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OBJECTIVE: We previously demonstrated the potential of radiomics for prediction of severe histological Placenta Accreta Spectrum (PAS) subtypes using T2-weighted MRI. We aim is to validate our model using an additional dataset. Secondly, we explore whether performance is improved using a new approach to develop a new multivariate radiomics model. METHODS: Multi-centre retrospective analysis conducted between 2018-2023. Inclusion criteria: MRI performed for suspicion of PAS from ultrasound, clinical findings of PAS at laparotomy and/or histopathological confirmation. Radiomic features were extracted from T2-weighted MRI. The previous multivariate model was validated. Secondly, a 5-radiomic feature random forest classifier was selected from a randomised feature selection scheme to predict invasive placenta increta PAS cases. Prediction performance was assessed based on several metrics including Area Under the Curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity and specificity. RESULTS: We present 100 women (mean age 34.6 (±3.9) with PAS, 64 of whom had placenta increta. Firstly, we validated the previous multivariate model and found a Support Vector Machine classifier had a sensitivity of 0.620 (95% CI: 0.068; 1.0), specificity of 0.619 (95% CI: 0.059; 1.0), an AUC of 0.671 (95% CI: 0.440; 0.922) and accuracy of 0.602 (95% CI: 0.353; 0.817) for predicting placenta increta. From the new multivariate model, the best 5-feature subset selected via the random subset feature selection scheme comprised of 4 radiomic features and 1 clinical variable (number of previous caesareans). This clinical-radiomic model achieved an AUC of 0.713 (95% CI: 0.551; 0.854), accuracy of 0.695 (95% CI 0.563; 0.793), sensitivity of 0.843 (95% CI 0.682; 0.990) and specificity of 0.447 (95% CI 0.167; 0.667). CONCLUSION: We validated our previous model and present a new multivariate radiomic model for prediction of severe placenta increta from a well-defined, cohort of PAS cases. ADVANCES IN KNOWLEDGE: Radiomic features demonstrate good predictive potential for identifying placenta increta. This suggests radiomics may be a useful adjunct to clinicians caring for women with this high-risk pregnancy condition.
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Automated Motion Artefact Detection (MAD) in Magnetic Resonance Imaging (MRI) is a field of study that aims to automatically flag motion artefacts in order to prevent the requirement for a repeat scan. In this paper, we identify and tackle the three current challenges in the field of automated MAD; (1) reliance on fully-supervised training, meaning they require specific examples of Motion Artefacts (MA), (2) inconsistent use of benchmark datasets across different works and use of private datasets for testing and training of newly proposed MAD techniques and (3) a lack of sufficiently large datasets for MRI MAD. To address these challenges, we demonstrate how MAs can be identified by formulating the problem as an unsupervised Anomaly Detection (AD) task. We compare the performance of three State-of-the-Art AD algorithms DeepSVDD, Interpolated Gaussian Descriptor and FewSOME on two open-source Brain MRI datasets on the task of MAD and MA severity classification, with FewSOME achieving a MAD AUC >90% on both datasets and a Spearman Rank Correlation Coefficient of 0.8 on the task of MA severity classification. These models are trained in the few shot setting, meaning large Brain MRI datasets are not required to build robust MAD algorithms. This work also sets a standard protocol for testing MAD algorithms on open-source benchmark datasets. In addition to addressing these challenges, we demonstrate how our proposed 'anomaly-aware' scoring function improves FewSOME's MAD performance in the setting where one and two shots of the anomalous class are available for training. Code available at https://github.com/niamhbelton/Unsupervised-Brain-MRI-Motion-Artefact-Detection/.
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Algoritmos , Artefatos , Encéfalo , Imageamento por Ressonância Magnética , Movimento (Física) , Imageamento por Ressonância Magnética/métodos , Humanos , Encéfalo/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodosRESUMO
Musculoskeletal research should synergistically investigate bone and muscle to inform approaches for maintaining mobility and to avoid bone fractures. The relationship between sarcopenia and osteoporosis, integrated in the term 'osteosarcopenia', is underscored by the close association shown between these two conditions in many studies, whereby one entity emerges as a predictor of the other. In a recent workshop of Working Group (WG) 2 of the EU Cooperation in Science and Technology (COST) Action 'Genomics of MusculoSkeletal traits Translational Network' (GEMSTONE) consortium (CA18139), muscle characterization was highlighted as being important, but currently under-recognized in the musculoskeletal field. Here, we summarize the opinions of the Consortium and research questions around translational and clinical musculoskeletal research, discussing muscle phenotyping in human experimental research and in two animal models: zebrafish and mouse.
