Jurgen Fripp

Amyloid imaging results, from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging

The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, a participant of the worldwide Alzheimers Disease Neuroimaging Initiative (ADNI), performed (11)C-Pittsburgh Compound B (PiB) scans in 177 healthy controls (HC), 57 mild cognitive impairment (MCI) subjects, and 53 mild Alzheimers disease (AD) patients. High PiB binding was present in 33% of HC (49% in ApoE-epsilon4 carriers vs 21% in noncarriers) and increased with age, most strongly in epsilon4 carriers. 18% of HC aged 60-69 had high PiB binding rising to 65% in those over 80 years. Subjective memory complaint was only associated with elevated PiB binding in epsilon4 carriers. There was no correlation with cognition in HC or MCI. PiB binding in AD was unrelated to age, hippocampal volume or memory. Beta-amyloid (Abeta) deposition seems almost inevitable with advanced age, amyloid burden is similar at all ages in AD, and secondary factors or downstream events appear to play a more direct role than total beta amyloid burden in hippocampal atrophy and cognitive decline.

An Atlas-Based Electron Density Mapping Method for Magnetic Resonance Imaging (MRI)-Alone Treatment Planning and Adaptive MRI-Based Prostate Radiation Therapy

PURPOSE Prostate radiation therapy dose planning directly on magnetic resonance imaging (MRI) scans would reduce costs and uncertainties due to multimodality image registration. Adaptive planning using a combined MRI-linear accelerator approach will also require dose calculations to be performed using MRI data. The aim of this work was to develop an atlas-based method to map realistic electron densities to MRI scans for dose calculations and digitally reconstructed radiograph (DRR) generation. METHODS AND MATERIALS Whole-pelvis MRI and CT scan data were collected from 39 prostate patients. Scans from 2 patients showed significantly different anatomy from that of the remaining patient population, and these patients were excluded. A whole-pelvis MRI atlas was generated based on the manually delineated MRI scans. In addition, a conjugate electron-density atlas was generated from the coregistered computed tomography (CT)-MRI scans. Pseudo-CT scans for each patient were automatically generated by global and nonrigid registration of the MRI atlas to the patient MRI scan, followed by application of the same transformations to the electron-density atlas. Comparisons were made between organ segmentations by using the Dice similarity coefficient (DSC) and point dose calculations for 26 patients on planning CT and pseudo-CT scans. RESULTS The agreement between pseudo-CT and planning CT was quantified by differences in the point dose at isocenter and distance to agreement in corresponding voxels. Dose differences were found to be less than 2%. Chi-squared values indicated that the planning CT and pseudo-CT dose distributions were equivalent. No significant differences (p > 0.9) were found between CT and pseudo-CT Hounsfield units for organs of interest. Mean ± standard deviation DSC scores for the atlas-based segmentation of the pelvic bones were 0.79 ± 0.12, 0.70 ± 0.14 for the prostate, 0.64 ± 0.16 for the bladder, and 0.63 ± 0.16 for the rectum. CONCLUSIONS The electron-density atlas method provides the ability to automatically define organs and map realistic electron densities to MRI scans for radiotherapy dose planning and DRR generation. This method provides the necessary tools for MRI-alone treatment planning and adaptive MRI-based prostate radiation therapy.

Automatic Segmentation and Quantitative Analysis of the Articular Cartilages From Magnetic Resonance Images of the Knee

In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm.

Cross-sectional and Longitudinal Analysis of the Relationship Between Aβ Deposition, Cortical Thickness, and Memory in Cognitively Unimpaired Individuals and in Alzheimer Disease

