Automatic selection of arterial input function on dynamic contrast-enhanced MR images

Peruzzo, Denis; Bertoldo, Alessandra; Zanderigo, Francesca; Cobelli, Claudio

doi:10.1016/j.cmpb.2011.02.012

Cited by 56 publications

(113 citation statements)

References 38 publications

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“…To assess the feasibility of AH clustering method for AIF detection, a simulation study was also conducted in the same manner proposed by Peruzzo et al [1] and Wu et al [21], where a ‘true’ and a ‘false’ AIF was modeled. Partial volume effect (PVE) was simulated using three kinds of tissues, normal gray matter (GM), normal white matter (WM) and pathological GM.…”

Section: Methodsmentioning

confidence: 99%

“…A recirculation process was added that comprised a copy of the aforementioned function with a delay of , which was convolved with an exponential with a time constant of . We used values of = 3.0, = 1.5 s, = 8 s, = 30 s [1], [6], [8], [16], and = 26 s, which approximated the arrival time of the contrast agent in our clinical perfusion data.…”

Section: Methodsmentioning

confidence: 99%

“…Cerebral perfusion describes the steady-state delivery of nutrients and oxygen via blood to the brain tissue parenchyma, and it comprises several cerebral hemodynamic parameters, i.e., cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT), which play important roles in the diagnosis and management of many diseases [1]–[5]. At present, several imaging techniques are used to analyze cerebral perfusion, including positron emission tomography, single photon emission computed tomography, and CT.…”

Section: Introductionmentioning

confidence: 99%

“…The original methods for AIF detection were based on the manual extraction of a region of interest, such as the middle cerebral artery (MCA) and internal carotid artery (ICA), which depended on the user's experience and subjective judgments, thereby leading to low accuracy and consistency [1], [8], [17]–[19]. Thus, the development of an automatic AIF detection method has been an interesting and demanding research problem.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Feasibility of an Agglomerative Hierarchy Clustering Algorithm for the Automatic Detection of the Arterial Input Function Using DSC-MRI

2014

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During dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI), it has been demonstrated that the arterial input function (AIF) can be obtained using fuzzy c-means (FCM) and k-means clustering methods. However, due to the dependence on the initial centers of clusters, both clustering methods have poor reproducibility between the calculation and recalculation steps. To address this problem, the present study developed an alternative clustering technique based on the agglomerative hierarchy (AH) method for AIF determination. The performance of AH method was evaluated using simulated data and clinical data based on comparisons with the two previously demonstrated clustering-based methods in terms of the detection accuracy, calculation reproducibility, and computational complexity. The statistical analysis demonstrated that, at the cost of a significantly longer execution time, AH method obtained AIFs more in line with the expected AIF, and it was perfectly reproducible at different time points. In our opinion, the disadvantage of AH method in terms of the execution time can be alleviated by introducing a professional high-performance workstation. The findings of this study support the feasibility of using AH clustering method for detecting the AIF automatically.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the Feasibility of an Agglomerative Hierarchy Clustering Algorithm for the Automatic Detection of the Arterial Input Function Using DSC-MRI

2014

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“…2, a set of 3D TOF-MRA (Time-of-Flight magnetic resonance angiography) images were first obtained by DCE-MRI in order to monitor the position of the middle cerebral artery (MCA) in the rat's brain [21]. The parameters of the TOF-MRA were the number of slices = 160, TR/TE = 14/5.42 ms, flip angle = 20°, slice thickness = 0.3 mm, FOV = 85×85 mm 2 , matrix = 256×256, slab = 5, and scanning time = 10 min 27 s.…”

Section: Methodsmentioning

confidence: 99%

Focused Ultrasound Simultaneous Irradiation/MRI Imaging, and Two-Stage General Kinetic Model

Huang¹,

Chen

et al. 2014

PLoS ONE

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Many studies have investigated how to use focused ultrasound (FUS) to temporarily disrupt the blood-brain barrier (BBB) in order to facilitate the delivery of medication into lesion sites in the brain. In this study, through the setup of a real-time system, FUS irradiation and injections of ultrasound contrast agent (UCA) and Gadodiamide (Gd, an MRI contrast agent) can be conducted simultaneously during MRI scanning. By using this real-time system, we were able to investigate in detail how the general kinetic model (GKM) is used to estimate Gd penetration in the FUS irradiated area in a rat's brain resulting from UCA concentration changes after single FUS irradiation. Two-stage GKM was proposed to estimate the Gd penetration in the FUS irradiated area in a rat's brain under experimental conditions with repeated FUS irradiation combined with different UCA concentrations. The results showed that the focal increase in the transfer rate constant of K trans caused by BBB disruption was dependent on the doses of UCA. Moreover, the amount of in vivo penetration of Evans blue in the FUS irradiated area in a rat's brain under various FUS irradiation experimental conditions was assessed to show the positive correlation with the transfer rate constants. Compared to the GKM method, the Two-stage GKM is more suitable for estimating the transfer rate constants of the brain treated with repeated FUS irradiations. This study demonstrated that the entire process of BBB disrupted by FUS could be quantitatively monitored by real-time dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

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Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering

Shi

Wang

Liu

et al. 2013

J. Magn. Reson. Imaging

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Automatic selection of arterial input function on dynamic contrast-enhanced MR images

Cited by 56 publications

References 38 publications

Evaluating the Feasibility of an Agglomerative Hierarchy Clustering Algorithm for the Automatic Detection of the Arterial Input Function Using DSC-MRI

Evaluating the Feasibility of an Agglomerative Hierarchy Clustering Algorithm for the Automatic Detection of the Arterial Input Function Using DSC-MRI

Focused Ultrasound Simultaneous Irradiation/MRI Imaging, and Two-Stage General Kinetic Model

Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering

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