This study proposed a registration framework to fuse 2D echocardiography images of the aortic valve with preoperative cardiac CT volume. The registration facilitates the fusion of CT and echocardiography to aid the diagnosis of aortic valve diseases and provide surgical guidance during transcatheter aortic valve replacement and implantation. The image registration framework consists of two major steps: temporal synchronization and spatial registration. Temporal synchronization allows time stamping of echocardiography time series data to identify frames that are at similar cardiac phase as the CT volume. Spatial registration is an intensity-based normalized mutual information method applied with pattern search optimization algorithm to produce an interpolated cardiac CT image that matches the echocardiography image. Our proposed registration method has been applied on the short-axis “Mercedes Benz” sign view of the aortic valve and long-axis parasternal view of echocardiography images from ten patients. The accuracy of our fully automated registration method was 0.81 ± 0.08 and 1.30 ± 0.13 mm in terms of Dice coefficient and Hausdorff distance for short-axis aortic valve view registration, whereas for long-axis parasternal view registration it was 0.79 ± 0.02 and 1.19 ± 0.11 mm, respectively. This accuracy is comparable to gold standard manual registration by expert. There was no significant difference in aortic annulus diameter measurement between the automatically and manually registered CT images. Without the use of optical tracking, we have shown the applicability of this technique for effective fusion of echocardiography with preoperative CT volume to potentially facilitate catheter-based surgery.

Abstract: Background: Early diagnosis of liver cancer may increase life expectancy. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) play a vital role in diagnosing liver cancer. Together, both modalities offer significant individual and specific diagnosis data to physicians; however, they lack the integration of both types of information. To address this concern, a registration process has to be utilized for the purpose, as multimodal details are crucial in providing the physician with complete information. Objective: The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities. Methods: In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel (R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively. Results: The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution. Conclusion: The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant’s technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.

Background This study presents a registration system that integrates preoperative cardiac Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) volume data with 2D Ultrasound (US) images of the aortic valve. The registration process aims to combine three different imaging modalities (US-CT-MRI) to improve the accuracy of diagnosing aortic valve disorders and provide surgical guidance during the implantation and replacement of the transcatheter aortic valve. Methods The registration framework involves two key components: temporal synchronization and spatial registration. Temporal synchronization allows the identification of frames in the CT and MRI volume that correspond to the same cardiac phase as the US time-series data. For spatial registration, an intensity-based normalized mutual information method combined with a pattern search optimization algorithm is used to produce interpolated cardiac CT and MRI images that align with the US image. Results The accuracy of the trimodality registration method is evaluated using the Dice similarity coefficient. The obtained coefficients are 0.92±0.05 and 0.92±0.04 for comparisons between US-CT and US-MRI, respectively, in short-axis "Mercedes Benz" sign views. The Hausdorff distance, which measures the dissimilarity between two sets of points, was found to be 1.49±0.20 and 1.49±0.19 for both US-CT and US-MRI pairings, respectively. Notably, these values are comparable to the precision achieved when an expert manually registers each image. Conclusions The proposed registration technique demonstrates excellent accuracy in enhancing image-guided systems for aortic valve surgical guidance. It shows promise in the context of Transcatheter Aortic Valve Implantation (TAVI) and Transcatheter Aortic Valve Replacement (TAVR) procedures. The successful integration of US-CT-MRI imaging modalities enables better diagnosis and surgical planning for aortic valve disorders, potentially leading to improved patient outcomes in these procedures.

A computerized bone age assessment requires segmentation of the X-ray carpal bones from other undesired tissue regions. This paper presents segmentation and area measurement of carpal bones in X-ray images. The locally weighted K-means variational level set was applied in segmenting 67 X-ray carpal bone datasets. Dice coefficient and Hausdorff distance measures show mean values above 0.7 and around 3 pixels, respectively. These satisfying segmentation outcomes enable the carpal bone areas to be measured on the segmented images. The carpal bone area measurement ranged from 4.24 mm to 48.96 mm with a mean value of 20.70 ± 10.51 mm and various values of the Pearson’s correlation coefficient implies that the segmentation method is insensitive to different carpal bone areas and locations. These results suggest that the methods can be applied in the bone age assessment by quantifying changes in the carpal bone area over certain time intervals.