Experimental and analytical comparative study of optical coefficient of fresh and frozen rat tissues

International audienceOptical properties of fresh and frozen tissues of rat heart, kidney, brain, liver, and muscle were measured in the 450-to 700-nm range. The total reflectance and transmittance were measured using a well-calibrated integral sphere setup. Absorption coefficient μ a and reduced scattering coefficient μ 0 s were derived from the experimental measurements using the inverse adding doubling technique. The influence of cryogenic processing on optical properties was studied. Interindividual and intraindividual variations were assessed. These new data aim at filling the lack of validated optical properties in the visible range especially in the blue-green region of particular interest for fluorescence and optogenetics preclinical studies. Furthermore, we provide a unique comparison of the optical properties of different organs obtained using the same measurement setup for fresh and frozen tissues as well as an estimate of the intraindividual and interindividual variability

Measurement of myocardial wall thickening from PET/SPECT images: comparison of two methods

Purpose: We compared two methods for measuring myocardial wall thickening from nuclear medicine perfusion scans. The first method uses the percent change in peak activity, and the second method models a profile measured across the myocardium. Method: Mathematical simulations of the myocardium were used. In addition, images with PET or SPECT resolution were created from real MR images. Known amounts of noise were then added. Results: The percent peak thickening (%PT) is nonlinear with true percent thickening, especially for PET resolutions [7 mm full width at half-maximum (FWHM)]. For the peak method, low levels of noise (10%) introduced an error of 8%PT for PET and of 16%PT for SPECT. Additional smoothing reduced these errors. For the fitted model, at 10% noise, the error in thickening was large: 2.3 mm for PET and 7.8 mm for SPECT. Conclusion: The fitted model works well only with good resolution and low noise (e.g., 7 mm FWHM and 10%). The peak method is also sensitive to noise, especially for poorer resolutions. Additional smoothing gives more reliable results for the peak method but not the fitted method. The peak method is therefore the more generally reliable, but even this method may only allow classification of myocardial thickening into broad categories.Publicad

Validation of a method to compensate multicenter effects affecting CT radiomic features

International audienc

3D+t segmentation of PET images using spectral clustering

International audienceSegmentation of dynamic PET images is often needed to extract the time activity curve (TAC) of regions. While clustering methods have been proposed to segment the PET sequence, they are generally either sensitive to initial conditions or favor convex shaped clusters. Recently, we have proposed a deterministic and automatic spectral clustering method (AD-KSC) of PET images. It has the advantage of handling clusters with arbitrary shape in the space in which they are identified. While improved results were obtained with AD-KSC compared to other methods, its use for clinical applications is constrained to 2D+t PET data due to its computational complexity. In this paper, we propose an extension of AD-KSC to make it applicable to 3D+t PET data. First, a preprocessing step based on a recursive principle component analysis and a Global K-means approach is used to generate many small seed clusters. AD-KSC is then applied on the generated clusters to obtain the final partition of the data. We validated the method with GATE Monte Carlo simulations of Zubal head phantom. The proposed approach improved the region of interest (ROI) definition and outperformed the K-means algorithm

Medical physics and imaging-a timely perspective

Radiolog

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Purpose: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. Methods: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. Result: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. Conclusion: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. Critical relevance statement: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. Key points: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score (https://metricsscore.github.io/metrics/METRICS.html) and a repository created to collect feedback from the radiomics community (https://github.com/metricsscore/metrics). Graphical Abstract: [Figure not available: see fulltext.

Correction de la diffusion en imagerie scintigraphique

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 84513 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Thorough scrutinization of radiomic features makes it possible to convert complicated radiomic models into comprehensible parsimonious signatures

International audienc

Thorough scrutinization of radiomic features makes it possible to convert complicated radiomic models into comprehensible parsimonious signatures

International audienc