Jochen G. Hirsch¹, Alexander Köhn¹, Daniel C. Hoinkiss¹, Jonas Peter¹, Andreas Thomsen¹, Matthias Günther^1,2, and the German National Cohort MRI Study Investigators^3
1Fraunhofer MEVIS, Bremen, Germany, ²University Bremen, Bremen, Germany, ³NAKO MR Imaging Core, Munich, Germany

We present a fully automated data management workflow and quality assurance, which is set up for large, multicentric cohort studies including whole-body MR imaging. The workflow includes a modality worklist, exam-synchronous DICOM transfer to centralized storage, quality control of MR acquisition, various image-based quality measures, web-based radiological image review for incidental findings, visual quality scores, as well as long-term archiving. This workflow, implemented in the MRI Study of the German National Cohort, enables to acquire and process more than 30 whole-body MRI scans per day, available for IF reading within 4 hours. Deviations, outliers, technical failures are pointed out on-the-fly.

Takao Goto¹ and Mirai Araki^1
1MR Engineering, GE Healthcare, Hino-shi, Japan

Accurate placement of a 2D plane across the aorta while examining scout images is a complex task and  makes the operator's workflow difficult in bolus tracking of DCE-MRI. We present a new method for automated slice positioning for 2D MRA used to monitor bolus arrival. The 2D plane was planned by aorta detection using both Hough Forests and AdaBoost classifiers following the classification of axial images. A dataset with 40 patients was tested, and 35 cases depicted the cross section of the aorta clearly. This automation will help the operator and decrease the total study time.

Emilie Poirion¹, Daniel Garcia Lorenzo¹, Isaac Adanyeguh¹, Marie-Stéphane Aigrot¹, Alexandra Petiet², and Bruno Stankoff^1,3
1Brain and Spine Institute, INSERM U1127/CNRS UMR 7225, Sorbonnes Universités, UPMC, CHU Pitié-Salpêtrière, 47 Bd de l'hôpital, 75013 Paris, Paris, France, ²Brain and Spine Institute, Center for Neuroimaging Research (CENIR), CHU Pitié-Salpêtrière, 47 Bd de l'hôpital, 75013 Paris, Paris, France, ³AP-HP, Saint Antoine Hospital, Department of Neurology, 184 Bd du Faubourg Saint Antoine, 75012 Paris, Paris, France

Experimental studies in mouse models offer the opportunity to combine in-vivo longitudinal high-field MRI and histological analyses. However, automatic MRI tools for processing rodent data to avoid manual processing are lacking. We proposed an automatic pipeline to perform systematic analyses on large murine cohorts with longitudinal data. We first applied artifacts correction as bias correction to optimize the subsequent steps. We then registered masks of regions of interest (ROIs) for our analyses onto each subject from which we extracted the quantitative data. This pipeline provides a way of quickly analyzing ROI regardless of disease models or the MRI sequence.

Takamasa Sugiura¹, Shuhei Nitta¹, Taichiro Shiodera¹, Yuko Hara¹, Yasunori Taguchi¹, Tomoyuki Takeguchi¹, Takuya Fujimaki², Kensuke Shinoda², Hiroshi Takai², and Ayako Ninomiya^2
1TOSHIBA CORPORATION, Kawasaki, Japan, ²TOSHIBA MEDICAL SYSTEMS CORPORATION, Otawara, Japan

We propose an improved automatic slice positioning algorithm for knee MR which combines conventional machine-learning based landmark detection with advanced image processing techniques. Conventional slice positioning methods determine the diagnostic slice center and orientation by detecting anatomical landmarks in the scout image. However, computing slice positions from landmarks can be inadequate since landmarks vary across patients and can be cut-off from scout images. Here, we use not only landmark detection but also image processing based contour detection of the femoral condyle and angle estimation of the femur and tibia to enable slice positioning for a wider range of scout images.

