Kevin Ma

Effective staging of fibrosis by the selected texture features of liver: Which one is better, CT or MR imaging?

PURPOSE Texture patterns of hepatic fibrosis are one of the important biomarkers to diagnose and classify chronic liver disease from initial to end stage on computed tomography (CT) or magnetic resonance (MR) images. Computer-aided diagnosis (CAD) of liver cirrhosis using texture features has become popular in recent research advances. To date, however, properly selecting effective texture features and image parameters is still mostly undetermined and not well-defined. In this study, different types of datasets acquired from CT and MR images are investigated to select the optimal parameters and features for the proper classification of fibrosis. METHODS A total of 149 patients were scanned by multi-detector computed tomography (MDCT) and 218 patients were scanned using 1.5T and 3T superconducting MR scanners for an abdominal examination. All cases were verified by needle biopsies as the gold standard of our experiment, ranging from 0 (no fibrosis) to 5 (cirrhosis). For each case, at least four sequenced phase images are acquired by CT or MR scanners: pre-contrast, arterial, portal venous and equilibrium phase. For both imaging modalities, 15 texture features calculated from gray level co-occurrence matrix (GLCM) are extracted within an ROI in liver as one set of input vectors. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: non-cirrhosis or cirrhosis. In addition, 10 ROIs in the liver are manually selected in a disperse manner by experienced radiologist from each sequenced image and each of the 15 features are averaged across the 10 ROIs for each case to reduce the validation time. The number of input items is selected from the various combinations of 15 features, from which the accuracy rate (AR) is calculated by counting the percentage of correct answers on each combination of features aggregated to determine a liver stage score and then compared to the gold standard. RESULTS According to the accuracy rate (AR) calculated from each combination, the optimal number of texture features to classify liver fibrosis degree ranges from 4 to 7, no matter which modality was utilized. The overall performance calculated by the average sum of maximum AR value of all 15 features is 66.83% in CT images, while 68.14%, and 71.98% in MR images, respectively; among the 15 texture features, mean gray value and entropy are the most commonly used features in all 3 imaging datasets. The correlation feature has the lowest AR value and was removed as an effective feature in all datasets. AR value tends to increase with the injection of contrast agency, and both CT and MR images reach the highest AR performance during the equilibrium phase. CONCLUSIONS Comparing the accuracy of classification with two imaging modalities, the MR images have an advantage over CT images with regards to AR performance of the 15 selected texture features, while 3T MRI is better than 1.5T MRI to classify liver fibrosis. Finally, the texture analysis is more effective during equilibrium phase than in any of the other phased images.

Improvement of MS (multiple sclerosis) CAD (computer aided diagnosis) performance using C/C++ and computing engine in the graphical processing unit (GPU)

Multiple Sclerosis (MS) is a disease which is caused by damaged myelin around axons of the brain and spinal cord. Currently, MR Imaging is used for diagnosis, but it is very highly variable and time-consuming since the lesion detection and estimation of lesion volume are performed manually. For this reason, we developed a CAD (Computer Aided Diagnosis) system which would assist segmentation of MS to facilitate physicians diagnosis. The MS CAD system utilizes K-NN (k-nearest neighbor) algorithm to detect and segment the lesion volume in an area based on the voxel. The prototype MS CAD system was developed under the MATLAB environment. Currently, the MS CAD system consumes a huge amount of time to process data. In this paper we will present the development of a second version of MS CAD system which has been converted into C/C++ in order to take advantage of the GPU (Graphical Processing Unit) which will provide parallel computation. With the realization of C/C++ and utilizing the GPU, we expect to cut running time drastically. The paper investigates the conversion from MATLAB to C/C++ and the utilization of a high-end GPU for parallel computing of data to improve algorithm performance of MS CAD.

Integration of imaging informatics-based multiple sclerosis eFolder system for multi-site clinical trials utilizing IHE workflow profiles

At last year’s SPIE, we presented a multiple sclerosis (MS) eFolder as an integrated imaging-informatics based system to provide several functionalities to both clinical and research environments. The eFolder system combines patients’ clinical data, radiological images and computer-aided lesion detection and quantification results to aid in longitudinal tracking, data mining, decision support, and other clinical and research needs. To demonstrate how this system can be integrated in an existing imaging environment such as a large-scale multi-site MS clinical trial, we present a system infrastructure to streamline imaging and clinical data flow with postprocessing (CAD) steps. The system stores clinical and imaging data, provides CAD postprocessing algorithm and data storage, and a web-based graphical user interface (GUI) to view clinical trial data and monitor workflow. To evaluate the system infrastructure, the MS eFolder is set up in a simulated environment with workflow scenarios, including DICOM store, query, and retrieve, automatic CAD steps, and data mining based on CAD results. This project aims to discuss the methodology of setting up eFolder system simulation with a connection to a CAD server component, simulation performance and test results, and discussion of eFolder system deployment results.

