Spectral CT Hybrid Images in the Diagnostic Evaluation of Hypervascular Abdominal Tumors—Potential Advantages in Clinical Routine

Background: This study aimed to investigate the use of spectral computed tomography (SCT) hybrid images combining virtual monoenergetic images (VMIs) and iodine maps (IMs) as a potentially efficient search series for routine clinical imaging in patients with hypervascular abdominal tumors. Methods: A total of 69 patients with hypervascular abdominal tumors including neuroendocrine neoplasms (NENs, n = 48), renal cell carcinoma (RCC, n = 10), and primary hepatocellular carcinoma (HCC, n = 11) were analyzed retrospectively. Two radiological readers (blinded to clinical data) read three CT image sets (1st a reference set with 70 keV; 2nd a 50:50 hybrid 140 keV/40 keV set; 3rd a 50:50 hybrid140 keV/IM set). They assessed images subjectively by rating several parameters including image contrast, visibility of suspicious lesions, and diagnostic confidence on five-point Likert scales. In addition, reading time was estimated. Results: Median subjective Likert scores were highest for the 1st set, except for image contrast, for which the 2nd set was rated highest. Scores for diagnostic confidence, artifacts, noise, and visibility of suspicious lesions or small structures were significantly higher for the 1st set than for the 2nd or 3rd set (p < 0.001). Regarding image contrast, the 2nd set was rated significantly higher than the 3rd set (p < 0.001), while the median did not differ significantly compared with the 1st set. Agreement between the two readers was high for all sets. Estimated potential reading time was the same for hybrid and reference sets. Conclusions: Hybrid images have the potential to efficiently exploit the additional information provided by SCT in patients with hypervascular abdominal tumors. However, the use of rigid weighting did not significantly improve diagnostic performance in this study.

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort

Objective To evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state (sagDESS) MRI. Methods 2D U-Nets were trained using manual, quality-controlled femorotibial cartilage segmentations available for 92 Osteoarthritis Initiative healthy reference cohort participants from both corFLASH and sagDESS (n = 50/21/21 training/validation/test-set). Cartilage morphometry was computed from automated and manual segmentations for knees from the test-set. Agreement and accuracy were evaluated from baseline visits (dice similarity coefficient: DSC, correlation analysis, systematic offset). The longitudinal reproducibility was assessed from year-1 and -2 follow-up visits (root-mean-squared coefficient of variation, RMSCV%). Results Automated segmentations showed high agreement (DSC 0.89–0.92) and high correlations (r ≥ 0.92) with manual ground truth for both corFLASH and sagDESS and only small systematic offsets (≤ 10.1%). The automated measurements showed a similar test–retest reproducibility over 1 year (RMSCV% 1.0–4.5%) as manual measurements (RMSCV% 0.5–2.5%). Discussion The 2D U-Net-based automated segmentation method yielded high agreement compared with manual segmentation and also demonstrated high accuracy and longitudinal test–retest reproducibility for morphometric analysis of articular cartilage derived from it, using both corFLASH and sagDESS.

Precision, Reliability, and Responsiveness of a Novel Automated Quantification Tool for Cartilage Thickness: Data from the Osteoarthritis Initiative

Objective.Accurate automated segmentation of cartilage should provide rapid reliable outcomes for both epidemiological studies and clinical trials. We aimed to assess the precision and responsiveness of cartilage thickness measured with careful manual segmentation or a novel automated technique.Methods.Agreement of automated segmentation was assessed against 2 manual segmentation datasets: 379 magnetic resonance images manually segmented in-house (training set), and 582 from the Osteoarthritis Initiative with data available at 0, 1, and 2 years (biomarkers set). Agreement of mean thickness was assessed using Bland-Altman plots, and change with pairwise Student t test in the central medial femur (cMF) and tibia regions (cMT). Repeatability was assessed on a set of 19 knees imaged twice on the same day. Responsiveness was assessed using standardized response means (SRM).Results.Agreement of manual versus automated methods was excellent with no meaningful systematic bias (training set: cMF bias 0.1 mm, 95% CI ± 0.35; biomarkers set: bias 0.1 mm ± 0.4). The smallest detectable difference for cMF was 0.13 mm (coefficient of variation 3.1%), and for cMT 0.16 mm(2.65%). Reported change using manual segmentations in the cMF region at 1 year was −0.031 mm (95% CI −0.022, −0.039), p < 10−4, SRM −0.31 (−0.23, −0.38); and at 2 years was −0.071 (−0.058, −0.085), p < 10−4, SRM −0.43 (−0.36, −0.49). Reported change using automated segmentations in the cMF at 1 year was −0.059 (−0.047, −0.071), p < 10−4, SRM −0.41 (−0.34, −0.48); and at 2 years was −0.14 (−0.123, −0.157, p < 10−4, SRM −0.67 (−0.6, −0.72).Conclusion.A novel cartilage segmentation method provides highly accurate and repeatable measures with cartilage thickness measurements comparable to those of careful manual segmentation, but with improved responsiveness.