A Finnish researcher has invented a method that makes it possible to reduce myocardial perfusion SPECT imaging time by up to 50%, according to the University of Eastern Finland in Joensuu.
Tuija Kangasmaa's technique makes the scan session easier for the patient, the university said. Kangasmaa has also developed two additional methods that correct errors resulting from patient movement during the scan.
Kangasmaa's work focused on reducing myocardial perfusion imaging (MPI) SPECT procedure time by using collimator response compensation and performing stress/rest MPI scans simultaneously using different radiopharmaceuticals -- correcting image problems caused by a long procedure times. The methods validated in the study have been integrated into a commercial MP image reconstruction package, and they are currently in clinical use in dozens of hospitals, both in Finland and all over the world, according to the university.
The research results have been published in the International Journal of Molecular Imaging and Annals of Nuclear Medicine (July 2014, Vol. 28:6, pp. 580-585).
![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)
