Designing a new molecular probe : the potential role for Tilmanocept (Lymphoseek) in the assessment of patients with painful hip and knee joint prostheses

There is a long history of nuclear medicine developments in orthopaedics beginning in the early 20th century. Technetium-99m (99mTc) has a short half-life of six hours, emits 140 keV gamma rays and is the most widely used isotope, imaged with the Anger (gamma) camera. Gamma image quality and test sensitivity in painful prosthetic joints can be improved with single photon emission computed tomography (SPECT) and SPECT/CT. Positron Emission Tomography-Computed Tomography (PET-CT) with Sodium Fluoride (18F-NaF) and 18Fluorine-fluorodeoxyglucose (18F-FDG) PET have promising and limited roles respectively in the investigation of painful prosthetic joints. New SPECT/CT and PET-CT isotopes targeting activated macrophages with 99mTc Tilmanocept (Lymphoseek®) and 68Gallium labelled Tilmanocept respectively show potential as agents to demonstrate wear particles ingested by macrophages and multinucleated giant cells. An imaging algorithm using SPECT and/or PET agents is proffered as a cost effective way of speedily and accurately arriving a diagnosis. Methods: Review of the historical role of nuclear medicine in orthopaedics and research into the potential role of new radiopharmaceutical agents was undertaken. Guidelines and algorithms for the imaging of complicated joint prosthesis are provided. Results: There is an established role for nuclear medicine in orthopaedics and particularly in the investigation of complicated joint prostheses. Imaging with Tilmanocept provides new opportunities to shorten the time to diagnose loosened and infected joint prostheses. Conclusion: There is a potential new role for Tilmanocept, which can be utilised with both PET-CT and SPECT-CT technologies. Tilmanocept is a relatively new radiopharmaceutical which has a potential role in the imaging assessment of painful joint prosthesis

The promising role of dynamic 18F-NaF PET-CT in diagnosing symptomatic joint prosthesis

Our purpose was to establish proof of principle case study for the use of dynamic 18F-NaF PET-CT in the assessment of knee and hip prostheses. Approval was granted by the research ethics committee and informed consent was obtained. This is a case study investigating the role of dynamic 18F NaF PET-CT in a patient with bilateral knee prostheses (1 symptomatic/painful and 1 asymptomatic). Both knees were studied with dynamic 18F-NaF PET-CT technique to demonstrate the different pattern of uptake in normal/asymptomatic joint as well as painful joints with aseptic loosening. In addition, a knee aspirate was obtained from the symptomatic knee and serum C-reactive protein and erythrocyte sediment rate levels as well as a peripheral white cell count were obtained in addition to 12 month clinical follow up. Images were obtained with multi-sequential dynamic image acquisition in list mode using GE Healthcare® volume imaging protocol (ViP) after an intravenous injection of 250 MBq 18F-NaF. The images were interpreted as normal, loosening or septic loosening based on the graphical pattern of tracer uptake produced at the bone-prosthesis interface. A final diagnosis was made by a combination of joint aspiration microbiology and clinical follow-up for 1 year; in addition to C-reactive protein and erythrocyte sediment rate levels as well as peripheral white cell count. NaF PET results were compared with 3-phase dynamic bone scan results and plain radiographs. The degree of uptake in the symptomatic joint exceeded background levels and also levels of uptake in the asymptomatic knee. The pattern of uptake and curve slope in both the asymptomatic and symptomatic joints matched the pattern of uptake in our hypothesis. Dynamic 18F-NaF PET-CT is a useful imaging modality for assessing painful joint prosthesis. It can differentiate between asymptomatic joints and aseptic loosening. However, more work is required for the detection of septic loosening

Deformable appearance pyramids for anatomy representation, landmark detection and pathology classification

Purpose Representation of anatomy appearance is one of the key problems in medical image analysis. An appearance model represents the anatomies with parametric forms, which are then vectorised for prior learning, segmentation and classification tasks. Methods We propose a part-based parametric appearance model we refer to as a deformable appearance pyramid (DAP). The parts are delineated by multi-scale local feature pyramids extracted from an image pyramid. Each anatomy is represented by an appearance pyramid, with the variability within a population approximated by local translations of the multi-scale parts and linear appearance variations in the assembly of the parts. We introduce DAPs built on two types of image pyramids, namely Gaussian and wavelet pyramids, and present two approaches to model the prior and fit the model, one explicitly using a subspace Lucas–Kanade algorithm and the other implicitly using the supervised descent method (SDM). Results We validate the performance of the DAP instances with difference configurations on the problem of lumbar spinal stenosis for localising the landmarks and classifying the pathologies. We also compare them with classic methods such as active shape models, active appearance models and constrained local models. Experimental results show that the DAP built on wavelet pyramids and fitted with SDM gives the best results in both landmark localisation and classification. Conclusion A new appearance model is introduced with several configurations presented and evaluated. The DAPs can be readily applied for other clinical problems for the tasks of prior learning, landmark detection and pathology classification

Weakly-supervised evidence pinpointing and description

We propose a learning method to identify which specific regions and features of images contribute to a certain classification. In the medical imaging context, they can be the evidence regions where the abnormalities are most likely to appear, and the discriminative features of these regions supporting the pathology classification. The learning is weakly-supervised requiring only the pathological labels and no other prior knowledge. The method can also be applied to learn the salient description of an anatomy discriminative from its background, in order to localise the anatomy before a classification step. We formulate evidence pinpointing as a sparse descriptor learning problem. Because of the large computational complexity, the objective function is composed in a stochastic way and is optimised by the Regularised Dual Averaging algorithm. We demonstrate that the learnt feature descriptors contain more specific and better discriminative information than hand-crafted descriptors contributing to superior performance for the tasks of anatomy localisation and pathology classification respectively. We apply our method on the problem of lumbar spinal stenosis for localising and classifying vertebrae in MRI images. Experimental results show that our method when trained with only target labels achieves better or competitive performance on both tasks compared with strongly-supervised methods requiring labels and multiple landmarks. A further improvement is achieved with training on additional weakly annotated data, which gives robust localisation with average error within 2 mm and classification accuracies close to human performance

Active shape model unleashed with multi-scale local appearance

We focus on optimising the Active Shape Model (ASM) with several extensions. The modification is threefold. First, we tackle the over-constraint problem and obtain an optimal shape with minimum energy considering both the shape prior and the salience of local features, based on statistical theory: a compact closed form solution to the optimal shape is deduced. Second, we enhance the ASM searching method by modelling and removing the variations of local appearance presented in the training data. Third, we speed up the convergence of shape fitting by integrating information from multi-scale local features simultaneously. Experiments show significant improvement brought by these modifications, i.e., optimal shape against standard relaxation methods dealing with inadequate training samples; enhanced searching method against standard gradient descent methods in searching accuracy; multi-scale local features against popular coarse-to-fine strategies in convergence speed

Active appearance pyramids for object parametrisation and fitting

Object class representation is one of the key problems in various medical image analysis tasks. We propose a part-based parametric appearance model we refer to as an Active Appearance Pyramid (AAP). The parts are delineated by multi-scale Local Feature Pyramids (LFPs) for superior spatial specificity and distinctiveness. An AAP models the variability within a population with local translations of multi-scale parts and linear appearance variations of the assembly of the parts. It can fit and represent new instances by adjusting the shape and appearance parameters. The fitting process uses a two-step iterative strategy: local landmark searching followed by shape regularisation. We present a simultaneous local feature searching and appearance fitting algorithm based on the weighted Lucas and Kanade method. A shape regulariser is derived to calculate the maximum likelihood shape with respect to the prior and multiple landmark candidates from multi-scale LFPs, with a compact closed-form solution. We apply the 2D AAP on the modelling of variability in patients with lumbar spinal stenosis (LSS) and validate its performance on 200 studies consisting of routine axial and sagittal MRI scans. Intervertebral sagittal and parasagittal cross-sections are typically used for the diagnosis of LSS, we therefore build three AAPs on L3/4, L4/5 and L5/S1 axial cross-sections and three on parasagittal slices. Experiments show significant improvement in convergence range, robustness to local minima and segmentation precision compared with Constrained Local Models (CLMs), Active Shape Models (ASMs) and Active Appearance Models (AAMs), as well as superior performance in appearance reconstruction compared with AAMs. We also validate the performance on 3D CT volumes of hip joints from 38 studies. Compared to AAMs, AAPs achieve a higher segmentation and reconstruction precision. Moreover, AAPs have a significant improvement in efficiency, consuming about half the memory and less than 10% of the training time and 15% of the testing time

Psoriatic disease and body composition : a systematic review and narrative synthesis

