Two-Year Prognostic Utility of Plasma p217+tau across the Alzheimer's Continuum.

BACKGROUND: Plasma p217+tau has shown high concordance with cerebrospinal fluid (CSF) and positron emission tomography (PET) measures of amyloid-ß (Aß) and tau in Alzheimer's Disease (AD). However, its association with longitudinal cognition and comparative performance to PET Aß and tau in predicting cognitive decline are unknown. OBJECTIVES: To evaluate whether p217+tau can predict the rate of cognitive decline observed over two-year average follow-up and compare this to prediction based on Aß (18F-NAV4694) and tau (18F-MK6240) PET. We also explored the sample size required to detect a 30% slowing in cognitive decline in a 2-year trial and selection test cost using p217+tau (pT+) as compared to PET Aß (A+) and tau (T+) with and without p217+tau pre-screening. DESIGN: A prospective observational cohort study. SETTING: Participants of the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) and Australian Dementia Network (ADNeT). PARTICIPANTS: 153 cognitively unimpaired (CU) and 50 cognitively impaired (CI) individuals. MEASUREMENTS: Baseline p217+tau Simoa® assay, 18F-MK6240 tau-PET and 18F-NAV4694 Aß-PET with neuropsychological follow-up (MMSE, CDR-SB, AIBL-PACC) over 2.4 ± 0.8 years. RESULTS: In CI, p217+tau was a significant predictor of change in MMSE (ß = -0.55, p < 0.001) and CDR-SB (ß =0.61, p < 0.001) with an effect size similar to Aß Centiloid (MMSE ß = -0.48, p = 0.002; CDR-SB ß = 0.43, p = 0.004) and meta-temporal (MetaT) tau SUVR (MMSE: ß = -0.62, p < 0.001; CDR-SB: ß = 0.65, p < 0.001). In CU, only MetaT tau SUVR was significantly associated with change in AIBL-PACC (ß = -0.22, p = 0.008). Screening pT+ CI participants into a trial could lead to 24% reduction in sample size compared to screening with PET for A+ and 6-13% compared to screening with PET for T+ (different regions). This would translate to an 81-83% biomarker test cost-saving assuming the p217+tau test cost one-fifth of a PET scan. In a trial requiring PET A+ or T+, p217+tau pre-screening followed by PET in those who were pT+ would cost more in the CI group, compared to 26-38% biomarker test cost-saving in the CU. CONCLUSIONS: Substantial cost reduction can be achieved using p217+tau alone to select participants with MCI or mild dementia for a clinical trial designed to slow cognitive decline over two years, compared to participant selection by PET. In pre-clinical AD trials, p217+tau provides significant cost-saving if used as a pre-screening measure for PET A+ or T+ but in MCI/mild dementia trials this may add to cost both in testing and in the increased number of participants needed for testing.

Cross-Sectional and Longitudinal Comparison of Tau Imaging with 18F-MK6240 and 18F-Flortaucipir in Populations Matched for Age, MMSE and Brain Beta-Amyloid Burden.

OBJECTIVES: Longitudinal tau quantification may provide a useful marker of drug efficacy in clinical trials. Different tau PET tracers may have different sensitivity to longitudinal changes, but without a head-to-head dataset or a carefully designed case-matching procedure, comparing results in different cohorts can be biased. In this study, we compared the tau PET tracers, 18F-MK6240 and 18F-flortaucipir (FTP), both cross-sectionally and longitudinally by case-matching subjects in the AIBL and ADNI longitudinal cohort studies. METHODS: A subset of 113 participants from AIBL and 113 from ADNI imaged using 18F-MK6240 and 18F-FTP respectively, with baseline and follow-up, were matched based on baseline clinical diagnosis, MMSE, age and amyloid (Aß) PET centiloid value. Subjects were grouped as 64 Aß- cognitively unimpaired (CU), 22 Aß+ CU, 14 Aß+ mild cognitive impairment (MCI) and 13 Aß+ Alzheimer's disease (AD). Tracer retention was measured in the mesial, temporoparietal, rest of the cortex, and a meta-temporal region composed of entorhinal, inferior/middle temporal, fusiform, parahippocampus and amygdala. T-tests were employed to assess group separation at baseline using SUVR Z-scores and longitudinally using SUVR%/Yr. RESULTS: Both tracers detected statistically significant differences at baseline in most regions between all clinical groups. Only 18F-MK6240 showed statistically significant higher rate of SUVR increase in Aß+ CU compared to Aß- CU in the mesial, meta-temporal and temporoparietal regions. CONCLUSION: 18F-MK6240 appears to be a more sensitive tracer for change in tau level at the preclinical stage of AD.