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Fenótipo , Animais , Humanos , Músculo Esquelético/metabolismo , Peixe-Zebra , Camundongos , Sarcopenia/metabolismo , Sarcopenia/fisiopatologia , Doenças Musculoesqueléticas/fisiopatologia , Doenças Musculoesqueléticas/genética , Osteoporose/metabolismo , Osteoporose/patologiaRESUMO
BACKGROUND: Placenta accreta spectrum (PAS) is a rare, life-threatening complication of pregnancy. Predicting PAS severity is critical to individualise care planning for the birth. We aim to explore whether radiomic analysis of T2-weighted magnetic resonance imaging (MRI) can predict severe cases by distinguishing between histopathological subtypes antenatally. METHODS: This was a bi-centre retrospective analysis of a prospective cohort study conducted between 2018 and 2022. Women who underwent MRI during pregnancy and had histological confirmation of PAS were included. Radiomic features were extracted from T2-weighted images. Univariate regression and multivariate analyses were performed to build predictive models to differentiate between non-invasive (International Federation of Gynecology and Obstetrics [FIGO] grade 1 or 2) and invasive (FIGO grade 3) PAS using R software. Prediction performance was assessed based on several metrics including sensitivity, specificity, accuracy and area under the curve (AUC) at receiver operating characteristic analysis. RESULTS: Forty-one women met the inclusion criteria. At univariate analysis, 0.64 sensitivity (95% confidence interval [CI] 0.0-1.00), specificity 0.93 (0.38-1.0), 0.58 accuracy (0.37-0.78) and 0.77 AUC (0.56-.097) was achieved for predicting severe FIGO grade 3 PAS. Using a multivariate approach, a support vector machine model yielded 0.30 sensitivity (95% CI 0.18-1.0]), 0.74 specificity (0.38-1.00), 0.58 accuracy (0.40-0.82), and 0.53 AUC (0.40-0.85). CONCLUSION: Our results demonstrate a predictive potential of this machine learning pipeline for classifying severe PAS cases. RELEVANCE STATEMENT: This study demonstrates the potential use of radiomics from MR images to identify severe cases of placenta accreta spectrum antenatally. KEY POINTS: ⢠Identifying severe cases of placenta accreta spectrum from imaging is challenging. ⢠We present a methodological approach for radiomics-based prediction of placenta accreta. ⢠We report certain radiomic features are able to predict severe PAS subtypes. ⢠Identifying severe PAS subtypes ensures safe and individualised care planning for birth.
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Placenta Acreta , Gravidez , Humanos , Feminino , Placenta Acreta/diagnóstico por imagem , Estudos Prospectivos , Estudos Retrospectivos , Aprendizado de Máquina , Projetos de PesquisaRESUMO
Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging. In contrast enhanced whole-body scans accurate automatic segmentation is particularly difficult as low dose whole body protocols reduce image quality and make contrast enhanced regions more difficult to separate when relying on differences in pixel intensities. This paper outlines a U-net architecture with novel preprocessing techniques, based on the windowing of training data and the modification of sigmoid activation threshold selection to successfully segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans. The proposed method achieved mean Dice coefficients of 0.979 ± 0.02, 0.965 ± 0.03, and 0.934 ± 0.06 on two internal datasets and one external test dataset respectively. We have demonstrated that appropriate preprocessing is important for differentiating between bone and contrast dye, and that excellent results can be achieved with limited data.
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Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Osso e Ossos/diagnóstico por imagem , Meios de Contraste , Processamento de Imagem Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodosRESUMO
In recent years enterprise imaging (EI) solutions have become a core component of healthcare initiatives, while a simultaneous rise in big data has opened up a number of possibilities in how we can analyze and derive insights from large amounts of medical data. Together they afford us a range of opportunities that can transform healthcare in many fields. This paper provides a review of recent developments in EI and big data in the context of medical physics. It summarizes the key aspects of EI and big data in practice, with discussion and consideration of the steps necessary to implement an EI strategy. It examines the benefits that a healthcare service can achieve through the implementation of an EI solution by looking at it through the lenses of: compliance, improving patient care, maximizing revenue, optimizing workflows, and applications of artificial intelligence that support enterprise imaging. It also addresses some of the key challenges in enterprise imaging, with discussion and examples presented for those in systems integration, governance, and data security and privacy.
