Colin Studholme

An overlap invariant entropy measure of 3D medical image alignment

Abstract This paper is concerned with the development of entropy-based registration criteria for automated 3D multi-modality medical image alignment. In this application where misalignment can be large with respect to the imaged field of view, invariance to overlap statistics is an important consideration. Current entropy measures are reviewed and a normalised measure is proposed which is simply the ratio of the sum of the marginal entropies and the joint entropy. The effect of changing overlap on current entropy measures and this normalised measure are compared using a simple image model and experiments on clinical image data. Results indicate that the normalised entropy measure provides significantly improved behaviour over a range of imaged fields of view.

The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods

The Alzheimers Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimers disease. Magnetic resonance imaging (MRI), (18F)‐fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquiredat multiple time points. All data will be cross‐linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications thatguided protocol development. A major effort was devoted toevaluating 3D T1‐weighted sequences for morphometric analyses. Several options for this sequence were optimized for the relevant manufacturer platforms and then compared in a reduced‐scale clinical trial. The protocol selected for the ADNI study includes: back‐to‐back 3D magnetization prepared rapid gradient echo (MP‐RAGE) scans; B1‐calibration scans when applicable; and an axial proton density‐T2 dual contrast (i.e., echo) fast spin echo/turbo spin echo (FSE/TSE) for pathology detection. ADNI MRI methods seek to maximize scientific utility while minimizing the burden placed on participants. The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom‐based monitoring of all scanners could be used as a model for other multisite trials. J. Magn. Reson. Imaging 2008.

Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures

Approaches using measures of voxel intensity similarity are showing promise in fully automating magnetic resonance (MR) and positron emission tomography (PET) image registration in the head, without requiring extraction and identification of corresponding structures. In this paper a method of multiresolution optimization of these measures is described and five alternative measures are compared: cross correlation, minimization of corresponding PET intensity variation, moments of the distribution of values in the intensity feature space, entropy of the intensity feature space and mutual information. Their ability to recover registration is examined for ten clinically acquired image pairs with respect to the size of initial misregistration, the precision of the final result, and the accuracy assessed by visual inspection. The mutual information measure proved the most robust to initial starting estimate, successfully registering 98.8% of 900 trial misregistrations. Success is defined as providing a visually acceptable solution to a trained observer. A high resolution search (1/16 mm step size) of 30 trial misregistrations showed that optimization using the mutual information measure provided solutions with 0.13 mm, 0.11 mm and 0.17 mm standard deviations in the three Cartesian axes of the translation vector and 0.2 degree, 0.3 degree and 0.2 degree standard deviations for rotations about the three axes. The algorithm takes between 4 and 8 minutes to run on a typical workstation, including visual inspection of the result.

Automated 3-D registration of MR and CT images of the head.

This paper discusses the application of voxel similarity measures in the automated registration of clinically acquired MR and CT data of the head. We describe a novel single-start multi-resolution approach to the optimization of these measures, and the issues involved in applying this to data having a range of different fields of view and sampling resolution. We compare four proposed measures of voxel similarity using the same optimization scheme when presented with 10 pairs of images with a range of initial misregistrations. The registration estimates are compared with those provided by manual point-based registration and evaluated by visual inspection to give an assessment of the robustness and accuracy of the different measures. One full-volume CT image set is used to investigate the performance of each measure when used to align truncated images from different regions in the head. The soft tissue correlation and mutual information measures were found to provide the most robust measures of misregistration, providing results comparable to or better than those from manual point-based registration for all but the most truncated image volumes.

A Supervised Patch-Based Approach for Human Brain Labeling

We propose in this work a patch-based image labeling method relying on a label propagation framework. Based on image intensity similarities between the input image and an anatomy textbook, an original strategy which does not require any nonrigid registration is presented. Following recent developments in nonlocal image denoising, the similarity between images is represented by a weighted graph computed from an intensity-based distance between patches. Experiments on simulated and in vivo magnetic resonance images show that the proposed method is very successful in providing automated human brain labeling.

