Simon Konstandin¹, Cristoffer Cordes¹, and Matthias Günther^1,2
1MR Physics, Fraunhofer MEVIS, Bremen, Germany, ²MR-Imaging and Spectroscopy, Faculty 01 (Physics/Electrical Engineering), University of Bremen, Bremen, Germany

MR sequence development is usually carried out by means of manufacturer-specific frameworks that do not allow an easy sequence transfer to scanners from other manufacturers. Sequence development in the recently presented framework is performed by scripting that can be time consuming and confusing for sophisticated MR sequences. Here, a module editor prototype is presented that provides a graphical sequence representation and should allow for a fast and relatively easy-to-understand MR sequence development in the future. Functionality could already be demonstrated by exchanging the rf pulse and implementing a parallel acquisition technique into a gradient echo sequence in a few steps.

Hidenori Takeshima^1
1Advanced Technology Research Department, Research and Development Center, Canon Medical Systems Corporation, Kanagawa, Japan

Existing software tools for assisting scientific research mainly focus on increasing reproducibility of primitives and/or integrated algorithms (exchangeable reproducibility). While exchangeable reproducibility is clearly important, reproducing the outputs using past configurations again is also important in daily research activities (local reproducibility).
The author proposes a new software framework named reconstream. To improve local reproducibility, reconstream implemented a revision manager for reproducing past configurations easily. To improve exchangeable reproducibility, the author reviewed advantages of individual software tools and implemented them in the reconstream framework.
Experimental results demonstrate several examples using various features of reconstream.
 

Xinxin Xu¹, Caixia Fu², Robert Grimm³, Huimin Lin¹, Ruokun Li¹, and Fuhua Yan^1
1Radiology, Ruijin Hospital affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China, ²Siemens healthcare, Shanghai, China, ³Siemens healthcare, Erlangen, Germany

We aim to assess the utility of whole-liver texture analysis on T1 maps for the risk stratification of advanced fibrosis in patients with suspected NAFLD. Our experiments show that whole-liver histogram and texture parameters of T1 maps can distinguish NAFLD patients with an intermediate-to-high risk of advanced fibrosis. A combination of histogram and texture parameters as a multivariate model showed a better diagnostic performance than any sole parameter and noninvasive fibrosis test in risk stratification of advanced fibrosis.
 

Ryoichi Kose¹ and Katsumi Kose^1
1MRIsimulations,Inc., Tokyo, Japan

A software tool that can efficiently describe MRI pulse sequences in Python has been developed for a fast MRI simulator. The essential part of this tool consists of application program interface (API) developed in C++, and any pulse sequence can be written by calling the API from a Python program. Using this tool, several pulse sequences including magnetic resonance fingerprinting were developed, and their usefulness was evaluated by performing MRI simulations for numerical phantoms. As a result, the tool developed in this study was shown to be very efficient in developing MRI pulse sequences.

Yangzi Qiao¹, Chao Zou¹, Jianhong Wen¹, Sen Jia¹, Xin Liu¹, and Hairong Zheng^1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

An integrated focused ultrasound guidance software named MARFit was developed base on the framework of Gadgetron. The software realized automatically focus localization and real-time temperature change monitoring during HIFU therapy. These features has been evaluated in animal experiments. The software makes the post processing procedure easily translated between different vendors, and could be a powerful tool for MR guided focused ultrasound therapy.

Marina Manso Jimeno^1,2, Sairam Geethanath^1,2, Jon-Fredrik Nielsen³, and Douglas C. Noll^3
1Columbia University, New York, NY, United States, ²Columbia Magnetic Resonance Research Center (CMRRC), New York, NY, United States, ³University of Michigan, Ann Arbor, MI, United States

TOPPE and Pulseq are two examples of open-source,  vendor-agnostic frameworks for rapid prototyping and implementation of MR sequences. Both have demonstrated their ability to easily execute a variety of highly specific and customized sequences on different vendors. Cross-vendor translation of sequences generated by these two frameworks can enable multi-site and multi-vendor evaluation of a single sequence implementation. This study demonstrates this capability by porting a spiral-based fMRI sequence implemented in  TOPPE on one vendor platform to Pulseq to run on another vendor. Results show comparable in vivo SNR and image contrast for Pulseq and TOPPE implementations.

Marina Manso Jimeno^1,2, Sairam Geethanath^1,2, and John Thomas Vaughan Jr.^1,2
1Columbia University, New York, NY, United States, ²Columbia Magnetic Resonance Research Center (CMRRC), New York, NY, United States

Off-resonance blurring is one of the challenges with long read-out trajectories such as spirals. Existent techniques deblur the images by demodulating k-space data with the conjugate term of the phase accumulated from off-resonant spins. Continuous and frequency-segmented CPR and Multi-Frequency Interpolation are some examples. This work is aimed at sharing an open-source package for off-resonance correction, demonstrating on spiral datasets. The methods have been tested on simulations, in vitro and in vivo and show promising preliminary results. We believe that the availability of this package will benefit the spiral imaging community.

Jon-Fredrik Nielsen¹, Luis Hernandez-Garcia¹, and Douglas C. Noll^1
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

Vendor-agnostic MR pulse sequence programming tools such as Pulseq or TOPPE allow rapid prototyping of complex MR sequences, but are not compatible with existing graphical slice planning interfaces on all supported vendor platforms. We introduce a simple open-source slice planning tool that allows any number of 3D rectangular regions-of-interest (ROIs) to be defined interactively and exported to a portable data file format (HDF5). We use this tool to prescribe both a 2D labeling plane in the neck and a 3D imaging volume in the brain for a vendor-agnostic implementation of 3D stack-of-spirals fast spin-echo (FSE) pseudo-continuous Arterial Spin Labeling.

Bragi Sveinsson^1,2, Neha Koonjoo^1,2, and Matthew Rosen^1,2
1Massachusetts General Hospital, Boston, MA, United States, ²Harvard Medical School, Boston, MA, United States

We present a smartphone app to view 3D medical image data using Augmented Reality (AR). The app superimposes the imaged anatomy on the patient and allows for completely free and intuitive choice of viewing plane by rotating the phone and moving it towards and away from the patient. The app is shown to be enable free choice of viewing plane in a much easier and faster manner than standard medical image viewing software on a computer workstation. We envision this app to enable lower-cost, more mobile viewing of MRI data, potentially in combination with portable, low-field MR hardware. 

Katsumi Kose¹, Ryoichi Kose¹, Yasuhiko Terada², Daiki Tamada³, and Utaroh Motosugi^3
1MRIsimulations, Tokyo, Japan, ²University of Tsukuba, Tsukuba, Japan, ³University of Yamanashi, Chuo, Japan

A method for Bloch image simulation of living tissues was developed and the simulated results were compared with experiments performed for phantoms including T₂* distribution and various chemical components. The Bloch image simulation for living tissues is based on a four-dimensional numerical phantom consisting of spatially defined 3D datasets of proton density, T₁, and T₂ with spatially homogeneous B₀ (frequency) offset. The multiple gradient-echo images reconstructed from the MR signal obtained from the Bloch image simulation of the numerical phantom reproduced experimental results for samples that simulated living tissues.

Min-Gi Pak¹, Seong-Min Han², ChungSub Lee³, SeungJin Kim¹, Tae-Hoon Kim³, Chang-Won Jeong³, and Kwon-Ha Yoon^3,4
1Medical Science, Wonkwang University, Iksan, Republic of Korea, ²Computer Software Engineering, Wonkwang University, Iksan, Republic of Korea, ³Medical Convergence Research Center, Wonkwang University, Iksan, Republic of Korea, ⁴Radiology, Wonkwang University, Iksan, Republic of Korea

The Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) used in distributed research networks has low coverage of clinical data and does not reflect the latest trends of precision medicine. Radiology data have great merits to visual and identify the lesions in specific diseases. However, radiology data should be shared to obtain the sufficient scale and diversity required to provide strong evidence for improving patient care. Our study was to develop a web-based management system for radiology-CDM (R-CDM), as an extension of the OMOP-CDM, and to evaluate the feasibility of R-CDM dataset for application of radiological image data in clinical practice.

