Name
Abstract | ||
|---|---|---|
The measurement of cerebral blood volume (CBV) has been the topic of numerous neuroimaging studies. To date, however, most in vivo imaging approaches can only measure CBV summed over all types of blood vessels, including arterial, capillary and venous vessels in the microvasculature (i.e. total CBV or CBVtot). As different types of blood vessels have intrinsically different anatomy, function and physiology, the ability to quantify CBV in different segments of the microvascular tree may furnish information that is not obtainable from CBVtot, and may provide a more sensitive and specific measure for the underlying physiology. This review attempts to summarize major efforts in the development of MRI techniques to measure arterial (CBVa) and venous CBV (CBVv) separately. Advantages and disadvantages of each type of method are discussed. Applications of some of the methods in the investigation of flow-volume coupling in healthy brains, and in the detection of pathophysiological abnormalities in brain diseases such as arterial steno-occlusive disease, brain tumors, schizophrenia, Huntington's disease, Alzheimer's disease, and hypertension are demonstrated. We believe that the continual development of MRI approaches for the measurement of compartment-specific CBV will likely provide essential imaging tools for the advancement and refinement of our knowledge on the exquisite details of the microvasculature in healthy and diseased brains. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neuroimage.2018.02.027 | NeuroImage |
Keywords | Field | DocType |
MRI,CBV,Pial artery,Arteriole,Capillary,Venule,Vein | Cerebral blood volume,Venule,Internal medicine,Vein,Cardiology,Psychology,Cognitive psychology,Neuroimaging,Preclinical imaging | Journal |
Volume | ISSN | Citations |
187 | 1053-8119 | 2 |
PageRank | References | Authors |
0.37 | 27 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Hua | 1 | 2 | 1.39 |
| Peiying Liu | 2 | 29 | 3.51 |
| Tae Kim | 3 | 2 | 0.37 |
| Manus J Donahue | 4 | 6 | 1.52 |
| Swati Rane | 5 | 11 | 1.94 |
| J Jean Chen | 6 | 129 | 9.48 |
| Qin Qin | 7 | 2 | 0.37 |
| Seong-Gi Kim | 8 | 403 | 55.43 |