Evidence map›Paper›PMID 42566117›Full record

ArticleRadiological physics and technology2026

AI-based reconstruction from low-resolution acquisitions for diffusion MRI: evaluation of FOD similarity.

Akihiro Kasahara, Yuichi Suzuki, Kazuki Endo, Ryosuke Mori, Ryutaro Yano, Hiroshi Kusahara, Toshihiro Hayashi, Osamu Abe

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiological physics and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Akihiro KasaharaRadiology Center, The University of Tokyo Hospital, Tokyo, Japan. 3619105k@gmail.com.ORCID http://orcid.org/0009-0003-2547-0092
Yuichi SuzukiRadiology Center, The University of Tokyo Hospital, Tokyo, Japan.
Kazuki EndoRadiology Center, The University of Tokyo Hospital, Tokyo, Japan.
Ryosuke MoriRadiology Center, The University of Tokyo Hospital, Tokyo, Japan.
Ryutaro YanoCanon Medical Systems Corporation, Tochigi, Japan.
Hiroshi KusaharaCanon Medical Systems Corporation, Tochigi, Japan.
Toshihiro HayashiRadiology Center, The University of Tokyo Hospital, Tokyo, Japan.
Osamu AbeDepartment of Radiology, The University of Tokyo Hospital, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MRI provides essential insights into tissue microstructure, and high angular resolution diffusion imaging (HARDI) enables detailed assessment of complex white matter architecture through fiber orientation distribution (FOD) analysis. However, HARDI requires high b-values and multiple diffusion directions, leading to reduced signal-to-noise ratio (SNR) and long scan times. Conventional zero-fill interpolation processing (ZIP) is widely used for super-resolution but is limited by edge blurring and artifacts. This study evaluated an AI-based reconstruction method, Precise IQ Engine (PIQE). Specifically, low-resolution diffusion data were reconstructed to standard resolution and compared with standard-resolution acquisitions. Ten healthy volunteers underwent HARDI at 3T, and FOD were estimated using constrained spherical deconvolution. Quantitative comparisons with reference data demonstrated that PIQE exhibited higher distributional similarity (lower Jensen-Shannon divergence) and directional agreement (higher angular correlation coefficient) compared with ZIP+Advanced Intelligent Clear-IQ Engine (AiCE), with statistically significant similarity observed. These findings indicate potential usefulness in advanced diffusion MRI applications.

Indexed as

Artificial IntelligenceDiffusion Magnetic Resonance ImagingImage Processing, Computer-AssistedHumansMaleSignal-To-Noise RatioArtificial intelligenceDiffusion-weighted ImagingHigh angular resolution diffusion imagingMagnetic resonance imagingSuper-resolution

Identifiers

PMID42566117
PMCPMC13546164

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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.