Background Obesity is a leading comorbidity in psoriatic disease, including both psoriasis (PsO) and psoriatic arthritis (PsA), and is associated with adverse metabolic and cardiovascular (CV) outcomes. Anthropometric parameters, such as weight, body mass index (BMI) and waist-to-hip ratio, have been extensively reported in psoriatic disease. However, the associations of body composition and fat distribution with psoriasis have not yet been fully defined. Objectives To identify whether patients with psoriatic disease, including psoriatic arthritis, have altered body composition compared with the general population, and to review existing modalities for the assessment of body composition. Methods Electronic searches of the literature were conducted in PubMed, Medline (Ovid®), Embase (Ovid®), Cochrane Central Register of Controlled Trials (CENTRAL) and Google Scholar. Titles and abstracts were reviewed by two authors independently against a set of prespecified inclusion/exclusion criteria. The research question was answered with a systematic literature review and results were summarized narratively. Results Twenty-five full text articles met the inclusion criteria and were included in the final narrative analysis. The studies were of heterogeneous design and used a range of objective measures to assess body composition, including simple anthropometric measures, bioimpedance analysis (BIA), dual energy X-ray absorptiometry (DXA) and computed tomography (CT). Few studies met all the quality assessment criteria. Clinical heterogeneity prevented meta-analysis. Conclusions Patients with psoriatic disease reveal defined body composition changes that are independent of obesity and the customary metabolic syndrome, including higher overall body fat, visceral fat and sarcopenia. These findings emphasize that patients with psoriatic disease should be screened for abnormal adipose effects beyond their weight and body mass index (BMI). Our findings show that the last decade has seen an exciting expansion of research interest in the development and validation of new modalities for the assessment of body composition. There is no consensus on the optimal assessment method of body composition for this diverse group; hence there is a need for validation of existing modalities and standardization of assessment tools

The prevalence of cam hip morphology in a general population sample

Objective Cam hip morphology is associated with femoroacetabular impingement (FAI) syndrome and causes hip osteoarthritis (OA). We aimed to assess the prevalence of cam hip morphology in a sample representative of the general population, using a measure with a predefined diagnostic accuracy. Design Patients aged 16–65, who were admitted to a major trauma centre and received a computed tomography (CT) pelvis were retrospectively screened for eligibility. Subjects with proximal femoral, acetabular or pelvic fractures and those who were deceased were excluded. Eligible subjects were divided into 10 groups based on gender and age. 20 subjects from each group were included. Subjects' index of multiple deprivation (IMD) and ethnicity were recorded. CT imaging was assessed and alpha angles (a measure of cam morphology) measured in the anterosuperior aspect of the femoral head neck junction. An alpha angle greater than 60° was considered to represent cam morphology. This measure and technique has a predefined sensitivity of 80% and specificity of 73% to detect cam morphology associated with FAI syndrome. The prevalence of cam morphology was reported as a proportion of subjects affected with 95% confidence intervals. Results 200 subjects were included. The sample was broadly representative of the UK general population in terms of IMD. 155 subjects (86%) identified as white. Cam morphology was present in 47% (95% CI 42,51) of subjects. Conclusions In this sample, broadly representative of the UK general population 47% of subjects had cam hip morphology; a hip shape associated with FAI syndrome and OA

ABCD² risk score does not predict the presence of cerebral microemboli in patients with hyper-acute symptomatic critical carotid artery stenosis

ABCD² risk score and cerebral microemboli detected by transcranial Doppler (TCD) have been separately shown to the predict risk of recurrent acute stroke. We studied whether ABCD² risk score predicts cerebral microemboli in patients with hyper-acute symptomatic carotid artery stenosis. We studied 206 patients presenting within 2 weeks of transient ischaemic attack or minor stroke and found to have critical carotid artery stenosis (≥50%). 86 patients (age 70±1 (SEM: years), 58 men, 83 Caucasian) had evidence of microemboli; 72 (84%) of these underwent carotid endarterectomy (CEA). 120 patients (age 72±1 years, 91 men, 113 Caucasian) did not have microemboli detected; 102 (85%) of these underwent CEA. Data were analysed using X2 and Mann-Whitney U tests and receiver operating characteristic (ROC) curves. 140/206 (68%: 95% CI 61.63 to 74.37) patients with hyper-acute symptomatic critical carotid stenosis had an ABCD2 risk score ≥4. There was no significant difference in the NICE red flag criterion for early assessment (ABCD² risk score ≥4) for patients with cerebral microemboli versus those without microemboli (59/86 vs 81/120 patients: OR 1.05 ABCD² risk score ≥4 (95% CI 0.58 to 1.90, p=0.867)). The ABCD² risk score was <4 in 27 of 86 (31%: 95% CI 21 to 41) embolising patients and in 39 of 120 (31%: 95% CI 23 to 39) without cerebral microemboli. After adjusting for pre-neurological event antiplatelet treatment (APT), area under the curve (AUC) of ROC for ABCD2 risk score showed no prediction of cerebral microemboli (no pre-event APT, n=57: AUC 0.45 (95% CI 0.29 to 0.60, p=0.531); pre-event APT, n=147: AUC 0.51 (95% CI 0.42 to 0.60, p=0.804)). The ABCD² score did not predict the presence of cerebral microemboli or carotid disease in over one-quarter of patients with symptomatic critical carotid artery stenosis. On the basis of NICE guidelines (refer early if ABCD² ≥4), assessment of high stroke risk based on ABCD² scoring may lead to inappropriate delay in urgent treatment in many patients