IMPORTANCE β-amyloid (Aβ) deposition is one of the hallmarks of Alzheimer disease. Aβ deposition accelerates gray matter atrophy at early stages of the disease even before objective cognitive impairment is manifested. Identification of at-risk individuals at the presymptomatic stage has become a major research interest because it will allow early therapeutic interventions before irreversible synaptic and neuronal loss occur. We aimed to further characterize the cross-sectional and longitudinal relationship between Aβ deposition, gray matter atrophy, and cognitive impairment. OBJECTIVE To investigate the topographical relationship of Aβ deposition, gray matter atrophy, and memory impairment in asymptomatic individuals with Alzheimer disease pathology as assessed by Pittsburgh compound B positron emission tomography (PiB-PET). DESIGN Regional analysis was performed on the cortical surface to relate cortical thickness to PiB retention and episodic memory. SETTING The Australian Imaging, Biomarkers, and Lifestyle Study of Aging, Austin Hospital, Melbourne, Australia. PARTICIPANTS Ninety-three healthy elderly control subjects (NCs) and 40 patients with Alzheimer disease from the Australian Imaging, Biomarkers, and Lifestyle Study of Aging cohort. INTERVENTION Participants underwent neuropsychological evaluation as well as magnetic resonance imaging and PiB-PET scans. Fifty-four NCs underwent repeated scans and neuropsychological evaluation 18 and 36 months later. MAIN OUTCOMES AND MEASURES Correlations between cortical thickness, PiB retention, and episodic memory. RESULTS There was a significant reduction in cortical thickness in the precuneus and hippocampus associated with episodic memory impairment in the NC PiB-positive (NC+) group when compared with the NC- group. Cortical thickness was also correlated negatively with neocortical PiB in the NC+ group. Longitudinal analysis showed a faster rate of gray matter (GM) atrophy in the temporal lobe and the hippocampi of the NC+ group. Over time, GM atrophy became more extensive in the NC+ group, especially in the temporal lobe. CONCLUSIONS AND RELEVANCE In asymptomatic individuals, Aβ deposition is associated with GM atrophy and memory impairment. The earliest signs of GM atrophy were detected in the hippocampus and the posterior cingulate and precuneus regions, and with disease progression, atrophy became more extensive in the temporal lobes. These findings support the notion that Aβ deposition is not a benign process and that interventions with anti-Aβ therapy at these early stages have a higher chance to be effective.

A novel mesh processing based technique for 3D plant analysis

BackgroundIn recent years, imaging based, automated, non-invasive, and non-destructive high-throughput plant phenotyping platforms have become popular tools for plant biology, underpinning the field of plant phenomics. Such platforms acquire and record large amounts of raw data that must be accurately and robustly calibrated, reconstructed, and analysed, requiring the development of sophisticated image understanding and quantification algorithms. The raw data can be processed in different ways, and the past few years have seen the emergence of two main approaches: 2D image processing and 3D mesh processing algorithms. Direct image quantification methods (usually 2D) dominate the current literature due to comparative simplicity. However, 3D mesh analysis provides the tremendous potential to accurately estimate specific morphological features cross-sectionally and monitor them over-time.ResultIn this paper, we present a novel 3D mesh based technique developed for temporal high-throughput plant phenomics and perform initial tests for the analysis of Gossypium hirsutum vegetative growth. Based on plant meshes previously reconstructed from multi-view images, the methodology involves several stages, including morphological mesh segmentation, phenotypic parameters estimation, and plant organs tracking over time. The initial study focuses on presenting and validating the accuracy of the methodology on dicotyledons such as cotton but we believe the approach will be more broadly applicable. This study involved applying our technique to a set of six Gossypium hirsutum (cotton) plants studied over four time-points. Manual measurements, performed for each plant at every time-point, were used to assess the accuracy of our pipeline and quantify the error on the morphological parameters estimated.ConclusionBy directly comparing our automated mesh based quantitative data with manual measurements of individual stem height, leaf width and leaf length, we obtained the mean absolute errors of 9.34%, 5.75%, 8.78%, and correlation coefficients 0.88, 0.96, and 0.95 respectively. The temporal matching of leaves was accurate in 95% of the cases and the average execution time required to analyse a plant over four time-points was 4.9 minutes. The mesh processing based methodology is thus considered suitable for quantitative 4D monitoring of plant phenotypic features.

Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian–Eulerian PDE approach using partial volume maps

Accurate cortical thickness estimation is important for the study of many neurodegenerative diseases. Many approaches have been previously proposed, which can be broadly categorised as mesh-based and voxel-based. While the mesh-based approaches can potentially achieve subvoxel resolution, they usually lack the computational efficiency needed for clinical applications and large database studies. In contrast, voxel-based approaches, are computationally efficient, but lack accuracy. The aim of this paper is to propose a novel voxel-based method based upon the Laplacian definition of thickness that is both accurate and computationally efficient. A framework was developed to estimate and integrate the partial volume information within the thickness estimation process. Firstly, in a Lagrangian step, the boundaries are initialized using the partial volume information. Subsequently, in an Eulerian step, a pair of partial differential equations are solved on the remaining voxels to finally compute the thickness. Using partial volume information significantly improved the accuracy of the thickness estimation on synthetic phantoms, and improved reproducibility on real data. Significant differences in the hippocampus and temporal lobe between healthy controls (NC), mild cognitive impaired (MCI) and Alzheimers disease (AD) patients were found on clinical data from the ADNI database. We compared our method in terms of precision, computational speed and statistical power against the Eulerian approach. With a slight increase in computation time, accuracy and precision were greatly improved. Power analysis demonstrated the ability of our method to yield statistically significant results when comparing AD and NC. Overall, with our method the number of samples is reduced by 25% to find significant differences between the two groups.

Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

Appearance modeling of 11C PiB PET images : Characterizing amyloid deposition in Alzheimer's disease, mild cognitive impairment and healthy aging

Beta-amyloid (Abeta) deposition is one of the neuropathological hallmarks of Alzheimers disease (AD), Abeta burden can be quantified using (11)C PiB PET. Neuropathological studies have shown that the initial plaques are located in the temporal and orbitofrontal cortices, extending later to the cingulate, frontal and parietal cortices (Braak and Braak, 1997). Previous studies have shown an overlap in (11)C PiB PET retention between AD, mild cognitive impairment (MCI) patients and normal elderly control (NC) participants. It has also been shown that there is a relationship between Abeta deposition and memory impairment in MCI patients. In this paper we explored the variability seen in 15 AD, 15 MCI and 18 NC by modeling the voxel data from spatially and uptake normalized PiB images using principal component analysis. The first two principal components accounted for 80% of the variability seen in the data, providing a clear separation between AD and NC, and allowing subsequent classification. The MCI cases were distributed along an apparent axis between the AD and NC group, closely aligned with the first principal component axis. The NC cases that were PiB(+) formed a distinct cluster that was between, but separated from the AD and PiB(-) NC clusters. The PiB(+) MCI were found to cluster with the AD cases, and exhibited a similar deposition pattern. The primary principal component score was found to correlate with episodic memory scores and mini mental status examination and it was observed that by varying the first principal component, a change in amyloid deposition could be derived that is similar to the expected progression of amyloid deposition observed from post mortem studies.

Symmetric diffeomorphic registration of fibre orientation distributions

Registration of diffusion-weighted images is an important step in comparing white matter fibre bundles across subjects, or in the same subject at different time points. Using diffusion-weighted imaging, Spherical Deconvolution enables multiple fibre populations within a voxel to be resolved by computing the fibre orientation distribution (FOD). In this paper, we present a novel method that employs FODs for the registration of diffusion-weighted images. Registration was performed by optimising a symmetric diffeomorphic non-linear transformation model, using image metrics based on the mean squared difference, and cross-correlation of the FOD spherical harmonic coefficients. The proposed method was validated by recovering known displacement fields using FODs represented with maximum harmonic degrees (l(max)) of 2, 4 and 6. Results demonstrate a benefit in using FODs at l(max)=4 compared to l(max)=2. However, a decrease in registration accuracy was observed when l(max)=6 was used; this was likely caused by noise in higher harmonic degrees. We compared our proposed method to fractional anisotropy driven registration using an identical code base and parameters. FOD registration was observed to perform significantly better than FA in all experiments. The cross-correlation metric performed significantly better than the mean squared difference. Finally, we demonstrated the utility of this method by computing an unbiased group average FOD template that was used for probabilistic fibre tractography. This work suggests that using crossing fibre information aids in the alignment of white matter and could therefore benefit several methods for investigating population differences in white matter, including voxel based analysis, tensor based morphometry, atlas based segmentation and labelling, and group average fibre tractography.

Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models.

The hippocampus is affected at an early stage in the development of Alzheimers disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the subpopulations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotellings T2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS).