Eric Barnes[email protected]CTMultipurpose CT lung cancer screening more cost-effectiveCT lung cancer screening is likely to be more cost-effective if it is used to identify other tobacco-related conditions in high-risk people, according to a study published in the Journal of Thoracic Oncology.July 2, 2017CTSubsolid lung nodule assessment with CT evolvesAs their experience with subsolid nodules at CT grows, radiologists are learning more about their characteristics and potential risk of malignancy. Overall, the past several years have seen nodule size and growth rise in importance relative to CT density, a less reliable predictor of malignancy.June 27, 2017Advanced VisualizationHolography beckons as alternative to 3D printingFor all its proven value, 3D printing is just one of many ways to look at 3D data -- and it's a rather rigid one at that. For clinicians, understanding a patient's medical condition may require more than a printed skull or aneurysm. In these cases, a holographic dataset can be helpful.June 15, 2017Advanced VisualizationAuntMinnie.com Advanced Visualization InsiderJune 13, 2017CTAuntMinnie.com CT InsiderJune 12, 2017CTISCT: The long road to full automation in abdominal CTSAN FRANCISCO - The past few years have seen rapid progress toward the goal of automated abdominal CT analysis using multiple computer-aided-detection systems, all theoretically applied to a single abdominal CT angiography dataset, according to a Tuesday presentation at the International Society for Computed Tomography (ISCT) symposium.June 6, 2017CTISCT: Ultrahigh-resolution CT raises the barSAN FRANCISCO - Exquisitely detailed ultrahigh-resolution CT is on its way thanks to several technologies, although most haven't reached the market yet, according to a talk on Monday at the International Society for Computed Tomography (ISCT) meeting.June 5, 2017CTISCT: Value of CT depends on who's lookingSAN FRANCISCO - Gauging the value of a CT scan is the single most critical reason for deciding whether to perform an exam. But when you get down to it, value depends on what you're looking for and who's looking at it, according to talks on Sunday at the International Society for Computed Tomography (ISCT) annual meeting.June 4, 2017CTCT detects frailty and predicts survival in elderlyCT scans are going beyond identifying broken bones to detecting skeletal frailty in the elderly, and even predicting their ability to survive a fall, according to a study in the June edition of the American Journal of Roentgenology.June 1, 2017Advanced Visualization3D-printed mouse ovaries yield healthy offspringThe promise of 3D-printed functional organs took a big step forward with the recent implantation of 3D-printed ovaries in mice. The bioprosthetic ovaries enabled the animals to ovulate and give birth to healthy pups, according to a study published online May 16 in Nature Communications. The mothers were even able to nurse their young.May 29, 2017Page 1 of 258Next PageTop StoriesInterventionalRadiation segmentectomy can be optimized before liver transplantAs of 2023, yttrium 90 (Y-90) radioembolization has become the most frequently used local-regional therapy in the U.S. for bridging patients with HCC to liver transplant.Womens ImagingCardiac devices affect mammography image qualityMRIMRI reveals how hidden muscle fat increases risk to heart, metabolismMRIWhole body MRI, deep-learning can 'map' distribution of fat and muscleSponsor ContentTurning Innovation into Everyday Efficiency
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=167&q=70&w=250)
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
