Riccardo Forni , Andrea Colacino , Bruna Punzo , Carlo Cavaliere , Monica Franzese , Aevar Orn Ulfarsson , Cristiana Corsi , Paolo Gargiulo
{"title":"虚拟心脏组织学:在健康和病理条件下左心室心肌的放射密度测定特征","authors":"Riccardo Forni , Andrea Colacino , Bruna Punzo , Carlo Cavaliere , Monica Franzese , Aevar Orn Ulfarsson , Cristiana Corsi , Paolo Gargiulo","doi":"10.1016/j.cmpb.2025.108876","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cardiovascular imaging plays a crucial role in disease understanding and case severity. Despite good results in morphological assessment due to an elevated spatial resolution, functional evaluation about cardiac tissue status is still lacking. The aim of the work was to perform a virtual cardiac histology, meaning to characterize cardiac tissue of the left ventricle with Computed Tomography images and use densitometric distribution to detect the presence of cardiac diseases such as acute myocardial infarction and hypertrophic cardiomyopathy.</div></div><div><h3>Methods:</h3><div>The study retrospectively analyzed volumetric data from sixty subjects, equally distributed among classes, developing a pipeline of image processing for the semi-automatic extraction of 3D virtual samples from different levels and segments. From each sample’s densitometric profile, a set of statistical descriptor were extracted.</div></div><div><h3>Results:</h3><div>The densitometric characterization detected heterogeneity in the left ventricular tissue, differentiating the more conductive myocytes of the septum with the more contractive myocytes of the other segments. In addition, a gradient of radiodensity was found as moving from the valvular plane (basal) to the apex of the heart. The intraventricular septum was also found as an eloquent structure in pathological changes due to myocardial infarction since a geometrical modification and shift of the profile was observed (Amplitude = 0.02, Muscle HU = 57). The hypertrophic cardiomyopathy caused significative changes in the contractile segments intensity (Muscle 5-7 HU increase) and shape of the profile (Amplitude = 0.21 inferior wall) reporting the absence of physiological fat and connective tissue in those segments (fat volume = 0.2 %).</div></div><div><h3>Conclusion:</h3><div>This study introduces a novel methodology leveraging CT densitometric properties to characterize left ventricular myocardium and distinguish healthy from pathological tissue. Significant patterns associated with hypertrophic cardiomyopathy and acute myocardial infarction highlight the potential of this approach for cardiac risk stratification.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108876"},"PeriodicalIF":4.9000,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Virtual cardiac histology: Towards a radiodensitometric characterization of left ventricular cardiac muscle in healthy and pathological conditions\",\"authors\":\"Riccardo Forni , Andrea Colacino , Bruna Punzo , Carlo Cavaliere , Monica Franzese , Aevar Orn Ulfarsson , Cristiana Corsi , Paolo Gargiulo\",\"doi\":\"10.1016/j.cmpb.2025.108876\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and Objective:</h3><div>Cardiovascular imaging plays a crucial role in disease understanding and case severity. Despite good results in morphological assessment due to an elevated spatial resolution, functional evaluation about cardiac tissue status is still lacking. The aim of the work was to perform a virtual cardiac histology, meaning to characterize cardiac tissue of the left ventricle with Computed Tomography images and use densitometric distribution to detect the presence of cardiac diseases such as acute myocardial infarction and hypertrophic cardiomyopathy.</div></div><div><h3>Methods:</h3><div>The study retrospectively analyzed volumetric data from sixty subjects, equally distributed among classes, developing a pipeline of image processing for the semi-automatic extraction of 3D virtual samples from different levels and segments. From each sample’s densitometric profile, a set of statistical descriptor were extracted.</div></div><div><h3>Results:</h3><div>The densitometric characterization detected heterogeneity in the left ventricular tissue, differentiating the more conductive myocytes of the septum with the more contractive myocytes of the other segments. In addition, a gradient of radiodensity was found as moving from the valvular plane (basal) to the apex of the heart. The intraventricular septum was also found as an eloquent structure in pathological changes due to myocardial infarction since a geometrical modification and shift of the profile was observed (Amplitude = 0.02, Muscle HU = 57). The hypertrophic cardiomyopathy caused significative changes in the contractile segments intensity (Muscle 5-7 HU increase) and shape of the profile (Amplitude = 0.21 inferior wall) reporting the absence of physiological fat and connective tissue in those segments (fat volume = 0.2 %).</div></div><div><h3>Conclusion:</h3><div>This study introduces a novel methodology leveraging CT densitometric properties to characterize left ventricular myocardium and distinguish healthy from pathological tissue. Significant patterns associated with hypertrophic cardiomyopathy and acute myocardial infarction highlight the potential of this approach for cardiac risk stratification.</div></div>\",\"PeriodicalId\":10624,\"journal\":{\"name\":\"Computer methods and programs in biomedicine\",\"volume\":\"269 \",\"pages\":\"Article 108876\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer methods and programs in biomedicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169260725002937\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer methods and programs in biomedicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169260725002937","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Virtual cardiac histology: Towards a radiodensitometric characterization of left ventricular cardiac muscle in healthy and pathological conditions
Background and Objective:
Cardiovascular imaging plays a crucial role in disease understanding and case severity. Despite good results in morphological assessment due to an elevated spatial resolution, functional evaluation about cardiac tissue status is still lacking. The aim of the work was to perform a virtual cardiac histology, meaning to characterize cardiac tissue of the left ventricle with Computed Tomography images and use densitometric distribution to detect the presence of cardiac diseases such as acute myocardial infarction and hypertrophic cardiomyopathy.
Methods:
The study retrospectively analyzed volumetric data from sixty subjects, equally distributed among classes, developing a pipeline of image processing for the semi-automatic extraction of 3D virtual samples from different levels and segments. From each sample’s densitometric profile, a set of statistical descriptor were extracted.
Results:
The densitometric characterization detected heterogeneity in the left ventricular tissue, differentiating the more conductive myocytes of the septum with the more contractive myocytes of the other segments. In addition, a gradient of radiodensity was found as moving from the valvular plane (basal) to the apex of the heart. The intraventricular septum was also found as an eloquent structure in pathological changes due to myocardial infarction since a geometrical modification and shift of the profile was observed (Amplitude = 0.02, Muscle HU = 57). The hypertrophic cardiomyopathy caused significative changes in the contractile segments intensity (Muscle 5-7 HU increase) and shape of the profile (Amplitude = 0.21 inferior wall) reporting the absence of physiological fat and connective tissue in those segments (fat volume = 0.2 %).
Conclusion:
This study introduces a novel methodology leveraging CT densitometric properties to characterize left ventricular myocardium and distinguish healthy from pathological tissue. Significant patterns associated with hypertrophic cardiomyopathy and acute myocardial infarction highlight the potential of this approach for cardiac risk stratification.
期刊介绍:
To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine.
Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.