Analysis of the Influence of 4D MR Angiography Temporal Resolution on Time-to-Peak Estimation Error for Different Cerebral Vessel Structures

Forkert, Nils D.; Illies, Till; Möller, Dietmar P. F.; Handels, Heinz; Säring, Dennis; Fiehler, Jens

doi:10.3174/ajnr.a3089

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…The reference-based linear curve fitting approach enables a TTP estimation with a precision of 0.1 second given the temporal resolution of 0.5 seconds of the 4D MRA sequence. 37,38 The precision of TTP calculation can be seen in the depiction of intranidal flow patterns, which correlate well with in-and outflow characteristics as well as with the flow pattern displayed by digital subtraction angiography. 34 The exclusion of 10 patients in our study group due to insufficient image quality can be ascribed to artifacts that mainly occurred in the beginning of the study.…”

Section: Strokementioning

confidence: 91%

Persistent Hemodynamic Changes in Ruptured Brain Arteriovenous Malformations

Illies

Forkert

Saering

et al. 2012

Stroke

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Background and Purpose-Hemodynamic properties of brain arteriovenous malformations (AVMs) with risk factors for a future hemorrhage are essentially unknown. We hypothesized that AVMs with anatomic properties, which are associated with an increased rupture risk, exhibit different hemodynamic characteristics than those without these properties. Methods-Seventy-two consecutive patients with AVMs diagnosed by conventional angiography underwent MRI examination, including time-resolved 3-dimensional MR angiography. Signal-intensity curves derived from the timeresolved 3-dimensional MR angiography datasets were used to calculate relative blood flow transit times through the AVM nidus based on the time-to-peak parameter. For identification of characteristics associated with altered transit times, a multiple normal regression model was fitted with stepwise selection of the following regressors: intracranial hemorrhage, deep nidus location, infratentorial location, deep drainage, associated aneurysm, nidus size, draining venous stenosis, and number of draining veins. Results-A previous intracranial hemorrhage is the only characteristic that was associated with a significant alteration of the relative transit time, leading to an increase of 2.4 seconds (95% CI, 1.2-3.6 seconds;, P<0.001) without adjustment and 2.1 seconds (95% CI, 0.6-3.6 seconds; P=0.007) with adjustment for all other regressors considered. The association was independent of the bleeding age. Conclusion-Hemodynamic

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Section: Strokementioning

confidence: 91%

Persistent Hemodynamic Changes in Ruptured Brain Arteriovenous Malformations

Illies

Forkert

Saering

et al. 2012

Stroke

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“…Global segmentation of a neurovascular mask (Auto-Segmentation) Initially a universal threshold value was calculated to segment intravascular blood from surrounding cranial tissue (Figure 3A). All voxel intensities throughout the CTA, DSA, or TOF-MRA image were linearly divided into 𝑛 𝑏𝑖𝑛 = 50 bins from maximal to minimal voxel intensity, similar to Nyúl et al, 2000. Voxel intensity brightness, x, belonging to different tissue types, such as dim extravascular soft tissue versus bone versus bright intravascular blood, was estimated by a sum of lognormal distributions (Figure 3B) as previously performed for MRA images (Forkert et al, 2012):…”

Section: Methods and Calculationmentioning

confidence: 99%

Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms

Allenby

Liang

Harvey

et al. 2021

Computerized Medical Imaging and Graphics

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“…All voxel intensities throughout the CTA, DSA, or TOF-MRA image were linearly divided into = 50 bins from maximal to minimal voxel intensity, similar to Nyúl et al, 2000. Voxel intensity brightness, x, belonging to different tissue types, such as dim extravascular soft tissue versus bone versus bright intravascular blood, was estimated by a sum of lognormal distributions ( Figure 3B) as previously performed for MRA images (Forkert et al, 2012): The images first undergo a global Auto-Segmentation to generate 2-3 tissue masks, the segmented neurovascular mask undergoes several Mask Processing steps, followed by a Centreline Estimation throughout the mask. Using the centreline, several Morphometry metrics are applied to measure geometrical properties of each voxel within the segmented vascular mask.…”

Section: Methods and Calculationmentioning

confidence: 99%

Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms

Allenby

Liang

Harvey

et al. 2020

Preprint

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Unruptured intracranial aneurysms (UIAs) are prevalent neurovascular anomalies which, in rare circumstances, rupture to create a catastrophic subarachnoid haemorrhage. Although surgical management can reduce rupture risk, the majority of IAs exist undiscovered until rupture. Current computer-aided UIA diagnoses sensitively detect and measure UIAs within cranial angiograms, but remain limited to low specificities whose output requires considerable neuroradiologist interpretation not amenable to broad screening efforts. To address these limitations, we propose an analysis which interprets single-voxel morphometry of segmented neurovasculature to identify UIAs. Once neurovascular anatomy of a specified resolution is segmented, interrelationships between voxel-specific morphometries are estimated and spatially-clustered outliers are identified as UIA candidates. Our automated solution detects UIAs within magnetic resonance angiograms (MRA) at unmatched 86% specificity and 81% sensitivity using 3 minutes on a conventional laptop. Our approach does not rely on interpatient comparisons or training datasets which could be difficult to amass and process for rare incidentally discovered UIAs within large MRA files, and in doing so, is versatile to user-defined segmentation quality, to detection sensitivity, and across a range of imaging resolutions and modalities. We propose this method as a unique tool to aid UIA screening, characterisation of abnormal vasculature in at-risk patients, morphometry-based rupture risk prediction, and identification of other vascular abnormalities. Graphical AbstractHighlightsRapid and automated detection of unruptured intracranial aneurysms (UIAs) in MRAsHighly specific, sensitive UIA detection to reduce radiologist input for screeningDetection is versatile to image resolution, modality and has tuneable mm sensitivity

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Analysis of the Influence of 4D MR Angiography Temporal Resolution on Time-to-Peak Estimation Error for Different Cerebral Vessel Structures

Cited by 5 publications

References 23 publications

Persistent Hemodynamic Changes in Ruptured Brain Arteriovenous Malformations

Persistent Hemodynamic Changes in Ruptured Brain Arteriovenous Malformations

Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms

Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms

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