High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis

Østergaard, Leif; Weisskoff, Robert M.; Chesler, David A.; Gyldensted, Carsten; Rosen, Bruce R.

doi:10.1002/mrm.1910360510

Cited by 1,397 publications

(1,609 citation statements)

References 30 publications

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“…Therefore, deconvolution of the AIF must be performed to obtain the product function CBF ⅐ R(t) (i.e., the impulse response function), and CBF can then be calculated from its initial (or maximum) value (2). The AIF represents the concentration of tracer entering the tissue at time t. Although this function can vary throughout the slice, its shape is commonly estimated from a major artery (e.g., the internal carotid artery or the middle cerebral artery).…”

Section: C(t) Is the Tissue Concentration-time Curve Aif(t) Is The Amentioning

confidence: 99%

“…In that work the estimated AIF was modeled as a gamma-variate function, and the tissue residue function and the vascular transport function were modeled by single-exponential functions. A dispersion range was simulated, with the dispersion range given by the value ␦ of the time constant of the VTF modeled as VTF(t) ϭ (1/␦ ⅐ exp(-t/␦), and the deconvolution was performed using singular value decomposition (SVD) (2). The main finding of that study was that dispersion can introduce substantial CBF underestimation and mean transit time (MTT) overestimation (see Fig.…”

Section: Quantification Errors Due To Bolus Dispersionmentioning

confidence: 99%

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Bolus dispersion issues related to the quantification of perfusion MRI data

Calamante

2005

Magnetic Resonance Imaging

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Quantification of cerebral blood flow (CBF) using dynamicsusceptibility contrast (DSC) MRI relies on the deconvolution of the arterial input function (AIF). The AIF is commonly measured in a major artery (e.g., the middle cerebral artery), and the estimated function is used as a global AIF for the whole slice. However, the presence of bolus delay and dispersion between the artery and the tissue of interest can introduce significant errors in CBF quantification. While several methods have been introduced to minimize or eliminate the effects of bolus delay, the correction of bolus dispersion is more difficult to address because it requires a model for the vascular bed. This article summarizes how this dispersion effect can be incorporated into the model for CBF quantification, and discusses the magnitude of the errors introduced. Furthermore, alternative methods for correcting or minimizing the effects of bolus dispersion in the quantification of CBF are reviewed.

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Section: C(t) Is the Tissue Concentration-time Curve Aif(t) Is The Amentioning

confidence: 99%

Section: Quantification Errors Due To Bolus Dispersionmentioning

confidence: 99%

Bolus dispersion issues related to the quantification of perfusion MRI data

Calamante

2005

Magnetic Resonance Imaging

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“…2. Using the arterial input function and unenhanced lung T 1 maps, time‐contrast curves can be calculated for each voxel and peak contrast, mean transit time, and pulmonary perfusion can be calculated, as previously described 20, 21. These are calculated for each voxel over the time‐course of the perfusion dataset and can be presented in a parametric map.…”

Section: Discussionmentioning

confidence: 99%

Lung perfusion: MRI vs. SPECT for screening in suspected chronic thromboembolic pulmonary hypertension

Johns

Swift

Rajaram

et al. 2017

Magnetic Resonance Imaging

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PurposeTo assess the diagnostic accuracy of magnetic resonance imaging (MRI) perfusion against perfusion single photon emission tomography (SPECT) screening for chronic thromboembolic pulmonary hypertension (CTEPH). Ventilation/perfusion (V/Q) scintigraphy is recommended to screen for suspected CTEPH. It has previously been shown that 3D dynamic contrast‐enhanced (DCE) lung perfusion MRI has a similar sensitivity for diagnosing CTEPH in comparison to planar perfusion scintigraphy; however, planar scintigraphy has now been largely replaced by SPECT, due to higher spatial resolution and sensitivity.Materials and MethodsConsecutive patients with suspected CTEPH or unexplained pulmonary hypertension attending a referral center, who underwent lung DCE perfusion MRI at 1.5T, perfusion SPECT, and computed tomography pulmonary angiography (CTPA) within 14 days of right heart catheterization, from April 2013 to April 2014, were included. DCE‐MR, SPECT, and CTPA were independently analyzed by two blinded radiologists. Disagreements were corrected by consensus. The gold standard reference for the diagnosis of chronic thromboemboli was based on a review of multimodality imaging and clinical findings.ResultsIn all, 74 patients with suspected CTEPH underwent all three modalities. Forty‐six were diagnosed with CTEPH (36) or chronic thromboembolic disease (CTED) (10). 3D DCE perfusion MRI correctly identified all patients (sensitivity of 100%), compared with a 97% sensitivity for SPECT.ConclusionDCE lung perfusion MRI has increased sensitivity when compared with perfusion scintigraphy in screening for CTEPH. As MRI does not use ionizing radiation, it should be considered as a first‐line imaging modality in suspected CTEPH. Level of Evidence: 3 Technical Efficacy: Stage 3J. Magn. Reson. Imaging 2017;46:1693–1697.

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“…(4) and (7), respectively, and which should be proportional to the CM concentration, were subject to a numerical deconvolution from the arterial input function using the singular value decomposition (SVD) algorithm. ( 19 , 20 ) …”

Section: Introductionmentioning

confidence: 99%

MR perfusion measurements on pharyngeal tumors: Comparison of quantification strategies

Hietschold

Kittner

Schreyer

et al. 2004

J Applied Clin Med Phys

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For the case of pharyngeal carcinomas, the clinical value as well as the stability of several evaluation methods of MR tomographic perfusion measurement are compared. Eighteen patients suffering from histologically proven squamous cell carcinomas were investigated by MR tomography (1.5 T, 0.2 mmol/kg Gd‐DTPA) prior to and during radiation therapy. Perfusion measurements were performed using a double‐echo FLASH sequence. Parameters describing regional blood flow, blood volume, mean transit time, and interstitial concentration of contrast medium (CM) were calculated, applying seven different combinations of correction approaches (separating the shortening of T1 and T2∗, arterial input function (AIF), and tumor shunts). Their correlations to MR independent tumor physiological parameters were analyzed (metabolic activity measurements using 18F‐FDG‐PET, polarographical pO2 measurement, tumor volume). Significant improvements of the correlation between perfusion‐dependent and other tumor physiological parameters could be achieved by decoupling the shortening of T1 and T2∗ and by applying of the tumor shunt model. Deconvolution from the AIF deteriorated the correlation. Therefore, the elimination of the T1 shortening due to interstitial CM proves to be essential for MR perfusion measurements on contrast medium uptaking lesions. Depending on the measurement conditions (temporal resolution, signal‐to‐noise ratio), the consideration of the AIF can even make the results significantly worse by introducing additional measuring errors.PACS numbers: 87.61.‐c

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High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis

Cited by 1,397 publications

References 30 publications

Bolus dispersion issues related to the quantification of perfusion MRI data

Bolus dispersion issues related to the quantification of perfusion MRI data

Lung perfusion: MRI vs. SPECT for screening in suspected chronic thromboembolic pulmonary hypertension

MR perfusion measurements on pharyngeal tumors: Comparison of quantification strategies

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