AuntMinnie

POCUS assistance leads to no improvements in NICU lumbar punctures

Ultrasound assistance does not lead to improvements in first-attempt non-traumatic lumbar puncture success for NICU patients.

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Kaustav Bera, MD, discussed the rise of the radiologist-developer with AuntMinnie. In June, Bera will complete an abdominal imaging fellowship at Case Western Reserve University and plans to continue as an abdominal imager at Case Western.

Imaging fellow tries vibe coding to solve radiology challenges

ISMRM annual meeting in Cape Town to highlight South African 'Ubuntu'

Illustrations show the role of angiosome-to-tumor volume ratio (ATR) in different clinical scenarios based on tumor and treatment angiosome volume. The pale orange oval represents the tumor within the liver, and the pie-shaped region encompassed by the black lines represents the angiosome. (A) An adequate ATR (≥16) is reached when treating tumor and an accompanying appropriate volume of expendable surrounding normal parenchyma, such as a 2.5-cm tumor within a 150-mL treatment angiosome. Lower (inadequate) ATR can occur with superselective administrations, such as (B) attempting to treat a larger (4-cm) tumor within the same 150-mL angiosome, which proves suboptimal, and (C) attempting to treat the same 2.5-cm tumor within a reduced angiosome (40 mL).

Radiation segmentectomy can be optimized before liver transplant

May 5, 2026

Radiology is Under Pressure

May 2, 2026

MRI reveals how hidden muscle fat increases risk to heart, metabolism

May 5, 2026

$Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).$