Dear CT Radiology Insider,
In Part I of our CT Radiology Insider series on protocol design, Dr. Geoffrey Rubin from Stanford University pondered the tradeoffs involved in tailoring multislice imaging to the patient and the clinical question at hand.
This month's Part II belongs to another multislice maestro, Dr. Mathias Prokop from University Medical Center Utrecht in the Netherlands. Dr. Prokop is the principal author of Computed Tomography of the Body, the current reference standard in multislice imaging texts.
Dr. Prokop discussed the clinical, technical, and workflow issues involved in multidetector imaging at Stanford's recent International Symposium on Multidetector-Row CT in San Francisco. Philosophically, he favors a high-pitch, thin-slice approach to data acquisition, keeping a wary eye on dose, noise, and artifacts all the while.
And while Dr. Prokop believes that radiologists can generate high patient throughput by holding onto their old single-slice scanning habits, doing so means they're not making the best use of multislice technology. Real-time interactive viewing of isotropic 3-D volumes is the way to go, he said, and the option is increasingly available from vendors.
You'll find more of Dr. Prokop's tips on multislice acquisition in today's CT Radiology Insider story, published for you exclusively before it's made available to our other AuntMinnie members. And if you're interested in writing about your own facility's CT research project, please don't hesitate to let me know at [email protected].
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
