Clinical Consequences of Deep Learning Image Reconstruction at CT.

Deep learning reconstruction (DLR) offers a variety of advantages over the current standard iterative reconstruction techniques, including decreased image noise without changes in noise texture and less susceptibility to spatial resolution limitations at low dose. These advances may allow for more aggressive dose reduction in CT imaging while maintaining image quality and diagnostic accuracy. However, performance of DLRs is impacted by the type of framework and training data used.

Ratio of visceral-to-subcutaneous fat area improves long-term mortality prediction over either measure alone: automated CT-based AI measures with longitudinal follow-up in a large adult cohort.

Background: Fully automated AI-based algorithms can quantify adipose tissue on abdominal CT images. The aim of this study was to investigate the clinical value of these biomarkers by determining the association between adipose tissue measures and all-cause mortality.

Methods: This retrospective study included 151,141 patients who underwent abdominal CT for any reason between 2000 and 2021.

CT Colonography for Colorectal Cancer Screening and Prevention: 20-Year Programmatic Experience at a U.S. Academic Medical Center.

Utilization of CT colonography (CTC) for colorectal cancer (CRC) screening test may increase following CMS coverage starting in January 2025. To report 20-year programmatic results of a large institutional screening-focused CTC program. This retrospective study included all CTC examinations performed from April 2004 through March 2024 at a single institution that has adopted CTC standardization and quality assurance measures.

CT Biomarkers for Phenotypic Biological Aging: Emerging Concepts and Advantages.

Aging is a complex phenomenon reflecting the time-dependent accumulation of damage that results in progressive structural and functional decline, disease risk, and death. Chronological age (CA) is an imperfect measure of health but remains an important driver of health care decisions. Biological age (BA) is a construct that attempts to provide a more holistic evaluation of the cumulative effects of aging and aging-related disease.

Improved muscle and fat segmentation for body composition measures on quantitative CT.

Purpose: Body composition analysis on abdominal CT scans is useful for opportunistic screening. It also offers prognostic insights into mortality and cardiovascular risk. However, current segmentation methods for muscle and fat often fail on quantitative CT scans used for bone densitometry.

Optimizing region of interest size and placement for clinical opportunistic CT trabecular bone Hounsfield unit measurements.

Purpose: No formal guidance exists regarding optimal opportunistic computed tomography (CT) region of interest (ROI) size or placement to clinically obtain bone Hounsfield unit (HU) data. Using clinical CT scans, this study evaluated ROI size/placement and assessed HU reproducibility.

Methods: Three non-radiologists independently identified the L1 and L4 vertebral body centroid and then placed varying size circular ROIs on axial and sagittal images of 30 clinical CT scans.

CT Colonography versus Multitarget Stool DNA Test for Colorectal Cancer Screening: A Cost-Effectiveness Analysis.

Background Colorectal cancer (CRC) is largely preventable or curable with effective screening. Purpose To compare both the clinical efficacy and cost effectiveness of CRC screening with CT colonography (CTC) with those of multitarget stool DNA (mt-sDNA) testing. Materials and Methods A state-transition Markov model was constructed using updated natural history evidence for colorectal polyps applied to a hypothetical 10 000-person cohort representative of the 45-year-old U.

Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT.

Accurate, reproducible body composition analysis from abdominal computed tomography (CT) images is critical for both clinical research and patient care. We present a fully automated, artificial intelligence (AI)-based pipeline that streamlines the entire process-from data normalization and anatomical landmarking to automated tissue segmentation and quantitative biomarker extraction. Our methodology ensures standardized inputs and robust segmentation models to compute volumetric, density, and cross-sectional area metrics for a range of organs and tissues.

Leveraging prior knowledge in machine intelligence to improve lesion diagnosis for early cancer detection.

Background: Experts' interpretations of medical images for lesion diagnosis may not always align with the underlying in vivo tissue pathology and, therefore, cannot be considered the definitive truth regarding malignancy or benignity. While current machine learning (ML) models in medical imaging can replicate expert interpretations, their results may also diverge from the actual ground truth.

Purpose: This study investigates various factors contributing to these discrepancies and proposes solutions.

Automated abdominal CT imaging biomarkers and clinical frailty measures associated with postoperative deceased-donor liver transplant outcomes.

Objective: To quantify the potential of fully automated CT-based body composition metrics and clinical frailty data in predicting liver transplant recipient postoperative outcomes.

Methods: AI-enabled body composition tools were applied to pre-transplant abdominal CT scans in a retrospective cohort of first-time deceased-donor liver transplant recipients. Clinical frailty data (Fried frailty score) was obtained from an established transplant database.

Assessing the Reliability of Pancreatic CT Imaging Biomarkers for Diabetes Prediction: A Dual Center Retrospective Study.

Rationale And Objectives: Pancreatic imaging biomarkers on CT imaging are known to be associated with diabetes. However, no studies have examined if these imaging biomarkers are resilient to changes in segmentation quality and contrast status. Here, we assess if imaging biomarkers are robust to variations in pancreatic segmentation quality and contrast status, and how these factors affect their ability to predict diabetes.

AI-enabled body composition biomarkers at post-mortem CT for enriching autopsy: analysis of a large decedent cohort.

