Up-and-coming Radiotracers for Imaging Pain Generators

Chronic musculoskeletal pain is among the most highly prevalent diseases worldwide. Managing patients with chronic pain remains very challenging because current imaging techniques focus on morphological causes of pain that can be inaccurate and misleading. Moving away from anatomical constructs of disease, molecular imaging has emerged as a method to identify diseases according to their molecular, physiologic, or cellular signatures that can be applied to the variety of biomolecular changes that occur in nociception and pain processing and therefore have tremendous potential for precisely pinpointing the source of a patient's pain. Several molecular imaging approaches to image the painful process are now available, including imaging of voltage-gated sodium channels, calcium channels, hypermetabolic processes, the substance P receptor, the sigma-1 receptor, and imaging of macrophage trafficking. This article provides an overview of promising molecular imaging approaches for the imaging of musculoskeletal pain with a focus on preclinical methods.</p

Up-and-coming Radiotracers for Imaging Pain Generators

Chronic musculoskeletal pain is among the most highly prevalent diseases worldwide. Managing patients with chronic pain remains very challenging because current imaging techniques focus on morphological causes of pain that can be inaccurate and misleading. Moving away from anatomical constructs of disease, molecular imaging has emerged as a method to identify diseases according to their molecular, physiologic, or cellular signatures that can be applied to the variety of biomolecular changes that occur in nociception and pain processing and therefore have tremendous potential for precisely pinpointing the source of a patient's pain. Several molecular imaging approaches to image the painful process are now available, including imaging of voltage-gated sodium channels, calcium channels, hypermetabolic processes, the substance P receptor, the sigma-1 receptor, and imaging of macrophage trafficking. This article provides an overview of promising molecular imaging approaches for the imaging of musculoskeletal pain with a focus on preclinical methods.</p

Advanced Magnetic Resonance Imaging and Molecular Imaging of the Painful Knee

Chronic knee pain is a common condition. Causes of knee pain include trauma, inflammation, and degeneration, but in many patients the pathophysiology remains unknown. Recent developments in advanced magnetic resonance imaging (MRI) techniques and molecular imaging facilitate more in-depth research focused on the pathophysiology of chronic musculoskeletal pain and more specifically inflammation. The forthcoming new insights can help develop better targeted treatment, and some imaging techniques may even serve as imaging biomarkers for predicting and assessing treatment response in the future. This review highlights the latest developments in perfusion MRI, diffusion MRI, and molecular imaging with positron emission tomography/MRI and their application in the painful knee. The primary focus is synovial inflammation, also known as synovitis. Bone perfusion and bone metabolism are also addressed.</p

Advanced Magnetic Resonance Imaging and Molecular Imaging of the Painful Knee

Chronic knee pain is a common condition. Causes of knee pain include trauma, inflammation, and degeneration, but in many patients the pathophysiology remains unknown. Recent developments in advanced magnetic resonance imaging (MRI) techniques and molecular imaging facilitate more in-depth research focused on the pathophysiology of chronic musculoskeletal pain and more specifically inflammation. The forthcoming new insights can help develop better targeted treatment, and some imaging techniques may even serve as imaging biomarkers for predicting and assessing treatment response in the future. This review highlights the latest developments in perfusion MRI, diffusion MRI, and molecular imaging with positron emission tomography/MRI and their application in the painful knee. The primary focus is synovial inflammation, also known as synovitis. Bone perfusion and bone metabolism are also addressed.</p

Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association with Lung Function in Asbestos-exposed Patients

Purpose: Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests.Materials and Methods: Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient (r) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO).Results: We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC (P = 0.001) and FVC (P = 0.04) values for the higher PPV patients, but not for DLCO (P = 0.19).Conclusion: We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.</p

Pain during prolonged sitting is a common problem in persons with patellofemoral pain

STUDY DESIGN: Retrospective cohort. BACKGROUND: Although persons with patellofemoral pain (PFP) often report pain with prolonged sitting, little is known about the prevalence and characteristics of sitting pain. OBJECTIVES: To describe the proportion of persons with PFP who experience problems with prolonged sitting and to determine patient characteristics associated with sitting pain. METHODS: Four hundred fifty-eight participants with a diagnosis of PFP from 4 separate studies were included. Item 8 of the Anterior Knee Pain Scale was used to define the presence of problems with prolonged sitting with knee flexion, based on 3 categories: (1) "no difficulty," (2) "pain after exercise," or (3) "problems with prolonged sitting." Differences in demographic and clinical variables between categories were evaluated using Kruskal- Wallis tests (

Confidence maps for reliable estimation of proton density fat fraction and R*₂ in the liver

Purpose: The objective was to develop a fully automated algorithm that generates confidence maps to identify regions valid for analysis of quantitative proton density fat fraction (PDFF) and (Formula presented.) maps of the liver, generated with chemical shift–encoded MRI (CSE-MRI). Confidence maps are urgently needed for automated quality assurance, particularly with the emergence of automated segmentation and analysis algorithms. Methods: Confidence maps for both PDFF and (Formula presented.) maps are generated based on goodness of fit, measured by normalized RMS error between measured complex signals and the CSE-MRI signal model. Based on Cramér-Rao lower bound and Monte-Carlo simulations, normalized RMS error threshold criteria were developed to identify unreliable regions in quantitative maps. Simulation, phantom, and in vivo clinical studies were included. To analyze the clinical data, a board-certified radiologist delineated regions of interest (ROIs) in each of the nine liver segments for PDFF and (Formula presented.) analysis in consecutive clinical CSE-MRI data sets. The percent area of ROIs in areas deemed unreliable by confidence maps was calculated to assess the impact of confidence maps on real-world clinical PDFF and (Formula presented.) measurements. Results: Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF and (Formula presented.) measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF and (Formula presented.) maps, as identified by confidence maps. Conclusion: A proposed confidence map algorithm that identifies reliable areas of PDFF and (Formula presented.) measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.</p

Confidence maps for reliable estimation of proton density fat fraction and R*₂ in the liver

Purpose: The objective was to develop a fully automated algorithm that generates confidence maps to identify regions valid for analysis of quantitative proton density fat fraction (PDFF) and (Formula presented.) maps of the liver, generated with chemical shift–encoded MRI (CSE-MRI). Confidence maps are urgently needed for automated quality assurance, particularly with the emergence of automated segmentation and analysis algorithms. Methods: Confidence maps for both PDFF and (Formula presented.) maps are generated based on goodness of fit, measured by normalized RMS error between measured complex signals and the CSE-MRI signal model. Based on Cramér-Rao lower bound and Monte-Carlo simulations, normalized RMS error threshold criteria were developed to identify unreliable regions in quantitative maps. Simulation, phantom, and in vivo clinical studies were included. To analyze the clinical data, a board-certified radiologist delineated regions of interest (ROIs) in each of the nine liver segments for PDFF and (Formula presented.) analysis in consecutive clinical CSE-MRI data sets. The percent area of ROIs in areas deemed unreliable by confidence maps was calculated to assess the impact of confidence maps on real-world clinical PDFF and (Formula presented.) measurements. Results: Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF and (Formula presented.) measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF and (Formula presented.) maps, as identified by confidence maps. Conclusion: A proposed confidence map algorithm that identifies reliable areas of PDFF and (Formula presented.) measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.</p