IMAge/enGINE: a freely available software for rapid computation of high-dimensional quantification.

Background High-dimensional image data including diffusion weighted imaging, diffusion tensor imaging and dynamic imaging are important in exploring the connectivity, cellularity, pharmacokinetic and blood supply. IMAge/enGINE is software especially designed for high-dimensional medical image computing. Methods IMAge/enGINE is implemented based on open-source and cross-platform tools such as Qt, ITK and VTK. It processes the high-dimensional image data in a slice-by-slice computation mechanism. For computational efficiency, C++ is used for implementing IMAge/enGINE and multi-thread computing is handled in the scale of voxels. The architecture of IMAge/enGINE is modularized for easier extension. Results IMAge/enGINE has following features: (I) IMAge/enGINE is free for research use; (II) it has an easy-to-use graphic user interface designed for clinical users without programming or engineering background; (III) its frame work is open-source and extensible. Developers can implement algorithms as modules and integrate them into IMAge/enGINE or generate their own application. Conclusions The source of IMAge/enGINE is hosted at https://github.com/VusionMed/IMAge-enGINE. Multiple diffusion and perfusion models are implemented and integrated into IMAge/enGINE and its binaries can be downloaded freely at http://www.vusion.com.cn/?page_id=14971.

[1]  S. Payabvash Quantitative diffusion magnetic resonance imaging in head and neck tumors. , 2018, Quantitative imaging in medicine and surgery.

[2]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[3]  Bachir Taouli,et al.  Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice , 2015, Journal of magnetic resonance imaging : JMRI.

[4]  Olaf Dietrich,et al.  Technical aspects of MR diffusion imaging of the body. , 2010, European journal of radiology.

[5]  D. Le Bihan,et al.  Diffusion tensor imaging: Concepts and applications , 2001, Journal of magnetic resonance imaging : JMRI.

[6]  Atilla P Kiraly,et al.  Hepatocellular carcinoma: response to TACE assessed with semiautomated volumetric and functional analysis of diffusion-weighted and contrast-enhanced MR imaging data. , 2011, Radiology.

[7]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[8]  N Caroline,et al.  Hepatocellular carcinoma:response to TACE assessed with semiautomated volumetric and functional analysis of diffusion-weighted and contrast-enhanced MR imaging data , 2011 .

[9]  Leif Østergaard,et al.  Principles of cerebral perfusion imaging by bolus tracking , 2005, Journal of magnetic resonance imaging : JMRI.

[10]  P. Grenier,et al.  MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.

[11]  A. Luciani,et al.  Liver Cirrhosis : Intravoxel Incoherent Motion MR Imaging — Pilot Study 1 , 2008 .

[12]  B. Ardekani,et al.  Estimation of tensors and tensor‐derived measures in diffusional kurtosis imaging , 2011, Magnetic resonance in medicine.

[13]  Gerald E. York,et al.  Creation of DICOM—Aware Applications Using ImageJ , 2005, Journal of Digital Imaging.

[14]  Bradford A Moffat,et al.  The functional diffusion map: an imaging biomarker for the early prediction of cancer treatment outcome. , 2006, Neoplasia.

[15]  Zhuoli Zhang,et al.  Quantitative parameters of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI): potential application in predicting pathological grades of pancreatic ductal adenocarcinoma. , 2018, Quantitative imaging in medicine and surgery.

[16]  Osman Ratib,et al.  OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images , 2004, Journal of Digital Imaging.

[17]  Jelle Veraart,et al.  One diffusion acquisition and different white matter models: How does microstructure change in human early development based on WMTI and NODDI? , 2015, NeuroImage.

[18]  B. Rosen,et al.  Tracer arrival timing‐insensitive technique for estimating flow in MR perfusion‐weighted imaging using singular value decomposition with a block‐circulant deconvolution matrix , 2003, Magnetic resonance in medicine.

[19]  Thomas E Yankeelov,et al.  Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and Examples. , 2007, Current medical imaging reviews.