Image Registration in Longitudinal Bone Assessment Using Computed Tomography.

PURPOSE OF REVIEW: Rigid image registration is an important image processing tool for the assessment of musculoskeletal chronic disease. In this paper, we critically review applications of rigid image registration in terms of similarity measurement methods over the past three years (2019-2022) in the context of monitoring longitudinal changes to bone microstructure and mechanical properties using computed tomography. This review identifies critical assumptions and trade-offs underlying different similarity measurement methods used in image registration and demonstrates the effect of using different similarity measures on registration outcomes. RECENT FINDINGS: Image registration has been used in recent studies for: correcting positional shifts between longitudinal scans to quantify changes to bone microstructural and mechanical properties over time, developing registration-based workflows for longitudinal assessment of bone properties in pre-clinical and clinical studies, and developing and validating registration techniques for longitudinal studies. In evaluating the recent literature, it was found that the assumptions at the root of different similarity measures used in rigid image registration are not always confirmed and reported. Each similarity measurement has its advantages and disadvantages, as well as underlying assumptions. Breaking these assumptions can lead to poor and inaccurate registration results. Thus, care must be taken with regards to the choice of similarity measurement and interpretation of results. We propose that understanding and verifying the assumptions of similarity measurements will enable more accurate and efficient quantitative assessments of structural changes over time.

A microCT imaging protocol for reproducible and efficient quantitative morphometric analysis (QMA) of joint structures of the in situ mouse tibio-femoral joint.

Micro-computed tomography (microCT) offers a three-dimensional (3D), high-resolution technique for the visualisation and analysis of bone microstructure. Using contrast-enhanced microCT, this capability has been expanded in recent studies to include cartilage morphometry and whole joint measures, known together as quantitative morphometric analysis (QMA). However, one of the main challenges in quantitative analysis of joint images is sensitivity to joint pose and alignment, which may influence measures related to both joint space and joint biomechanics. Thus, this study proposes a novel microCT imaging protocol for reproducible and efficient QMA of in situ mouse tibio-femoral joint. This work consists of two parts: an in situ diffusion kinetics study for a known cationic iodinated contrast agent (CA4+) for QMA of the cartilage, and a joint positioning and image processing workflow for whole joint QMA. In the diffusion kinetics study, 8 mice were injected at both of their tibio-femoral joints with distinct CA4+ concentrations and diffusion times. The mice were scanned at different time points after injection, and evaluated using attenuation and cartilage QMA measures. Results show that cartilage segmentation and QMA could be performed for CA4+ solution at a concentration of 48 mg/ml, and that reliable measurement and quantification of cartilage were achieved after 5 min of diffusion following contrast agent injection. We established the joint positioning and image processing workflow by developing a novel positioning device to control joint pose during scanning, and a spherical harmonics-based image processing workflow to ensure consistent alignment during image processing. Both legs of seven mice were scanned 10 times, 5 prior to receiving CA4+ and 5 after, and evaluated using whole joint QMA parameters. Joint QMA evaluation of the workflow showed excellent reproducibility; intraclass correlation coefficients ranged from 0.794 to 0.930, confirming that the imaging protocol enables reproducible and efficient QMA of joint structures in preclinical models, and that contrast agent injection did not cause significant alteration to the measured parameters.

SPHARM-PDM based image preprocessing pipeline for quantitative morphometric analysis (QMA) for in situ joint assessment in rabbit and rat models.

The accessibility of quantitative measurements of joint morphometry depends on appropriate tibial alignment and volume of interest (VOI) selection of joint compartments; often a challenging and time-consuming manual task. In this work, we developed a novel automatic, efficient, and model-invariant image preprocessing pipeline that allows for highly reproducible 3D quantitative morphometric analysis (QMA) of the joint. The pipeline addresses the problem by deploying two modules: an alignment module and a subdivision module. Alignment is achieved by representing the tibia in its basic form using lower degree spherical harmonic basis functions and aligning using principal component analysis. The second module subdivides the joint into lateral and medial VOIs via a watershedding approach based on persistence homology. Multiple repeated micro-computed tomography scans of small (rat) and medium (rabbit) animal knees were processed using the pipeline to demonstrate model invariance. Existing QMA was performed to evaluate the pipeline's ability to generate reproducible measurements. Intraclass correlation coefficient and mean-normalised root-mean-squared error of more than 0.75 and lower than 9.5%, respectively, were achieved for joint centre of mass, joint contact area under virtual loading, joint space width, and joint space volume. Processing time and technical requirements were reduced compared to manual processing in previous studies.

Quantitative measures of bone shape, cartilage morphometry and joint alignment are associated with disease in an ACLT and MMx rat model of osteoarthritis.

Multi-scale, subject-specific quantitative methods to characterize and monitor osteoarthritis in animal models and therapeutic treatments could help reveal causal relationships in disease development and distinguish treatment strategies. In this work, we demonstrate a reproducible and sensitive quantitative image analysis to characterize bone, cartilage and joint measures describing a rat model of post-traumatic osteoarthritis. Eleven 3-month-old male Wistar rats underwent medial anterior cruciate ligament (ACL) transection and medial meniscectomy on the right knee to destabilise the right tibiofemoral joint. They were sacrificed 6 weeks post-surgery and a silicon-based micro-bead contrast agent was injected in the joint space, before scanning with micro-computed tomography (microCT). Subsequently, 3D quantitative morphometric analysis (QMA), previously developed for rabbit joints, was performed. This included cartilage, subchondral cortical and epiphyseal bone measures, as well as novel tibiofemoral joint metrics. Semi-quantitative evaluation was performed on matching two-dimensional (2D) histology and microCT images. Reproducibility of the QMA was tested on eleven age-matched additional joints. The results indicate the QMA method is accurate and reproducible and that microCT-derived cartilage measurements are valid for the analysis of rat joints. The pathologic changes caused by transection of the ACL and medial meniscectomy were reflected in measurements of bone shape, cartilage morphology, and joint alignment. Furthermore, we were able to identify model-specific predictive parameters based on morphometric parameters measured with the QMA.

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer's Disease.

Since age is the most significant risk factor for the development of Alzheimer's disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This article proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification accuracy, barring the group of participants below the age of 70, for resting state EEG recorded during eyes open. The developed approach is generic and can be used as a powerful tool to examine brain network characteristics and disruption in a user friendly and systematic way.

A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals.

Alzheimer's disease (AD) accounts for 60%-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This paper aims to explore a routine to gain such biomarkers using the quantitative analysis of electroencephalography (QEEG). This paper proposes a supervised classification framework that uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-nearest neighbor (KNN) classification, quantitative evaluation, and topographic visualization. Considering the human brain either as a stationary or a dynamical system, both the frequency-based and time-frequency-based features were tested in 40 participants. The results show that: 1) the proposed method can achieve up to a 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared with alternative machine learning approaches; 2) the features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; and 3) in the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and the localization of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD.