Background and Objectives: The increasing clinical use of torso positron emission tomography/computed tomography (PET/CT) demands automated segmentation of torso organs from PET/CT images. We attempt to use the multi-atlas segmentation approach for trunk organ segmentation from the low-dose CT images of PET/CT. Since atlas selection is a prerequisite step for multi-atlas segmentation, this study focuses on evaluating the performance of different atlas selection strategies for torso organ segmentation. Methods: We evaluated two criteria for atlas selection, including image similarity and body mass index (BMI) difference between the atlas and the target image. Based on the two criteria, ten atlases are selected and registered to the target image, followed by the label fusion step to achieve final segmentation. Results: The BMI criterion yields comparable segmentation accuracy to the image similarity criterion but with much less computation time. All the evaluated atlas selection methods have Dice >0.9 for the lungs, heart, and liver and Dice < 0.85 for the skeleton, spleen, and kidneys. The inter-method differences are not significant for the high-contrast and big-sized organs such as skeleton, lungs, heart, and liver. For the low-contrast and smaller-sized organs such as spleen and kidneys, none of the atlas selection methods significantly outperforms random atlas selection. Conclusions: BMI is an effective and efficient atlas selection criterion for low-dose torso CT images. The spleen and kidneys are difficult to get good segmentation, no matter which atlas selection method is used. It is important to develop more effective atlas selection methods for the spleen and kidneys.

Background and Objectives: Multimodal image registration is a crucial step in prostate cancer radiation therapy scheme. However, it can be challenging due to the obvious appearance difference between computed tomography (CT) and magnetic resonance imaging (MRI) and unavoidable organ motion. Accordingly, a nonrigid registration framework for precisely registering multimodal prostate images is proposed in this paper. Materials and Methods: In this work, multimodal prostate image registration between CT and MRI is achieved using a hybrid model that integrates multiresolution strategy and Demons algorithm. Furthermore, to precisely describe the deformation of prostate, B-spline-based registration is utilized to refine the initial registration result of multiresolution Demons algorithm. Results: To evaluate our method, experiments on clinical prostate data sets of nine participants and comparison with the conventional Demons algorithm are conducted. Experimental results demonstrate that the proposed registration method outperforms the Demons algorithm by a large margin in terms of mutual information and correlation coefficient. Conclusions: These results show that our method outperforms the Demons algorithm and can achieve excellent performance on multimodal prostate images even the appearances of prostate change significantly. In addition, the results demonstrate that the proposed method can help to localize the prostate accurately, which is feasible in clinical.

Background and Objectives: Three-dimensional (3D) printing has potential value in medical applications with increasing reports in the diagnostic assessment of cardiovascular diseases. The use of 3D printing in replicating pulmonary artery anatomy and diagnosing pulmonary embolism is very limited. The purpose of this study was to develop a 3D printed pulmonary artery model and test different computed tomography (CT) scanning protocols for determination of an optimal protocol with acceptable image quality but low radiation dose. Materials and Methods: A patient-specific 3D printed pulmonary artery model was created based on contrast-enhanced CT images in a patient with suspected pulmonary embolism. Different CT pulmonary angiography protocols consisting of 80, 100, and 120 kVp, pitch 0.7, 0.9, and 1.2 with 1 mm slice thickness, and 0.6 mm reconstruction interval were tested on the phantom. Quantitative assessment of image quality in terms of signal-to-noise ratio (SNR) was measured in the images acquired with different protocols. Measurements in pulmonary artery diameters were conducted and compared between pre- and post-3D printed images and 3D printed model. Results: The 3D printed model was found to replicate normal pulmonary artery with high accuracy. The mean difference in diameter measurements was <0.8 mm (<0.5% deviation in diameter). There was no significant difference in SNR measured between these CT protocols (P = 0.96–0.99). Radiation dose was reduced by 55% and 75% when lowering kVp from 120 to 100 and 80 kVp, without affecting image quality. Conclusions: It is feasible to produce a 3D printed pulmonary artery model with high accuracy in replicating normal anatomy. Different CT scanning protocols are successfully tested on the model with 80 kVp and pitch 0.9 being the optimal one with resultant diagnostic images but at much lower radiation dose.

Background and Objectives: The aim of this study is to investigate the application of three-dimensional (3D) visualization technique in the surgical treatment of malignant tumors of the hilar and common bile ducts. Materials and Methods: A total of 23 patients admitted from January 2015 to April 2017 for surgical treatment were analyzed, of whom 13 patients underwent medical image 3D visualization system (treatment group) while 10 underwent surgery alone (control group). Indicators to the treatment effects were recorded and compared between the two groups, including the operation time, intraoperative bleeding volume, postoperative hospitalization time, the number of dissected lymph nodes, the incidence of all postoperative complications, and the alanine transaminase (ALT), albumin (ALB), and total bilirubin (TBIL) monitored on days 1, 3, 5, and 7 after the operation. Results: In the treatment group and control group, the operation time was 194.66 ± 13.79 and 230.81 ± 27.07 min (t = 3.857, P = 0.002), the intraoperative bleeding volume was 274.28 ± 44.57 and 320.69 ± 35.90 mL (t = 2.686, P = 0.014), the postoperative hospitalization time was 11.15 ± 1.25 and 15.25 ± 1.75d (t = 6.557, P = 0.000), the number of dissected lymph nodes 10.46 ± 1.71 and 7.40 ± 0.97 (t = 5.050, P = 0.000), and the incidence of all postoperative complications 7.69% and 60% (χ² = 7.304, P = 0.019), respectively. The level of ALT and TBIL was significantly higher in the treatment group than in the control group, whereas the level of ALB was significantly lower in the treatment group than in the control group on days 3, 5, and 7 (P < 0.05). The recovery of liver function was better in the treatment group than in the control group. In addition, no perioperative deaths were found in either group. Conclusions: In the surgical treatment of malignant tumors occurring in the hilar to the common bile duct, 3D visualization technology can reduce the operation injury and intraoperative bleeding, decrease the incidence of postoperative complications, improve the safety and effectiveness of the operation, and promote the recovery of liver function, thus demonstrating promising short-term efficacy.

The pursuit of digital biomarkers wherein signal outputs from biosensors are collated to inform health-care decisions continues to evolve at a rapid pace. In the field of neurodegenerative disorders, a goal is to augment subjective patient-reported inputs with patient-independent verifiable data that can be used to recommend interventive measures. For example, in the case of Alzheimer's disease, such tools might preselect patients in the presymptomatic and prodromal phases for definitive positron emission tomographic analysis, allowing accurate staging of disease and providing a reference point for subsequent therapeutic and other measures. Selection of appropriate and meaningful digital biomarkers to pursue, however, requires deep understanding of the disease state and its ecological relationship to the instrumental activities of daily living scale. Similar opportunities and challenges exist in a number of other chronic disease states including Parkinson's, Huntington's, and Duchenne's disease, multiple sclerosis, and cardiovascular disease. This review will highlight progress in device technology, the need for holistic approaches for data inputs, and regulatory pathways for adoption. The review focuses on published work from the period 2012–2017 derived from online searches of the most widely used abstracting portals.