Brain MR perfusion imaging is used to evaluate local perfusion in patients with cerebral vascular disease. Quantitative measurements on the hemodynamic parameters and volume of brain with abnormal perfusion provide an estimation of the severity of the brain perfusion defect. However, quantitative measurements of these focal cerebral hemodynamic parameters are limited by the presence of cerebrospinal fluid (CSF) pixels. We noticed that the CSF has a higher signal than other tissue types on the first perfusion image, which is usually discarded in routine parametric image calculations. This signal difference, however, can be used to segment CSF pixels on the perfusion images. An image division was used to generate ratio images to compensate for spatially dependent signal variation caused by the inhomogeneity of excitation radiofrequency field. By applying an appropriate signal threshold to the ratio images, CSF pixels can be identified and removed from the parametric images. With the removal of CSF pixels, the volume of delayed-perfusion brain parenchyma can be better visualized and the interference from the CSF can be avoided. The proposed technique is simple, fast, automatic, and effective, and no extra scanning is needed to use this technique.
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http://dx.doi.org/10.1002/mrm.22402 | DOI Listing |
Background: Spontaneous subarachnoid hemorrhage (SAH) often results in altered cerebrospinal fluid (CSF) flow and secondary hydrocephalus, yet the mechanisms behind these phenomena remain poorly understood. This study aimed to elucidate the impact of SAH on individual CSF flow patterns and their association with secondary hydrocephalus.
Methods: In patients who had experienced SAH, changes in CSF flow were assessed using cardiac-gated phase-contrast magnetic resonance imaging (PC-MRI) at the Sylvian aqueduct and cranio-cervical junction (CCJ).
Magn Reson Med Sci
September 2024
Department of Neurosurgery, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo, Japan.
Sci Rep
August 2024
Lero and ADAPT Research Centres, School of Computer Science, University of Galway, Galway, Ireland.
Analyses of complex behaviors of Cerebrospinal Fluid (CSF) have become increasingly important in diseases diagnosis. The changes of the phase-contrast magnetic resonance imaging (PC-MRI) signal formed by the velocity of flowing CSF are represented as a set of velocity-encoded images or maps, which can be thought of as signal data in the context of medical imaging, enabling the evaluation of pulsatile patterns throughout a cardiac cycle. However, automatic segmentation of the CSF region in a PC-MRI image is challenging, and implementing an explained ML method using pulsatile data as a feature remains unexplored.
View Article and Find Full Text PDFFront Neurosci
February 2024
Department of Neurosurgery and Neuro-Oncology, Barlicki University Hospital, Medical University of Lodz, Łódź, Poland.
Fluids Barriers CNS
February 2024
Institute of Neuroradiology, Kantonsspital Aarau, 5000, Aarau, Switzerland.
Background: Impaired cerebrospinal fluid (CSF) dynamics is involved in the pathophysiology of neurodegenerative diseases of the central nervous system and the optic nerve (ON), including Alzheimer's and Parkinson's disease, as well as frontotemporal dementia. The smallness and intricate architecture of the optic nerve subarachnoid space (ONSAS) hamper accurate measurements of CSF dynamics in this space, and effects of geometrical changes due to pathophysiological processes remain unclear. The aim of this study is to investigate CSF dynamics and its response to structural alterations of the ONSAS, from first principles, with supercomputers.
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