Objective: To correlate fully-automated PMCT-based body composition measures with causes of death and comorbidities.

Materials And Methods: Retrospective study of New Mexico Decedent Image Database (NMDID) with non-contrast PMCT scans between 2010 and 2017. Automated pipeline of AI-driven algorithms for quantifying skeletal muscle, subcutaneous/visceral fat, and aortic calcification from the abdominal component of PMCT scans was used.

CT Colonography for Colorectal Cancer Prevention and Detection: Integration Into Clinical Practice, From the Special Series on Screening.

CT colonography (CTC) is a CT examination, performed with low dose and typically without IV contrast media, optimized to detect colorectal polyps and cancer. Despite extensive supporting data, CTC has had variable acceptance and use over the past two decades, particularly for a main indication of colorectal cancer screening. CTC is now at an inflection point after the approval in 2025 by CMS for reimbursement of CTC performed for colorectal cancer screening.

Photon-Counting CT: Technology, Current and Potential Future Clinical Applications, and Overview of Approved Systems and Those in Various Stages of Research and Development.

Photon-counting CT (PCCT) has emerged as a transformative technology, with the potential to herald a new era of clinical capabilities. This review provides an overview of the current status and potential future developments of PCCT, including basic physics principles and technical implementation by different vendors, with special attention to applications that have not, to date, been emphasized in the literature. The technologic underpinnings that distinguish PCCT scanners from traditional energy-integrating detector (EID) CT scanners with dual-energy capability are discussed.

Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality.

Background: Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are available from these CT examinations, but they remain an underused source of data, often missing from prognostication models, as these measurements require organ segmentation not routinely performed clinically by radiologists. We hypothesize that artificial intelligence facilitates accurate extraction of abdominal CT body composition data, allowing better prediction of outcomes.

Opportunistic screening for osteoporosis using artificial intelligence-based morphometric analysis of chest computed tomography images: a retrospective multi-center study in Russia leveraging the COVID-19 pandemic.

Study Design: Retrospective cohort study.

Purpose: To evaluate the effectiveness of opportunistic osteoporosis screening using an artificial intelligence (AI) algorithm for detecting vertebral compression deformity (VCD >25%) and reduced bone mineral density (BMD) from routine chest computed tomography (CT) scans.

Overview Of Literature: Osteoporosis is an insidious metabolic disease that often remains asymptomatic for a long time, and is typically diagnosed due to the occurrence of complications.

Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity.

We derive and test a CT-based biological age model for predicting longevity, using an automated pipeline of explainable AI algorithms that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. We apply these AI tools to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women; median follow-up, 5.

Effect of Patient Positioning on CT Number Accuracy: A Phantom Study Comparing Energy Integrating and Deep Silicon Photon Counting Detector CT.

Objective: Patient positioning during clinical practice can be challenging, and mispositioning leads to a change in CT number. CT number fluctuation was assessed in single-energy (SE) EID, dual-energy (DE) EID, and deep silicon photon-counting detector (PCD) CT over water-equivalent diameter (WED) with different mispositions.

Methods: A phantom containing five clinically relevant inserts (Mercury Phantom, Gammex) was scanned on a clinical EID CT and a deep silicon PCD CT prototype at vertical positions of 0, 4, 8, and 12 cm.

CT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence Tools in Over 100,000 Patients.

CT-based abdominal body composition measures have shown associations with important health outcomes. Advances in artificial intelligence (AI) now allow deployment of tools that measure body composition in large patient populations. The purpose of this study was to assess associations of age, sex, and common systemic diseases with CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample.

Validating a Practical Correction for Intravenous Contrast on Computed Tomography-Based Muscle Density.

Objective: Computed tomography (CT) measured muscle density is prognostic of health outcomes. However, the use of intravenous contrast obscures prognoses by artificially increasing CT muscle density. We previously established a correction to equalize contrast and noncontrast muscle density measurements.

Image-guided percutaneous mesenteric biopsy: diagnostic yield and safety profile.

Purpose: To evaluate the diagnostic yield and safety profile of percutaneous image-guided biopsy of mesenteric lesions.

Materials, Methods, And Procedures: Image-guided percutaneous biopsies of the mesentery at a single institution from 2000 to 2022 were identified and reviewed. Relevant demographic and procedural data were abstracted from the medical record.

Noninvasive Quantitative CT for Diffuse Liver Diseases: Steatosis, Iron Overload, and Fibrosis.

Chronic diffuse liver disease continues to increase in prevalence and represents a global health concern. Noninvasive detection and quantification of hepatic steatosis, iron overload, and fibrosis are critical, especially given the many relative disadvantages and potential risks of invasive liver biopsy. Although MRI techniques have emerged as the preferred reference standard for quantification of liver fat, iron, and fibrosis, CT can play an important role in opportunistic detection of unsuspected disease and is performed at much higher volumes.

Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT.

Purpose: To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).

Methods: This retrospective study included 88 contrast-enhanced abdominopelvic CTs acquired with rapid-kVp switching DECT. Images were reconstructed into five VMI levels (40, 55, 70, 85, 100 keV).

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Purpose: To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian cancer.

Materials And Methods: This retrospective study included contrast-enhanced and non-contrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age, 60 years ± 11 [s.