Nonrigid registration of three-dimensional ultrasound and magnetic resonance images of the carotid arteries

Atherosclerosis at the carotid bifurcation can result in cerebral emboli, which in turn can block the blood supply to the brain causing ischemic strokes. Noninvasive imaging tools that better characterize arterial wall, and atherosclerotic plaque structure and composition may help to determine the factors which lead to the development of unstable lesions, and identify patients at risk of plaque disruption and stroke. Carotid magnetic resonance (MR) imaging allows for the characterization of carotid vessel wall and plaque composition, the characterization of normal and pathological arterial wall, the quantification of plaque size, and the detection of plaque integrity. On the other hand, various ultrasound (US) measurements have also been used to quantify atherosclerosis, carotid stenosis, intima-media thickness, total plaque volume, total plaque area, and vessel wall volume. Combining the complementary information provided by 3D MR and US carotid images may lead to a better understanding of the underlying compositional and textural factors that define plaque and wall vulnerability, which may lead to better and more effective stroke prevention strategies and patient management. Combining these images requires nonrigid registration to correct the nonlinear misalignments caused by relative twisting and bending in the neck due to different head positions during the two image acquisition sessions. The high degree of freedom and large number of parameters associated with existing nonrigid image registration methods causes several problems including unnatural plaque morphology alteration, high computational complexity, and low reliability. Thus, a twisting and bending model was used with only six parameters to model the normal movement of the neck for nonrigid registration. The registration technique was evaluated using 3D US and MR carotid images at two field strengths, 1.5 and 3.0 T, of the same subject acquired on the same day. The mean registration error between the segmented carotid artery wall boundaries in the target US image and the registered MR images was calculated using a distance-based error metric after applying a twisting and bending model based nonrigid registration algorithm. An average registration error of 1.4 +/- 0.3 mm was obtained for 1.5 T MR and 1.5 +/- 0.4 mm for 3.0 T MR, when registered with 3D US images using the nonrigid registration technique presented in this paper. Visual inspection of segmented vessel surfaces also showed a substantial improvement of alignment with this nonrigid registration technique compared to rigid registration

Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines

© 2019 Haddad et al. The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data

Development of a PID based closed loop controller for shape memory alloy actuators

Shape Memory Alloy (SMA) spring actuator was designed and fabricated using commercially available NiTiNOL material by shape setting with the use of a special fixture. So, before applying the actuator to an application, a force characterization was conducted and force variation respect to uncontrolled temperature was analyzed. Due to the difference between force and temperature sensor's response time, a lag can occur between force and temperature measurements. Therefore, a more controlled technique was further implemented by developing a Proportional-Integral-Derivative (PID) based closed loop controller, together with a Graphical User Interface (GUI) which supports parameter control and sensor calibration. Finally, a force feedback controlling method also developed using the same PID technique for a force sensitive applications, where controlled forces need to be maintained by varying temperature of SMA accordingly.</p

In situ 4D tomography image analysis framework to follow sintering within 3D-printed glass scaffolds

We propose a novel image analysis framework to automate analysis of X‐ray microtomography images of sintering ceramics and glasses, using open‐source toolkits and machine learning. Additive manufacturing (AM) of glasses and ceramics usually requires sintering of green bodies. Sintering causes shrinkage, which presents a challenge for controlling the metrology of the final architecture. Therefore, being able to monitor sintering in 3D over time (termed 4D) is important when developing new porous ceramics or glasses. Synchrotron X‐ray tomographic imaging allows in situ, real‐time capture of the sintering process at both micro and macro scales using a furnace rig, facilitating 4D quantitative analysis of the process. The proposed image analysis framework is capable of tracking and quantifying the densification of glass or ceramic particles within multiple volumes of interest (VOIs) along with structural changes over time using 4D image data. The framework is demonstrated by 4D quantitative analysis of bioactive glass ICIE16 within a 3D‐printed scaffold. Here, densification of glass particles within 3 VOIs were tracked and quantified along with diameter change of struts and interstrut pore size over the 3D image series, delivering new insights on the sintering mechanism of ICIE16 bioactive glass particles in both micro and macro scales

Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity

Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function

Soluble Epoxide Hydrolase Derived Linoleic Acid Oxylipins, Small Vessel Disease Markers, and Neurodegeneration in Stroke

Background Cerebral small vessel disease is associated with higher ratios of soluble-epoxide hydrolase derived linoleic acid diols (12,13-dihydroxyoctadecenoic acid [DiHOME]&nbsp;and 9,10-DiHOME) to their parent epoxides (12(13)-epoxyoctadecenoic acid [EpOME]&nbsp;and 9(10)-EpOME); however, the relationship has not yet been examined in stroke. Methods and Results Participants with mild to moderate small vessel stroke or large vessel stroke were selected based on clinical and imaging&nbsp;criteria. Metabolites were quantified by ultra-high-performance liquid chromatography-mass spectrometry. Volumes of stroke, lacunes, white matter hyperintensities, magnetic resonance imaging visible perivascular spaces, and free water diffusion were quantified from structural and diffusion magnetic resonance imaging (3 Tesla). Adjusted linear regression models were used for analysis. Compared with&nbsp;participants with large vessel stroke (n=30), participants with small vessel stroke (n=50) had a higher 12,13-DiHOME/12(13)-EpOME ratio (β=0.251, P=0.023). The 12,13-DiHOME/12(13)-EpOME ratio was associated with more lacunes (β=0.266, P=0.028) but not with large vessel stroke volumes. Ratios of 12,13-DiHOME/12(13)-EpOME and 9,10-DiHOME/9(10)-EpOME were associated with greater volumes of white matter hyperintensities (β=0.364, P&lt;0.001; β=0.362, P&lt;0.001) and white matter MRI-visible perivascular spaces (β=0.302, P=0.011; β=0.314, P=0.006). In small vessel stroke, the 12,13-DiHOME/12(13)-EpOME ratio was associated with higher white matter free water diffusion (β=0.439, P=0.016), which was specific to the temporal lobe in exploratory regional analyses. The 9,10-DiHOME/9(10)-EpOME ratio was associated with temporal lobe atrophy (β=-0.277, P=0.031). Conclusions Linoleic acid markers of cytochrome P450/soluble-epoxide hydrolase activity were associated with small versus large vessel stroke, with small vessel disease markers consistent with blood brain barrier and neurovascular-glial disruption, and temporal lobe atrophy. The findings may indicate a novel modifiable risk factor for small vessel disease and related neurodegeneration