Statistical properties of cerebral CT perfusion imaging systems. Part I. Cerebral blood volume maps generated from nondeconvolution‐based systems

Li, Ké; Strother, Charles M.; Chen, Guang‐Hong

doi:10.1002/mp.13806

Cited by 4 publications

(7 citation statements)

References 47 publications

(55 reference statements)

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“… Cerebral blood volume (CBV) maps of the three in vivo canine subjects used in the Part I paper . CTDI

normalvol

of the computed tomography perfusion (CTP) exam with modulated mA was matched to that of the constant mA CTP exam.…”

Section: Resultsmentioning

confidence: 99%

“…The numerical validation study used the same digital CTP phantom as the one in the Part I paper . Descriptions of this digital phantom can be found in the Part I paper.…”

Section: Numerical and Experimental Methodsmentioning

confidence: 99%

“…In addition to this canine study, image data of another three canine subjects used in the Part I paper were retrospectively processed by the deconvolution‐based processing method to demonstrate an example application of the theoretical model, which is on the prediction of the benefit of baseline noise reduction to deconvolution results. Data acquisition methods for these three canine studies can be found in the Part I paper.…”

Section: Numerical and Experimental Methodsmentioning

confidence: 99%

“…Motivated by this theoretical analysis, we retrospectively analyzed CTP data of the three canine subjects used in the Part I paper . For each subject, a CTP acquisition was performed using an x‐ray tube current (mA) modulation scheme optimized for nondeconvolution‐based CBV imaging, so that

σ_{b}^{2}

is only 5% of

σ_{0}^{2}

.…”

Section: Example Application Of the Theoretical Model: Impact Of Basementioning

confidence: 99%

“…In a separate Part I paper, the signal and noise properties of cerebral blood volume (CBV) generated from nondeconvolution‐based computed tomography perfusion (CTP) systems were analyzed . In this Part II paper, the focus will be switched to the signal and noise properties of deconvolution‐based systems.…”

Section: Introductionmentioning

confidence: 99%

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Statistical properties of cerebral CT perfusion imaging systems. Part II. Deconvolution‐based systems

Chen

2019

Medical Physics

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Purpose: The purpose of this work was to develop a theoretical framework to pinpoint the quantitative relationship between input parameters of deconvolution-based cerebral computed tomography perfusion (CTP) imaging systems and statistical properties of the output perfusion maps. Methods: Deconvolution-based CTP systems assume that the arterial input function, tissue enhancement curve, and flow-scaled residue function k(t) are related to each other through a convolution model, and thus by reversing the convolution operation, k(t) and the associated perfusion parameters can be estimated. The theoretical analysis started by deriving analytical formulas for the expected value and autocovariance of the residue function estimated using the singular value decompositionbased deconvolution method. Next, it analyzed statistical properties of the "max" and "arg max" operators, based on which the signal and noise properties of cerebral blood flow (CBF) and time-tomax (t max ) are quantitatively related to the statistical model of the estimated residue function [k Ã ðtÞ] and system parameters. To validate the theory, CTP images of a digital head phantom were simulated, from which signal and noise of each perfusion parameter were measured and compared with values calculated using the theoretical model. In addition, an in vivo canine experiment was performed to validate the noise model of cerebral blood volume (CBV).Results: For the numerical study, the relative root mean squared error between the measured and theoretically calculated value is ≤0.21% for the autocovariance matrix of k Ã ðtÞ, and is ≤0.13% for the expected form of k Ã ðtÞ. A Bland-Altman analysis demonstrated no significant difference between measured and theoretical values for the mean or noise of each perfusion parameter. For the animal study, the theoretical CBV noise fell within the 25th and 75th percentiles of the experimental values. To provide an example of the theory's utility, an expansion of the CBV noise formula was performed to unveil the dominant role of the baseline image noise in deconvolution-based CBV. Correspondingly, data of the three canine subjects used in the Part I paper were retrospectively processed to confirm that preferentially partitioning dose to the baseline frames benefits both nondeconvolution-and deconvolution-based CBV maps. Conclusions: Quantitative relationships between the statistical properties of deconvolution-based CTP maps, source image acquisition and reconstruction parameters, contrast injection protocol, and deconvolution parameters are established.

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“… Cerebral blood volume (CBV) maps of the three in vivo canine subjects used in the Part I paper . CTDI

normalvol

of the computed tomography perfusion (CTP) exam with modulated mA was matched to that of the constant mA CTP exam.…”

Section: Resultsmentioning

confidence: 99%

“…The numerical validation study used the same digital CTP phantom as the one in the Part I paper . Descriptions of this digital phantom can be found in the Part I paper.…”

Section: Numerical and Experimental Methodsmentioning

confidence: 99%

Section: Numerical and Experimental Methodsmentioning

confidence: 99%

σ_{b}^{2}

is only 5% of

σ_{0}^{2}

.…”

Section: Example Application Of the Theoretical Model: Impact Of Basementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical properties of cerebral CT perfusion imaging systems. Part II. Deconvolution‐based systems

Chen

2019

Medical Physics

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Iterative reconstruction for low-dose cerebral perfusion computed tomography using prior image induced diffusion tensor

Niu¹,

Liu²,

Zhang³

et al. 2021

Phys. Med. Biol.

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Cerebral perfusion computed tomography (CPCT) can depict the functional status of cerebral circulation at the tissue level; hence, it has been increasingly used to diagnose patients with cerebrovascular disease. However, there is a significant concern that CPCT scanning protocol could expose patients to excessive radiation doses. Although reducing the x-ray tube current when acquiring CPCT projection data is an effective method for reducing radiation dose, this technique usually results in degraded image quality. To enhance the image quality of low-dose CPCT, we present a prior image induced diffusion tensor (PIDT) for statistical iterative reconstruction, based on the penalized weighted least-squares (PWLS) criterion, which we referred to as PWLS-PIDT, for simplicity. Specifically, PIDT utilizes the geometric features of pre-contrast scanned high-quality CT image as a structure prior for PWLS reconstruction; therefore, the low-dose CPCT images are enhanced while preserving important features in the target image. An effective alternating minimization algorithm is developed to solve the associated objective function in the PWLS-PIDT reconstruction. We conduct qualitative and quantitative studies to evaluate the PWLS-PIDT reconstruction with a digital brain perfusion phantom and patient data. With this method, the noise in the reconstructed CPCT images is more substantially reduced than that of other competing methods, without sacrificing structural details significantly. Furthermore, the CPCT sequential images reconstructed via the PWLS-PIDT method can derive more accurate hemodynamic parameter maps than those of other competing methods.

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Leveraging non-contrast head CT to improve the image quality of cerebral CT perfusion maps

Harvey

2020

J. Med. Imag.

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Statistical properties of cerebral CT perfusion imaging systems. Part I. Cerebral blood volume maps generated from nondeconvolution‐based systems

Cited by 4 publications

References 47 publications

Statistical properties of cerebral CT perfusion imaging systems. Part II. Deconvolution‐based systems

Statistical properties of cerebral CT perfusion imaging systems. Part II. Deconvolution‐based systems

Iterative reconstruction for low-dose cerebral perfusion computed tomography using prior image induced diffusion tensor

Leveraging non-contrast head CT to improve the image quality of cerebral CT perfusion maps

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