SHIP-MR and Radiology: 12 Years of Whole-Body Magnetic Resonance Imaging in a Single Center

Abstract

The Study of Health in Pomerania (SHIP), a population-based study from a rural state in northeastern Germany with a relatively poor life expectancy, supplemented its comprehensive examination program in 2008 with whole-body MR imaging at 1.5 T (SHIP-MR). We reviewed more than 100 publications that used the SHIP-MR data and analyzed which sequences already produced fruitful scientific outputs and which manuscripts have been referenced frequently. Upon reviewing the publications about imaging sequences, those that used T1-weighted structured imaging of the brain and a gradient-echo sequence for R2* mapping obtained the highest scientific output; regarding specific body parts examined, most scientific publications focused on MR sequences involving the brain and the (upper) abdomen. We conclude that population-based MR imaging in cohort studies should define more precise goals when allocating imaging time. In addition, quality control measures might include recording the number and impact of published work, preferably on a bi-annual basis and starting 2 years after initiation of the study. Structured teaching courses may enhance the desired output in areas that appear underrepresented.

Population Based Average Parotid Gland Volume and Prevalence of Incidental Tumors in T1-MRI

“…The overall aim of the SHIP is not only a long-term longitudinal observation of cohorts, but also the comparison of transversal cohorts, as well as the cross-regional comparability of epidemiological studies. It is the first population-based study to implement whole-body MRI [ 16 ]. Besides this fact, one of the first SHIP revelations was the pronounced prevalence of common diseases, such as type 2 diabetes mellitus and arterial hypertension in Pomerania compared to the national average.…”

Functional MRI in Radiology—A Personal Review

“…We performed a number of cross-sectional investigations on grey matter volume in association with lifestyle factors (e.g., nicotine consumption [ 62 ], income and education [ 92 ], gender [ 68 ], and leisure sports [ 93 ]) in thousands of participants. The unique challenge, however, was a longitudinal evaluation of the epidemiologic datasets [ 94 ], which is currently funded by the German Research Community (DFG LO 795/37-1).…”

Application of a Deep Learning Approach to Analyze Large-Scale MRI Data of the Spine

“…As mentioned before, in previous studies, the spine was just partially analyzed, or segmentation was performed just manually. In addition, the data of these studies were always acquired on a geographically local basis, like in the “Study of Health in Pomerania” [ 15 , 16 ]. In our study, for the first time, the morphometry of the entire spine of more than 11,000 representative subjects from all over Germany was evaluated by using AI.…”

“…Although there are a variety of grading scales to categorize the imaging findings [ 14 ], little is known regarding the extent to which DDD is physiological in a certain population, depending on their age and gender. Currently, there are large-scale-based quantitative biomarkers and data for morphometric analysis of the spine in relation to age, sex and height available [ 15 , 16 ] but no real standardized reference values [ 17 ]. In these studies, the spine was just partially analyzed, or segmentation was performed just manually.…”