Exploring the Biological and Mechanical Properties of Abdominal Aortic Aneurysms Using USPIO MRI and Peak Tissue Stress: A Combined Clinical and Finite Element Study.

Inflammation detected through the uptake of ultrasmall superparamagnetic particles of iron oxide (USPIO) on magnetic resonance imaging (MRI) and finite element (FE) modelling of tissue stress both hold potential in the assessment of abdominal aortic aneurysm (AAA) rupture risk. This study aimed to examine the spatial relationship between these two biomarkers. Patients (n = 50) > 40 years with AAA maximum diameters > = 40 mm underwent USPIO-enhanced MRI and computed tomography angiogram (CTA). USPIO uptake was compared with wall stress predictions from CTA-based patient-specific FE models of each aneurysm. Elevated stress was commonly observed in areas vulnerable to rupture (e.g. posterior wall and shoulder). Only 16% of aneurysms exhibited co-localisation of elevated stress and mural USPIO enhancement. Globally, no correlation was observed between stress and other measures of USPIO uptake (i.e. mean or peak). It is suggested that cellular inflammation and stress may represent different but complimentary aspects of AAA disease progression.

Nonlinear multiscale regularisation in MR elastography: Towards fine feature mapping.

Fine-featured elastograms may provide additional information of radiological interest in the context of in vivo elastography. Here a new image processing pipeline called ESP (Elastography Software Pipeline) is developed to create Magnetic Resonance Elastography (MRE) maps of viscoelastic parameters (complex modulus magnitude |G*| and loss angle ϕ) that preserve fine-scale information through nonlinear, multi-scale extensions of typical MRE post-processing techniques. METHODS: A new MRE image processing pipeline was developed that incorporates wavelet-domain denoising, image-driven noise estimation, and feature detection. ESP was first validated using simulated data, including viscoelastic Finite Element Method (FEM) simulations, at multiple noise levels. ESP images were compared with MDEV pipeline images, both in the FEM models and in three ten-subject cohorts of brain, thigh, and liver acquisitions. ESP and MDEV mean values were compared to 2D local frequency estimation (LFE) mean values for the same cohorts as a benchmark. Finally, the proportion of spectral energy at fine frequencies was quantified using the Reduced Energy Ratio (RER) for both ESP and MDEV. RESULTS: Blind estimates of added noise (σ) were within 5.3% ± 2.6% of prescribed, and the same technique estimated σ in the in vivo cohorts at 1.7 ± 0.8%. A 5 × 5 × 5 truncated Gabor filter bank effectively detects local spatial frequencies at wavelengths λ ≤ 10px. For FEM inversions, mean |G*| of hard target, soft target, and background remained within 8% of prescribed up to σ=20%, and mean ϕ results were within 10%, excepting hard target ϕ, which required redrawing around a ring artefact to achieve similar accuracy. Inspection of FEM |G*| images showed some spatial distortion around hard target boundaries and inspection of ϕ images showed ring artefacts around the same target. For the in vivo cohorts, ESP results showed mean correlation of R=0.83 with MDEV and liver stiffness estimates within 7% of 2D-LFE results. Finally, ESP showed statistically significant increase in fine feature spectral energy as measured with RER for both |G*| (p<1×10-9) and ϕ (p<1×10-3). CONCLUSION: Information at finer frequencies can be recovered in ESP elastograms in typical experimental conditions, however scatter- and boundary-related artefacts may cause the fine features to have inaccurate values. In in vivo cohorts, ESP delivers an increase in fine feature spectral energy, and better performance with longer wavelengths, than MDEV while showing similar stability and robustness.

An anatomically unbiased approach for analysis of renal BOLD magnetic resonance images.

Oxygenation defects may contribute to renal disease progression, but the chronology of events is difficult to define in vivo without recourse to invasive methodologies. Blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) provides an attractive alternative, but the R2* signal is physiologically complex. Postacquisition data analysis often relies on manual selection of region(s) of interest. This approach excludes from analysis significant quantities of biological information and is subject to selection bias. We present a semiautomated, anatomically unbiased approach to compartmentalize voxels into two quantitatively related clusters. In control F344 rats, low R2* clustering was located predominantly within the cortex and higher R2* clustering within the medulla (70.96 ± 1.48 vs. 79.00 ± 1.50; 3 scans per rat; n = 6; P < 0.01) consistent anatomically with a cortico-medullary oxygen gradient. An intravenous bolus of acetylcholine caused a transient reduction of the R2* signal in both clustered segments (P < 0.01). This was nitric oxide dependent and temporally distinct from the hemodynamic effects of acetylcholine. Rats were then chronically infused with angiotensin II (60 ng/min) and rescanned 3 days later. Clustering demonstrated a disruption of the cortico-medullary gradient, producing less distinctly segmented mean R2* clusters (71.30 ± 2.00 vs. 72.48 ± 1.27; n = 6; NS). The acetylcholine-induced attenuation of the R2* signal was abolished by chronic angiotensin II infusion, consistent with reduced nitric oxide bioavailability. This global map of oxygenation, defined by clustering individual voxels on the basis of quantitative nearness, might be more robust in defining deficits in renal oxygenation than the absolute magnitude of R2* in small, manually selected regions of interest defined exclusively by anatomical nearness.