Search Results for author: Berkin Bilgic

Found 22 papers, 13 papers with code

NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction

1 code implementation22 Jan 2024 Xinrui Jiang, Yohan Jun, Jaejin Cho, Mengze Gao, Xingwang Yong, Berkin Bilgic

Typical quantitative MRI (qMRI) methods estimate parameter maps after image reconstructing, which is prone to biases and error propagation.

MRI Reconstruction Zero-Shot Learning

SubZero: Subspace Zero-Shot MRI Reconstruction

1 code implementation28 Nov 2023 Heng Yu, Yamin Arefeen, Berkin Bilgic

Recently introduced zero-shot self-supervised learning (ZS-SSL) has shown potential in accelerated MRI in a scan-specific scenario, which enabled high-quality reconstructions without access to a large training dataset.

MRI Reconstruction Self-Supervised Learning

Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning Reconstruction

1 code implementation9 Aug 2023 Jaejin Cho, Yohan Jun, Xiaoqing Wang, Caique Kobayashi, Berkin Bilgic

In this study, we introduce a novel msEPI reconstruction approach called zero-MIRID (zero-shot self-supervised learning of Multi-shot Image Reconstruction for Improved Diffusion MRI).

Image Reconstruction Self-Supervised Learning

Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS

1 code implementation4 Jul 2023 Yohan Jun, Yamin Arefeen, Jaejin Cho, Shohei Fujita, Xiaoqing Wang, P. Ellen Grant, Borjan Gagoski, Camilo Jaimes, Michael S. Gee, Berkin Bilgic

Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques.

SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS

no code implementations28 Feb 2023 Yohan Jun, Jaejin Cho, Xiaoqing Wang, Michael Gee, P. Ellen Grant, Berkin Bilgic, Borjan Gagoski

Conclusion: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.

Self-Supervised Learning Transfer Learning

3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping

no code implementations1 Dec 2022 Zhifeng Chen, Congyu Liao, Xiaozhi Cao, Benedikt A. Poser, Zhongbiao Xu, Wei-Ching Lo, Manyi Wen, Jaejin Cho, Qiyuan Tian, Yaohui Wang, Yanqiu Feng, Ling Xia, Wufan Chen, Feng Liu, Berkin Bilgic

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping.

Image Reconstruction

SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data

no code implementations10 Nov 2022 Jiaxin Xiao, Zihan Li, Berkin Bilgic, Jonathan R. Polimeni, Susie Huang, Qiyuan Tian

Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a barrier to feasible and accessible implementation.

Denoising Super-Resolution

Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI

1 code implementation27 Aug 2022 Yamin Arefeen, Junshen Xu, Molin Zhang, Zijing Dong, Fuyixue Wang, Jacob White, Berkin Bilgic, Elfar Adalsteinsson

Purpose: Training auto-encoders on simulated signal evolution and inserting the decoder into the forward model improves reconstructions through more compact, Bloch-equation-based representations of signal in comparison to linear subspaces.

Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction

1 code implementation6 Feb 2022 Jaejin Cho, Borjan Gagoski, Taehyung Kim, Qiyuan Tian, Stephen Robert Frost, Itthi Chatnuntawech, Berkin Bilgic

Purpose: To propose a wave-encoded model-based deep learning (wave-MoDL) strategy for highly accelerated 3D imaging and joint multi-contrast image reconstruction, and further extend this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T2 preparation pulse (3D-QALAS).

Image Reconstruction

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI

1 code implementation14 Nov 2021 Qiyuan Tian, Ziyu Li, Qiuyun Fan, Jonathan R. Polimeni, Berkin Bilgic, David H. Salat, Susie Y. Huang

The noise in diffusion-weighted images (DWIs) decreases the accuracy and precision of diffusion tensor magnetic resonance imaging (DTI) derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR).

Image Denoising

Highly Accelerated EPI with Wave Encoding and Multi-shot Simultaneous Multi-Slice Imaging

1 code implementation3 Jun 2021 Jaejin Cho, Congyu Liao, Qiyuan Tian, Zijing Zhang, Jinmin Xu, Wei-Ching Lo, Benedikt A. Poser, V. Andrew Stenger, Jason Stockmann, Kawin Setsompop, Berkin Bilgic

We introduce wave encoded acquisition and reconstruction techniques for highly accelerated echo planar imaging (EPI) with reduced g-factor penalty and image artifacts.

Covariance-Free Sparse Bayesian Learning

no code implementations21 May 2021 Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba Ba

The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix.

MRI Reconstruction Uncertainty Quantification

Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI

no code implementations2 Apr 2021 Yamin Arefeen, Onur Beker, Jaejin Cho, Heng Yu, Elfar Adalsteinsson, Berkin Bilgic

Conclusion: SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.

SRDTI: Deep learning-based super-resolution for diffusion tensor MRI

1 code implementation17 Feb 2021 Qiyuan Tian, Ziyu Li, Qiuyun Fan, Chanon Ngamsombat, Yuxin Hu, Congyu Liao, Fuyixue Wang, Kawin Setsompop, Jonathan R. Polimeni, Berkin Bilgic, Susie Y. Huang

High-resolution diffusion tensor imaging (DTI) is beneficial for probing tissue microstructure in fine neuroanatomical structures, but long scan times and limited signal-to-noise ratio pose significant barriers to acquiring DTI at sub-millimeter resolution.

Super-Resolution

Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning

no code implementations30 Sep 2019 Daniel Polak, Itthi Chatnuntawech, Jaeyeon Yoon, Siddharth Srinivasan Iyer, Jong-Ho Lee, Peter Bachert, Elfar Adalsteinsson, Kawin Setsompop, Berkin Bilgic

We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-the-art reconstruction techniques.

Highly Accelerated Multishot EPI through Synergistic Machine Learning and Joint Reconstruction

no code implementations8 Aug 2018 Berkin Bilgic, Itthi Chatnuntawech, Mary Kate Manhard, Qiyuan Tian, Congyu Liao, Stephen F. Cauley, Susie Y. Huang, Jonathan R. Polimeni, Lawrence L. Wald, Kawin Setsompop

While msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image.

BIG-bench Machine Learning Image Reconstruction

Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network

2 code implementations29 May 2018 Itthi Chatnuntawech, Kittipong Tantisantisom, Paisan Khanchaitit, Thitikorn Boonkoom, Berkin Bilgic, Ekapol Chuangsuwanich

In this work, we develop a non-destructive rice variety classification system that benefits from the synergy between hyperspectral imaging and deep convolutional neural network (CNN).

Classification General Classification

Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet

1 code implementation15 Mar 2018 Jaeyeon Yoon, Enhao Gong, Itthi Chatnuntawech, Berkin Bilgic, Jingu Lee, Woojin Jung, Jingyu Ko, Hosan Jung, Kawin Setsompop, Greg Zaharchuk, Eung Yeop Kim, John Pauly, Jong-Ho Lee

The QSMnet maps of the test dataset were compared with those from TKD and MEDI for image quality and consistency in multiple head orientations.

Image and Video Processing

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