Modeling and Reconstruction of Mixed Functional and Molecular Patterns

Functional medical imaging promises powerful tools for the visualization and elucidation of important disease-causing biological processes in living tissue. Recent research aims to dissect the distribution or expression of multiple biomarkers associated with disease progression or response, where the signals often represent a composite of more than one distinct source independent of spatial resolution. Formulating the task as a blind source separation or composite signal factorization problem, we report here a statistically principled method for modeling and reconstruction of mixed functional or molecular patterns. The computational algorithm is based on a latent variable model whose parameters are estimated using clustered component analysis. We demonstrate the principle and performance of the approaches on the breast cancer data sets acquired by dynamic contrast-enhanced magnetic resonance imaging

Merging of intersecting triangulations for finite element modeling

Surface mesh generation over intersecting triangulations is a problem common to many branches of biomechanics. A new strategy for merging intersecting triangulations is described. The basis of the method is that object surfaces are represented as the zero-level iso-surface of the distance-to-surface function defined on a background grid. Thus, the triangulation of intersecting objects reduces to the extraction of an iso-surface from an unstructured grid. In a first step, a regular background mesh is constructed. For each point of the background grid, the closest distance to the surface of each object is computed. Background points are then classified as external or internal by checking the direction of the surface normal at the closest location and assigned a positive or negative distance, respectively. Finally, the zero-level iso-surface is constructed. This is the final triangulation of the intersecting objects. The overall accuracy is enhanced by adaptive refinement of the background grid elements. The resulting surface models are used as support surfaces to generate three-dimensional grids for finite element analysis. The algorithms are demonstrated by merging arterial branches independently reconstructed from contrast-enhanced magnetic resonance images and by adding extra features such as vascular stents. Although the methodology is presented in the context of finite element analysis of blood flow, the algorithms are general and can be applied in other areas as well

Theoretical study of the mechanism of dry oxidation of 4H-SiC

Possible defect structures, arising from the interaction of O-2 molecules with an ideal portion of the SiC/SiO2 interface, have been investigated systematically using density functional theory. Based on the calculated total energies and assuming thermal quasiequilibrium during oxidation, the most likely routes leading to complete oxidation have been determined. The defect structures produced along these routes will remain at the interface in significant concentration when stopping the oxidation process. The results obtained for their properties are well supported by experimental findings about the SiC/SiO2 interface. It is found that carbon-carbon bonds can explain most of the observed interface states but not the high density near the conduction band of 4H-SiC

Unsupervised Deconvolution of Dynamic Imaging Reveals Intratumor Vascular Heterogeneity and Repopulation Dynamics

With the existence of biologically distinctive malignant cells originated within the same tumor, intratumor functional heterogeneity is present in many cancers and is often manifested by the intermingled vascular compartments with distinct pharmacokinetics. However, intratumor vascular heterogeneity cannot be resolved directly by most in vivo dynamic imaging. We developed multi-tissue compartment modeling (MTCM), a completely unsupervised method of deconvoluting dynamic imaging series from heterogeneous tumors that can improve vascular characterization in many biological contexts. Applying MTCM to dynamic contrast-enhanced magnetic resonance imaging of breast cancers revealed characteristic intratumor vascular heterogeneity and therapeutic responses that were otherwise undetectable. MTCM is readily applicable to other dynamic imaging modalities for studying intratumor functional and phenotypic heterogeneity, together with a variety of foreseeable applications in the clinic

Monitoring of Tumor Promotion and Progression in a Mouse Model of Inflammation-Induced Colon Cancer with Magnetic Resonance Colonography

AbstractEarly detection of precancerous tissue has significantly improved survival of most cancers including colorectal cancer (CRC). Animal models designed to study the early stages of cancer are valuable for identifying molecular events and response indicators that correlate with the onset of disease. The goal of this work was to investigate magnetic resonance (MR) colonography in a mouse model of CRC on a clinical MR imager. Mice treated with azoxymethane and dextran sulfate sodium were imaged by serial MR colonography (MRC) from initiation to euthanasia. Magnetic resonance colonography was obtained with both T1- and T2-weighted images after administration of a Fluorinert enema to remove residual luminal signal and intravenous contrast to enhance the colon wall. Individual tumor volumes were calculated and validated ex vivo. The Fluorinert enema provided a clear differentiation of the lumen of the colon from the mucosal lining. Inflammation was detected 3 days after dextran sulfate sodium exposure and subsided during the next week. Tumors as small as 1.2 mm3 were detected and as early as 29 days after initiation. Individual tumor growths were followed over time, and tumor volumes were measured by MR imaging correlated with volumes measured ex vivo. The use of a Fluorinert enema during MRC in mice is critical for differentiating mural processes from intraluminal debris. Magnetic resonance colonography with Fluorinert enema and intravenous contrast enhancement will be useful in the study of the initial stages of colon cancer and will reduce the number of animals needed for preclinical trials of prevention or intervention

CT and MRI of Hepatic Abscess in Patients with Chronic Granulomatous Disease

We describe the spectrum of radiologic appearances of hepatic abscesses in patients with chronic granulomatous disease (CGD), a hereditary immunodeficiency presenting in childhood that occurs at a rate of 1 in 200,000-250,000 live births and predisposes patients to infection with catalase-positive organisms. CONCLUSION: Hepatic abscesses in patients with CGD show an atypical radiologic appearance compared with sporadic hepatic abscesses, and they are characterized by homogeneous enhancement and multiseptal enhancement. In the appropriate clinical setting, the appearance of an enhancing mass should suggest the possibility of a CGD-related hepatic absces

Assessing and testing anomaly detection for finding prostate cancer in spatially registered multi-parametric MRI

BackgroundEvaluating and displaying prostate cancer through non-invasive imagery such as Multi-Parametric MRI (MP-MRI) bolsters management of patients. Recent research quantitatively applied supervised target algorithms using vectoral tumor signatures to spatially registered T1, T2, Diffusion, and Dynamic Contrast Enhancement images. This is the first study to apply the Reed-Xiaoli (RX) multi-spectral anomaly detector (unsupervised target detector) to prostate cancer, which searches for voxels that depart from the background normal tissue, and detects aberrant voxels, presumably tumors.MethodsMP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images, or seven components) were prospectively collected from 26 patients and then resized, translated, and stitched to form spatially registered multi-parametric cubes. The covariance matrix (CM) and mean μ were computed from background normal tissue. For RX, noise was reduced for the CM by filtering out principal components (PC), regularization, and elliptical envelope minimization. The RX images were compared to images derived from the threshold Adaptive Cosine Estimator (ACE) and quantitative color analysis. Receiver Operator Characteristic (ROC) curves were used for RX and reference images. To quantitatively assess algorithm performance, the Area Under the Curve (AUC) and the Youden Index (YI) points for the ROC curves were computed.ResultsThe patient average for the AUC and [YI] from ROC curves for RX from filtering 3 and 4 PC was 0.734[0.706] and 0.727[0.703], respectively, relative to the ACE images. The AUC[YI] for RX from modified Regularization was 0.638[0.639], Regularization 0.716[0.690], elliptical envelope minimization 0.544[0.597], and unprocessed CM 0.581[0.608] using the ACE images as Reference Image. The AUC[YI] for RX from filtering 3 and 4 PC was 0.742[0.711] and 0.740[0.708], respectively, relative to the quantitative color images. The AUC[YI] for RX from modified Regularization was 0.643[0.648], Regularization 0.722[0.695], elliptical envelope minimization 0.508[0.605], and unprocessed CM 0.569[0.615] using the color images as Reference Image. All standard errors were less than 0.020.ConclusionsThis first study of spatially registered MP-MRI applied anomaly detection using RX, an unsupervised target detection algorithm for prostate cancer. For RX, filtering out PC and applying Regularization achieved higher AUC and YI using ACE and color images as references than unprocessed CM, modified Regularization, and elliptical envelope minimization

Clinical value of prostate segmentation and volume determination on MRI in benign prostatic hyperplasia

Benign prostatic hyperplasia (BPH) is a nonmalignant pathological enlargement of the prostate, which occurs primarily in the transitional zone. BPH is highly prevalent and is a major cause of lower urinary tract symptoms in aging males, although there is no direct relationship between prostate volume and symptom severity. The progression of BPH can be quantified by measuring the volumes of the whole prostate and its zones, based on image segmentation on magnetic resonance imaging. Prostate volume determination via segmentation is a useful measure for patients undergoing therapy for BPH. However, prostate segmentation is not widely used due to the excessive time required for even experts to manually map the margins of the prostate. Here, we review and compare new methods of prostate volume segmentation using both manual and automated methods, including the ellipsoid formula, manual planimetry, and semiautomated and fully automated segmentation approaches. We highlight the utility of prostate segmentation in the clinical context of assessing BPH