Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimation.

PURPOSE: Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation's effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. METHODS: The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. RESULTS: Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. CONCLUSIONS: The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC.

Dynamic Contrast-Enhanced MR Imaging in Head and Neck Cancer.

Dynamic contrast-enhanced (DCE) MR imaging uses rapid sequential MR image acquisition before, during, and after intravenous contrast administration to elucidate information on the microvascular biologic function of tissues. The derived pharmacokinetic parameters provide useful information on tissue perfusion and permeability that may help to evaluate entities that otherwise appear similar by conventional imaging. When specifically applied to the evaluation of head and neck cancer, DCE-MR imaging may provide valuable information to help predict treatment response, discriminate between posttreatment changes and residual tumor, and discriminate between various head and neck neoplasms.

Strategies for Improving the Value of the Radiology Report: A Retrospective Analysis of Errors in Formally Over-read Studies.

PURPOSE: The radiology report is a critical component of the Imaging Value Chain. Unfortunately, the quality of this aspect of a radiologist's work is often heterogeneous and fails to add significant value to the referring provider and, ultimately, the patient. Gauging what defines quality can be elusive; however, we elucidate techniques that can be employed to ensure that reports are more comprehensible, actionable, and useful to our customers. METHODS: Four hundred consecutive studies (July-August 2015) submitted to our institution with request for a formal over-read were reviewed retrospectively, specifically focused on analyzing differences in language, organization, and impression between the outside reports and the formal over-reads performed at our institution. The formal over-reads were classified into one of the following categories: (1) no clinically significant change; (2) emergent clinically significant change; (3) nonemergent clinically significant change. Clinically significant changes were further classified as either perceptual or cognitive errors. RESULTS: A total of 12.4% of formally over-read reports had clinically significant changes. Of these, 22.2% were emergent changes. Clinically significant changes were composed of 64.4% perceptual error and 35.6% cognitive error. Four strategies were discovered specifically related to reporting techniques that helped mitigate these errors on formal over-reads: (1) synthesizing varied anatomic findings into a cohesive disease process; (2) integration of relevant electronic health record data; (3) use of structured reporting; and (4) forming actionable impressions. CONCLUSIONS: We identify, through examples, four strategies for reporting that add value through reduction of radiologic error, helping to mitigate the 12.4% clinically significant error rate found in reinterpretation of outside studies.

Feasibility of geometric-intensity-based semi-automated delineation of the tentorium cerebelli from MRI scans.

This paper describes a feasibility study of a method for delineating the tentorium cerebelli in magnetic resonance imaging (MRI) brain scans. The tentorium cerebelli is a thin sheet of dura matter covering the cerebellum and separating it from the posterior part of the temporal lobe and the occipital lobe of the cerebral hemispheres. Cortical structures such as the parahippocampal gyrus can be indistinguishable from tentorium in magnetized prepared rapid gradient echo and T1-weighted MRI scans. Similar intensities in these neighboring regions make it difficult to perform accurate cortical analysis in neuroimaging studies of schizophrenia and Alzheimer's disease. A semi-automated, geometric, intensity-based procedure for delineating the tentorium from a whole-brain scan is described. Initial and final curves are traced within the tentorium. A cost function, based on intensity and Euclidean distance, is computed between the two curves using the Fast Marching method. The initial curve is then evolved to the final curve based on the gradient of the computed costs, generating a series of intermediate curves. These curves are then used to generate a triangulated surface of the tentorium. For 3 scans, surfaces were found to be within 2 voxels from hand segmentations.