Multipath Curved Planar Reformations of Peripheral CT Angiography: Diagnostic Accuracy and Time Efficiency

Curved Planar Reformation: A Useful Method for Screening Dental Pathologies in Chronic Rhinosinusitis via Paranasal Sinus Computed Tomography

(1) Background: Curved planar reformation (CPR) is a multiplanar reformatting technique of computed tomography (CT) commonly used during dental cone-beam CT (CBCT) to generate panorex-like images for dental evaluation. Here, we evaluated the utility of an additional CPR sequence in detecting dental pathologies in patients with chronic rhinosinusitis (CRS). (2) Methods: CRS patients who underwent paranasal sinus CT were enrolled retrospectively. The CT images featured three orthogonal sequences and a reconstructed CPR sequence. Additional dental CBCT was performed in patients with pathologies with a strongly suspected odontogenic origin. Dental pathologies detected by CT, CPR, and CBCT were analyzed. (3) Results: A total of 82 CRS patients (37 females and 45 males; mean age 47.3 ± 13.7 years) were included, of whom 23 underwent dental CBCT. In total, 1058 maxillary teeth were evaluated. Compared with paranasal sinus CT, CPR identified greater frequencies of dental pathologies, particularly caries (p < 0.001), periapical lesions (p < 0.001), and fistulae (p = 0.014). CBCT identified greater frequencies of periodontal dental pathologies (p = 0.046) and premolar caries (p = 0.002) compared with CPR. CBCT and CPR detected molar dental pathologies at similar frequencies. (4) Conclusions: CPR could increase the diagnostic rate of odontogenic pathologies compared with standard CT orthogonal views, especially when the sinusitis is caused by caries, periapical lesions, or fistulae. The addition of a CPR sequence allows for simple screening of dental pathologies in CRS patients without a need for additional radiation.

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

This paper presents an in-depth study and analysis of the 3D arterial centerline in spiral CT coronary angiography, and constructs its detection and extraction technique. The first time, the distance transform is used to complete the boundary search of the original figure; the second time, the distance transform is used to calculate the value of the distance transform of all voxels, and according to the value of the distance transform, unnecessary voxels are deleted, to complete the initial contraction of the vascular region and reduce the computational consumption in the next process; then, the nonwitnessed voxels are used to construct the maximum inner joint sphere model and find the skeletal voxels that can reflect the shape of the original figure. Finally, the skeletal lines were optimized on these initially extracted skeletal voxels using a dichotomous-like principle to obtain the final coronary artery centerline. Through the evaluation of the experimental results, the algorithm can extract the coronary centerline more accurately. In this paper, the segmentation method is evaluated on the test set data by two kinds of indexes: one is the index of segmentation result evaluation, including dice coefficient, accuracy, specificity, and sensitivity; the other is the index of clinical diagnosis result evaluation, which is to refine the segmentation result for vessel diameter detection. The results obtained in this paper were compared with the physicians' labeling results. In terms of network performance, the Dice coefficient obtained in this paper was 0.89, the accuracy was 98.36%, the sensitivity was 93.36%, and the specificity was 98.76%, which reflected certain advantages in comparison with the advanced methods proposed by previous authors. In terms of clinical evaluation indexes, by performing skeleton line extraction and diameter calculation on the results obtained by the segmentation method proposed in this paper, the absolute error obtained after comparing with the diameter of the labeled image was 0.382 and the relative error was 0.112, which indicates that the segmentation method in this paper can recover the vessel contour more accurately. Then, the results of coronary artery centerline extraction with and without fine branch elimination were evaluated, which proved that the coronary artery centerline has higher accuracy after fine branch elimination. The algorithm is also used to extract the centerline of the complete coronary artery tree, and the results prove that the algorithm has better results for the centerline extraction of the complete coronary vascular tree.