The artificial intelligence (AI)-powered technology reportedly offers decreased noise magnitude while enhancing image reconstruction for cardiovascular computed tomography (CT) scanners.
The Food and Drug Adminstration (FDA) has granted 510(k) clearance for the addition of deep learning image reconstruction (DLIR) to the Spotlight™ cardiovascular computed tomography (CT) and Spotlight Duo cardiothoracic CT systems.
Utilizing an advanced convolutional neural network (CNN), the DLIR technology maintains high-contrast spatial resolution while reducing pixel-wise noise magnitude, according to Arineta Cardio Imaging, the manufacturer of the aforementioned CT systems.
Trained on more than three billion image data points, the DLIR technology enhances the use of cross-sectional images for cardiovascular imaging assessment in a variety of settings.
“We have used Arineta cardiac CT systems for several years, and they provide the highest quality cardiac CT clinical images for our practice. From our FDA 510(k) reader study, Arineta’s DLIR technology continues that excellence. Arineta’s SpotLight systems make the highest performance cardiac CT available at point-of-care, in an office, mobile, or cath lab setting,” said Matthew Budoff, M.D., the director of Cardiac Computed Tomography within the Division of Cardiology at the Harbor-UCLA Medical Center in Torrance, Ca.
CT Study: AI Algorithm Comparable to Radiologists in Differentiating Small Renal Masses
May 14th 2024An emerging deep learning algorithm had a lower AUC and sensitivity than urological radiologists for differentiating between small renal masses on computed tomography (CT) scans but had a 21 percent higher sensitivity rate than non-urological radiologists, according to new research.
What a New Meta-Analysis Reveals About Fractional Flow Reserve Assessment with Computed Tomography
May 13th 2024While acknowledging variable accuracy overall with CT-derived fractional flow reserve (FFR-CT) values, researchers found that the accuracy rate increased to 90 percent for FFR-CT values greater > 0.90 and < 0.49.
MRI-Based Deep Learning Algorithm Shows Comparable Detection of csPCa to Radiologists
May 8th 2024In a study involving over 1,000 visible prostate lesions on biparametric MRI, a deep learning algorithm detected 96 percent of clinically significant prostate cancer (csPCa) in comparison to a 98 percent detection rate for an expert genitourinary radiologist.