Development of a data mining and imaging informatics display tool for a multiple sclerosis e-folder system

Multiple sclerosis (MS) is a debilitating autoimmune disease of the central nervous system that damages axonal pathways through inflammation and demyelination. In order to address the need for a centralized application to manage and study MS patients, the MS e-Folder - a web-based, disease-specific electronic medical record system - was developed. The e-Folder has a PHP and MySQL based graphical user interface (GUI) that can serve as both a tool for clinician decision support and a data mining tool for researchers. This web-based GUI gives the e-Folder a user friendly interface that can be securely accessed through the internet and requires minimal software installation on the client side. The e-Folder GUI displays and queries patient medical records--including demographic data, social history, past medical history, and past MS history. In addition, DICOM format imaging data, and computer aided detection (CAD) results from a lesion load algorithm are also displayed. The GUI interface is dynamic and allows manipulation of the DICOM images, such as zoom, pan, and scrolling, and the ability to rotate 3D images. Given the complexity of clinical management and the need to bolster research in MS, the MS e-Folder system will improve patient care and provide MS researchers with a function-rich patient data hub.

The development of a disease oriented eFolder for multiple sclerosis decision support

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system. The chronic nature of MS necessitates multiple MRI studies to track disease progression. Currently, MRI assessment of multiple sclerosis requires manual lesion measurement and yields an estimate of lesion volume and change that is highly variable and user-dependent. In the setting of a longitudinal study, disease trends and changes become difficult to extrapolate from the lesions. In addition, it is difficult to establish a correlation between these imaged lesions and clinical factors such as treatment course. To address these clinical needs, an MS specific e-Folder for decision support in the evaluation and assessment of MS has been developed. An e-Folder is a disease-centric electronic medical record in contrast to a patient-centric electronic health record. Along with an MS lesion computer aided detection (CAD) package for lesion load, location, and volume, clinical parameters such as patient demographics, disease history, clinical course, and treatment history are incorporated to make the e-Folder comprehensive. With the integration of MRI studies together with related clinical data and informatics tools designed for monitoring multiple sclerosis, it provides a platform to improve the detection of treatment response in patients with MS. The design and deployment of MS e-Folder aims to standardize MS lesion data and disease progression to aid in decision making and MS-related research.

An automatic quantification system for MS lesions with integrated DICOM structured reporting (DICOM-SR) for implementation within a clinical environment

Multiple Sclerosis (MS) is a common neurological disease affecting the central nervous system characterized by pathologic changes including demyelination and axonal injury. MR imaging has become the most important tool to evaluate the disease progression of MS which is characterized by the occurrence of white matter lesions. Currently, radiologists evaluate and assess the multiple sclerosis lesions manually by estimating the lesion volume and amount of lesions. This process is extremely time-consuming and sensitive to intra- and inter-observer variability. Therefore, there is a need for automatic segmentation of the MS lesions followed by lesion quantification. We have developed a fully automatic segmentation algorithm to identify the MS lesions. The segmentation algorithm is accelerated by parallel computing using Graphics Processing Units (GPU) for practical implementation into a clinical environment. Subsequently, characterized quantification of the lesions is performed. The quantification results, which include lesion volume and amount of lesions, are stored in a structured report together with the lesion location in the brain to establish a standardized representation of the disease progression of the patient. The development of this structured report in collaboration with radiologists aims to facilitate outcome analysis and treatment assessment of the disease and will be standardized based on DICOM-SR. The results can be distributed to other DICOM-compliant clinical systems that support DICOM-SR such as PACS. In addition, the implementation of a fully automatic segmentation and quantification system together with a method for storing, distributing, and visualizing key imaging and informatics data in DICOM-SR for MS lesions improves the clinical workflow of radiologists and visualizations of the lesion segmentations and will provide 3-D insight into the distribution of lesions in the brain.