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Inteligência Artificial , Diagnóstico por Imagem , Big Data , Humanos , Física , Fluxo de TrabalhoRESUMO
UNLABELLED: The assessment of regional skeletal metabolism using (18)F-fluoride PET ((18)F-PET) requires segmentation of the tissue region of interest (ROI). The aim of this study was to validate a novel approach to define multiple ROIs at the proximal femur similar to those used in dual x-ray absorptiometry. Regions were first drawn on low-dose CT images acquired as a routine part of the PET/CT study and transferred to the (18)F-PET images for the quantitative analysis of bone turnover. METHODS: Four healthy postmenopausal women with a mean age of 65.1 y (range, 61.8-70.0 y), and with no history of metabolic bone disorder and not currently being administered treatment affecting skeletal metabolism, underwent dynamic (18)F-PET/CT at the hip with an injected activity of 180 MBq. The ROIs at the proximal femur included femoral shaft, femoral neck, and total hip and were segmented using both a semiautomatic method and manually by 8 experts at manual ROI delineation. The mean of the 8 manually drawn ROIs was considered the gold standard against which the performances of the semiautomatic and manual methods were compared in terms of percentage overlap and percentage difference. The time to draw the ROIs was also compared. RESULTS: The percentage overlaps between the gold standard and the semiautomatic ROIs for total hip, femoral neck, and femoral shaft were 86.1%, 37.8%, and 96.1%, respectively, and the percentage differences were 14.5%, 89.7%, and 4.7%, respectively. In the same order, the percentage overlap between the gold standard and the manual ROIs were 85.2%, 39.1%, and 95.2%, respectively, and the percentage differences were 19.9%, 91.6%, and 12.2%, respectively. The semiautomatic method was approximately 9.5, 2.5, and 67 times faster than the manual method for segmenting total-hip, femoral-neck, and femoral-shaft ROIs, respectively. CONCLUSION: We have developed and validated a semiautomatic procedure whereby ROIs at the hip are defined using the CT component of an (18)F-PET/CT scan. The percentage overlap and percentage difference results between the semiautomatic method and the manual method for ROI delineation were similar. Two advantages of the semiautomatic method are that it is significantly quicker and eliminates some of the variability associated with operator or reader input. The tube current used for the CT scan was associated with an effective dose 8 times lower than that associated with a typical diagnostic CT scan. These results suggest that it is possible to segment bone ROIs from low-dose CT for later transfer to PET in a single PET/CT procedure without the need for an additional high-resolution CT scan.
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Fêmur/diagnóstico por imagem , Fluoretos , Radioisótopos de Flúor , Processamento de Imagem Assistida por Computador/métodos , Imagem Multimodal/métodos , Tomografia por Emissão de Pósitrons , Tomografia Computadorizada por Raios X , Idoso , Automação , Feminino , Humanos , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Fatores de TempoRESUMO
BACKGROUND: The localization of proteins to specific subcellular structures in eukaryotic cells provides important information with respect to their function. Fluorescence microscopy approaches to determine localization distribution have proved to be an essential tool in the characterization of unknown proteins, and are now particularly pertinent as a result of the wide availability of fluorescently-tagged constructs and antibodies. However, there are currently very few image analysis options able to effectively discriminate proteins with apparently similar distributions in cells, despite this information being important for protein characterization. FINDINGS: We have developed a novel method for combining two existing image analysis approaches, which results in highly efficient and accurate discrimination of proteins with seemingly similar distributions. We have combined image texture-based analysis with quantitative co-localization coefficients, a method that has traditionally only been used to study the spatial overlap between two populations of molecules. Here we describe and present a novel application for quantitative co-localization, as applied to the study of Rab family small GTP binding proteins localizing to the endomembrane system of cultured cells. CONCLUSIONS: We show how quantitative co-localization can be used alongside texture feature analysis, resulting in improved clustering of microscopy images. The use of co-localization as an additional clustering parameter is non-biased and highly applicable to high-throughput image data sets.
