Body CT examinations in oncologic patients: the impact of subspecialty radiology on radiation exposure in the clinical practice. A quality care study.

PURPOSES: The primary objective of this retrospective study was to assess whether the CT dose delivered to oncologic patients was different in a subspecialty radiology department, compared to a general radiology department. The secondary explorative objective was to assess whether the objective image quality of CT examinations was different in the two settings. MATERIALS AND METHODS: Chest and abdomen CT scans performed for oncologic indications were selected from a general radiology department and a subspecialty radiology department. By using a radiation dose management platform, we extracted and compared CT dose index (CTDIvol) and dose length product (DLP) both for each phase and for the entire CT exams. For objective image quality evaluation, we calculated the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) at the level of the liver and of the aorta. A P-value < 0.05 was considered significant. RESULTS: A total of 7098 CT examinations were included. CTDIvol was evaluated in 12,804 phases; DLP in 10,713 phases and in 6714 examinations. The CTDIvol and DLP overall were significantly lower in the subspecialty radiology department compared to the general radiology department CTDI median (IQR) 5.19 (3.91-7.00) and 5.51 (4.17-7.72), DLP median and IQR of 490.0 (342.4-710.6) and 503.4 (359.9-728.8), p < 0.001 and p = 0.01, respectively. The objective image quality showed no significant difference in the general and subspecialty radiology departments, with median and IQR of 4.03 (2.82-5.51) and 3.84 (3.09-4.94) for SNRLiv (p = 0.58); 4.81 (2.70-7.62) and 4.34 (3.05-6.25) for SNRAo (p = 0.30); 0.83 (0.20-1.89) and 1.00 (0.35-1.57) for CNRLiv (p = 0.99); 2.23 (0.09-3.83) and 1.01 (0.15-2.84) for CNRAo (p = 0.24) with SNRLiv (p = 0.58), SNRAo (p = 0.30), CNRLiv (p = 0.99) and CNRAo (p = 0.24). CONCLUSION: In a subspecialty radiology department, CT protocols are optimized compared to a general radiology department leading to lower doses to oncologic patients without significant objective image quality degradation.

Impact of retraining a deep learning algorithm for improving guideline-compliant aortic diameter measurements on non-gated chest CT.

PURPOSE/OBJECTIVE: Reliable detection of thoracic aortic dilatation (TAD) is mandatory in clinical routine. For ECG-gated CT angiography, automated deep learning (DL) algorithms are established for diameter measurements according to current guidelines. For non-ECG gated CT (contrast enhanced (CE) and non-CE), however, only a few reports are available. In these reports, classification as TAD is frequently unreliable with variable result quality depending on anatomic location with the aortic root presenting with the worst results. Therefore, this study aimed to explore the impact of re-training on a previously evaluated DL tool for aortic measurements in a cohort of non-ECG gated exams. METHODS & MATERIALS: A cohort of 995 patients (68 ± 12 years) with CE (n = 392) and non-CE (n = 603) chest CT exams was selected which were classified as TAD by the initial DL tool. The re-trained version featured improved robustness of centerline fitting and cross-sectional plane placement. All cases were processed by the re-trained DL tool version. DL results were evaluated by a radiologist regarding plane placement and diameter measurements. Measurements were classified as correctly measured diameters at each location whereas false measurements consisted of over-/under-estimation of diameters. RESULTS: We evaluated 8948 measurements in 995 exams. The re-trained version performed 8539/8948 (95.5%) of diameter measurements correctly. 3765/8948 (42.1%) of measurements were correct in both versions, initial and re-trained DL tool (best: distal arch 655/995 (66%), worst: Aortic sinus (AS) 221/995 (22%)). In contrast, 4456/8948 (49.8%) measurements were correctly measured only by the re-trained version, in particular at the aortic root (AS: 564/995 (57%), sinotubular junction: 697/995 (70%)). In addition, the re-trained version performed 318 (3.6%) measurements which were not available previously. A total of 228 (2.5%) cases showed false measurements because of tilted planes and 181 (2.0%) over-/under-segmentations with a focus at AS (n = 137 (14%) and n = 73 (7%), respectively). CONCLUSION: Re-training of the DL tool improved diameter assessment, resulting in a total of 95.5% correct measurements. Our data suggests that the re-trained DL tool can be applied even in non-ECG-gated chest CT including both, CE and non-CE exams.

Practical instructions for using drugs in CT and MR cardiac imaging.

The progressive increase in numbers of noninvasive cardiac imaging examinations broadens the spectrum of knowledge radiologists are expected to acquire in the management of drugs during CT coronary angiography (CTCA) and cardiac MR (CMR) to improve image quality for optimal visualization and assessment of the coronary arteries and adequate MR functional analysis. Aim of this review is to provide an overview on different class of drugs (nitrate, beta-blockers, ivabradine, anxiolytic, adenosine, dobutamine, atropine, dipyridamole and regadenoson) that can be used in CTCA and CMR, illustrating their main indications, contraindications, efficacy, mechanism of action, metabolism, safety, side effects or complications, and providing advices in their use.