The development of an MRI lesion quantifying system for multiple sclerosis patients undergoing treatment

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system that affects approximately 2.5 million people worldwide. Magnetic resonance imaging (MRI) is an established tool for the assessment of disease activity, progression and response to treatment. The progression of the disease is variable and requires routine follow-up imaging studies. Currently, MRI quantification of multiple sclerosis requires a manual approach to lesion measurement and yields an estimate of lesion volume and interval change. In the setting of several prior studies and a long treatment history, trends related to treatment change quickly become difficult to extrapolate. Our efforts seek to develop an imaging informatics based MS lesion computer aided detection (CAD) package to quantify and track MS lesions including lesion load, volume, and location. Together, with select clinical parameters, this data will be incorporated into an MS specific e- Folder to provide decision support to evaluate and assess treatment options for MS in a manner tailored specifically to an individual based on trends in MS presentation and progression.

Assuring image authenticity within a data grid using lossless digital signature embedding and a HIPAA-compliant auditing system

A Data Grid for medical images has been developed at the Image Processing and Informatics Laboratory, USC to provide distribution and fault-tolerant storage of medical imaging studies across Internet2 and public domain. Although back-up policies and grid certificates guarantee privacy and authenticity of grid-access-points, there still lacks a method to guarantee the sensitive DICOM images have not been altered or corrupted during transmission across a public domain. This paper takes steps toward achieving full image transfer security within the Data Grid by utilizing DICOM image authentication and a HIPAA-compliant auditing system. The 3-D lossless digital signature embedding procedure involves a private 64 byte signature that is embedded into each original DICOM image volume, whereby on the receiving end the signature can to be extracted and verified following the DICOM transmission. This digital signature method has also been developed at the IPILab. The HIPAA-Compliant Auditing System (H-CAS) is required to monitor embedding and verification events, and allows monitoring of other grid activity as well. The H-CAS system federates the logs of transmission and authentication events at each grid-access-point and stores it into a HIPAA-compliant database. The auditing toolkit is installed at the local grid-access-point and utilizes Syslog [1], a client-server standard for log messaging over an IP network, to send messages to the H-CAS centralized database. By integrating digital image signatures and centralized logging capabilities, DICOM image integrity within the Medical Imaging and Informatics Data Grid can be monitored and guaranteed without loss to any image quality.

Evaluation of longitudinal tracking and data mining for an imaging informatics-based multiple sclerosis e-folder (Conference Presentation)

We have designed and developed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results to allow patient tracking. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and data analysis. The system quantifies lesion volumes, identify and register lesion locations to track shifts in volume and quantity of lesions in a longitudinal study. We aim to evaluate the two most important features of the system, data mining and longitudinal lesion tracking, to demonstrate the MS eFolder’s capability in improving clinical workflow efficiency and outcome analysis for research. In order to evaluate data mining capabilities, we have collected radiological and neurological data from 72 patients, 36 Caucasian and 36 Hispanic matched by gender, disease duration, and age. Data analysis on those patients based on ethnicity is performed, and analysis results are displayed by the system’s web-based user interface. The data mining module is able to successfully separate Hispanic and Caucasian patients and compare their disease profiles. For longitudinal lesion tracking, we have collected 4 longitudinal cases and simulated different lesion growths over the next year. As a result, the eFolder is able to detect changes in lesion volume and identifying lesions with the most changes. Data mining and lesion tracking evaluation results show high potential of eFolder’s usefulness in patientcare and informatics research for multiple sclerosis.

Lesion registration for longitudinal disease tracking in an imaging informatics-based multiple sclerosis eFolder

We have designed and developed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results stored in DICOM-SR format. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and data analysis. The system needs to quantify lesion volumes, identify and register lesion locations to track shifts in volume and quantity of lesions in a longitudinal study. In order to perform lesion registration, we have developed a brain warping and normalizing methodology using Statistical Parametric Mapping (SPM) MATLAB toolkit for brain MRI. Patients’ brain MR images are processed via SPM’s normalization processes, and the brain images are analyzed and warped according to the tissue probability map. Lesion identification and contouring are completed by neuroradiologists, and lesion volume quantification is completed by the eFolder’s CAD program. Lesion comparison results in longitudinal studies show key growth and active regions. The results display successful lesion registration and tracking over a longitudinal study. Lesion change results are graphically represented in the web-based user interface, and users are able to correlate patient progress and changes in the MRI images. The completed lesion and disease tracking tool would enable the eFolder to provide complete patient profiles, improve the efficiency of patient care, and perform comprehensive data analysis through an integrated imaging informatics system.