Who will be the person to take over at the helm of Insights into Imaging, the open-access journal of the European Society of Radiology (ESR)? The ESR is embarking on a quest to find a successor for its current editor in chief, Prof. Luis Martí-Bonmatí, who will conclude his term of office in December 2024.
The successful candidate will be expected to assume the role in mid-2024 with a gradual handover of duties until January 2025. The first term has a duration of three years, with the possibility of extension through mutual agreement. But you'll have to be quick because applications must be in by 31 March.
Luis Martí-Bonmatí, PhD, is head of radiology, Quirónsalud Hospital, Valencia, Spain. He is director of the Research Group on Biomedical Imaging (GIBI230) within La Fe Health Research Center, and he also co-founded QUIBIM (Quantitative Imaging Biomarkers in Medicine). He has been an active member of the editorial advisory board of AuntMinnieEurope.com for several years.
Stepping into Martí-Bonmatí's shoes will require a seasoned expert with proven scientific leadership skills in the realm of radiology to oversee all editorial processes, ensuring the integrity of the journal while strategically collaborating with the ESR journal family group of editors and working in liaison with Prof. Dr. Bernd Hamm, the editor in chief of the ESR journal family, to facilitate any significant changes in editorial strategy and policy of the journal.
Responsibilities encompass a wide array of other tasks including upholding publishing ethics standards, commissioning articles, providing guidance and leadership to the editorial team, as well as fostering and maintaining a strong partnership with the Editorial Board. Attendance of scientific meetings to stimulate submissions will be integral, the ESR stated in the job description.
The ideal candidate must possess strong diplomatic acumen, for effective networking within and outside the radiological community, it noted. They should exhibit an organized and dynamic character with adept time management to meet deadlines. Essential qualifications include a comprehensive understanding of various radiological branches and an awareness of international variations in practice. Prospective candidates should hold full ESR membership and have an affiliation with an esteemed research or academic institution.
Insights into Imaging has made it its mission to enhance critical thinking within the field of radiology by shedding light on current practices and conducting analytical assessments to pinpoint areas for improvement. The journal publishes original and educational articles, critical reviews, and position and recommendation papers from leading societies and institutions. With over 3.6 million annual downloads and an Impact Factor of 4.7 as of 2022, the journal is a "cornerstone in the field," according to ESR.
For further details, see the ESR website.


![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=100&q=70&w=100)







![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=112&q=70&w=112)









