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St. Louis, MO, United States

Kim H.,Case Western Reserve University | Park S.B.,National Cancer Center | Monroe J.I.,Case Western Reserve University | Monroe J.I.,St Anthonys Medical Center | And 16 more authors.
Technology in Cancer Research and Treatment | Year: 2015

This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref. Applying DVFref to the original reference image deforms T into a new image R’. The data set, R’, T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest. For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck. © The Author(s) 2014.

Coleman J.,Mid-America Transplant Services | Brockmeier D.,Mid-America Transplant Services | Kappel D.,Mid-America Transplant Services | Marklin G.,St Anthonys Medical Center | Wright R.,Ssm St Marys Health Center
Journal of Critical Care | Year: 2013

Purpose: Corticosteroids are used to promote hemodynamic stability and reduce inflammatory organ injury after brain death. High-dose (HD) methylprednisolone has become the standard regimen based on comparisons to untreated/historical controls. However, this protocol may exacerbate hyperglycemia. Our objective was to compare a lower-dose (LD) steroid protocol (adequate for hemodynamic stabilization in adrenal insufficiency and sepsis) to the traditional HD regimen in the management of brain-dead organ donors. Methods: We evaluated 132 consecutive brain-dead donors managed before and after changing the steroid protocol from 15 mg/kg methylprednisolone (HD) to 300 mg hydrocortisone (LD). Primary outcome measures were glycemic control, oxygenation, hemodynamic stability, and organs transplanted. Results: Groups were balanced except for nonsignificantly higher baseline Pao2 in the LD cohort. Final Pao2 remained higher (394 mm Hg LD vs 333 mm Hg HD, P=03); but improvement in oxygenation was comparable (+37 mm Hg LD vs +28 mm Hg HD, P=43), as was the proportion able to come off vasopressor support (39% LD vs 47% HD, P=38). Similar proportions of lungs (44% vs 33%) and hearts (31% vs 27%) were transplanted in both groups. After excluding diabetics, median glucose values at 4 hours (170 mmol/L vs 188 mmol/L, P=06) and final insulin requirements (2.9 U/h vs 8.4 U/h, P=01) were lower with LD steroids; and more patients were off insulin infusions (74% LD vs 53% HD, P=02). Conclusions: A lower-dose corticosteroid protocol did not result in worsened donor pulmonary or cardiac function, with comparable organs transplanted compared with the traditional HD regimen. Insulin requirements and glycemic control were improved. High-dose methylprednisolone may not be required to support brain-dead donors. © 2013 Elsevier Inc.

Bierig S.M.,St Anthonys Medical Center | Hill J.,Boston College
Journal of Diagnostic Medical Sonography | Year: 2011

Left ventricular diastolic dysfunction is common in patients with heart failure. Echocardiographic evaluation of diastolic function provides the clinician with important information about ventricular relaxation and estimation of filling pressures. Optimal evaluation includes the integration of multiple echocardiographic parameters such as Doppler, M-mode, and volumes. This article aims to review the components of diastolic filling and ventricular performance, as well as discuss the techniques used for the assessment of left ventricular diastolic function. © The Author(s) 2011.

Kim H.,Proton Therapy | Monroe J.I.,Case Western Reserve University | Monroe J.I.,St Anthonys Medical Center | Lo S.,Case Western Reserve University | And 8 more authors.
Medical Physics | Year: 2015

Purpose: A quantitative and objective metric, the medical similarity index (MSI), has been developed for evaluating the accuracy of a medical image segmentation relative to a reference segmentation. The MSI uses the medical consideration function (MCF) as its basis. Methods: Currently, no indices provide quantitative evaluations of segmentation accuracy with medical considerations. Variations in segmentation can occur due to individual skill levels and medical relevance-curable or palliative intent, boundary uncertainty due to olume averaging, contrast levels, spatial resolution, and unresolved motion all affect the accuracy of a patient segmentation. Current accuracy measuring indices are not medically relevant. For example, undercontouring the tumor volume is not differentiated from overcontouring tumor. Dice similarity coefficient (DSC) and Hausdorff distance (HD) are two similarity measures often used. However, these metrics consider only geometric difference without considering medical implications. Two segments (under- vs overcontouring tumor) with similar DSC and HD measures could produce significantly different medical treatment results. The authors are proposing a MSI involving a user-defined MCF derived from an asymmetric Gaussian function. The shape of the MCF can be determined by a user, reflecting the anatomical location and characteristics of a particular tissue, organ, or tumor type. The peak of MCF is set along the reference contour; the inner and outer slopes are selected by the user. The discrepancy between the test and reference contours is calculated at each pixel by using a bidirectional local distance measure. The MCF value corresponding to that distance is summed and averaged to produce the MSI. Synthetic segmentations and clinical data from a 15 multi-institutional trial for a head-and-neck case are scored and compared by using MSI, DSC, and Hausdorff distance. Results: The MSI was shown to reflect medical considerations through the choice of MCF penalties for under- and overcontouring. Existing similarity scores were either insensitive to medical realities or simply inaccurate. Conclusions: The medical similarity index, a segmentation evaluation metric based on medical considerations, has been proposed, developed, and tested to incorporate clinically relevant considerations beyond geometric parameters alone. ©2015 American Association of Physicists in Medicine.

Kim H.S.,Case Western Reserve University | Kim H.S.,University Hospitals of Cleveland | Park S.B.,Case Western Reserve University | Park S.B.,University Hospitals of Cleveland | And 4 more authors.
Medical Physics | Year: 2012

Purpose: To accurately quantify the local difference between two contour surfaces in two- or three-dimensional space, a new, robust point-to-surface distance measure is developed. Methods: To evaluate and visualize the local surface differences, point-to-surface distance measures have been utilized. However, previously well-known point-to-surface distance measures have critical shortfalls. Previous distance measures termed normal distance (ND), radial distance, or minimum distance (MD) can report erroneous results at certain points where the surfaces under comparison meet certain conditions. These skewed results are due to the monodirectional characteristics of these methods. ComGrad distance was also proposed to overcome asymmetric characteristics of previous point-to-surface distance measures, but their critical incapability of dealing with a fold or concave contours. In this regard, a new distance measure termed the bidirectional local distance (BLD) is proposed which minimizes errors of the previous methods by taking into account the bidirectional characteristics with the forward and backward directions. BLD measure works through three steps which calculate the maximum value between the forward minimum distance (FMinD) and the backward maximum distance (BMaxD) at each point. The first step calculates the FMinD as the minimum distance to the test surface from a point, pref on the reference surface. The second step involves calculating the minimum distances at every point on the test surface to the reference surface. During the last step, the BMaxD is calculated as the maximum distance among the minimum distances found at pref on the reference surface. Tests are performed on two- and three-dimensional artificial contour sets in comparison to MD and ND measure techniques. Three-dimensional tests performed on actual liver and head-And-neck cancer patients. Results: The proposed BLD measure provides local distances between segmentations, even in situations where ND, MD, or ComGrad measures fail. In particular, the standard deviation measure is not distorted at certain geometries where ND, MD, and ComGrad measures report skewed results. Conclusions: The proposed measure provides more reliable statistics on contour comparisons. From the statistics, specific local and global distances can be extracted. Bidirectional local distance is a reliable distance measure in comparing two- or three-dimensional organ segmentations. © 2012 American Association of Physicists in Medicine.

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