Search research articles

お問い合わせ

JoVEについて

概要リーダーシップブログ JoVEヘルプセンター

著者向け

出版プロセス編集委員会範囲と方針査読よくある質問投稿

図書館員向け

推薦の声購読アクセスリソース図書館諮問委員会よくある質問

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments アーカイブ

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教員リソースセンター教員サイト

プライバシーポリシー

関連する概念動画

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

Imaging Studies I: CT and MRI

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...

Imaging Studies for Cardiovascular System IV: CMRI

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え

Same author

Reasoning in machine vision by learning fast and slow thinking.

Nature communications·2026

Same author

InnerEye-HS: a disease-agnostic clinical tool for hippocampal segmentation.

Brain communications·2026

Same author

Real-Time, Inline Quantitative MRI Enabled by Scanner-Integrated Machine Learning: A Proof of Principle With NODDI.

Magnetic resonance in medicine·2026

Same author

Expanded detection of early fibrotic phenotypes using lobar traction bronchiolectasis in lung cancer screening.

American journal of respiratory and critical care medicine·2026

Same author

Understanding pre-training data effects in retinal foundation models using two large fundus cohorts.

Nature communications·2026

Same author

A computationally frugal, open-source chest CT foundation model for thoracic disease detection in lung cancer screening programmes.

Communications medicine·2026

Search research articles

関連する実験動画

Updated: May 7, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

自己監督のディープラーニングによる定量MRIのリシアン確率損失

Christopher S Parker¹, Anna Schroder¹, Sean C Epstein¹

¹UCL Hawkes Institute, Department of Computer Science, University College London, London, UK.

NMR in biomedicine

|September 4, 2025

まとめ

新しいリシアン確率損失は,騒々しい画像から定量的なMRIパラメータの推定を改善します. この自己監視型ディープラーニング法では,信号対ノイズ比が低い場合のバイアスを減らし,医療用イメージングアプリケーションの精度を高めます.

キーワード:

リシアンディープラーニング拡散MRI イントラベクセル不協和運動可能性平均二乗の誤差定量的なMRI 自己監督する

さらに関連する動画

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

関連する実験動画

Last Updated: May 7, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

科学分野:

医療用イメージング
機械学習
バイオ物理学

背景:

定量的MRIは,自己監視のディープラーニングを使用して,トレーニングラベルなしで堅実なパラメータ推定を可能にします.
平均二乗誤差 (MSE) 損失を使用する既存の方法は,低い信号対ノイズ比 (SNR) で有意なバイアスを示しています.
MSEの損失は,MRの大きさの信号と相容れないので,推定バイアスを引き起こす可能性があります.

研究の目的:

定量MRIにおける自己監督学習のための新しいリシアン確率損失 (NLR) を導入する.
低SNRによるパラメータ推定におけるバイアスの問題に対処する.
定量的なMRIの精度と信頼性を向上させる

主な方法:

負のログリシアン (NLR) 確率損失の安定した数値近似を開発した.
イントラベクセル不一致運動 (IVIM) モデルを用いて,従来のMSE損失と比較した.
様々なSNRでシミュレートされた実際のMRIデータを用いて評価されたパラメータ推定性能.

主要な成果:

低SNRでIVIM拡散係数の推定におけるバイアスを著しく減少させた.
低SNRでは,NLRの損失は精度を犠牲にして精度を向上させました.
NLRとMSEの損失は,より高いSNRで収束し,より高い精度,精度,およびより低い総誤差が得られます.

結論:

NLRの損失は,特に騒音のある環境で,定量的なMRIパラメータの推定の精度を高めます.
この方法は,低SNRデータからMRI分析を改善するために広く適用できます.
NLRの損失は,自己監視のMRIの MSEの損失よりもより堅固な代替手段を提供します.