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Imageamento Tridimensional/classificação , Imageamento Tridimensional/métodos , Microscopia/métodos , Análise por Conglomerados , Células HeLa , Humanos , Transporte Proteico , Frações Subcelulares/metabolismo , Proteínas rab de Ligação ao GTP/metabolismoRESUMO
AIM: The aim of this study was to evaluate the relationship between different quantification methods used for the measurement of bone plasma clearance (K(i)) using F-PET at the hip and lumbar spine. METHODS: Twelve healthy postmenopausal women aged 52-71 years were recruited. Each participant underwent 60-min dynamic F-PET scans at the lumbar spine and hip on two separate occasions with an injected activity of 90 and 180 MBq, respectively. Image-derived input functions were obtained at the aorta from the lumbar spine scans. K(i) was evaluated using a three-compartment four-parameter model (K(i-4k)), three-compartment three-parameter model (K(i-3k)), Patlak analysis (K(i-Pat)), spectral analysis (K(i-Spec)) and deconvolution (K(i-Decon)). Standardized uptake values (SUVs) were also measured. RESULTS: The Pearson correlation between K(i-4k) and K(i-3k), K(i-Pat), K(i-Spec), K(i-Decon) and SUV were 0.91, 0.97, 0.94, 0.95 and 0.93, respectively, with a significance of P less than 0.0001. The differences between the correlations measured using Fisher's Z-test were not significant (P>0.05). Bland-Altman analysis showed that the limits of agreement for K(i) measured as the SD of the differences were 0.0082 (25.9%), 0.0062 (11.7%), 0.0098 (20.1%) and 0.0056 (25.5%) ml/min/ml, respectively, and the biases were -0.0081 (-23.8%), -0.0075 (-23.7%), -0.0107 (-29.5%) and -0.0015 (0.8%) ml/min/ml, respectively. CONCLUSION: All five methods of quantification (K(i-3k), K(i-Pat), K(i-Spec), K(i-Decon) and SUV) strongly correlated with K(i-4k). Although systematic differences of up to 29% were found between K(i-4k) and the other methods (K(i-3k), K(i-Pat), K(i-Spec) and K(i-Decon)), these should not affect the conclusions of clinical studies, provided the methods are applied consistently. However, care should be taken when comparing reports that use different methods of quantification.
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Remodelação Óssea/fisiologia , Articulação do Quadril/diagnóstico por imagem , Vértebras Lombares/diagnóstico por imagem , Imagem Multimodal/métodos , Tomografia por Emissão de Pósitrons , Tomografia Computadorizada por Raios X , Idoso , Algoritmos , Feminino , Fluordesoxiglucose F18 , Articulação do Quadril/metabolismo , Humanos , Vértebras Lombares/metabolismo , Pessoa de Meia-Idade , Compostos Radiofarmacêuticos , Reprodutibilidade dos TestesRESUMO
BACKGROUND: Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial. RESULTS: We have developed a novel algorithm to quantify co-localization that improves on and addresses the major shortcomings of existing co-localization measures. This algorithm uses a non-parametric ranking of pixel intensities in each channel, and the difference in ranks of co-localizing pixel positions in the two channels is used to weight the coefficient. This weighting is applied to co-occurring pixels thereby efficiently combining both co-occurrence and correlation. Tests with synthetic data sets show that the algorithm is sensitive to both co-occurrence and correlation at varying levels of intensity. Analysis of biological data sets demonstrate that this new algorithm offers high sensitivity, and that it is capable of detecting subtle changes in co-localization, exemplified by studies on a well characterized cargo protein that moves through the secretory pathway of cells. CONCLUSIONS: This algorithm provides a novel way to efficiently combine co-occurrence and correlation components in biological images, thereby generating an accurate measure of co-localization. This approach of rank weighting of intensities also eliminates the need for manual thresholding of the image, which is often a cause of error in co-localization quantification. We envisage that this tool will facilitate the quantitative analysis of a wide range of biological data sets, including high resolution confocal images, live cell time-lapse recordings, and high-throughput screening data sets.
