3D cone-beam CT with a twin robotic x-ray system in elbow imaging: comparison of image quality to high-resolution multidetector CT

Background Elbow imaging is challenging with conventional multidetector computed tomography (MDCT), while cone-beam CT (CBCT) provides superior options. We compared intra-individually CBCT versus MDCT image quality in cadaveric elbows. Methods A twin robotic x-ray system with new CBCT mode and a high-resolution clinical MDCT were compared in 16 cadaveric elbows. Both systems were operated with a dedicated low-dose (LD) protocol (equivalent volume CT dose index [CTDIvol(16 cm)] = 3.3 mGy) and a regular clinical scan dose (RD) protocol (CTDIvol(16 cm) = 13.8 mGy). Image quality was evaluated by two radiologists (R1 and R2) on a seven-point Likert scale, and estimation of signal intensity in cancellous bone was conducted. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) statistics were used. Results The CBCT prototype provided superior subjective image quality compared to MDCT scans (for RD, p ≤ 0.004; for LD, p ≤ 0.001). Image quality was rated very good or excellent in 100% of the cases by both readers for RD CBCT, 100% (R1) and 93.8% (R2) for LD CBCT, 62.6% and 43.8% for RD MDCT, and 0.0% and 0.0% for LD MDCT. Single-measure ICC was 0.95 (95% confidence interval 0.91–0.97; p < 0.001). Software-based assessment supported subjective findings with less “undecided” pixels in CBCT than dose-equivalent MDCT (p < 0.001). No significant difference was found between LD CBCT and RD MDCT. Conclusions In cadaveric elbow studies, the tested cone-beam CT prototype delivered superior image quality compared to high-end multidetector CT and showed a potential for considerable dose reduction.

Automatic Detection of the Aortic Annular Plane and Coronary Ostia from Multidetector Computed Tomography

Anatomic landmark detection is crucial during preoperative planning of transcatheter aortic valve implantation (TAVI) to select the proper device size and assess the risk of complications. The detection is currently a time-consuming manual process influenced by the image quality and subject to operator variability. In this work, we propose a novel automatic method to detect the relevant aortic landmarks from MDCT images using deep learning techniques. We trained three convolutional neural networks (CNNs) with 344 multidetector computed tomography (MDCT) acquisitions to detect five anatomical landmarks relevant for TAVI planning: the three basal attachment points of the aortic valve leaflets and the left and right coronary ostia. The detection strategy used these three CNN models to analyse a single MDCT image and yield three segmentation volumes as output. These segmentation volumes were averaged into one final segmentation volume, and the final predicted landmarks were obtained during a postprocessing step. Finally, we constructed the aortic annular plane, defined by the three predicted hinge points, and measured the distances from this plane to the predicted coronary ostia (i.e., coronary height). The methodology was validated on 100 patients. The automatic landmark detection was able to detect all the landmarks and showed high accuracy as the median distance between the ground truth and predictions is lower than the interobserver variations (1.5 mm [1.1–2.1], 2.0 mm [1.3–2.8] with a paired difference −0.5 ± 1.3 mm and p value <0.001). Furthermore, a high correlation is observed between predicted and manually measured coronary heights (for both R2 = 0.8). The image analysis time per patient was below one second. The proposed method is accurate, fast, and reproducible. Embedding this tool based on deep learning in the preoperative planning routine may have an impact in the TAVI environments by reducing the time and cost and improving accuracy.

A Three-Dimensional Evaluation of EndoButton Displacement Direction After Anterior Cruciate Ligament Reconstruction in CT Image Using Tunnel Axis

The goal of this study was to propose a threedimensional evaluation of the EndoButton displacement direction after anterior cruciate ligament reconstruction in the multidetector-row computed tomography (MDCT) image by using the tunnel axis. The proposed method was applied experimentally to six subjects. The result of the simulated experiment revealed that the proposed method could analyze EndoButton displacement direction satisfactorily because the error was less than that of the MDCT image resolution. The clinical experiment results revealed displacement relative to the tunnel between time-zero and the followup point. We conclude that the proposed method can quantitatively evaluate the EndoButton displacement direction from the raw MDCT image after anterior cruciate ligament reconstruction; further, our findings suggest that the EndoButton was displaced relative to the tunnel between time-zero and the follow-up point.