1naresh<pre>Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => Controlled
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

)
1naresh<pre>Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => FreeToRead
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

)
PT  - JOURNAL ARTICLE
AU  - Elsheikh, S.
AU  - Urbach, H.
AU  - Reisert, M.
TI  - Intracranial Vessel Segmentation in 3D High-Resolution T1 Black-Blood MRI
AID  - 10.3174/ajnr.A7700
DP  - 2022 Dec 01
TA  - American Journal of Neuroradiology
PG  - 1719--1721
VI  - 43
IP  - 12
4099  - http://www.ajnr.org/content/43/12/1719.short
4100  - http://www.ajnr.org/content/43/12/1719.full
SO  - Am. J. Neuroradiol.2022 Dec 01; 43
AB  - SUMMARY: We demonstrate the feasibility of intracranial vascular segmentation based on the hypointense signal in non-contrast-enhanced black-blood MR imaging using convolutional neural networks. We selected 37 cases. Qualitatively, we observed no degradation due to stent artifacts, a comparable recognition of an aneurysm recurrence with TOF-MRA, and consistent success in the differentiation of intracranial arteries and veins. False-positive and false-negative results were observed. Quantitatively, our model achieved a promising Dice similarity coefficient of 0.72.BBMRIblack-blood compressed-sensing MRICNNconvolutional neural networksDSCDice similarity coefficient