating high quality apparent diffusion
coefficient (ADC) maps. Methods: A deep learning method was developed to
generate accurate ADC maps from accelerated DWI data acquired with the
Rad-DW-SE method. The deep learning method integrates convolutional neural
networks (CNNs) with vision transformers to generate high quality ADC maps from
accelerated DWI data, regularized by a monoexponential ADC model fitting term.
A model was trained on DWI data of 147 mice and evaluated on DWI data of 36
mice, with acceleration factors of 4x and 8x compared to the original
acquisition parameters. We have made our code publicly available at GitHub:
https://github.com/ymli39/DeepADC-Net-Learning-Apparent-Diffusion-Coefficient-Maps,
and our dataset can be downloaded at
https://pennpancreaticcancerimagingresource.github.io/data.html. Results:
Ablation studies and experimental results have demonstrated that the proposed
deep learning model generates higher quality ADC maps from accelerated DWI data
than alternative deep learning methods under comparison when their performance
is quantified in whole images as well as in regions of interest, including
tumors, kidneys, and muscles. Conclusions: The deep learning method with
integrated CNNs and transformers provides an effective means to accurately
compute ADC maps from accelerated DWI data acquired with the Rad-DW-SE method.Comment: Accepted by Magnetic Resonance in Medicin
DWI Metrics Differentiating Benign Intraductal Papillary Mucinous Neoplasms from Invasive Pancreatic Cancer: A Study in GEM Models
KPC (KrasG12D:Trp53R172H:Pdx1-Cre) and CKS (KrasG12D:Smad4L/L:Ptf1a-Cre) mice are genetically engineered mouse (GEM) models that capture features of human pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasms (IPMN), respectively. We compared these autochthonous tumors using quantitative imaging metrics from diffusion-weighted MRI (DW-MRI) and dynamic contrast enhanced (DCE)-MRI in reference to quantitative histological metrics including cell density, fibrosis, and microvasculature density. Our results revealed distinct DW-MRI metrics between the KPC vs. CKS model (mimicking human PDAC vs. IPMN lesion): the apparent diffusion coefficient (ADC) of CKS tumors is significantly higher than that of KPC, with little overlap (mean ± SD 2.24±0.2 vs. 1.66±0.2, p<10−10) despite intratumor and intertumor variability. Kurtosis index (KI) is also distinctively separated in the two models. DW imaging metrics are consistent with growth pattern, cell density, and the cystic nature of the CKS tumors. Coregistration of ex vivo ADC maps with H&E-stained sections allowed for regional comparison and showed a correlation between local cell density and ADC value. In conclusion, studies in GEM models demonstrate the potential utility of diffusion-weighted MRI metrics for distinguishing pancreatic cancer from benign pancreatic cysts such as IPMN