With regard to optimized iodine CNR, it is more efficient to perform dual-energy scans and compute virtual monoenergetic images at 40 keV using the Mono+ technique than to perform low kilovolt scans. Given the improved CNR, the Mono+ algorithm could be very useful in improving both detection and differential diagnosis of abdominal lesions, specifically low-contrast lesions, as well as in other anatomical regions where improved iodine CNR is beneficial.

Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFR) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFR)-against coronary CT angiography and quantitative coronary angiography (QCA). Materials and Methods A total of 85 patients (mean age, 62 years ± 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived on-site from coronary CT angiography data sets by using both FFR and FFR. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard. Results On a per-lesion and per-patient level, FFR showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFR resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P = .86 and P = .92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFR and 0.89 for FFR showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P < .0001). Also, on a per-patient level, FFR (AUC, 0.91) and FFR (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P < .0001). Processing time for FFR was significantly shorter compared with that of FFR (40.5 minutes ± 6.3 vs 43.4 minutes ± 7.1; P = .042). Conclusion The FFR algorithm performs equally in detecting lesion-specific ischemia when compared with the FFR approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis.

Organ-based TCM provided superior image quality to that with bismuth shielding while similarly reducing dose to the eye. Simply reducing tube current globally by about 30% provides the same dose reduction to the eye as bismuth shielding; however, CT number accuracy is maintained and dose is reduced to all parts of the head.

For the same applied radiation level, the use of integrated electronics in a CT detector showed a substantially reduced level of electronic noise, resulting in reductions in image noise and artifacts, compared with detectors having distributed electronics.

Organ-based tube-current modulation can reduce the dose to the anterior surface of patients without increasing image noise by commensurately increasing the dose to the posterior surface. This technique can reduce the dose to anterior radiosensitive organs for head and thoracic CT scans.