Evidence map›Paper›PMID 42618219›Full record

ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2026

Parameter Estimation for the Intravoxel Incoherent Motion-diffusional Kurtosis Imaging Model Using Synthetic Q-space Learning Toward Breast Tumor Diagnosis.

Kousei Konya, Yuki Ichinoseki, Erina Kato, Naoko Mori, Hideki Ota, Shunji Mugikura, Kei Takase, Yoshitaka Masutani

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Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 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

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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Kousei KonyaDepartment of Medical Image Computation, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.
Yuki IchinosekiDepartment of Medical Image Computation, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.
Erina KatoDepartment of Diagnostic Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan.
Naoko MoriDepartment of Radiology, Akita University Graduate School of Medicine, Akita, Akita, Japan.
Hideki OtaDepartment of Diagnostic Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan.
Shunji MugikuraDivision of Image Statistics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan.
Kei TakaseDepartment of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.
Yoshitaka MasutaniDepartment of Medical Image Computation, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSynthetic Q-space learning (synQSL), which uses only synthetic data in the training of regressors, has demonstrated promising results in parameter estimation for brain diffusion MRI (dMRI). This study aimed to evaluate the performance of synQSL in breast dMRI for intravoxel incoherent motion-diffusional kurtosis imaging (IVIM-DKI) parameter estimation, with comparing several types of regressors and conventional fitting by nonlinear least squares fitting (NL-LSF).

methodsIn synthesizing data for synQSL, IVIM-DKI parameters were sampled from uniform distributions and substituted into the signal model along with b-values to generate diffusion-weighted imaging (DWI) signals. In addition, Rician noise was mixed to the signals. We prepared datasets of 10

resultsIn the digital phantom, the regressors of synQSL achieved significantly lower root mean square error (RMSE) for f, D

conclusionsynQSL demonstrated superior parameter estimation performance compared to NL-LSF in breast IVIM-DKI analysis. In this study, MLP was considered the most suitable regressor for synQSL among those we examined, based on the balance between estimation accuracy and computational costs.

Indexed as

Breast NeoplasmsDiffusion Magnetic Resonance ImagingImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMachine LearningAlgorithmsBreastFemaleHumansLeast-Squares AnalysisMotionMultilayer PerceptronsPhantoms, Imagingbreast magnetic resonance imagingdiffusion magnetic resonance imagingintravoxel incoherent motion–diffusional kurtosis imagingmachine learningparameter estimation

Identifiers

PMID42618219

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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.