Leonardo da Vinci thought he had it all figured out. But now, thanks to eight Dutch couples who crawled into an MRI machine and had intercourse, researchers have seen what anatomical changes actually happen during lovemaking.
It took the Groningen University research team seven years to complete the series of 13 experiments -- aided by volunteers who had a sense of humor and often a need for Viagra. And the researchers said the results advance our knowledge of anatomy and dispel some myths about the sex act.
Reporting in the current issue of the British Medical Journal, doctors said they had to slightly alter the MRI bore to allow the couples to engage in "missionary position" sex.
The work was not without its academic and professional skeptics and critics, said Dr. Willibrord Weijmar Schultz, MD, associate professor of gynecology at University Hospital Groningen, The Netherlands.
"Of course this is pioneering work," Weijmar Schultz said. "We were breaking new ground and you have to break down a taboo. You have to convince your colleagues as well of the scientific importance of the research. Fortunately hospital officials on duty had the intellectual courage to allow us to do this kind of experiment."
He said that the anatomical changes during intercourse and female orgasm have been difficult to investigate, but MRI, with its ability to noninvasively visualize internal organs, made the study possible.
Weijmar Schultz and colleagues reported that the study was valuable in that they have shown:
The study involved eight couples and three single women, who also engaged in self-arousal.
"It was difficult to recruit volunteers," Weijmar Schultz said. "This was one of the reasons that it took so long. We didn't use incentives to recruit the subjects. They were adventurous, curious, motivated people convinced of the scientific importance of the research project, and of course they enjoyed eroticism."
He said the subjects were approached by the research staff and through a scientific television program.
Another problem that beset the study was that the men had difficulty performing. But Viagra tablets saved the day, helping the men overcome performance impotence. The women, Weijmar Schultz said, had no problems with engaging in sex or in self-arousal.
The researchers tried to afford the couples some degree of privacy, he added.
"The tube in which the couple would have intercourse stood in a room next to a control room where the researchers were sitting behind the scanning console and screen," he explained. "An improvised curtain covered the window between the rooms, so the intercom was the only means of communication."
The MRI machine also had to be slightly altered to allow even the slim Dutch couples to engage in sex.
"We had to remove the rails of the MRI machine in order to get a diameter of 50 centimeters (about 20 inches). That's about 15 cm (6 inches) more than usual." He said the couples were asked not to move during imaging.
Dr. Leonardo Martin, a consultant radiologist at McMaster University in Hamilton, Ontario, congratulated the researchers. "Truly a Dutch treat and one that will garner world attention for them and British Medical Journal for all the right or wrong reasons."
Martin noted with obvious amusement, "I am concerned the conclusions drawn may be 'premature' and 'phallatious.' Further investigation may be warranted."
By Edward Susman
AuntMinnie.com contributing writer
December 31, 1999
Copyright © 1999 AuntMinnie.com



![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.auntminnie.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.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)








