5 research outputs found

    Positron Emission Tomography and Magnetic Resonance Imaging of Cellular Inflammation in Patients with Abdominal Aortic Aneurysms.

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    OBJECTIVES: Inflammation is critical in the pathogenesis of abdominal aortic aneurysm (AAA) disease. Combined (18)F-fludeoxyglucose ((18)F-FDG) positron emission tomography with computed tomography (PET-CT) and ultrasmall superparamagnetic particles of iron oxide (USPIO)-enhanced magnetic resonance imaging (MRI) are non-invasive methods of assessing tissue inflammation. The aim of this study was to compare these techniques in patients with AAA. MATERIALS AND METHODS: Fifteen patients with asymptomatic AAA with diameter 46 ± 7 mm underwent PET-CT with (18)F-FDG, and T2*-weighted MRI before and 24 hours after administration of USPIO. The PET-CT and MRI data were then co-registered. Standardised uptake values (SUVs) were calculated to measure (18)F-FDG activity, and USPIO uptake was determined using the change in R2*. Comparisons between the techniques were made using a quadrant analysis and a voxel-by-voxel evaluation. RESULTS: When all areas of the aneurysm were evaluated, there was a modest correlation between the SUV on PET-CT and the change in R2* on USPIO-enhanced MRI (n = 70,345 voxels; r = .30; p < .0001). Although regions of increased (18)F-FDG and USPIO uptake co-localised on occasion, this was infrequent (kappa statistic 0.074; 95% CI 0.026-0.122). (18)F-FDG activity was commonly focused in the shoulder region whereas USPIO uptake was more apparent in the main body of the aneurysm. Maximum SUV was lower in patients with mural USPIO uptake. CONCLUSIONS: Both (18)F-FDG PET-CT and USPIO-MRI uptake identify vascular inflammation associated with AAA. Although they demonstrate a modest correlation, there are distinct differences in the pattern and distribution of uptake, suggesting a differential detection of macrophage glycolytic and phagocytic activity respectively.This research was supported by grants from the National Institutes of Health Research (NIHR) Efficacy and Mechanism Evaluation Programme (11/20/03), the British Heart Foundation (PG/09/083) and the Evelyn Trust (09/22). Dr. McBride is supported by the Academic Department of Military Surgery and Trauma, Royal Centre for Defence Medicine. Dr. Joshi is supported by Chief Scientist Office (ETM/160). Dr. van Beek is supported by the Scottish Imaging Network e a Platform of Scientific Excellence. The work of Dr. Rudd is part-supported by the NIHR Cambridge Biomedical Research Centre, the British Heart Foundation and the Wellcome Trust. Dr. Newby is supported by the British Heart Foundation (CH/09/002). The Wellcome Trust Clinical Research Facility and the Clinical Research Imaging Centre are supported by National Health Service Research Scotland through National Health Service Lothian.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ejvs.2015.12.01

    Ferumoxytol-enhanced magnetic resonance imaging methodology and normal values at 1.5 and 3T