A Conformational Variant of p53 (U-p53AZ) as Blood-Based Biomarker for the Prediction of the Onset of Symptomatic Alzheimer's Disease.

BACKGROUND: Ongoing research seeks to identify blood-based biomarkers able to predict onset and progression of Alzheimer's disease (AD). OBJECTIVE: The unfolded conformational variant of p53 (U-p53AZ), previously observed in AD individuals, was evaluated in plasma samples from individuals participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) cohort for diagnostic and prognostic assessment, validated on a neuropsychological-based diagnosis, over the course of six years. DESIGN: Retrospective Longitudinal Prognostic biomarker study. SETTING: Single-center study based on the AIBL cohort. PARTICIPANTS: 482 participants of the AIBL cohort, aged 60-85 years, without uncontrolled diabetes, vascular disease, severe depression or psychiatric illnesses. MEASUREMENTS: The AlzoSure® Predict test, consisting of immunoprecipitation (IP) followed by liquid chromatography (LC) tandem mass spectrometry (MS/MS), was performed to quantify the AZ 284® peptide as readout of U-p53AZ and compared with an independent neuropsychological diagnosis. The amyloid load via amyloid ß-positron emission tomography (Aß-PET) and supporting clinical information were included where possible. RESULTS: U-p53AZ diagnostic and prognostic performance was assessed in both time-independent and time-dependent (36, 72 and 90 months following initial sampling) analyses. Prognostic performance of Aß-PET and survival analyses with different risk factors (gender, Aß-PET and APOE ε4 allele status) were also performed. U-p53AZ differentiated neuropsychologically graded AD from non-AD samples, and its detection at intermediate/high levels precisely identified present and future symptomatic AD. In both time-independent and time-dependent prognostic analyses U-p53AZ achieved area under the curve (AUC) >98%, significantly higher than Aß-PET AUCs (between 84% and 93%, P respectively <0.0001 and <0.001). As single factor, U-p53AZ could clearly determine the risk of AD neuropsychological diagnosis over time (low versus intermediate/high U-p53AZ hazard ratio=2.99). Proportional hazards regression analysis identified U-p53AZ levels as a major independent predictor of AD onset. CONCLUSIONS: These findings support use of U-p53AZ as blood-based biomarker predicting whether individuals would reach neuropsychologically-defined AD within six years prior to AD diagnosis. Integration of U-p53AZ in screening processes could support refined participant stratification for interventional studies.

Automating Quantitative Measures of an Established Conventional MRI Scoring System for Preterm-Born Infants Scanned between 29 and 47 Weeks' Postmenstrual Age.

BACKGROUND AND PURPOSE: Conventional MR imaging scoring is a valuable tool for risk stratification and prognostication of outcomes, but manual scoring is time-consuming, operator-dependent, and requires high-level expertise. This study aimed to automate the regional measurements of an established brain MR imaging scoring system for preterm neonates scanned between 29 and 47 weeks' postmenstrual age. MATERIALS AND METHODS: This study used T2WI from the longitudinal Prediction of PREterm Motor Outcomes cohort study and the developing Human Connectome Project. Measures of biparietal width, interhemispheric distance, callosal thickness, transcerebellar diameter, lateral ventricular diameter, and deep gray matter area were extracted manually (Prediction of PREterm Motor Outcomes study only) and automatically. Scans with poor quality, failure of automated analysis, or severe pathology were excluded. Agreement, reliability, and associations between manual and automated measures were assessed and compared against statistics for manual measures. Associations between measures with postmenstrual age, gestational age at birth, and birth weight were examined (Pearson correlation) in both cohorts. RESULTS: A total of 652 MRIs (86%) were suitable for analysis. Automated measures showed good-to-excellent agreement and good reliability with manual measures, except for interhemispheric distance at early MR imaging (scanned between 29 and 35 weeks, postmenstrual age; in line with poor manual reliability) and callosal thickness measures. All measures were positively associated with postmenstrual age (r = 0.11-0.94; R2 = 0.01-0.89). Negative and positive associations were found with gestational age at birth (r = -0.26-0.71; R2 = 0.05-0.52) and birth weight (r = -0.25-0.75; R2 = 0.06-0.56). Automated measures were successfully extracted for 80%-99% of suitable scans. CONCLUSIONS: Measures of brain injury and impaired brain growth can be automatically extracted from neonatal MR imaging, which could assist with clinical reporting.

