Samuel James Wharton¹, and Richard Bowtell^1
1Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom

Recent work has shown that magnitude images acquired using gradient echo (GE) techniques at high field are sensitive to the fiber-orientation in white matter. Here, we show that frequency difference mapping (FDM) based on acquisition of multi-echo GE data allows the strong fiber-orientation contrast in phase images to be exploited in producing high resolution, 3D fiber-orientation maps. This approach was tested on post-mortem brain samples by comparing the resulting orientation maps to DTI data and fiber-orientation maps generated from simultaneously acquired R2* maps. The results suggest that FDM at high field may be used for high resolution fiber-orientation mapping.

Frederick William Damen^1,2, Yi Sui^1,3, and Xiaohong Joe Zhou^1,4
1Center for MR Research, University of Illinois at Chicago, Chicago, IL, United States, ²Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States, ³Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States, ⁴Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois at Chicago, Chicago, IL, United States

Many diffusion MRI applications require co-registering a diffusion image based on EPI acquisitions to a high-resolution anatomical image; however, the contrast difference between diffusion and anatomical images poses a problem. We report a novel method, coined as multi-channel hybrid-aided registration of multiple spaces (mCHARM), to co-reregister diffusion images to T1-weighted images using a synthesized diffusion multi-channel hybrid (DMCH) image that mimics the contrast of a T1-weighted image. Our results from ten human volunteers have demonstrated that mCHARM improves the registration accuracy and produces reliable FA skeleton mask for tract-based spatial statistics analysis.

Valentin Hamy¹, Andrew Melbourne², Benjamin Trémoulhéac², Shonit Punwani¹, and David Atkinson^1
1Centre for Medical Imaging, UCL, London, United Kingdom, ²Centre for Medical Image Computing, UCL, London, United Kingdom

Dynamic Contrast Enhanced (DCE) MRI is a technique used in oncology to extract quantitative information about tumours. However, when it comes to image organs in the thorax or the abdomen, several types of motion (e.g. patient’s breathing, heartbeat and bowel peristalsis) can lead to errors in the estimation of that quantitative information. The study we present proposes a new technique aimed at compensating deformations/displacements due to motion in DCE images acquired using a repeat breath-hold protocol or a free breathing protocol.

Oleh Dzyubachyk¹, Boudewijn Lelieveldt^1,2, Jorik Blaas¹, Monique Reijnierse¹, Andrew Webb¹, and Rob van der Geest^1
1Department of Radiology, Leiden University Medical Center, Leiden, Netherlands, ²Mediamatics Department, Delft University of Technology, Delft, Netherlands

Initial feasibility studies have shown the promise of 7T MRI for studying spine disorders. However, diagnosis based on such data is compromised by the presence of high intensity inhomogeneities and the need to analyze several separately acquired image stacks. In practice, both intensity inhomogeneity correction and volume stitching must be performed manually. Here we present our novel fully automated algorithm for reconstructing a whole spine volume from a set of image stacks acquired on a 7T MR scanner.

Bryan T Addeman¹, Melanie Beaton², Robert A Hegele³, Abraam S Soliman^3,4, Curtis N Wiens⁵, and Charlies A McKenzie^1,5
1Department of Medical Biophysics, University of Western Ontario, London, ON, Canada, ²Department of Medicine, University of Western Ontario, London, ON, Canada,³The Robarts Research Institute, London, ON, Canada, ⁴Biomedical Engineering, University of Western Ontario, London, ON, Canada, ⁵Department of Physics, University of Western Ontario, London, ON, Canada

The distribution of adipose tissue is associated with the long-term development of type 2 diabetes and cardiovascular disease. Most adipose tissue volume quantification techniques require manual input, are susceptible to human error, and are time consuming. We propose a novel automated process for the quantification and segmentation of Total Adipose Tissue, Subcutaneous Adipose Tissue, and Intra-Abdominal Adipose Tissue using quantitative fat fraction maps. Segmentation is robust and requires no prior knowledge or complex machine learning. Results show that automated fat volume measurements are similar to manual segmentation techniques and can be calculated very rapidly.

