scholarly journals A Framework for Cortical Layer Composition Analysis using Low Resolution T1 MRI Images (August 2018)

2018 ◽  
Author(s):  
Ittai Shamir ◽  
Omri Tomer ◽  
Zvi Baratz ◽  
Galia Tsarfaty ◽  
Maya Faraggi ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Image Resolution ◽  
Accurate Estimation ◽  
Composition Analysis ◽  
Low Resolution ◽  
Partial Volume ◽  
Partial Volume Effects ◽  
T1 Relaxation ◽  
Layer Composition ◽  
Volume Effects

The layer composition of the cerebral cortex represents a unique anatomical fingerprint of brain development, function, connectivity and pathology. Historically the cortical layers were investigated solely ex-vivo using histological means, but recent magnetic resonance imaging (MRI) studies suggest that T1 relaxation images can be utilized to separate the layers. Despite technological advancements in the field of high resolution MRI, accurate estimation of whole brain layer composition has remained limited due to partial volume effects, leaving some layers far beyond the image resolution. In this study we offer a simple and accurate method for layer composition analysis, resolving partial volume effects and cortical curvature heterogeneity. We use a low resolution echo planar imaging inversion recovery (EPI IR) MRI scan protocol that provides fast acquisition (~12 minutes) and enables extraction of multiple T1 relaxation time components per voxel, which are assigned to types of brain tissue and utilized to extract the subvoxel composition of each T1 layer. While previous investigation of the layers required the estimation of cortical normals or smoothing of layer widths (similar to VBM), here we developed a sphere-based approach to explore the inner mesoscale architecture of the cortex. Our novel algorithm conducts spatial analysis using volumetric sampling of a system of virtual spheres dispersed throughout the entire cortical space. The methodology offers a robust and powerful framework for quantification and visualization of the layers on the cortical surface, providing a basis for quantitative investigation of their role in cognition, physiology and pathology.

Analysis of right ventricular mass from magnetic resonance imaging data: a simple post-processing algorithm for correction of partial-volume effects

2020 ◽  
Author(s):  
Emil Knut Stenersen Espe ◽  
Bård Andre Bendiksen ◽  
Lili Zhang ◽  
Ivar Sjaastad
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Ex Vivo ◽  
Processing Algorithm ◽  
Post Processing ◽  
Resonance Imaging ◽  
Partial Volume ◽  
Preclinical Data ◽  
Partial Volume Effects ◽  
Volume Effects

Background Magnetic resonance imaging (MRI) of the right ventricle (RV) offers important diagnostic information, but the accuracy of this information is hampered by the complex geometry of the RV. In this project, we propose a novel post-processing algorithm that corrects for partial-volume effects in the analysis of standard MRI cine images of RV mass (RVm), and evaluate the method in clinical and preclinical data. Methods Self-corrected RVm measurement was compared with conventionally measured RVm in 16 patients who showed different clinical indications for cardiac MRI, and in 17 Wistar rats with different degrees of pulmonary congestion. The rats were studied under isoflurane anaesthesia. To evaluate the reliability of the proposed method, the measured end-systolic and end-diastolic RVm were compared. Accuracy was evaluated by comparing preclinical RVm to ex-vivo RV weight (RVw). Results We found that use of the self-correcting algorithm improved reliability compared with conventional segmentation. For clinical data, the limits of agreement (LOAs) were -1.8±8.6g (self-correcting) vs. 5.8±7.8g (conventional) and coefficients of variation (CoVs) were 7.0% (self-correcting) vs. 14.3% (conventional). For preclinical data, LOAs were 21±45mg (self-correcting) vs. 64±89mg (conventional) and CoVs were 9.0% (self-correcting) and 17.4% (conventional). Self-corrected RVm also showed better correspondence with the ex vivo RVw: LOAs were -5±80mg (self-correcting) vs. 94±116mg (conventional) in end-diastole and -26±74mg (self-correcting) vs. 31±98mg (conventional) in end-systole. Conclusions The new self-correcting algorithm improves the reliability and accuracy of RVm measurements in both clinical and preclinical MRI. It is simple, easy to implement and does not require any additional MRI data.

Reconstructed Image Resolution of the LabPET Small Animal Scanner Measured Using Simulated Partial Volume Effects

2009 ◽  
Vol 56 (5) ◽  
pp. 2689-2695 ◽  
Author(s):  
Tyler Dumouchel ◽  
Vitali V. Selivanov ◽  
Jules Cadorette ◽  
Roger Lecomte ◽  
Robert A. deKemp
Keyword(s):  
Small Animal ◽  
Image Resolution ◽  
Reconstructed Image ◽  
Partial Volume ◽  
Partial Volume Effects ◽  
Volume Effects

scholarly journals Evaluation of nuclear medicine planar images based on partial volume effects and recovery coefficients at different activity levels of radionuclides and matrix sizes

2021 ◽  
Vol 1106 (1) ◽  
pp. 012015
Author(s):  
Mohd Fahmi Mohd Yusof ◽  
Nor Amalyna Ghazali ◽  
Ummi Solehah Ab Ghani ◽  
Ahmad Thaifur Khaizul ◽  
Puteri Nor Khatijah Abd Hamid
Keyword(s):  
Nuclear Medicine ◽  
Activity Levels ◽  
Partial Volume ◽  
Partial Volume Effects ◽  
Planar Images ◽  
Volume Effects

scholarly journals Comparison of Partial Volume Effects in Arterial and Venous Contrast Curves in CT Brain Perfusion Imaging

PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e97586 ◽  
Author(s):  
Alan J. Riordan ◽  
Edwin Bennink ◽  
Jan Willem Dankbaar ◽  
Max A. Viergever ◽  
Birgitta K. Velthuis ◽  
...  
Keyword(s):  
Perfusion Imaging ◽  
Brain Perfusion ◽  
Partial Volume ◽  
Partial Volume Effects ◽  
Brain Perfusion Imaging ◽  
Ct Brain ◽  
Volume Effects
Sign in / Sign up

Export Citation Format

Share Document