Multi T1-weighted contrast MRI with fluid and white matter suppression at 1.5 T.

INTRODUCTION: The fluid and white matter suppression sequence (FLAWS) provides two T1-weighted co-registered datasets: a white matter (WM) suppressed contrast (FLAWS1) and a cerebrospinal fluid (CSF) suppressed contrast (FLAWS2). FLAWS has the potential to improve the contrast of the subcortical brain regions that are important for Deep Brain Stimulation surgery planning. However, to date FLAWS has not been optimized for 1.5 T. In this study, the FLAWS sequence was optimized for use at 1.5 T. In addition, the contrast-enhancement properties of FLAWS image combinations were investigated using two voxel-wise FLAWS combined images: the division (FLAWS-div) and the high contrast (FLAWS-hc) image. METHODS: FLAWS sequence parameters were optimized for 1.5 T imaging using an approach based on the use of a profit function under constraints for brain tissue signal and contrast maximization. MR experiments were performed on eleven healthy volunteers (age 18-30). Contrast (CN) and contrast to noise ratio (CNR) between brain tissues were measured in each volunteer. Furthermore, a qualitative assessment was performed to ensure that the separation between the internal globus pallidus (GPi) and the external globus pallidus (GPe) is identifiable in FLAWS1. RESULTS: The optimized set of sequence parameters for FLAWS at 1.5 T provided contrasts similar to those obtained in a previous study at 3 T. The separation between the GPi and the GPe was clearly identified in FLAWS1. The CN of FLAWS-hc was higher than that of FLAWS1 and FLAWS2, but was not different from the CN of FLAWS-div. The CNR of FLAWS-hc was higher than that of FLAWS-div. CONCLUSION: Both qualitative and quantitative assessments validated the optimization of the FLAWS sequence at 1.5 T. Quantitative assessments also showed that FLAWS-hc provides an enhanced contrast compared to FLAWS1 and FLAWS2, with a higher CNR than FLAWS-div.

Validation of an MRI Brain Injury and Growth Scoring System in Very Preterm Infants Scanned at 29- to 35-Week Postmenstrual Age.

BACKGROUND AND PURPOSE: The diagnostic and prognostic potential of brain MR imaging before term-equivalent age is limited until valid MR imaging scoring systems are available. This study aimed to validate an MR imaging scoring system of brain injury and impaired growth for use at 29 to 35 weeks postmenstrual age in infants born at <31 weeks gestational age. MATERIALS AND METHODS: Eighty-three infants in a prospective cohort study underwent early 3T MR imaging between 29 and 35 weeks' postmenstrual age (mean, 32+2 ± 1+3 weeks; 49 males, born at median gestation of 28+4 weeks; range, 23+6-30+6 weeks; mean birthweight, 1068 ± 312 g). Seventy-seven infants had a second MR scan at term-equivalent age (mean, 40+6 ± 1+3 weeks). Structural images were scored using a modified scoring system which generated WM, cortical gray matter, deep gray matter, cerebellar, and global scores. Outcome at 12-months corrected age (mean, 12 months 4 days ± 1+2 weeks) consisted of the Bayley Scales of Infant and Toddler Development, 3rd ed. (Bayley III), and the Neuro-Sensory Motor Developmental Assessment. RESULTS: Early MR imaging global, WM, and deep gray matter scores were negatively associated with Bayley III motor (regression coefficient for global score ß = -1.31; 95% CI, -2.39 to -0.23; P = .02), cognitive (ß = -1.52; 95% CI, -2.39 to -0.65; P < .01) and the Neuro-Sensory Motor Developmental Assessment outcomes (ß = -1.73; 95% CI, -3.19 to -0.28; P = .02). Early MR imaging cerebellar scores were negatively associated with the Neuro-Sensory Motor Developmental Assessment (ß = -5.99; 95% CI, -11.82 to -0.16; P = .04). Results were reconfirmed at term-equivalent-age MR imaging. CONCLUSIONS: This clinically accessible MR imaging scoring system is valid for use at 29 to 35 weeks postmenstrual age in infants born very preterm. It enables identification of infants at risk of adverse outcomes before the current standard of term-equivalent age.

