Intracranial Aneurysm Classifier Using Phenotypic Factors: An International Pooled Analysis.

Intracranial aneurysms (IAs) are usually asymptomatic with a low risk of rupture, but consequences of aneurysmal subarachnoid hemorrhage (aSAH) are severe. Identifying IAs at risk of rupture has important clinical and socio-economic consequences. The goal of this study was to assess the effect of patient and IA characteristics on the likelihood of IA being diagnosed incidentally versus ruptured. Patients were recruited at 21 international centers. Seven phenotypic patient characteristics and three IA characteristics were recorded. The analyzed cohort included 7992 patients. Multivariate analysis demonstrated that: (1) IA location is the strongest factor associated with IA rupture status at diagnosis; (2) Risk factor awareness (hypertension, smoking) increases the likelihood of being diagnosed with unruptured IA; (3) Patients with ruptured IAs in high-risk locations tend to be older, and their IAs are smaller; (4) Smokers with ruptured IAs tend to be younger, and their IAs are larger; (5) Female patients with ruptured IAs tend to be older, and their IAs are smaller; (6) IA size and age at rupture correlate. The assessment of associations regarding patient and IA characteristics with IA rupture allows us to refine IA disease models and provide data to develop risk instruments for clinicians to support personalized decision-making.

Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors.

Clinical decision making regarding the treatment of unruptured intracranial aneurysms (IA) benefits from a better understanding of the interplay of IA rupture risk factors. Probabilistic graphical models can capture and graphically display potentially causal relationships in a mechanistic model. In this study, Bayesian networks (BN) were used to estimate IA rupture risk factors influences. From 1248 IA patient records, a retrospective, single-cohort, patient-level data set with 9 phenotypic rupture risk factors (n=790 complete entries) was extracted. Prior knowledge together with score-based structure learning algorithms estimated rupture risk factor interactions. Two approaches, discrete and mixed-data additive BN, were implemented and compared. The corresponding graphs were learned using non-parametric bootstrapping and Markov chain Monte Carlo, respectively. The BN models were compared to standard descriptive and regression analysis methods. Correlation and regression analyses showed significant associations between IA rupture status and patient's sex, familial history of IA, age at IA diagnosis, IA location, IA size and IA multiplicity. BN models confirmed the findings from standard analysis methods. More precisely, they directly associated IA rupture with familial history of IA, IA size and IA location in a discrete framework. Additive model formulation, enabling mixed-data, found that IA rupture was directly influenced by patient age at diagnosis besides additional mutual influences of the risk factors. This study establishes a data-driven methodology for mechanistic disease modelling of IA rupture and shows the potential to direct clinical decision-making in IA treatment, allowing personalised prediction.

On probing intravoxel incoherent motion in the heart-spin-echo versus stimulated-echo DWI.

PURPOSE: Mapping intravoxel incoherent motion (IVIM) in the heart remains challenging despite advances in cardiac DWI and DTI. In the present work, simulations and experimental imaging are used to compare the IVIM encoding efficiency of spin-echo- and stimulated-echo-based DWI/DTI for assessing myocardial perfusion. METHODS: Using normalized phase distributions and statistical models of capillary networks derived from histological studies, along with typical diffusion gradient waveforms for in vivo cardiac DWI/DTI, Monte Carlo simulations were performed. The simulation results were compared to IVIM measurements of perfused porcine hearts regarding both magnitude and phase modulation. An IVIM tensor model was used to account for anisotropy of the capillary network, and potential bias of parameter estimation was reported based on simulations. RESULTS: Both computer simulations and experimental data demonstrate a low sensitivity of spin-echo DWI/DTI sequences for IVIM parameters, whereas stimulated-echo-based DWI/DTI with typical mixing times can differentiate between no-flow baseline and perfused myocardium (+129% IVIM-derived flow). In addition, ischemic territories induced by coronary occlusion could be successfully detected. With increasing order of motion compensation (M0/M1/M2) of the diffusion encoding gradients, as required for cardiac in vivo spin-echo DWI/DTI, the low IVIM sensitivity of spin-echo DWI/DTI decreased further in simulations: maximum attenuations of perfusion compartment 52/13/5% (b = 500 s/mm2 ). CONCLUSION: Given the short encoding time of spin-echo-based DWI/DTI sequences, a limited perfusion sensitivity results, in particular in combination with motion-compensated diffusion gradients. In contrast, stimulated-echo based DWI/DTI has the potential to identify perfusion changes in cardiac IVIM in vivo.

Enhancing intravoxel incoherent motion parameter mapping in the brain using k-b PCA.

