SpinDoctor-IVIM: A virtual imaging framework for intravoxel incoherent motion MRI

Intravoxel incoherent motion (IVIM) imaging is increasingly recognised as an important tool in clinical MRI, where tissue perfusion and diffusion information can aid disease diagnosis, monitoring of patient recovery, and treatment outcome assessment. Currently, the discovery of biomarkers based on IVIM imaging, similar to other medical imaging modalities, is dependent on long preclinical and clinical validation pathways to link observable markers derived from images with the underlying pathophysiological mechanisms. To speed up this process, virtual IVIM imaging is proposed. This approach provides an efficient virtual imaging tool to design, evaluate, and optimise novel approaches for IVIM imaging. In this work, virtual IVIM imaging is developed through a new finite element solver, SpinDoctor-IVIM, which extends SpinDoctor, a diffusion MRI simulation toolbox. SpinDoctor-IVIM simulates IVIM imaging signals by solving the generalised Bloch-Torrey partial differential equation. The input velocity to SpinDoctor-IVIM is computed using HemeLB, an established Lattice Boltzmann blood flow simulator. Contrary to previous approaches, SpinDoctor-IVIM accounts for volumetric microvasculature during blood flow simulations, incorporates diffusion phenomena in the intravascular space, and accounts for the permeability between the intravascular and extravascular spaces. The above-mentioned features of the proposed framework are illustrated with simulations on a realistic microvasculature model.

Authors

Institutions

Publication Details

Journal
Medical Image Analysis
Published
2024-10-16
DOI
https://doi.org/10.1016/j.media.2024.103369
Citations
5
Primary Topic
MRI in cancer diagnosis
Type
article
Field-Weighted Citation Impact
2.02

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SpinDoctor-IVIM: A virtual imaging framework for intravoxel incoherent motion MRI

Miguel O. Bernabéu, Mojtaba Lashgari, Jing‐Rebecca Li, Alejandro Federico Frangi et al.
5 citations
Medical Image Analysis
MRI in cancer diagnosis
2.02
article

SpinDoctor-IVIM: A virtual imaging framework for intravoxel incoherent motion MRI

Miguel O. Bernabéu, Mojtaba Lashgari, Jing‐Rebecca Li, Alejandro Federico Frangi, Zheyi Yang
article en
5 citations

Abstract

Intravoxel incoherent motion (IVIM) imaging is increasingly recognised as an important tool in clinical MRI, where tissue perfusion and diffusion information can aid disease diagnosis, monitoring of patient recovery, and treatment outcome assessment. Currently, the discovery of biomarkers based on IVIM imaging, similar to other medical imaging modalities, is dependent on long preclinical and clinical validation pathways to link observable markers derived from images with the underlying pathophysiological mechanisms. To speed up this process, virtual IVIM imaging is proposed. This approach provides an efficient virtual imaging tool to design, evaluate, and optimise novel approaches for IVIM imaging. In this work, virtual IVIM imaging is developed through a new finite element solver, SpinDoctor-IVIM, which extends SpinDoctor, a diffusion MRI simulation toolbox. SpinDoctor-IVIM simulates IVIM imaging signals by solving the generalised Bloch-Torrey partial differential equation. The input velocity to SpinDoctor-IVIM is computed using HemeLB, an established Lattice Boltzmann blood flow simulator. Contrary to previous approaches, SpinDoctor-IVIM accounts for volumetric microvasculature during blood flow simulations, incorporates diffusion phenomena in the intravascular space, and accounts for the permeability between the intravascular and extravascular spaces. The above-mentioned features of the proposed framework are illustrated with simulations on a realistic microvasculature model.

Medical Image AnalysisVol. 99
École Nationale Supérieure de Techniques Avancées (FR), Centre de Mathématiques Appliquées de l'École polytechnique (FR), Centre Inria de Saclay (FR), Leeds Beckett University (GB), University of Edinburgh (GB), KU Leuven (BE)
Edinburgh and Lothians Health Foundation, Alan Turing Institute, British Heart Foundation, Royal Academy of Engineering, Royal College of Surgeons of Edinburgh, European Commission, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 13%
MRI in cancer diagnosis
2.02
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.