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    Background: Ultrasmall superparamagnetic particles of iron oxide (USPIO)-enhanced magnetic resonance imaging (MRI) can detect tissue-resident macrophage activity and identify cellular inflammation. Clinical studies using this technique are now emerging. We aimed to report a range of normal R2* values at 1.5 and 3 T in the myocardium and other tissues following ferumoxytol administration, outline the methodology used and suggest solutions to commonly encountered analysis problems. Methods: Twenty volunteers were recruited: 10 imaged each at 1.5 T and 3 T. T2* and late gadolinium enhanced (LGE) MRI was conducted at baseline with further T2* imaging conducted approximately 24 h after USPIO infusion (ferumoxytol, 4 mg/kg). Regions of interest were selected in the myocardium and compared to other tissues. Results: Following administration, USPIO was detected by changes in R2* from baseline (1/T2*) at 24 h in myocardium, skeletal muscle, kidney, liver, spleen and blood at 1.5 T, and myocardium, kidney, liver, spleen, blood and bone at 3 T (p &lt; 0.05 for all). Myocardial changes in R2* due to USPIO were 26.5 ± 7.3 s-1 at 1.5 T, and 37.2 ± 9.6 s-1 at 3 T (p &lt; 0.0001 for both). Tissues showing greatest ferumoxytol enhancement were the reticuloendothelial system: the liver, spleen and bone marrow (216.3 ± 32.6 s-1, 336.3 ± 60.3 s-1, 69.9 ± 79.9 s-1; p &lt; 0.0001, p &lt; 0.0001, p = ns respectively at 1.5 T, and 275.6 ± 69.9 s-1, 463.9 ± 136.7 s-1, 417.9 ± 370.3 s-1; p &lt; 0.0001, p &lt; 0.0001, p &lt; 0.01 respectively at 3 T). Conclusion: Ferumoxytol-enhanced MRI is feasible at both 1.5 T and 3 T. Careful data selection and dose administration, along with refinements to echo-time acquisition, post-processing and analysis techniques are essential to ensure reliable and robust quantification of tissue enhancement

    Iterative reconstruction incorporating background correction improves quantification of [18F]-NaF PET/CT images of patients with abdominal aortic aneurysm

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    Background A confounding issue in [18F]-NaF PET/CT imaging of abdominal aortic aneurysms (AAA) is the spill in contamination from the bone into the aneurysm. This study investigates and corrects for this spill in contamination using the background correction (BC) technique without the need to manually exclude the part of the AAA region close to the bone. Methods Seventy-two (72) datasets of patients with AAA were reconstructed with the standard ordered subset expectation maximization (OSEM) algorithm incorporating point spread function (PSF) modelling. The spill in effect in the aneurysm was investigated using two target regions of interest (ROIs): one covering the entire aneurysm (AAA), and the other covering the aneurysm but excluding the part close to the bone (AAAexc). ROI analysis was performed by comparing the maximum SUV in the target ROI (SUVmax(T)), the corrected cSUVmax (SUVmax(T) − SUVmean(B)) and the target-to-blood ratio (TBR = SUVmax(T)/SUVmean(B)) with respect to the mean SUV in the right atrium region. Results There is a statistically significant higher [18F]-NaF uptake in the aneurysm than normal aorta and this is not correlated with the aneurysm size. There is also a significant difference in aneurysm uptake for OSEM and OSEM + PSF (but not OSEM + PSF + BC) when quantifying with AAA and AAAexc due to the spill in from the bone. This spill in effect depends on proximity of the aneurysms to the bone as close aneurysms suffer more from spill in than farther ones. Conclusion The background correction (OSEM + PSF + BC) technique provided more robust AAA quantitative assessments regardless of the AAA ROI delineation method, and thus it can be considered as an effective spill in correction method for [18F]-NaF AAA studies

    Maximum principal AAA wall stress is proportional to wall thickness

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    Abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is an asymptomatic condition which if left untreated can expand to the point of rupture. Rupture of an artery will occur when the local wall stress exceeds the local wall strength. Therefore, estimation of a patient’s AAA wall stress non-invasively, quickly, and reliably is desirable. One solution to this problem is to use recently-published methods to compute AAA wall stress, using geometry from CT scans, and median arterial pressure as the load. Our method is embedded in the software platform BioPARR—Biomechanics based Prediction of Aneurysm Rupture Risk, freely available from http://bioparr.mech.uwa.edu.au/. Experience with over 50 patient-specific stress analyses, as well as common sense, suggests that the AAA wall stress is critically dependent on the local AAA wall thickness. This thickness is currently very difficult to measure in the clinical environment. Therefore, we conducted a simulation study to elucidate the relationship between the wall thickness and the maximum principal stress. The results of the analysis of three cases presented here unequivocally demonstrate that this relationship is approximately linear, bringing us closer to being able to compute predictive stress envelopes for every patient
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