Evidence map›Paper›PMID 40033952›Full record

ArticleMagnetic resonance in medicine2025

Diffusion-prepared imaging with amplitude navigation for correction of motion-induced signal loss.

Philip K Lee, Xuetong Zhou, Brian A Hargreaves

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Combined ADC and TMagnetic resonance in medicine · 2026
    Article
  2. 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

3 authors.

Philip K LeeRadiology, Stanford University, Stanford, California, USA.ORCID https://orcid.org/0000-0002-7945-902X
Xuetong ZhouRadiology, Stanford University, Stanford, California, USA.ORCID https://orcid.org/0009-0007-2584-0558
Brian A HargreavesRadiology, Stanford University, Stanford, California, USA.

Funding

Abbreviated Non-Contrast-Enhanced MRI for Breast Cancer ScreeningR01CA249893 · NCI · STANFORD UNIVERSITY · PI DANIEL, BRUCE L, HARGREAVES, BRIAN ANDREW · 2021 to 2025
$3.1M
GE HealthcareNCI NIH HHS R01 CA249893NIH HHS NIH R01-CA249893
6 · The paper itself

Abstract

purposeDiffusion-prepared imaging is a flexible alternative to conventional spin-echo diffusion-weighted EPI that allows selection of different imaging readouts and k-space traversals, and permits control of image contrast or image artifacts. We investigate a new signal model and reconstruction for diffusion-prepared imaging that addresses signal variations caused by motion-sensitizing diffusion gradients.

methodsA signal model, sampling theory, and reconstruction framework were developed assuming that motion-induced phases and the measured signals are random variables. The reconstruction incorporates real-valued amplitude weights estimated from low-resolution images into a linear system. A diffusion-prepared sequence was applied in phantom and in vivo acquisitions using different methods for managing phase errors from eddy currents or motion. This acquisition was performed with a high number of NEX and retrospectively undersampled to analyze errors in ADC estimation, and compared to spin-echo diffusion-weighted EPI, as well as conventional diffusion-prepared methods. The B

resultsImages reconstructed using the proposed method had similar image structures when compared to conventional spin-echo diffusion-weighted EPI, and demonstrated improved SNR efficiency compared to previous diffusion-prepared approaches. ADC errors followed a trend consistent with the derived signal model, sampling theory, and expected B

conclusionEmploying amplitude weights in the reconstruction of a diffusion-prepared sequence can improve SNR efficiency at the cost of a greater minimum sampling time and increased sensitivity to B

Indexed as

ArtifactsBrainDiffusion Magnetic Resonance ImagingImage EnhancementImage Processing, Computer-AssistedAlgorithmsComputer SimulationHumansMotionPhantoms, ImagingReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedSignal-To-Noise Ratiodiffusion MRIimage reconstructionlinear systems

Identifiers

PMID40033952
PMCPMC12974092

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Registered trials

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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.