Longitudinal stability of MRI for mapping brain change using tensor-based morphometry

Measures of brain change can be computed from sequential MRI scans, providing valuable information on disease progression, e.g., for patient monitoring and drug trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy, but its sensitivity depends on the contrast and geometric stability of the images. As part of the Alzheimers Disease Neuroimaging Initiative (ADNI), 17 normal elderly subjects were scanned twice (at a 2-week interval) with several 3D 1.5 T MRI pulse sequences: high and low flip angle SPGR/FLASH (from which Synthetic T1 images were generated), MP-RAGE, IR-SPGR (N = 10) and MEDIC (N = 7) scans. For each subject and scan type, a 3D deformation map aligned baseline and follow-up scans, computed with a nonlinear, inverse-consistent elastic registration algorithm. Voxelwise statistics, in ICBM stereotaxic space, visualized the profile of mean absolute change and its cross-subject variance; these maps were then compared using permutation testing. Image stability depended on: (1) the pulse sequence; (2) the transmit/receive coil type (birdcage versus phased array); (3) spatial distortion corrections (using MEDIC sequence information); (4) B1-field intensity inhomogeneity correction (using N3). SPGR/FLASH images acquired using a birdcage coil had least overall deviation. N3 correction reduced coil type and pulse sequence differences and improved scan reproducibility, except for Synthetic T1 images (which were intrinsically corrected for B1-inhomogeneity). No strong evidence favored B0 correction. Although SPGR/FLASH images showed least deviation here, pulse sequence selection for the ADNI project was based on multiple additional image analyses, to be reported elsewhere.

Functional MRI of Language Processing: Dependence on Input Modality and Temporal Lobe Epilepsy

Summary:  Purpose: Functional magnetic resonance imaging (MRI) using two language‐comprehension tasks was evaluated to determine its ability to lateralize language processing and identify regions that must be spared in surgery.

Deformation-based morphometry of brain changes in alcohol dependence and abstinence

Brain atrophy associated with chronic alcohol consumption is partially reversible after cessation of drinking. Recovering alcoholics (RA, 45+/-8 years) were studied with MRI within 1 week of entering treatment, with follow-up at 8 months. Light drinkers (LD) were studied with MRI twice 1 year apart. For each participant, deformation maps of baseline structure and longitudinal size changes between baseline and follow-up scans were created using nonlinear registration techniques. ANCOVA assessed group differences and regression methods examined relationships between deformation maps and measures of drinking severity or baseline atrophy. At baseline, RA showed significant atrophy in the frontal and temporal lobes. Longitudinally, abstainers recovered tissue volumes significantly faster than LD in parietal and frontal lobes. When comparing abstainers to relapsers, additional regions with significantly greater recovery in abstainers were temporal lobes, thalamus, brainstem, cerebellum, corpus callosum, anterior cingulate, insula, and subcortical white matter. Gray matter volume at baseline predicted volume recovery during abstinence better than white matter. Drinking severity was not significantly related to brain structural changes assessed with this method. Longitudinally, deformation-based morphometry confirmed tissue recovery in RAs who maintain long-term sobriety. Abstinence-associated tissue volume gains are significant in focal parts of the fronto-ponto-cerebellar circuit that is adversely affected by heavy drinking.

Voxel similarity measures for automated image registration

We present the concept of the feature space sequence: 2D distributions of voxel features of two images generated at registration and a sequence of misregistrations. We provide an explanation of the structure seen in these images. Feature space sequences have been generated for a pair of MR image volumes identical apart from the addition of Gaussian noise to one, MR image volumes with and without Gadolinium enhancement, MR and PET-FDG image volumes and MR and CT image volumes, all of the head. The structure seen in the feature space sequences was used to devise two new measures of similarity which in turn were used to produce plots of cost versus misregistration for the 6 degrees of freedom of rigid body motion. One of these, the third order moment of the feature space histogram, was used to register the MR image volumes with and without Gadolinium enhancement. These techniques have the potential for registration accuracy to within a small fraction of a voxel or resolution element and therefore interpolation errors in image transformation can be the dominant source of error in subtracted images. We present a method for removing these errors using sinc interpolation and show how interpolation errors can be reduced by over two orders of magnitude.

Mapping of brain metabolite distributions by volumetric proton MR spectroscopic imaging (MRSI).

Distributions of proton MR‐detected metabolites have been mapped throughout the brain in a group of normal subjects using a volumetric MR spectroscopic imaging (MRSI) acquisition with an interleaved water reference. Data were processed with intensity and spatial normalization to enable voxel‐based analysis methods to be applied across a group of subjects. Results demonstrate significant regional, tissue, and gender‐dependent variations of brain metabolite concentrations, and variations of these distributions with normal aging. The greatest alteration of metabolites with age was observed for white‐matter choline and creatine. An example of the utility of the normative metabolic reference information is then demonstrated for analysis of data acquired from a subject who suffered a traumatic brain injury. This study demonstrates the ability to obtain proton spectra from a wide region of the brain and to apply fully automated processing methods. The resultant data provide a normative reference for subsequent utilization for studies of brain injury and disease. Magn Reson Med, 2009.