Sandy Napel

Characterization of spatial distortion in magnetic resonance imaging and its implications for stereotactic surgery.

The different sources of spatial distortion in magnetic resonance images are reviewed from the point of view of stereotactic target localization. The extents of the two most complex sources of spatial distortion, gradient field nonlinearities and magnetic field inhomogeneities, are discussed both qualitatively and quantitatively. Several ways by which the spatial distortion resulting from these sources can be minimized are discussed. The clinical relevance of the spatial distortion along with some strategies to minimize the localization errors in magnetic resonance-guided stereotaxy are presented.

Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.

Content-based image retrieval in radiology: current status and future directions.

Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography

Adenomatous polyps in the colon are believed to be the precursor to colorectal carcinoma, the second leading cause of cancer deaths in United States. In this paper, we propose a new method for computer-aided detection of polyps in computed tomography (CT) colonography (virtual colonoscopy), a technique in which polyps are imaged along the wall of the air-inflated, cleansed colon with X-ray CT. Initial work with computer aided detection has shown high sensitivity, but at a cost of too many false positives. We present a statistical approach that uses support vector machines to distinguish the differentiating characteristics of polyps and healthy tissue, and uses this information for the classification of the new cases. One of the main contributions of the paper is the new three-dimensional pattern processing approach, called random orthogonal shape sections method, which combines the information from many random images to generate reliable signatures of shape. The input to the proposed system is a collection of volume data from candidate polyps obtained by a high-sensitivity, low-specificity system that we developed previously. The results of our tenfold cross-validation experiments show that, on the average, the system increases the specificity from 0.19 (0.35) to 0.69 (0.74) at a sensitivity level of 1.0 (0.95).

STS-MIP: a new reconstruction technique for CT of the chest.

The authors present sliding thin-slab maximum intensity projection (STS-MIP) as a technique for improved visualization of blood vessels and airways from rapidly acquired thin-section CT data. The STS-MIP reconstructions can be computed rapidly and without operator intervention directly from the transaxial sections. The resulting images retain the high contrast resolution of thin-section (1-3 mm) CT while providing vascular or airway visibility within a sequence of overlapping thin-slabs (3-10 mm). Examples are presented of pulmonary vessels and airways derived from spiral CT and of pulmonary vessels and coronary arteries derived from electron-beam CT.

Three-dimensional spiral computed tomographic angiography: An alternative imaging modality for the abdominal aorta and its branches

PURPOSE We sought to apply a new technique of computed tomographic angiography (CTA) to the preoperative and postoperative assessment of the abdominal aorta and its branches. METHODS After a peripheral intravenous contrast injection, the patient is continuously advanced through a spiral CT scanner, while maintaining a 30-second breath-hold. Thirty-five patients with abdominal aortic, renal, and visceral arterial disease have undergone CTA. RESULTS Diagnostic three-dimensional images were obtained in patients with aortic aneurysms (n = 9), aortic dissections (n = 4), and mesenteric artery stenoses (n = 4). The technique has also been used to assess vessels after operative reconstruction or endovascular intervention in 18 patients. These preliminary studies have correlated well with conventional arteriographic findings. In aneurysmal disease both the lumen and mural thrombus and associated renal artery stenoses are visualized. The true and false channels of aortic dissections and the perfusion source of the visceral vessels are clearly shown; patency of visceral and renal reconstruction or stent placement are confirmed. CTA is relatively noninvasive and can be completed in less time than conventional angiography with less radiation exposure. CONCLUSIONS This initial experience suggests that CTA may be a valuable alternative to conventional arteriography in the evaluation of the aorta and its branches.

Automated flight path planning for virtual endoscopy

In this paper, a novel technique for rapid and automatic computation of flight paths for guiding virtual endoscopic exploration of three-dimensional medical images is described. While manually planning flight paths is a tedious and time consuming task, our algorithm is automated and fast. Our method for positioning the virtual camera is based on the medial axis transform but is much more computationally efficient. By iteratively correcting a path toward the medial axis, the necessity of evaluating simple point criteria during morphological thinning is eliminated. The virtual camera is also oriented in a stable viewing direction, avoiding sudden twists and turns. We tested our algorithm on volumetric data sets of eight colons, one aorta and one bronchial tree. The algorithm computed the flight paths in several minutes per volume on an inexpensive workstation with minimal computation time added for multiple paths through branching structures (10%-13% per extra path). The results of our algorithm are smooth, centralized paths that aid in the task of navigation in virtual endoscopic exploration of three-dimensional medical images.

Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results

PURPOSE To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets. MATERIALS AND METHODS A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available. RESULTS There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance. CONCLUSION This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.

Fetal blood velocity waveforms.

In this paper a method is described for obtaining and characterizing fetal blood velocity waveforms. The signals were recorded with a range-gated Doppler instrument and characterized after spectral analysis. Preliminary observations indicate differences in the waveforms obtained during normal pregnancies compared with some complicated pregnancies.

Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features

PURPOSE To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data. MATERIALS AND METHODS Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the modules expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways. RESULTS Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes. CONCLUSION Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively. Online supplemental material is available for this article.