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Algoritmos , Microscopia/métodos , Chaperonina 60/análise , Células HeLa , Humanos , Mitocôndrias/química , Sensibilidade e EspecificidadeRESUMO
INTRODUCTION: The use of image-derived arterial input functions (IDAIF) for the dynamic quantification of bone metabolism using 18F-fluoride positron emission tomography 18F-PET is an attractive alternative to direct arterial blood sampling. PURPOSES: (a) To validate a method for obtaining the IDAIF by imaging the femoral artery against a method for deriving the IDAIF at the aorta that was previously validated against direct arterial sampling. (b) To compare the accuracy of bone plasma clearance measurements (Ki) at the total hip site obtained using the femoral artery IDAIF against Ki values at the same site obtained using the aorta IDAIF. METHODS: Twelve healthy postmenopausal women with a mean age of 62.6 years (range, 52.3-70.6 years) had 60-min dynamic 18F-PET scans of the lumbar spine and proximal femur 2 weeks apart. The femoral artery IDAIF was obtained from the proximal femur scan using four different algorithms: (a) fixed partial volume correction (PVC) method; (b) variable PVC method; (c) Chen method; and (d) Cook-Lodge method. The aorta IDAIF was obtained from the lumbar spine scan using a previously validated method and the respective Ki values in the hip were used to assess the performance of each of the femoral artery algorithms. RESULTS: When the femoral artery IDAIF methods were compared with the aorta IDAIF in terms of the area under the curve AUC values calculated in 4-min time intervals over 0-60 min, the absolute root mean square errors were: (a) fixed PVC, 0.52; (b) variable PVC, 0.54; (c) Chen, 0.72; and (d) Cook-Lodge, 0.49 in MBq s/ml. There were small, but statistically significant differences, in the Ki values found by all four femoral artery IDAIF methods when compared with the figures obtained using the aorta IDAIF. Bland-Altman plots of Ki values showed the best agreement for the fixed PVC method with a standard deviation of 0.0020 ml/min/ml, followed by variable PVC, Cook-Lodge and Chen method with standard deviations of 0.0022, 0.0024 and 0.0042 ml/min/ml, respectively. CONCLUSION: We have demonstrated that it is possible to measure regional bone turnover at the hip without the need to perform direct arterial sampling to acquire the arterial input function (AIF). The differences in the Ki values obtained at the hip by using aorta IDAIF and any of the four image-based AIF methods at the femoral artery were small and clinically insignificant. The performance of fixed PVC, variable PVC and Cook-Lodge method was similar although the latter was less robust than the other two methods.
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Artéria Femoral/diagnóstico por imagem , Artéria Femoral/fisiologia , Fluoretos , Radioisótopos de Flúor , Tomografia por Emissão de Pósitrons , Idoso , Osso e Ossos/metabolismo , Feminino , Artéria Femoral/metabolismo , Fluoretos/farmacocinética , Humanos , Cinética , Masculino , Taxa de Depuração Metabólica , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Estudos Retrospectivos , Veias/metabolismoRESUMO
OBJECTIVES: (i) To validate two new image-based methods for finding the plasma arterial input function (AIF) and evaluate the performance of these and two similar techniques against arterial sampling. (ii) To evaluate the performance of all four image-derived AIF (IDAIF) methods against arterial sampling for measuring the F plasma clearance (Ki) to the lumbar spine. METHODS: Eight healthy postmenopausal women had a F-fluoride positron emission tomography scan of the lumbar spine. Venous blood samples were used to estimate the IDAIFs from: (i) a fixed population-based partial volume correction (PVC) factor method, (ii) a variable PVC factor method, (iii) the Chen method, and (iv) the Cook-Lodge method. Continuous arterial sampling and the respective Ki values were used as the gold standard against which the performance of the IDAIF methods was compared. RESULTS: The IDAIFs were compared with direct arterial sampling in terms of the area under the curve values. The percentage root mean square error in area under the curves compared with arterial sampling were: (i) fixed PVC: 12.7%, (ii) variable PVC: 12.0%, (iii) Chen: 39.0%, and (iv) Cook-Lodge: 17.3%. There were small but significant differences in the Ki values found by all four methods compared with arterial sampling. Bland-Altman plots of Ki values showed the best agreement for the variable and fixed PVC methods with a standard deviation of 0.0026 and 0.0030 ml/min/ml, respectively. CONCLUSION: The differences in the Ki values obtained at the lumbar spine using direct arterial sampling and any of the IDAIF methods at the aorta were clinically nonsignificant. The variable PVC and fixed PVC methods performed better than the Cook-Lodge and Chen methods.
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Aorta/diagnóstico por imagem , Aorta/fisiologia , Fluoretos , Radioisótopos de Flúor , Tomografia por Emissão de Pósitrons , Idoso , Aorta/metabolismo , Osso e Ossos/irrigação sanguínea , Osso e Ossos/diagnóstico por imagem , Osso e Ossos/metabolismo , Feminino , Humanos , Pessoa de Meia-Idade , Modelos Teóricos , Reprodutibilidade dos Testes , Estudos RetrospectivosRESUMO
The in-vivo mechanical response of neural tissue during impact loading of the head is simulated using geometrically accurate finite element (FE) head models. However, current FE models do not account for the anisotropic elastic material behaviour of brain tissue. In soft biological tissue, there is a correlation between internal microscopic structure and macroscopic mechanical properties. Therefore, constitutive equations are important for the numerical analysis of the soft biological tissues. By exploiting diffusion tensor techniques the anisotropic orientation of neural tissue is incorporated into a non-linear viscoelastic material model for brain tissue and implemented in an explicit FE analysis. The viscoelastic material parameters are derived from published data and the viscoelastic model is used to describe the mechanical response of brain tissue. The model is formulated in terms of a large strain viscoelastic framework and considers non-linear viscous deformations in combination with non-linear elastic behaviour. The constitutive model was applied in the University College Dublin brain trauma model (UCDBTM) (i.e. three-dimensional finite element head model) to predict the mechanical response of the intra-cranial contents due to rotational injury.