Pedro A. Gómez^1,2, Guido Buonincontri³, Miguel Molina-Romero^1,2, Cagdas Ulas^1,2, Jonatahn I. Sperl², Marion I. Menzel², and Bjoern H. Menze^1
1Technische Universität München, Garching, Germany, ²GE Global Research, Garching, Germany, ³Istituto Nazionale di Fisica Nucleare, Pisa, Italy

We present a method for creating a spatiotemporal dictionary for magnetic resonance fingerprinting (MRF). Our technique is based on the clustering of multi-parametric spatial kernels from training data and the posterior simulation of a temporal fingerprint for each voxel in every cluster. We show that the parametric maps estimated with a clustered dictionary agree with maps estimated with a full dictionary, and are also robust to undersampling and shorter sequences, leading to increased efficiency in parameter mapping with MRF. 

Laurent Lamalle^1,2, Georgios Gousios³, and Matthieu Urvoy^3
1Inserm US 17 & CNRS UMS 3552, Grenoble, France, ²Université Joseph Fourier & CHU de Grenoble, UMS IRMaGe, Grenoble, France, ³SFR RMN Biomédicale et Neurosciences, Université Joseph Fourier, Grenoble, France

Phase information of MR images can provide quantitative access to various physical properties of the examined sample, such as local $$$B_0$$$ values, magnetic susceptibility or flow. Phase is a continuous information whose estimation typically requires unwrapping. In this study, we propose a novel phase estimation algorithm which: (1) relies on a numeric scheme that is robust to phase jumps, and (2) is optimized for execution on modern parallel processors.

Arathi Sreekumari¹, K S Shriram¹, Uday Patil¹, Ersin Bayram², Dattesh Shanbhag¹, and Rakesh Mullick^1
1GE Global Research, Bangalore, India, ²GE Healthcare, Houston, TX, United States

In this work we are focusing on automating the scan coverage and FOV for liver MRI acquisitions. We demonstrate that using fast scout images, we can achieve very good localization of liver FOV, irrespective of anatomy differences and hand-up / hands-down positioning.

Hyungseok Jang^1,2, Curtis N Wiens¹, and Alan B McMillan^1
1Department of Radiology, University of Wisconsin, Madison, WI, United States, ²Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, United States

In this study, we propose a highly time efficient quantitative imaging scheme where short and long T2* components can be simultaneously estimated. This method is based on a multi-echo UTE hybrid encoding scheme, where the central SPI region is oversampled to allow measurement of short T2* across a wide range of TEs. The UTE acquisition is immediately followed by a gradient echo train to measure long T2*. We show the proposed method can obtain an extensive number of images (e.g., approximately 750 images) within a single acquisition and reasonable scan time.

Ricardo Jorge Maximiano¹, Tiago Constantino^1,2,3, André Santos-Ribeiro^1,4, and Hugo Alexandre Ferreira^1
1Institute of Biophysics and Biomedical Engineering, Faculty of Sciences of the University of Lisbon, Lisbon, Portugal, ²Spitalzentrum Biel, Bienne, Switzerland, ³Lisbon School of Health Technology - ESTeSL, Lisbon, Portugal, ⁴Centre for Neuropsychopharmacology, Imperial College London, London, United Kingdom

In this work, a user-friendly toolbox that aims to classify automatically brain connectivity matrices is described. To test this tool, we used the Parkinson’s Progression Markers Initiative (PPMI) data which includes structural and functional Magnetic Resonance Imaging data of healthy subjects, patients with “scans without evidence for dopaminergic deficit” (SWEDD) and patients diagnosed with Parkinson’s Disease (PD). Using default parameters, this tool was able to achieve a maximum accuracy of 85.4% in classifying the 3 groups of subjects by selecting features that were related to the rostral middle frontal gyrus and splenium, which are in agreement with PD literature.

Christian Kames^1,2, Vanessa Wiggermann^1,3,4, and Alexander Rauscher^1,4
1UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada, ²Department of Engineering Physics, University of British Columbia, Vancouver, BC, Canada, ³Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada, ⁴Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada

Current state-of-the-art QSM reconstruction algorithms are plagued by the trade-off between reconstruction speed and quality. We propose a novel two-step dipole inversion algorithm 20x faster than MEDI and HEIDI, while producing qualitatively appealing images with a root-mean-square error less than MEDI’s and HEIDI’s when compared to COSMOS. The proposed method works by first reconstructing the well-conditioned k-space region through the use of a Krylov subspace solver, followed by a total variation minimization to fill in the ill-conditioned k-space region.