Local contrast‐enhanced <scp>MR</scp> images via high dynamic range processing

Chandra, Shekhar S.; Engstrom, Craig; Fripp, Jürgen; Neubert, Aleš; Jin, Jin; Walker, Duncan; Salvado, Olivier; Ho, Charles P.; Crozier, Stuart

doi:10.1002/mrm.27109

Cited by 3 publications

(3 citation statements)

References 41 publications

(57 reference statements)

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“…A custom main window object can be inherited from the milxQtMain object to instantly create a fully featured application as shown in figure 2. This has been recently demonstrated by deploying a High Dynamic Range (HDR) algorithm for magnetic resonance (MR) images [16]. The cross-platform nature of SMILI allows the imaging data to be visualised and processed identically across Windows, Linux and Mac OSX operating systems.…”

Section: Overviewmentioning

confidence: 99%

“…distortion for the purposes of MR alone treatment planning, but also demonstrates that end-users can utilise SMILI built applications, such as sMILX, for clinical research. Recent work has been completed in using SMILI and its milxQtMain class (as described in figure 2) to deploy HDR algorithms for multi-channel and multi-sequence MR images [16,see also GitHub].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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A lightweight rapid application development framework for biomedical image analysis

Chandra

Dowling

Engstrom

et al. 2018

Computer Methods and Programs in Biomedicine

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Biomedical imaging analysis typically comprises a variety of complex tasks requiring sophisticated algorithms and visualising high dimensional data. The successful integration and deployment of the enabling software to clinical (research) partners, for rigorous evaluation and testing, is a crucial step to facilitate adoption of research innovations within medical settings. In this paper, we introduce the Simple Medical Imaging Library Interface (SMILI), an object oriented open-source framework with a compact suite of objects geared for rapid biomedical imaging (cross-platform) application development and deployment. SMILI supports the development of both command-line (shell and Python scripting) and graphical applications utilising the same set of processing algorithms. It provides a substantial subset of features when compared to more complex packages, yet it is small enough to ship with clinical applications with limited overhead and has a license suitable for commercial use. After describing where SMILI fits within the existing biomedical imaging software ecosystem, by comparing it to other state-of-the-art offerings, we demonstrate its capabilities in creating a clinical application for manual measurement of cam-type lesions of the femoral head-neck region for the investigation of femoro-acetabular impingement (FAI) from three dimensional (3D) magnetic resonance (MR) images of the hip. This application for the investigation of FAI proved to be convenient for radiological analyses and resulted in high intra (ICC=0.97) and inter-observer (ICC=0.95) reliabilities for measurement of α-angles of the femoral head-neck region. We believe that SMILI is particularly well suited for prototyping biomedical imaging applications requiring user interaction and/or visualisation of 3D mesh, scalar, vector or tensor data.

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Section: Overviewmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

A lightweight rapid application development framework for biomedical image analysis

Chandra

Dowling

Engstrom

et al. 2018

Computer Methods and Programs in Biomedicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…N3 and N4 algorithms [36] (nonparametric non-uniform intensity normalization) were tried, but could not correct the strong signal loss. Enhancing contrast methods were tried, but they were found to be time consuming given the number of images to process [37].…”

Section: Introductionmentioning

confidence: 99%