Seungwook Yang¹, Eung Yeop Kim², Min-Oh Kim¹, Yoonho Nam¹, Jaeseok Park³, and Dong-Hyun Kim^1
1Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seoul, Korea, Republic of, ²Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea, Republic of, ³Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea, Republic of

The purpose of this study was to develop a computer-aided detection (CAD) system for brain metastases detection from magnetic resonance (MR) black blood images and to assess the applicability of MR black-blood imaging to CAD. Twenty-six patients with brain metastases of various sizes were imaged with a contrast-enhanced, three dimensional, whole brain MR black blood pulse sequence. The CAD system uses 3D template matching which measures normalized cross-correlation coefficient (NCCC) to generate possible metastases candidates from the patient data. Various image features were extracted from each candidate, then principal component analysis (PCA) was performed to determine dominant features. Artificial neural network (ANN) training and testing scheme were incorporated for classification.

Tobias Kober¹, Alexis Roche^1,2, Oscar Esteban^3,4, Subrahmanyam Gorthi⁴, Delphine Ribes⁵, Meritxell Bach-Cuadra^2,4, Reto Meuli², and Gunnar Krueger^1,2
1Advanced Clinical Imaging Technology, Siemens Healthcare Sector IM&WS S, Lausanne, Switzerland, ²Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland, ³Biomedical Image Technology (BIT), Universidad Politécnica de Madrid, Madrid, Spain, ⁴Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁵ARTORG Center for Computer Aided Surgery, University of Bern, Switzerland

Typical brain tissue segmentation algorithms use prior knowledge in the form of pre-segmented templates, i.e. atlases. This can lead to erroneous segmentation results if the actual image differs too much from the template, e.g. due to a too big age difference or a disease. In this work, the homogenous T1-contrast of the MP2RAGE sequence is combined with a Dixon water/fat imaging approach. It is shown that a single MP2RAGE-Dixon acquisition provides enough information for reproducible atlas-free brain tissue segmentation.

Bénédicte Maréchal¹, Tobias Kober¹, Tom Hilbert², Delphine Ribes³, Nicolas Chevrey⁴, Alexis Roche¹, Jean-Philippe Thiran⁵, Reto Meuli⁴, and Gunnar Krueger^1
1Advanced Clinical Imaging Technology, Siemens Healthcare Sector - CIBM, Renens, Switzerland, ²Universität Heidelberg, Germany, ³ARTORG Center for Computer Aided Surgery, Univ. of Bern, Switzerland, ⁴Centre Hospitalier Universitaire Vaudois and Univ. of Lausanne, ⁵Signal Processing Laboratory (LTS5) Ecole Polytechnique Fédérale de Lausanne

Normal aging and a wide range of neurologic, inflammatory or psychiatric diseases lead to changes in the brain tissue over time. In the interest of diagnosis, prognosis and treatment monitoring, it is highly desirable to have robust tools that reliably measure brain morphometry. We explore the ability of an automated MR image quality assessment technique to predict the accuracy of subsequent algorithms for brain quantitative analysis. The approach proofs to be a very promising candidate to objectively assess quality prior to any post-processing in order to attribute tissue changes to a potential pathology rather than to image degradation.

Chris J Rose^1,2, Penny L Hubbard^1,2, Tim F Cootes^1,2, Chris J Taylor^1,2, Josephine H Naish^1,2, Geoff J Parker^1,2, Simon S Young³, and John C Waterton^1,4
1University of Manchester Biomedical Imaging Institute, Manchester, Greater Manchester, United Kingdom, ²University of Manchester Academic Health Science Centre, Manchester, Greater Manchester, United Kingdom, ³AstraZeneca R&D, Charnwood, Loughborough, Leicestershire, United Kingdom, ⁴AstraZeneca, Alderley Park, Macclesfield, Cheshire, United Kingdom

MRI is used in natural history and intervention studies to spatially map parameters throughout organs or tumors. However, the problem of choosing the best method for drawing inferences about the populations being studied is unsolved. Conventionally, a significance test is applied using averaged parameter values, but this method neglects heterogeneity. We developed significance tests for application to a sample of density estimates (smooth histograms). Using ventilation-to-perfusion (V/Q) ratio data from an oxygen-enhanced MRI study of COPD patients and age-matched controls, we can identify biologically meaningful significant differences in V/Q where the conventional method fails.