Search

Existing parallel MRI methods are limited by a fundamental trade-off in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly over the entire image, resulting in blurred edges. Here we introduce an edge-preserving prior (EPP) that instead assumes that intensities are piecewise smooth, and propose a new approach to efficiently compute its Bayesian estimate. The estimation task is formulated as an optimization problem that requires a non-convex objective function to be minimized in a space with thousands of dimensions. As a result, traditional continuous minimization methods cannot be applied. This optimization task is closely related to some problems in the field of computer vision for which discrete optimization methods have been developed in the last few years. We adapt these algorithms, which are based on graph cuts, to address our optimization problem. The results of several parallel imaging experiments on brain and torso regions performed under challenging conditions with high acceleration factors are shown and compared with the results of conventional sensitivity encoding (SENSE) methods. An empirical analysis indicates that the proposed method visually improves overall quality compared to conventional methods.

Surgical breast reconstruction after mastectomy requires precise perforator coordinates/dimensions, perforator course, and fat volume in a radiology report. Automatic perforator reporting software was implemented as an OsiriX Digital Imaging and Communications in Medicine (DICOM) viewer plugin. For perforator analysis, the user identifies a reference point (e.g., umbilicus) and marks each perforating artery/vein bundle with multiple region of interest (ROI) points along its course beginning at the muscle-fat interface. Computations using these points and analysis of image data produce content for the report. Post-processing times were compared against conventional/manual methods using de-identified images of 26 patients with surgically confirmed accuracy of perforator locations and caliber. The time from loading source images to completion of report was measured. Significance of differences in mean processing times for this automated approach versus the conventional/manual approach was assessed using a paired t test. The mean conventional reporting time for our radiologists was 76 ± 27 min (median 65 min) compared with 25 ± 6 min (median 25 min) using our OsiriX plugin (p < 0.01). The conventional approach had three reports with transcription errors compared to none with the OsiriX plugin. Otherwise, the reports were similar. In conclusion, automated reporting of perforator magnetic resonance angiography (MRA) studies is faster compared with the standard, manual approach, and transcription errors which are eliminated.

Background: Identifying risk factors for conversion from laparoscopic to open appendectomy could select patients who may benefit from primary open appendectomy. We aimed to develop a predictive scoring model for conversion from laparoscopic to open based on pre-operative patient characteristics., Methods: A retrospective review of the State Inpatient Database (2007-2011) was performed using derivation (N = 71,617) and validation (N = 143,235) cohorts of adults ≥ 18 years with acute appendicitis treated by laparoscopic-only (LA), conversion from laparoscopic to open (CA), or primary open (OA) appendectomy. Pre-operative variables independently associated with CA were identified and reported as odds ratios (OR) with 95% confidence intervals (CI). A weighted integer-based scoring model to predict CA was designed based on pre-operative variable ORs, and complications between operative subgroups were compared., Results: Independent predictors of CA in the derivation cohort were age ≥40 (OR 1.67; CI 1.55-1.80), male sex (OR 1.25; CI 1.17-1.34), black race (OR 1.46; CI 1.28-1.66), diabetes (OR 1.47; CI 1.31-1.65), obesity (OR 1.56; CI 1.40-1.74), and acute appendicitis with abscess or peritonitis (OR 7.00; CI 6.51-7.53). In the validation cohort, the CA predictive scoring model had an optimal cutoff score of 4 (range 0-9). The risk of conversion-to-open was ≤5% for a score <4, compared to 10-25% for a score ≥4. On composite outcomes analysis controlling for all pre-operative variables, CA had a higher likelihood of infectious/inflammatory (OR 1.44; CI 1.31-1.58), hematologic (OR 1.31; CI 1.17-1.46), and renal (OR 1.22; CI 1.06-1.39) complications compared to OA. Additionally, CA had a higher likelihood of infectious/inflammatory, respiratory, cardiovascular, hematologic, and renal complications compared to LA., Conclusions: CA patients have an unfavorable complication profile compared to OA. The predictors identified in this scoring model could help select for patients who may benefit from primary open appendectomy., (Copyright © 2017 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.)