Intracranial aneurysm wall displacement depicted by amplified Flow predicts growth.

BACKGROUND: Abnormal intracranial aneurysm (IA) wall motion has been associated with IA growth and rupture. Recently, a new image processing algorithm called amplified Flow (aFlow) has been used to successfully track IA wall motion by combining the amplification of cine and four-dimensional (4D) Flow MRI. We sought to apply aFlow to assess wall motion as a potential marker of IA growth in a paired-wise analysis of patients with growing versus stable aneurysms. METHODS: In this retrospective case-control study, 10 patients with growing IAs and a matched cohort of 10 patients with stable IAs who had baseline 4D Flow MRI were included. The aFlow was used to amplify and extract IA wall displacements from 4D Flow MRI. The associations of aFlow parameters with commonly used risk factors and morphometric features were assessed using paired-wise univariate and multivariate analyses. RESULTS: aFlow quantitative results showed significantly (P=0.035) higher wall motion displacement depicted by mean±SD 90th% values of 2.34±0.72 in growing IAs versus 1.39±0.58 in stable IAs with an area under the curve of 0.85. There was also significantly (P<0.05) higher variability of wall deformation across IA geometry in growing versus stable IAs depicted by the dispersion variables including 121-150% larger standard deviation ([Formula: see text]) and 128-161% wider interquartile range [Formula: see text]. CONCLUSIONS: aFlow-derived quantitative assessment of IA wall motion showed greater wall motion and higher variability of wall deformation in growing versus stable IAs.

Is there a morphometric cause of Chiari malformation type I? Analysis of existing literature.

Although many etiologies have been proposed for Chiari malformation type I (CM-I), there currently is no singular known cause of CM-I pathogenesis. Advances in imaging have greatly progressed the study of CM-I. This study reviews the literature to determine if an anatomical cause for CM-I could be proposed from morphometric studies in adult CM-I patients. After conducting a literature search using relevant search terms, two authors screened abstracts for relevance. Full-length articles of primary morphometric studies published in peer-reviewed journals were included. Detailed information regarding methodology and symptomatology, craniocervical instability, syringomyelia, operative effects, and genetics were extracted. Forty-six studies met inclusion criteria, averaging 93.2 CM-I patients and 41.4 healthy controls in size. To obtain measurements, 40 studies utilized MRI and 10 utilized CT imaging, whereas 41 analyzed parameters within the posterior fossa and 20 analyzed parameters of the craniovertebral junction. The most commonly measured parameters included clivus length (n = 30), tonsillar position or descent (n = 28), McRae line length (n = 26), and supraocciput length (n = 26). While certain structural anomalies including reduced clivus length have been implicated in CM-I, there is a lack of consensus on how several other morphometric parameters may or may not contribute to its development. Heterogeneity in presentation with respect to the extent of tonsillar descent suggests alternate methods utilizing morphometric measurements that may help to identify CM-I patients and may benefit future research to better understand underlying pathophysiology and sequelae such as syringomyelia.

3D amplified MRI (aMRI).

PURPOSE: Amplified MRI (aMRI) has been introduced as a new method of detecting and visualizing pulsatile brain motion in 2D. Here, we improve aMRI by introducing a novel 3D aMRI approach. METHODS: 3D aMRI was developed and tested for its ability to amplify sub-voxel motion in all three directions. In addition, 3D aMRI was qualitatively compared to 2D aMRI on multi-slice and 3D (volumetric) balanced steady-state free precession cine data and phase contrast (PC-MRI) acquired on healthy volunteers at 3T. Optical flow maps and 4D animations were produced from volumetric 3D aMRI data. RESULTS: 3D aMRI exhibits better image quality and fewer motion artifacts compared to 2D aMRI. The tissue motion was seen to match that of PC-MRI, with the predominant brain tissue displacement occurring in the cranial-caudal direction. Optical flow maps capture the brain tissue motion and display the physical change in shape of the ventricles by the relative movement of the surrounding tissues. The 4D animations show the complete brain tissue and cerebrospinal fluid (CSF) motion, helping to highlight the "piston-like" motion of the ventricles. CONCLUSIONS: Here, we introduce a novel 3D aMRI approach that enables one to visualize amplified cardiac- and CSF-induced brain motion in striking detail. 3D aMRI captures brain motion with better image quality than 2D aMRI and supports a larger amplification factor. The optical flow maps and 4D animations of 3D aMRI may open up exciting applications for neurological diseases that affect the biomechanics of the brain and brain fluids.

Evaluation and Verification of Fast Computational Simulations of Stent-Graft Deployment in Endovascular Aneurysmal Repair.

Fenestrated Endovascular Aortic Repair, also known as FEVAR, is a minimally invasive procedure that allows surgeons to repair the aorta while still preserving blood flow to kidneys and other critical organs. Given the high complexity of FEVAR, there is a pressing need to develop numerical tools that can assist practitioners at the preoperative planning stage and during the intervention. The aim of the present study is to introduce and to assess an assistance solution named Fast Method for Virtual Stent-graft Deployment for computer assisted FEVAR. This solution, which relies on virtual reality, is based on a single intraoperative X-ray image. It is a hybrid method that includes the use of intraoperative images and a simplified mechanical model based on corotational beam elements. The method was verified on a phantom and validated on three clinical cases, including a case with fenestrations. More specifically, we quantified the errors induced by the different simplifications of the mechanical model, related to fabric simulation and aortic wall mechanical properties. Overall, all errors for both stent and fenestration positioning were less than 5 mm, making this method compatible with clinical expectations. More specifically, the errors related to fenestration positioning were less than 3 mm. Although requiring further validation with a higher number of test cases, our method could achieve an accuracy compatible with clinical specifications within limited calculation time, which is promising for future implementation in a clinical context.

Amplified Flow Imaging (aFlow): A Novel MRI-Based Tool to Unravel the Coupled Dynamics Between the Human Brain and Cerebrovasculature.

With each heartbeat, periodic variations in arterial blood pressure are transmitted along the vasculature, resulting in localized deformations of the arterial wall and its surrounding tissue. Quantification of such motions may help understand various cerebrovascular conditions, yet it has proven technically challenging thus far. We introduce a new image processing algorithm called amplified Flow (aFlow) which allows to study the coupled brain-blood flow motion by combining the amplification of cine and 4D flow MRI. By incorporating a modal analysis technique known as dynamic mode decomposition into the algorithm, aFlow is able to capture the characteristics of transient events present in the brain and arterial wall deformation. Validating aFlow, we tested it on phantom simulations mimicking arterial walls motion and observed that aFlow displays almost twice higher SNR than its predecessor amplified MRI (aMRI). We then applied aFlow to 4D flow and cine MRI datasets of 5 healthy subjects, finding high correlations between blood flow velocity and tissue deformation in selected brain regions, with correlation values r = 0.61 , 0.59, 0.52 for the pons, frontal and occipital lobe ( ). Finally, we explored the potential diagnostic applicability of aFlow by studying intracranial aneurysm dynamics, which seems to be indicative of rupture risk. In two patients, aFlow successfully visualized the imperceptible aneurysm wall motion, additionally quantifying the increase in the high frequency wall displacement after a one-year follow-up period (20%, 76%). These preliminary data suggest that aFlow may provide a novel imaging biomarker for the assessment of aneurysms evolution, with important potential diagnostic implications.