Stepwise local stitching ultrasound image algorithms based on adaptive iterative threshold Harris corner features

Herein, a Harris corner detection algorithm is proposed based on the concepts of iterated threshold segmentation and adaptive iterative threshold (AIT-Harris), and a stepwise local stitching algorithm is used to obtain wide-field ultrasound (US) images.Cone-beam computer tomography (CBCT) and US images from 9 cervical cancer patients and 1 prostate cancer patient were examined. In the experiment, corner features were extracted based on the AIT-Harris, Harris, and Morave algorithms. Accordingly, wide-field ultrasonic images were obtained based on the extracted features after local stitching, and the corner matching rates of all tested algorithms were compared. The accuracies of the drawn contours of organs at risk (OARs) were compared based on the stitched ultrasonic images and CBCT.The corner matching rate of the Morave algorithm was compared with those obtained by the Harris and AIT-Harris algorithms, and paired sample t tests were conducted (t = 6.142, t = 31.859, P < .05). The results showed that the differences were statistically significant. The average Dice similarity coefficient between the automatically delineated bladder region based on wide-field US images and the manually delineated bladder region based on ground truth CBCT images was 0.924, and the average Jaccard coefficient was 0.894.The proposed algorithm improved the accuracy of corner detection, and the stitched wide-field US image could modify the delineation range of OARs in the pelvic cavity.

Research on pseudo-CT imaging technique based on an ultrasound deformation field with binary mask in radiotherapy

This study aimed to investigate the reliability of pseudo-computed tomography (pseudo-CT) imaging based on ultrasound (US) deformation fields under different binary masks in radiotherapy.We used 3-dimensional (3D) CT and US images, including those acquired during CT simulation positioning, and cone-beam CT (CBCT) and US images acquired 1 week after treating 3 patients with cervical cancer. Image data of 3 different layers were selected from the US images, and 3D CT images of each patient were selected. For US image registration, the following were created and applied: binary masks of the region of interest overlapping (ROIO) between the US image based on simulation positioning and US image for positioning verification, region of interest (ROI), whole overlapping (wholeO), and whole imaging region (whole). Accordingly, the deformation field was obtained and applied to CT images (CTsim), and different pseudo-CT images were acquired. Similarities between the pseudo-CT and CBCT images were compared, and registration accuracies between pseudo-CT images under different binary masks and CTsim were compared and discussed.A pair t test was conducted to normalized mutual information values of the registration accuracy between the pseudo-CT image based on ROIO binary mask and CTsim with other methods (P < .05), and the difference was statistically significant. A pair t test of normalized gray mean-squared errors was also performed (P < .05), and the difference was statistically significant. The similarity function means between pseudo-CT, that is, based on ROIO, ROI, wholeO, whole, and no binary mask, and CBCT were 0.9084, 0.8365, 0.7800, 0.6830, and 0.5518, respectively.Pseudo-CT based on ROIO binary mask best matched with CTsim and achieved the highest similarity with CBCT.

Registration of Real-Time 3-D Ultrasound to Tomographic Images of the Abdominal Aorta

The purpose of this study was to develop an image-based method for registration of real-time 3-D ultrasound to computed tomography (CT) of the abdominal aorta, targeting future use in ultrasound-guided endovascular intervention. We proposed a method in which a surface model of the aortic wall was segmented from CT, and the approximate initial location of this model relative to the ultrasound volume was manually indicated. The model was iteratively transformed to automatically optimize correspondence to the ultrasound data. Feasibility was studied using data from a silicon phantom and in vivo data from a volunteer with previously acquired CT. Through visual evaluation, the ultrasound and CT data were seen to correspond well after registration. Both aortic lumen and branching arteries were well aligned. The processing was done offline, and the registration took approximately 0.2 s per ultrasound volume. The results encourage further patient studies to investigate accuracy, robustness and clinical value of the approach.