Automatic segmentation of the glenohumeral cartilages from magnetic resonance images.

PURPOSE: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. METHODS: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone-cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. RESULTS: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head and glenoid fossa, respectively. Mean DSC scores of 0.74 and 0.72 were obtained for the humeral and glenoid cartilage volumes, respectively. The manual interobserver reliability evaluated by DSC was 0.80 ± 0.03 and 0.76 ± 0.04 for the two cartilages, implying that the automated results were within an acceptable 10% difference. The MASD between the automatic and the corresponding manual cartilage segmentations was less than 0.4 mm (previous studies reported mean cartilage thickness of 1.3 mm). CONCLUSIONS: This work shows the feasibility of volumetric segmentation and separation of the glenohumeral cartilages from MR images. To their knowledge, this is the first fully automated algorithm for volumetric segmentation of the individual glenohumeral cartilages from MR images. The approach was validated against manual segmentations from experienced analysts. In future work, the approach will be validated on imaging datasets acquired with various MR contrasts in patients.

Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images--data from the Osteoarthritis Initiative.

OBJECTIVE: To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee. METHOD: We analysed sagittal water-excited double-echo steady-state MR images of the knee from a subset of the Osteoarthritis Initiative (OAI) cohort. The MM and LM were automatically segmented in the MR images based on a deformable model approach. Quantitative parameters including volume, subluxation and tibial-coverage were automatically calculated for comparison (Wilcoxon tests) between knees with variable radiographic osteoarthritis (rOA), medial and lateral joint space narrowing (mJSN, lJSN) and pain. Automatic segmentations and estimated parameters were evaluated for accuracy using manual delineations of the menisci in 88 pathological knee MR examinations at baseline and 12 months time-points. RESULTS: The median (95% confidence-interval (CI)) Dice similarity index (DSI) (2 ∗|Auto ∩ Manual|/(|Auto|+|Manual|)∗ 100) between manual and automated segmentations for the MM and LM volumes were 78.3% (75.0-78.7), 83.9% (82.1-83.9) at baseline and 75.3% (72.8-76.9), 83.0% (81.6-83.5) at 12 months. Pearson coefficients between automatic and manual segmentation parameters ranged from r = 0.70 to r = 0.92. MM in rOA/mJSN knees had significantly greater subluxation and smaller tibial-coverage than no-rOA/no-mJSN knees. LM in rOA knees had significantly greater volumes and tibial-coverage than no-rOA knees. CONCLUSION: Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development.

Three-dimensional morphological and signal intensity features for detection of intervertebral disc degeneration from magnetic resonance images.

BACKGROUND AND OBJECTIVES: Advances in MRI hardware and sequences are continually increasing the amount and complexity of data such as those generated in high-resolution three-dimensional (3D) scanning of the spine. Efficient informatics tools offer considerable opportunities for research and clinically based analyses of magnetic resonance studies. In this work, we present and validate a suite of informatics tools for automated detection of degenerative changes in lumbar intervertebral discs (IVD) from both 3D isotropic and routine two-dimensional (2D) clinical T2-weighted MRI. MATERIALS AND METHODS: An automated segmentation approach was used to extract morphological (traditional 2D radiological measures and novel 3D shape descriptors) and signal appearance (extracted from signal intensity histograms) features. The features were validated against manual reference, compared between 2D and 3D MRI scans and used for quantification and classification of IVD degeneration across magnetic resonance datasets containing IVD with early and advanced stages of degeneration. RESULTS AND CONCLUSIONS: Combination of the novel 3D-based shape and signal intensity features on 3D (area under receiver operating curve (AUC) 0.984) and 2D (AUC 0.988) magnetic resonance data deliver a significant improvement in automated classification of IVD degeneration, compared to the combination of previously used 2D radiological measurement and signal intensity features (AUC 0.976 and 0.983, respectively). Further work is required regarding the usefulness of 2D and 3D shape data in relation to clinical scores of lower back pain. The results reveal the potential of the proposed informatics system for computer-aided IVD diagnosis from MRI in large-scale research studies and as a possible adjunct for clinical diagnosis.

