Despite impressive strides, multidetector CT scanner technology could still use some tweaking to maximize its utility, according to a speaker at the seventh annual Symposium on Multidetector-Row CT in San Francisco.
Despite impressive strides, multidetector CT scanner technology could still use some tweaking to maximize its utility, according to a speaker at the seventh annual Symposium on Multidetector-Row CT in San Francisco.
"We need more flexibility in handling the imaging data," said Dr. Willi Kalender, a radiology researcher at the University of Erlangen in Germany. "These are not necessarily features that are technology-driven. We have to ask ourselves what is needed clinically."
Tasked to come up with five features still needed to optimize multislice CT, Kalendar first outlined general goals for CT systems: shorter scan times for reliable cardiac imaging and larger z-axis coverage for perfusion measurements in the brain, heart, and lungs.
Achieving those goals means developing a system with multiple sources and detectors, for higher peak x-ray power and shorter scan times, he said. Such a CT system, whether using multiple or segmented detectors, would also be capable of higher spatial resolution for fluoroscopy and radiography.
A third item on the top five wish list would be a system with more tissue parameters to allow radiologists to escape the Hounsfield unit "cage," Kalendar said. His fourth desirable feature was interactively variable isotropic spatial resolution, which would be particularly useful in cardiac imaging.
Last but not least, Kalender would like to see CT established as a low-dose modality.
"With the advent of automatic exposure control, CT already is a low-dose modality, but that is not the widely held perception," he said. "We need to change that."
Photon-Counting Computed Tomography: Eleven Takeaways from a New Literature Review
May 27th 2025In a review of 155 studies, researchers examined the capabilities of photon-counting computed tomography (PCCT) for enhanced accuracy, tissue characterization, artifact reduction and reduced radiation dosing across thoracic, abdominal, and cardiothoracic imaging applications.
Can AI Predict Future Lung Cancer Risk from a Single CT Scan?
May 19th 2025In never-smokers, deep learning assessment of single baseline low-dose computed tomography (CT) scans demonstrated a 79 percent AUC for predicting lung cancer up to six years later, according to new research presented today at the American Thoracic Society (ATS) 2025 International Conference.
Can Emerging AI Software Offer Detection of CAD on CCTA on Par with Radiologists?
May 14th 2025In a study involving over 1,000 patients who had coronary computed tomography angiography (CCTA) exams, AI software demonstrated a 90 percent AUC for assessments of cases > CAD-RADS 3 and 4A and had a 98 percent NPV for obstructive coronary artery disease.