Ignacio Xavier Partarrieu¹, Elliot Fox^1,2, Frederic Brochu¹, Matt Cashmore¹, Neha Koonjoo³, Sheng Shen³, David Sinden¹, Matthew Rosen³, and Matt G. Hall^1,4
1National Physical Laboratory, Teddington, United Kingdom, ²Durham University, Durham, United Kingdom, ³Harvard, Boston, MA, United States, ⁴University College London, London, United Kingdom

The large expense and running costs of traditional 1.5T scanners have meant that as demand for scans has grown supply has not been able to keep up. Novel, ultra-low field systems aim to address this problem by producing images with some resolution losses as a trade-off for  reduced costs. However, access to such devices is currently restricted, limiting the possible research output. In this work we develop and test a simulation model of 6.5 mT b-SSFP acquisitions, which we compare to actual scanner acquisitions. We find that such simulations produce qualitatively and quantitatively similar images to real scans.

SeungJin Kim¹, Min-Gi Pak¹, Tae-Hoon Kim², Chang-Won Jeong², and Kwon-Ha Yoon^2,3
1Medical Science, Wonkwang University, Iksan, Republic of Korea, ²Medical Convergence Research Center, Wonkwang University, Iksan, Republic of Korea, ³Radiology, Wonkwang University, Iksan, Republic of Korea

In 2016, sarcopenia has been classified by the international classification of diseases(ICD-10-CM), with the code(M62.84). The role of imaging techniques has rapidly increased in the field of sarcopenia. In the past decade, the importance of muscle and fat mass has been emphasized on imaging evaluation of body composition including of muscle and body fat such as visceral fat or subcutaneous fat. However, there are diverse quantification methods for assessing muscle and fat mass by imaging and thus, these methods must be standardized. This study developed a customized quantification software based on ImageJ-platform and evaluated in the patient with sarcopenia.

Behzad Babaei¹, Daniel Fovargue², David Nordsletten^2,3, and Lynne Bilston^1,4
1Neuroscience Research Australia, Sydney, Australia, ²King's College London, London, United Kingdom, ³Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ⁴Prince of Wales Clinical School, University of New South Wales, Randwick, Australia

MR elastography is well established for measuring the mechanical properties of soft tissues in a broad range of clinical populations. However, unlike liver tissue, which is generally considered to be isotropic, muscle is significantly anisotropic, with stiffer mechanical behaviour along the muscle fibre directions. Identifying anisotropic properties is challenging due to the need to track fibre directions and additional numerical difficulties in estimating properties. Here, we describe a novel finite element inversion method for estimating anisotropic mechanical properties of ex vivo tissue data, and validate this method using three sets of simulated data.

Thomas B Shaw¹, Steffen Bollmann¹, Ashley York¹, Maryam Ziaei¹, and Markus Barth^1
1The University of Queensland, Brisbane, Australia

Characterising longitudinal changes in the subfields of the hippocampus using high-resolution MRI is important for understanding neurological disorders and diseases including Alzheimer’s. Hippocampus subfields are differentially involved in these disorders and can be used as biomarkers for disease. Here, we introduce an automatic longitudinal pipeline for measuring the subfields of the hippocampus (LASHiS). Unlike other longitudinal pipelines, LASHiS harnesses multiple MR contrasts and is therefore more sensitive to changes in these subfields. We compared LASHiS with four other established methods including Freesurfer and found that our method yields higher test-retest reliability and robust Bayesian longitudinal linear mixed effects modelling results.

Simon Daniel Robinson^1,2,3, Korbinian Eckstein¹, Siegfried Trattnig¹, Karin Shmueli⁴, and Barbara Dymerska^4
1Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ²Department of Neurology, Medical University of Graz, Graz, Austria, ³Centre for Advanced Imaging, University of Queensland, Queensland, Australia, ⁴University College London, London, United Kingdom

We present a phase unwrapping algorithm (ROMEO), which improves on previous path-based unwrapping methods by using weights (the values of which determine the order in which voxels are unwrapped) that guide the unwrapping on paths through the object which avoid noise and a computationally efficient minimum spanning tree algorithm.  ROMEO was tested against the region-growing method PRELUDE and the voxel-based method Best Path in unwrapping simulated data (a complex topography) and in-vivo GRE and EPI data acquired at 7T. ROMEO was found to be faster and more reliable than PRELUDE and Best Path.