Evidence map›Paper›PMID 39566835›Full record

ArticleMagnetic resonance imaging2025

Blip-up blip-down circular EPI (BUDA-cEPI) for distortion-free dMRI with rapid unrolled deep learning reconstruction.

Uten Yarach, Itthi Chatnuntawech, Congyu Liao, Surat Teerapittayanon, Siddharth Srinivasan Iyer, Tae Hyung Kim, Justin Haldar, Jaejin Cho, Berkin Bilgic, Yuxin Hu and 2 more

Abstract read
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In one paragraph

Article in Magnetic resonance imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Uten YarachRadiologic Technology Department, Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand.
Itthi ChatnuntawechNational Nanotechnology Center, National Science and Technology Development Agency, Pathum Thani, Thailand.
Congyu LiaoDepartment of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Surat TeerapittayanonNational Nanotechnology Center, National Science and Technology Development Agency, Pathum Thani, Thailand.
Siddharth Srinivasan IyerDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Tae Hyung KimDepartment of Computer Engineering, Hongik University, Seoul, South Korea.
Justin HaldarSignal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
Jaejin ChoAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
Berkin BilgicAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
Yuxin HuDepartment of Bioengineering, Stanford University, Stanford, California, USA.
Brian HargreavesDepartment of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, California, USA.
Kawin SetsompopDepartment of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA. Electronic address: kawins@stanford.edu.

Funding

MRI CORTICOGRAPHY: DEVELOPING NEXT GENERATION MICROSCALE HUMAN CORTEX MRI SCANNERU01EB025162 · NIBIB · UNIVERSITY OF CALIFORNIA BERKELEY · PI FEINBERG, DAVID ALAN, LIU, CHUNLEI · 2017 to 2021
$13.4M
Training and Dissemination CoreP41EB030006 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Susie Yi Huang · 2020 to 2026
$10.9M
Next generation in-vivo diffusion imaging at submillimeter resolutionR01MH116173 · NIMH · STANFORD UNIVERSITY · PI Yogesh Rathi, Kawin Setsompop · 2018 to 2026
$6.2M
fMRI Technologies for Imaging at the Limit of Biological Spatiotemporal Resolution: Administrative SupplementR01EB019437 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI POLIMENI, JONATHAN RIZZO, SETSOMPOP, KAWIN · 2015 to 2024
$5.5M
Rapid MRI acquisition for pediatric low-grade gliomasR01EB020613 · NIBIB · STANFORD UNIVERSITY · PI SETSOMPOP, KAWIN · 2016 to 2019
$2.5M
An acquisition and reconstruction framework to enable mesoscale human fMRI on clinical 3 Tesla scannersR01EB033206 · NIBIB · STANFORD UNIVERSITY · PI SETSOMPOP, KAWIN · 2022 to 2022
$853k
NIBIB NIH HHS P41 EB030006NIBIB NIH HHS R01 EB019437NIBIB NIH HHS R01 EB020613NIBIB NIH HHS R01 EB033206NIBIB NIH HHS U01 EB025162NIMH NIH HHS R01 MH116173
6 · The paper itself

Abstract

purposeBUDA-cEPI has been shown to achieve high-quality, high-resolution diffusion magnetic resonance imaging (dMRI) with fast acquisition time, particularly when used in conjunction with S-LORAKS reconstruction. However, this comes at a cost of more complex reconstruction that is computationally prohibitive. In this work we develop rapid reconstruction pipeline for BUDA-cEPI to pave the way for its deployment in routine clinical and neuroscientific applications. The proposed reconstruction includes the development of ML-based unrolled reconstruction as well as rapid ML-based B0 and eddy current estimations that are needed. The architecture of the unroll network was designed so that it can mimic S-LORAKS regularization well, with the addition of virtual coil channels.

methodsBUDA-cEPI RUN-UP - a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also introduced into the reconstruction to effectively take advantage of the smooth phase prior and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS.

resultsThe introduction of the Virtual Coil concept into the unrolled network was shown to be key to achieving high-quality reconstruction for BUDA-cEPI. With the inclusion of an additional non-diffusion image (b-value = 0 s/mm

conclusionBUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ∼88× when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application.

Indexed as

BrainDeep LearningImage Processing, Computer-AssistedAlgorithmsDiffusion Magnetic Resonance ImagingEcho-Planar ImagingHumansNeural Networks, ComputerBUDA-cEPIDeep learningDiffusion MRIEddy currentGeometric distortionOff-resonanceUnrolled network

Identifiers

PMID39566835
PMCPMC12124459

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.