MilxXplore: a web-based system to explore large imaging datasets.

OBJECTIVE: As large-scale medical imaging studies are becoming more common, there is an increasing reliance on automated software to extract quantitative information from these images. As the size of the cohorts keeps increasing with large studies, there is a also a need for tools that allow results from automated image processing and analysis to be presented in a way that enables fast and efficient quality checking, tagging and reporting on cases in which automatic processing failed or was problematic. MATERIALS AND METHODS: MilxXplore is an open source visualization platform, which provides an interface to navigate and explore imaging data in a web browser, giving the end user the opportunity to perform quality control and reporting in a user friendly, collaborative and efficient way. DISCUSSION: Compared to existing software solutions that often provide an overview of the results at the subject's level, MilxXplore pools the results of individual subjects and time points together, allowing easy and efficient navigation and browsing through the different acquisitions of a subject over time, and comparing the results against the rest of the population. CONCLUSIONS: MilxXplore is fast, flexible and allows remote quality checks of processed imaging data, facilitating data sharing and collaboration across multiple locations, and can be easily integrated into a cloud computing pipeline. With the growing trend of open data and open science, such a tool will become increasingly important to share and publish results of imaging analysis.

Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models.

Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

Beta-amyloid burden in the temporal neocortex is related to hippocampal atrophy in elderly subjects without dementia.

OBJECTIVE: To investigate whether global and regional beta-amyloid (Abeta) burden as measured with 11C Pittsburgh compound B (PIB) PET is associated with hippocampal atrophy characterized using MRI in healthy controls and patients with amnestic mild cognitive impairment (aMCI) or Alzheimer disease (AD). METHODS: Ninety-two elderly healthy controls, 32 subjects with aMCI, and 35 patients with AD were imaged using 11C-PIB PET and MRI. Hippocampal volume was measured and PIB standardized uptake value ratio was extracted after partial volume correction within 41 regions of interest. Global, regional, and voxel-based correlations between PIB and hippocampal volume were computed for each group. RESULTS: In healthy control participants with elevated neocortex PIB retention, significant correlation was found between PIB retention in the inferior temporal region and hippocampal volume using both region-based and voxel-based approaches. No correlation was found in any other group. CONCLUSIONS: The strong correlation between hippocampal atrophy and beta-amyloid (Abeta) burden in the Pittsburgh compound B-positive healthy control group suggests that Abeta deposition in the inferior temporal neocortex is related to hippocampal synaptic and neuronal degeneration.

Automated segmentation of the quadratus lumborum muscle from magnetic resonance images using a hybrid atlas based - geodesic active contour scheme.

This study presents a novel method for the automatic segmentation of the quadratus lumborum (QL) muscle from axial magnetic resonance (MR) images using a hybrid scheme incorporating the use of non-rigid registration with probabilistic atlases (PAs) and geodesic active contours (GACs). The scheme was evaluated on an MR database of 7mm axial images of the lumbar spine from 20 subjects (fast bowlers and athletic controls). This scheme involved several steps, including (i) image pre-processing, (ii) generation of PAs for the QL, psoas (PS) and erector spinae+multifidus (ES+MT) muscles and (iii) segmentation, using 3D GACs initialized and constrained by the propagation of the PAs using non-rigid registration. Pre-processing of the images involved bias field correction based on local entropy minimization with a bicubic spline model and a reverse diffusion interpolation algorithm to increase the slice resolution to 0.98 x 0.98 x 1.75mm. The processed images were then registered (affine and non-rigid) and used to generate an average atlas. The PAs for the QL, PS and ES+MT were then generated by propagation of manual segmentations. These atlases were further analysed with specialised filtering to constrain the QL segmentation from adjacent non-muscle tissues (kidney, fat). This information was then used in 3D GACs to obtain the final segmentation of the QL. The automatic segmentation results were compared with the manual segmentations using the Dice similarity metric (DSC), with a median DSC for the right and left QL muscles of 0.78 (mean = 0.77, sd=0.07) and 0.75 (mean =0.74, sd=0.07), respectively.