no code implementations • 28 Dec 2023 • Hang Zhang, Thanh D. Nguyen, Jinwei Zhang, Renjiu Hu, Susan A. Gauthier, Yi Wang
We validated RimSet using simulated QSM images and an in vivo dataset of 172 MS subjects with 177 rim+ and 3986 rim-lesions.
no code implementations • 17 Nov 2023 • Renjiu Hu, Qihao Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
The trained network was further tested in a synthetic brain ASL image based on vasculature network extracted from magnetic resonance (MR) angiography.
no code implementations • 5 May 2023 • Jinwei Zhang, Alexey Dimov, Chao Li, Hang Zhang, Thanh D. Nguyen, Pascal Spincemaille, Yi Wang
Purpose: To improve the generalization ability of convolutional neural network (CNN) based prediction of quantitative susceptibility mapping (QSM) from high-pass filtered phase (HPFP) image.
1 code implementation • 22 Apr 2023 • Alexandra G. Roberts, Dominick J. Romano, Mert Şişman, Alexey V. Dimov, Pascal Spincemaille, Thanh D. Nguyen, Ilhami Kovanlikaya, Susan A. Gauthier, Yi Wang
To develop a tissue field filtering algorithm, called maximum Spherical Mean Value (mSMV), for reducing shadow artifacts in quantitative susceptibility mapping (QSM) of the brain without requiring brain tissue erosion. Residual background field is a major source of shadow artifacts in QSM.
no code implementations • 7 Apr 2023 • Jinwei Zhang, Thanh D. Nguyen, Eddy Solomon, Chao Li, Qihao Zhang, Jiahao Li, Hang Zhang, Pascal Spincemaille, Yi Wang
Results: The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without multi-contrast sampling pattern optimization or image feature fusion, and negligible bias and narrow 95% limits of agreement on regional T1, T2, T2* and QSM values were obtained by the under-sampled reconstructions compared to the fully sampled reconstruction.
1 code implementation • 1 Nov 2022 • Jinwei Zhang, Pascal Spincemaille, Hang Zhang, Thanh D. Nguyen, Chao Li, Jiahao Li, Ilhami Kovanlikaya, Mert R. Sabuncu, Yi Wang
In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM.
no code implementations • 4 May 2021 • Chao Li, Hang Zhang, Jinwei Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed.
no code implementations • 10 Mar 2021 • Jinwei Zhang, Hang Zhang, Chao Li, Pascal Spincemaille, Mert Sabuncu, Thanh D. Nguyen, Yi Wang
Quantitative imaging in MRI usually involves acquisition and reconstruction of a series of images at multi-echo time points, which possibly requires more scan time and specific reconstruction technique compared to conventional qualitative imaging.
no code implementations • 16 Oct 2020 • Tianyu Ma, Hang Zhang, Hanley Ong, Amar Vora, Thanh D. Nguyen, Ajay Gupta, Yi Wang, Mert Sabuncu
Our core idea is straightforward: A diverse ensemble of low precision and high recall models are likely to make different false positive errors (classifying background as foreground in different parts of the image), but the true positives will tend to be consistent.
no code implementations • 29 Sep 2020 • Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Susan A. Gauthier, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
Multiple sclerosis (MS) lesions occupy a small fraction of the brain volume, and are heterogeneous with regards to shape, size and locations, which poses a great challenge for training deep learning based segmentation models.
no code implementations • 13 Sep 2020 • Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed to capture global contextual information for performance enhancement.
no code implementations • 28 Jul 2020 • Jinwei Zhang, Hang Zhang, Alan Wang, Qihao Zhang, Mert Sabuncu, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
The previously established LOUPE (Learning-based Optimization of the Under-sampling Pattern) framework for optimizing the k-space sampling pattern in MRI was extended in three folds: firstly, fully sampled multi-coil k-space data from the scanner, rather than simulated k-space data from magnitude MR images in LOUPE, was retrospectively under-sampled to optimize the under-sampling pattern of in-vivo k-space data; secondly, binary stochastic k-space sampling, rather than approximate stochastic k-space sampling of LOUPE during training, was applied together with a straight-through (ST) estimator to estimate the gradient of the threshold operation in a neural network; thirdly, modified unrolled optimization network, rather than modified U-Net in LOUPE, was used as the reconstruction network in order to reconstruct multi-coil data properly and reduce the dependency on training data.
1 code implementation • 27 Feb 2020 • Hang Zhang, Jinwei Zhang, Qihao Zhang, Jeremy Kim, Shun Zhang, Susan A. Gauthier, Pascal Spincemaille, Thanh D. Nguyen, Mert R. Sabuncu, Yi Wang
Brain lesion volume measured on T2 weighted MRI images is a clinically important disease marker in multiple sclerosis (MS).