Hepatocellular Carcinoma is the most common type of liver cancer having a strong relation with cirrhosis. Undoubtedly, cirrhosis may be caused by the virus infection of hepatitis B (HBV) and hepatitis C (HBC) or through alchoholism. However, even when cirrhosis has not been developed, patients with hepatitis viral infections are still at the risk of liver cancer. Apparently, among the numerous medical imaging techniques, Computed Tomography (CT) is the best in defining liver tumor borders. Unfortunately, these imaging techniques, including the CT procedures, usually rely on an appended application to reconstruct the generated 2-D slices to 3-D model. This may involve high performance computation, may be time-consuming or costly. Moreover, even with the outstanding performances of CT in defining the liver tumor boundaries, contrast between tumor tissues and the surrounding liver parenchyma is too low in CT slices. With such a close proxity in the tumor and the surrounding liver tissues, accurate characterization of liver tumor is a challenge. Previously, algorithms were developed to reveal abnormalities in brain's MRI datasets and CT abdominal pelvic, however, introducing a framework that could accurately characterize liver tumor and its surrounding tissues in CT datasets would go a long way in contributing to medical diagnosis and therapy planning of Hepatocellular Carcinoma. This paper proposes an Hepatocellular Carcinoma framework by extending the functionalities of SurLens Visualization System with an automatic liver tumor localization technique using Compute Unified Device Architecture (CUDA). The study was evaluated with liver CT datasets from the Imaging Science and Information Systems (ISIS) Center, the Georgetown University Medical Center. Significantly, visualization of liver CT datasets and the localization of the entangled tumor was achieved without prior datasets segmentation. Interestingly, the framework achieved remarkably good processing speed at a reasonably cheaper cost with an immediate reconstruction of the datasets and mapping of the tumor tissues within the surrounding liver parenchyma.
In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging devices. Unfortunately, these image scanners could only present the 3-D human anatomical structure in 2-D. Traditionally, this requires medical professional concerned to study and analyze the 2-D images based on their expert experience. This is tedious, time consuming and prone to error; expecially when certain features are occluding the desired region of interest. Reconstruction procedures was earlier proposed to handle such situation. However, 3-D reconstruction system requires high performance computation and longer processing time. Integrating efficient reconstruction system into clinical procedures involves high resulting cost. Previously, brain's blood vessels reconstruction with MRA was achieved using SurLens Visualization System. However, adapting such system to other image modalities, applicable to the entire human anatomical structures, would be a meaningful contribution towards achieving a resourceful system for medical diagnosis and disease therapy. This paper attempts to adapt SurLens to possible visualisation of abnormalities in human anatomical structures using CT and MR images. The study was evaluated with brain MR images from the department of Surgery, University of North Carolina, United States and CT abdominal pelvic, from the Swedish National Infrastructure for Computing. The MR images contain around 109 datasets each of T1-FLASH, T2-Weighted, DTI and T1-MPRAGE. Significantly, visualization of human anatomical structure was achieved without prior segmentation. SurLens was adapted to visualize and display abnormalities, such as an indication of walderstrom's macroglobulinemia, stroke and penetrating brain injury in the human brain using Magentic Resonance (MR) images. Moreover, possible abnormalities in abdominal pelvic was also visualized using Computed Tomography (CT) slices. The study shows SurLens' functionality as a 3-D Multimodal Visualization System.
CT and MRI scans are widely used in medical diagnosis procedures, but they only produce 2-D images. However, the human anatomical structure, the abnormalities, tumors, tissues and organs are in 3-D. 2-D images from these devices are difficult to interpret because they only show cross-sectional views of the human structure. Consequently, such circumstances require doctors to use their expert experiences in the interpretation of the possible location, size or shape of the abnormalities, even for large datasets of enormous amount of slices. Previously, the concept of reconstructing 2-D images to 3-D was introduced. However, such reconstruction model requires high performance computation, may either be time-consuming or costly. Furthermore, detecting the internal features of human anatomical structure, such as the imaging of the blood vessels, is still an open topic in the computer-aided diagnosis of disorders and pathologies. This paper proposes a volume visualization framework using Compute Unified Device Architecture (CUDA), augmenting the widely proven ray casting technique in terms of superior qualities of images but with slow speed. Considering the rapid development of technology in the medical community, our framework is implemented on Microsoft.NET environment for easy interoperability with other emerging revolutionary tools. The framework was evaluated with brain datasets from the department of Surgery, University of North Carolina, United States, containing around 109 MRA datasets. Uniquely, at a reasonably cheaper cost, our framework achieves immediate reconstruction and obvious mappings of the internal features of human brain, reliable enough for instantaneous locations of possible blockages in the brain blood vessels.