Evaluation of local and global atrophy measurement techniques with simulated Alzheimer's disease data

The main goal of this work was to evaluate several well-known methods which provide global (BSI and SIENA) or local (Jacobian integration) estimates of atrophy in brain structures using Magnetic Resonance images. For that purpose, we have generated realistic simulated Alzheimer's disease images in which volume changes are modelled with a Finite Element thermoelastic model, which mimic the patterns of change obtained from a cohort of 19 real controls and 27 probable Alzheimer's disease patients. SIENA and BSI results correlate very well with gold standard data (BSI mean absolute error <0.29%; SIENA <0.44%). Jacobian integration was guided by both fluid and FFD-based registration techniques and resulting deformation fields and associated Jacobians were compared, region by region, with gold standard ones. The FFD registration technique provided more satisfactory results than the fluid one. Mean absolute error differences between volume changes given by the FFD-based technique and the gold standard were: sulcal CSF <2.49%; lateral ventricles 2.25%; brain <0.36%; hippocampi <0.42%

Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefa- - cts is also presented. Cross-sectional and

Progressive brain changes in patients with chronic fatigue syndrome: a longitudinal MRI study

To examine progressive brain changes associated with chronic fatigue syndrome (CFS).We investigated progressive brain changes with longitudinal MRI in 15 CFS and 10 normal controls (NCs) scanned twice 6 years apart on the same 1.5 Tesla (T) scanner. MR images yielded gray matter (GM) volumes, white matter (WM) volumes, and T1- and T2-weighted signal intensities (T1w and T2w). Each participant was characterized with Bell disability scores, and somatic and neurological symptom scores. We tested for differences in longitudinal changes between CFS and NC groups, inter group differences between pooled CFS and pooled NC populations, and correlations between MRI and symptom scores using voxel based morphometry. The analysis methodologies were first optimized using simulated atrophy.We found a significant decrease in WM volumes in the left inferior fronto-occipital fasciculus (IFOF) in CFS while in NCs it was unchanged (family wise error adjusted cluster level P value, PFWE < 0.05). This longitudinal finding was consolidated by the group comparisons which detected significantly decreased regional WM volumes in adjacent regions (PFWE < 0.05) and decreased GM and blood volumes in contralateral regions (PFWE < 0.05). Moreover, the regional GM and WM volumes and T2w in those areas showed significant correlations with CFS symptom scores (PFWE < 0.05).The results suggested that CFS is associated with IFOF WM deficits which continue to deteriorate at an abnormal rate. J. Magn. Reson. Imaging 2016;44:1301-1311.Zack Y. Shan, Richard Kwiatek, Richard Burnet, Peter Del Fante, Donald R. Staines, Sonya M. Marshall-Gradisnik and Leighton R. Barnde

Comparison of Two Methods for Automatic Brain Morphometry Analysis

The methods of computational neuroanatomy are widely used; the data on their individual strengths and limitations from direct comparisons are, however, scarce. The aim of the present study was direct comparison of DBM based on high-resolution spatial transforms with widely used VBM analysis based on segmented high-resolution images. We performed DBM and VBM analyses on simulated volume changes in a set of 20 3-D MR images, compared to 30 MR images, where only random spatial transforms were introduced. The ability of the two methods to detect regions with the simulated volume changes was determined using overlay index together with the ground truth regions of the simulations; the precision of the detection in space was determined using the distance measures between the centers of detected and simulated regions. DBM was able to detect all the regions with simulated local volume changes with high spatial precision. On the other hand, VBM detected only changes in vicinity of the largest simulated change, with a poor overlap of the detected changes and the ground truth. Taken together we suggest that the analysis of high-resolution deformation fields is more convenient, sensitive, and precise than voxel-wise analysis of tissue-segmented images

Longitudinal segmentation of age-related white matter hyperintensities

Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns

Automating the multimodal analysis of musculoskeletal imaging in the presence of hip implants

In patients treated with hip arthroplasty, the muscular condition and presence of inflammatory reactions are assessed using magnetic resonance imaging (MRI). As MRI lacks contrast for bony structures, computed tomography (CT) is preferred for clinical evaluation of bone tissue and orthopaedic surgical planning. Combining the complementary information of MRI and CT could improve current clinical practice for diagnosis, monitoring and treatment planning. In particular, the different contrast of these modalities could help better quantify the presence of fatty infiltration to characterise muscular condition after hip replacement. In this thesis, I developed automated processing tools for the joint analysis of CT and MR images of patients with hip implants. In order to combine the multimodal information, a novel nonlinear registration algorithm was introduced, which imposes rigidity constraints on bony structures to ensure realistic deformation. I implemented and thoroughly validated a fully automated framework for the multimodal segmentation of healthy and pathological musculoskeletal structures, as well as implants. This framework combines the proposed registration algorithm with tailored image quality enhancement techniques and a multi-atlas-based segmentation approach, providing robustness against the large population anatomical variability and the presence of noise and artefacts in the images. The automation of muscle segmentation enabled the derivation of a measure of fatty infiltration, the Intramuscular Fat Fraction, useful to characterise the presence of muscle atrophy. The proposed imaging biomarker was shown to strongly correlate with the atrophy radiological score currently used in clinical practice. Finally, a preliminary work on multimodal metal artefact reduction, using an unsupervised deep learning strategy, showed promise for improving the postprocessing of CT and MR images heavily corrupted by metal artefact. This work represents a step forward towards the automation of image analysis in hip arthroplasty, supporting and quantitatively informing the decision-making process about patient’s management

Statistical normalization techniques for magnetic resonance imaging☆☆☆

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers