Aim: To assess Magnetic resonance colonography with fat enema as a method for detection of colorectal neoplasm.
Methods: Consecutive twenty-two patients underwent MR colonography with fat enema before colonoscopy. T1-weighted three-dimensional fast spoiled gradient-echo with inversion recovery sequence was acquired with the patient in the supine position before and 75 s after Gadopentetate Dimelumine administration. Where by, pre and post MR coronal images were obtained with a single breath hold for about 20 s to cover the entire colon. The quality of MR colonographs and patients' tolerance to fat contrast medium was investigated. Colorectal neoplasms identified by MR colonography were compared with those identified on colonoscopy and sensitivity of detecting the lesions was calculated accordingly.
Results: MR colonography with fat enema was well tolerated without sedation and analgesia. 120 out of 132 (90.9%) colonic segments were well distended and only 1 (0.8%) colonic segment was poor distension. After contrast enhancement scan, mean contrast-to-noise ratio (CNR) value between the normal colonic wall and lumen was 18.5 +/- 2.9 while mean CNR value between colorectal neoplasm and lumen was 20.2 +/- 3.1. By Magnetic resonance colonography, 26 of 35 neoplasms (sensitivity 74.3%) were detected. However, sensitivity of MRC was 95.5% (21 of 22) for neoplasm larger than 10 mm and 55.6% (5 of 9) for 5-10 mm neoplasm.
Conclusion: MR colonography with fat enema and T1-weighted three-dimensional fast spoiled gradient-echo with inversion recovery sequence is feasible in detecting colorectal neoplasm larger than 10 mm.
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http://dx.doi.org/10.3748/wjg.v13.i40.5371 | DOI Listing |
Eur Radiol
January 2025
Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA.
Objectives: To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population.
Methods: Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes.
Abdom Radiol (NY)
April 2024
Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792, USA.
J Appl Clin Med Phys
May 2023
Radiology & Medical Imaging, School of Medicine, University of Virginia, Charlottesville, Virginia, USA.
The purpose of this study was to determine the lower limit of radiation dose required to measure visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) volumes when a fat quantification and noise reduction techniques (NRTs) are combined. For this purpose, we utilized CT colonography (CTC) images taken at low doses and manually segmented VAT and SAT fat volumes as ground truth. In order to derive the acceptable precision of the measurements needed to estimate the lower limit of radiation dose, we estimated the effect of different positioning during CT scanning on fat measurements using manually segmented VAT and SAT against normal dose.
View Article and Find Full Text PDFScott Med J
August 2022
Academic Unit of Surgery, 3526University of Glasgow, Royal Infirmary, Glasgow, G31 2ER, UK.
Introduction: Obesity is associated with an increased risk of colorectal cancer (CRC). Unlike the indirect measures such as BMI, CT-Body composition (CT-BC) allows for the assessment of both volume and distribution of adipose tissue. Therefore, the aim of this study was to examine the relationship between host characteristics, BMI, CT-BC measurements and the incidence of colorectal neoplasia.
View Article and Find Full Text PDFRadiology
July 2022
From the Department of Radiology and Imaging Sciences (H.T., D.C.E., S.L., R.M.S.) and Department of Biostatistics and Clinical Epidemiology Service (P.W.), Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Room 1C224D, MSC 1182, Bethesda, MD 20892-1182; and Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (P.J.P.).
Background CT biomarkers both inside and outside the pancreas can potentially be used to diagnose type 2 diabetes mellitus. Previous studies on this topic have shown significant results but were limited by manual methods and small study samples. Purpose To investigate abdominal CT biomarkers for type 2 diabetes mellitus in a large clinical data set using fully automated deep learning.
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