Dr. Emanuele Neri[email protected]CTWhat does the new year hold for radiology in Italy?Italy will hold the presidency of ESR/ECR in 2024. We look at the main issues facing the nation’s radiologists, including the urgent need to address the gender gap and to prepare for the Winter Olympics.December 12, 2023AIOpinion: Radiographers do not equal radiologistsStudies that compare the reading skills of radiographers against those of radiologists are flawed, says Dr. Emanuele Neri. Trying to prove that "radiographers = radiologists" is the wrong exercise because the skillsets of the two groups are so different, but "radiographers + radiologists = better care" is valid, he writes.June 19, 2018Clinical NewsEuroPACS changes name to seek new identity and focusAt the recent Computer Assisted Radiology and Surgery congress in Heidelberg, Germany, the EuroPACS General Assembly approved a historic bylaws change, amending the name of the association and adopting a new mission statement. The new name is European Society of Medical Imaging Informatics.July 22, 2013Imaging InformaticsEuroPACS puts strong emphasis on training in 2012EuroPACS President Dr. Emanuele Neri is upbeat about 2012. In June, the society's annual meeting will take place in his home city of Pisa, Italy. Training initiatives are also planned in 2012, he said.November 20, 2011Page 1 of 1Top StoriesMRIMRI reveals how hidden muscle fat increases risk to heart, metabolismThe results suggest that MRI exams could "augment traditional risk factor screening," researchers noted.Molecular ImagingThe EANM joins EMUC26 and pushes its policy agenda in BrusselsMRIWhole body MRI, deep-learning can 'map' distribution of fat and muscleCTFirst signals from Germany's lung cancer screeningSponsor 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.auntminnieeurope.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.auntminnieeurope.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)