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Lesões Encefálicas/fisiopatologia , Imagem de Tensor de Difusão/métodos , Análise de Elementos Finitos , Cabeça/fisiopatologia , Modelos Biológicos , Aceleração , Anisotropia , Módulo de Elasticidade , Humanos , Dinâmica não Linear , Rotação , Índices de Gravidade do Trauma , ViscosidadeRESUMO
Bioluminescent imaging (BLI) is a non-invasive imaging modality widely used in the field of pre-clinical oncology research. Imaging of small animal tumour models using BLI involves the generation of light by luciferase-expressing cells in the animal following administration of substrate. This light may be imaged using an external detector. The technique allows a variety of tumour-associated properties to be visualized dynamically in living models. The increasing use of BLI as a small-animal imaging modality has led to advances in the development of xenogeneic, orthotopic, and genetically engineered animal models expressing luciferase genes. This review aims to provide insight into the principles of BLI and its applications in cancer research. Many studies to assess tumour growth and development, as well as efficacy of candidate therapeutics, have been performed using BLI. More recently, advances have also been made using bioluminescent imaging in studies of protein-protein interactions, genetic screening, cell-cycle regulators, and spontaneous cancer development. Such novel studies highlight the versatility and potential of bioluminescent imaging in future oncological research.
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Modelos Animais de Doenças , Medições Luminescentes/métodos , Neoplasias Experimentais/diagnóstico , Animais , Progressão da Doença , Luciferases/metabolismo , Camundongos , Metástase Neoplásica , Neoplasias Experimentais/fisiopatologia , Neoplasias Experimentais/terapiaRESUMO
UNLABELLED: (18)F-Fluoride PET allows noninvasive evaluation of regional bone metabolism and has the potential to become a useful tool for assessing patients with metabolic bone disease and evaluating novel drugs being developed for these diseases. The main PET parameter of interest, termed K(i), reflects regional bone metabolism. The aim of this study was to compare the long-term precision of (18)F-fluoride PET with that of biochemical markers of bone turnover assessed over 6 mo. METHODS: Sixteen postmenopausal women with osteoporosis or significant osteopenia and a mean age of 64 y underwent (18)F-fluoride PET of the lumbar spine and measurements of biochemical markers of bone formation (bone-specific alkaline phosphatase and osteocalcin) and bone resorption (urinary deoxypyridinoline) at baseline and 6 mo later. Four different methods for analyzing the (18)F-fluoride PET data were compared: a 4k 3-compartmental model using nonlinear regression analysis (K(i-4k)), a 3k 3-compartmental model using nonlinear regression analysis (K(i-3k)), Patlak analysis (K(i-PAT)), and standardized uptake values. RESULTS: With the exception of a small but significant decrease in K(i-3k) at 6 mo, there were no significant differences between the baseline and 6-mo values for the PET parameters or biochemical markers. The long-term precision, expressed as the coefficient of variation (with 95% confidence interval in parentheses), was 12.2% (9%-19%), 13.8% (10%-22%), 14.4% (11%-22%), and 26.6% (19%-40%) for K(i-3k), K(i-PAT), mean standardized uptake value, and K(i-4k), respectively. For comparison, the precision of the biochemical markers was 10% (7%-15%), 18% (13%-27%), and 14% (10%-21%) for bone-specific alkaline phosphatase, osteocalcin, and urinary deoxypyridinoline, respectively. Intraclass correlation between the baseline and 6-mo values ranged from 0.44 for K(i-4k) to 0.85 for K(i-3k). No significant correlation was found between the repeated mean standardized uptake value measurements. CONCLUSION: The precision and intraclass correlation observed for K(i-3k) and K(i-PAT) was equivalent to that observed for biochemical markers. This study provided initial data on the long-term precision of (18)F-fluoride PET measured at the lumbar spine, which will aid in the accurate interpretation of changes in regional bone metabolism in response to treatment.