Intravoxel incoherent motion (IVIM) imaging of diffusion and perfusion parameters in the brain using parallel imaging suffers from local noise amplification. To address the issue, signal correlations in space and along the diffusion encoding dimension are exploited jointly using a constrained image reconstruction approach. IVIM imaging was performed on a clinical 3 T MR system with diffusion weighting along six gradient directions and 16 b-values encoded per direction across a range of 0-900 s/mm2 . Data were collected in 11 subjects, retrospectively undersampled in k-space with net factors ranging from 2 to 6 and reconstructed using CG-SENSE and the proposed k-b PCA approach. Results of k-b PCA and CG-SENSE from retrospectively undersampled data were compared with those from the fully sampled reference. In addition, prospective single-shot k-b undersampling was implemented and data were acquired in five additional volunteers. IVIM parameter maps were derived using a segmented least-squares method. The proposed k-b PCA method outperformed CG-SENSE in terms of reconstruction errors for effective undersampling factors of 3 and beyond. Undersampling artifacts were effectively removed with k-b PCA up to sixfold undersampling. At net sixfold undersampling, relative errors (compared with the fully sampled reference) of image magnitude and IVIM parameters (D, f and D* ) were (median ± interquartile range): 3.5 ± 3.7 versus 25.3 ± 25.8%, 2.7 ± 3.6 versus 14.2 ± 20.4%, 15.1 ± 26.1 versus 96.6 ± 67.4% and 14.8 ± 26.6 versus 100 ± 195.1% for k-b PCA versus CG-SENSE, respectively. Acquisition with sixfold prospective undersampling yielded average IVIM parameters in the brain of 0.79 ± 0.18 × 10-3  mm2 /s for D, 7.35 ± 7.27% for f and 7.11 ± 2.39 × 10-3  mm2 /s for D* . Constrained reconstruction using k-b PCA improves IVIM parameter mapping from undersampled data when compared with CG-SENSE reconstruction. Prospectively undersampled single-shot echo planar imaging acquisition was successfully employed using k-b PCA, demonstrating a reduction of image artifacts and noise relative to parallel imaging.

Bayesian intravoxel incoherent motion parameter mapping in the human heart.

BACKGROUND: Intravoxel incoherent motion (IVIM) imaging of diffusion and perfusion in the heart suffers from high parameter estimation error. The purpose of this work is to improve cardiac IVIM parameter mapping using Bayesian inference. METHODS: A second-order motion-compensated diffusion weighted spin-echo sequence with navigator-based slice tracking was implemented to collect cardiac IVIM data in early systole in eight healthy subjects on a clinical 1.5 T CMR system. IVIM data were encoded along six gradient optimized directions with b-values of 0-300 s/mm2. Subjects were scanned twice in two scan sessions one week apart to assess intra-subject reproducibility. Bayesian shrinkage prior (BSP) inference was implemented to determine IVIM parameters (diffusion D, perfusion fraction F and pseudo-diffusion D*). Results were compared to least-squares (LSQ) parameter estimation. Signal-to-noise ratio (SNR) requirements for a given fitting error were assessed for the two methods using simulated data. Reproducibility analysis of parameter estimation in-vivo using BSP and LSQ was performed. RESULTS: BSP resulted in reduced SNR requirements when compared to LSQ in simulations. In-vivo, BSP analysis yielded IVIM parameter maps with smaller intra-myocardial variability and higher estimation certainty relative to LSQ. Mean IVIM parameter estimates in eight healthy subjects were (LSQ/BSP): 1.63 ± 0.28/1.51 ± 0.14·10-3 mm2/s for D, 13.13 ± 19.81/13.11 ± 5.95% for F and 201.45 ± 313.23/13.11 ± 14.53·10-3 mm2/s for D ∗. Parameter variation across all volunteers and measurements was lower with BSP compared to LSQ (coefficient of variation BSP vs. LSQ: 9% vs. 17% for D, 45% vs. 151% for F and 111% vs. 155% for D ∗). In addition, reproducibility of the IVIM parameter estimates was higher with BSP compared to LSQ (Bland-Altman coefficients of repeatability BSP vs. LSQ: 0.21 vs. 0.26·10-3 mm2/s for D, 5.55 vs. 6.91% for F and 15.06 vs. 422.80·10-3 mm2/s for D*). CONCLUSION: Robust free-breathing cardiac IVIM data acquisition in early systole is possible with the proposed method. BSP analysis yields improved IVIM parameter maps relative to conventional LSQ fitting with fewer outliers, improved estimation certainty and higher reproducibility. IVIM parameter mapping holds promise for myocardial perfusion measurements without the need for contrast agents.