Stroke caused by an embolism accounts for about a third of all stroke cases. Understanding the source and cause of the embolism is critical for diagnosis and long-term treatment of such stroke cases. The complex nature of the transport of an embolus within large arteries is a primary hindrance to a clear understanding of embolic stroke etiology. Recent advances in medical image-based computational hemodynamics modeling have rendered increasing utility to such techniques as a probe into the complex flow and transport phenomena in large arteries. In this work, we present a novel, patient-specific, computational framework for understanding embolic stroke etiology, by combining image-based hemodynamics with discrete particle dynamics and a sampling-based analysis. The framework allows us to explore the important question of how embolism source manifests itself in embolus distribution across the various major cerebral arteries. Our investigations illustrate prominent numerical evidence regarding (i) the size/inertia-dependent trends in embolus distribution to the brain; (ii) the relative distribution of cardiogenic versus aortogenic emboli among the anterior, middle, and posterior cerebral arteries; (iii) the left versus right brain preference in cardio-emboli and aortic-emboli transport; and (iv) the source-destination relationship for embolisms affecting the brain.
Background: Brain-and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlasfree brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center. Methods: Proton-density, T 2-weighted, T 1-weighted, brain-free water, 3D FLAIR, 3D T 2-weighted, and 3D T 2 *-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV þ subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV þ subjects (cohort
Moving towards single-sided, low-field MRI systems can increase the portability and access to this invaluable clinical tool. However, the B0-fields generated by these systems are highly inhomogeneous, and severe artifacts arise when the RF pulse does not cover the full bandwidth of spin resonances. In this study, we have developed a simulation algorithm that will allow us to better simulate the phase variations induced by B1 and B0 inhomogeneities and help us design RF schemes to remove unwanted phase to produce higher quality images.
Myocardial strain is increasingly used to assess left ventricular (LV) function. Incorporation of LV deformation into finite element (FE) modeling environment with subsequent strain calculation will allow analysis to reach its full potential. We describe a new kinematic model-based analysis framework (KMAF) to calculate strain from 3D cine-DENSE (displacement encoding with stimulated echoes) MRI. Methods: Cine-DENSE allows measurement of 3D myocardial displacement with high spatial accuracy. The KMAF framework uses cine cardiovascular magnetic resonance (CMR) to facilitate cine-DENSE segmentation, interpolates cine-DENSE displacement, and kinematically deforms an FE model to calculate strain. This framework was validated in an axially compressed gel phantom and applied in 10 healthy sheep and 5 sheep after myocardial infarction (MI). Results: Excellent Bland-Altman agreement of peak circumferential (E cc ) and longitudinal (E ll ) strain (mean difference = 0.021 ± 0.04 and −0.006 ± 0.03, respectively), was found between KMAF estimates and idealized FE simulation. E rr had a 2106 | WANG et Al.
Since recovery time of the RF coil is long at low field MRI, the rising and the ring-down times of the square pulse are also long, which means the applied sinc pulse can easily be distorted from the changing amplitude. However, both the rising time and ring-down time can be calculated using Q-factor. Using this information, an RF square pulse were compensated by appending two square pulses before and after the RF pulse. The durations of these RF square pulses were calculated using the Q-factor. Since the amplitude of the sinc pulse changes continuously, a series of square pulses were applied to apply sinc pulse to the coil. The minimum number of square pulses and the amplitude of the square pulses were calculated. It was successfully demonstrated that the sinc pulse can be compensated using a series of square pulses. The more number of square pulses were used, the smoother sinc pulse was applied to the RF coil. The Q-factor was experimentally calculated from the ring-down time of a signal induced in a sniffer loop which was connected to an oscilloscope. The resulting Q-factor was then used to calculate both the duration and amplitude of the square pulses for compensation. Echo trains were also acquired in an inhomogeneous B0 field using the compensated RF pulses. In order to enhance the SNR of the echo trains, a pre-polarization pulse was added to the CPMG spin echo sequence. The SNRs of the echo signal acquired using compensated pulses were compared with those of signal obtained with uncompensated pulses and showed significant improvements of 61.1% and 51.5% for the square and sinc shaped pulses respectively.
Gradient-free imaging at low-fields can significantly reduce the cost of and increase access to MRI devices. Here we propose to exploit the Bloch-Siegert shift effect to perform gradient-free, RF spatial encoding at 24mT. We have developed a simulation algorithm that designs various nonlinear encoding schemes and evaluates their performance through image reconstruction. Our results indicate that this technique is tolerant to noise and B0 inhomogeneities; this is an important step towards demonstrating the feasibility of performing low-field imaging with nonlinear RF spatial encoding using the Bloch-Siegert shift.
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