Fast SMWI : Optimizing Classifier with FOV reduction simulation

Motivation: SMWI is an advanced SWI method capable of detecting nigral hyperintensity in the substantia nigra. Due to the long scan time, a reduced FOV sequence was proposed to decrease the scan time from 4min 15sec to 2min 45sec, along with the application of denoising techniques. However, when evaluated with the original classifier, the results showed changes, reporting increased FN, which may be a problem in clinic. Goal(s): Maintaining diagnostic results for the reduced FOV with denoised SMWI images. Approach: Optimizing classifier to denoised FOV-simulated SMWI images. Results: The diagnostic result of denoised FOV 64.5% SMWI images is comparable to FOV 100% SMWI images. Impact: The diagnostic result of denoised FOV 64.5% SMWI images is comparable to FOV 100% SMWI images with the optimizing classifier based on denoised FOV-simulated SMWI images.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/4389
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fast SMWI : Optimizing Classifier with FOV reduction simulation

Eung Yeop Kim, Hwan Heo, Jae‐Woo Choi, Soohwa Song et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Neural Networks and Applications
article

Fast SMWI : Optimizing Classifier with FOV reduction simulation

Eung Yeop Kim, Hwan Heo, Jae‐Woo Choi, Soohwa Song, Jong-Ho Lee, Juhyung Park, MyeongOh Lee, Sooyeon Ji, Donghoon Shin, Jonghyo Youn
article en

Abstract

Motivation: SMWI is an advanced SWI method capable of detecting nigral hyperintensity in the substantia nigra. Due to the long scan time, a reduced FOV sequence was proposed to decrease the scan time from 4min 15sec to 2min 45sec, along with the application of denoising techniques. However, when evaluated with the original classifier, the results showed changes, reporting increased FN, which may be a problem in clinic. Goal(s): Maintaining diagnostic results for the reduced FOV with denoised SMWI images. Approach: Optimizing classifier to denoised FOV-simulated SMWI images. Results: The diagnostic result of denoised FOV 64.5% SMWI images is comparable to FOV 100% SMWI images. Impact: The diagnostic result of denoised FOV 64.5% SMWI images is comparable to FOV 100% SMWI images with the optimizing classifier based on denoised FOV-simulated SMWI images.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 81%
Neural Networks and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.