Spectral CT quantification of organ mass and physical density: A non-invasive metric for assessing functional reserve in type 2 diabetes

Introduction: Conventional imaging fails to quantify the true physical density and mass of organs. This study aims to develop a spectral CT-based physical modeling approach to quantify organ remodeling in type 2 diabetes mellitus (T2DM). Methods: A total of 97 subjects (46 T2DM patients and 51 non-diabetic controls) were evaluated. Organ regions (liver, pancreas, spleen) were automatically segmented using a V-Net neural network. A physical density model was constructed based on spectral parameters. We compared organ CT value, effective atomic number, mass, volume, and physical density between groups and analyzed their correlation with HbA1c levels. Results: The physical density model showed high accuracy, with measured subcutaneous fat density aligning with standard physical references. In T2DM patients, pancreatic mass and physical density decreased significantly. Conversely, liver and spleen mass increased without significant changes in physical density. Pancreatic physical density showed a significant negative correlation with HbA1c, with rho = −0.411 and p < 0.001. Discussion: The spectral physical density model provides a precise, non-invasive metric for assessing organ quality. The strong correlation between pancreatic density and glycemic supports its potential utility in evaluating functional reserve for artificial organ.

Authors

Institutions

Publication Details

Journal
The International Journal of Artificial Organs
Published
2026-06-16
DOI
https://doi.org/10.1177/03913988261452658
Primary Topic
Advanced X-ray and CT Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spectral CT quantification of organ mass and physical density: A non-invasive metric for assessing functional reserve in type 2 diabetes

R Wang, Y F Li, Yanjun Gao, Ge Liu et al.
The International Journal of Artificial Organs
Advanced X-ray and CT Imaging
article

Spectral CT quantification of organ mass and physical density: A non-invasive metric for assessing functional reserve in type 2 diabetes

R Wang, Y F Li, Yanjun Gao, Ge Liu, Liangliang Shang
article en

Abstract

Introduction: Conventional imaging fails to quantify the true physical density and mass of organs. This study aims to develop a spectral CT-based physical modeling approach to quantify organ remodeling in type 2 diabetes mellitus (T2DM). Methods: A total of 97 subjects (46 T2DM patients and 51 non-diabetic controls) were evaluated. Organ regions (liver, pancreas, spleen) were automatically segmented using a V-Net neural network. A physical density model was constructed based on spectral parameters. We compared organ CT value, effective atomic number, mass, volume, and physical density between groups and analyzed their correlation with HbA1c levels. Results: The physical density model showed high accuracy, with measured subcutaneous fat density aligning with standard physical references. In T2DM patients, pancreatic mass and physical density decreased significantly. Conversely, liver and spleen mass increased without significant changes in physical density. Pancreatic physical density showed a significant negative correlation with HbA1c, with rho = −0.411 and p < 0.001. Discussion: The spectral physical density model provides a precise, non-invasive metric for assessing organ quality. The strong correlation between pancreatic density and glycemic supports its potential utility in evaluating functional reserve for artificial organ.

The International Journal of Artificial Organs
Northwest University (CN)
Openalex Percentile: Top 10%
Advanced X-ray and CT Imaging
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.