====== Magnetic resonance imaging for Moyamoya Disease Diagnosis ======

[[4D flow MRI for Moyamoya disease diagnosis]]
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[[Arterial spin labelled imaging for Moyamoya disease.]]

MRA usually discloses the stenosis or occlusion of the ICA. Moyamoya vessels appear as [[flow void]]s on MRI (especially in basal ganglia) and a fine network of vessels on MRA and are demonstrated better in children than adults. Parenchymal ischemic changes are commonly shown, usually in watershed areas.

[[MRI]] images show:

1) diminished [[blood flow]] in the [[internal carotid artery]] ([[ICA]]) and the [[middle cerebral artery]] (MCA) and [[anterior cerebral artery]] (ACA) and 2) prominent collateral [[blood flow]] at the base of the brain. To confirm the diagnosis of [[moyamoya disease]], an [[angiogram]] is typically required.

Diagnostic criteria of definitive moyamoya disease include all of the following items based on the conventional angiographic findings.

(1) Stenosis or occlusion of the terminal portion of the intracranial ICA or proximal portions of the anterior cerebral artery (ACA) and/or the middle cerebral artery (MCA). 

(2) Development of abnormal vascular networks near the occlusive or stenotic lesions in the arterial phase. 

(3) Bilateral lesion
((Research Committee on the Pathology and Treatment of Spontaneous Occlusion of 
the [[Circle of Willis]]; Health Labour Sciences Research Grant for Research on
Measures for Infractable Diseases. Guidelines for diagnosis and treatment of
moyamoya disease (spontaneous occlusion of the circle of Willis). Neurol Med Chir
(Tokyo). 2012;52(5):245-66. PubMed PMID: 22870528.
)).


Results demonstrate distinct alterations in the temporal correlations of low-frequency [[BOLD]] signals, predominantly in resting-state networks in [[moyamoya disease]]. Additionally, [[Resting-state functional magnetic resonance imaging]] (rs-fMRI) measures were associated with ischemic motor-related symptoms and cognitive performance in the patients. Thus, rs-fMRI may offer a useful non-invasive method of acquiring additional information beyond cerebral perfusion as part of clinical investigations in patients with [[moyamoya disease]]
((Kazumata K, Tha KK, Uchino H, Ito M, Nakayama N, Abumiya T. Mapping altered
brain connectivity and its clinical associations in adult moyamoya disease: A
resting-state functional MRI study. PLoS One. 2017 Aug 4;12(8):e0182759. doi:
10.1371/journal.pone.0182759. eCollection 2017. PubMed PMID: 28783763.
)).
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Territorial [[arterial spin labeling]] (t-ASL) could reveal comprehensive [[Moyamoya disease]] (MMD) cerebral blood [[perfusion]] and the vivid perfusion territory shifts fed by the unilateral ICA and ECA and bilateral [[vertebral artery]]s (VAs) in a noninvasive, straightforward, nonradioactive, and nonenhanced manner. 3D [[Time-of-flight magnetic resonance angiography]] (3D-TOF-MRA) could subdivide t-ASL perfusion territory shifts according to their shunt arteries. A perfusion territory shift attributable to the secondary collaterals is a potential independent risk factor for preoperative [[hemorrhage]] in MMD patients. A perfusion territory shift fed by the primary collaterals may not have a strong effect on preoperative hemorrhage in MMD patients. These findings make the combined modalities of t-ASL and 3D-TOF-MRA a feasible tool for MMD disease assessment, management, and surgical strategy planning
((Gao XY, Li Q, Li JR, Zhou Q, Qu JX, Yao ZW. A perfusion territory shift
attributable solely to the secondary collaterals in moyamoya patients: a
potential risk factor for preoperative hemorrhagic stroke revealed by t-ASL and
3D-TOF-MRA. J Neurosurg. 2019 Aug 9:1-9. doi: 10.3171/2019.5.JNS19803. [Epub
ahead of print] PubMed PMID: 31398708.
)).
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Akiyama et al. showed excellent results in terms of accuracy of differential diagnosis of moyamoya disease using AI with the conventional T2 weighted images. The authors suggest the possibility of diagnosing moyamoya disease using AI technique and demonstrate the area of interest on which AI focuses while processing magnetic resonance images
((Akiyama Y, Mikami T, Mikuni N. Deep Learning-Based Approach for the Diagnosis of Moyamoya Disease. J Stroke Cerebrovasc Dis. 2020 Sep 25;29(12):105322. doi: 10.1016/j.jstrokecerebrovasdis.2020.105322. Epub ahead of print. PMID: 32992181.)).