Defining a local arterial input function for perfusion MRI using independent component analysis

Calamante, Fernando; Mørup, Morten; Hansen, Lars Kai

doi:10.1002/mrm.20227

Cited by 162 publications

(181 citation statements)

References 20 publications

Supporting

Mentioning

178

Contrasting

Order By: Relevance

“…Researchers have proposed several methods to remedy such difficult situations. For example, a local AIF was proposed to improve the accuracy of quantification (29). An experimentally derived AIF is used for regions in which the measurement of AIF is not possible (25).…”

Section: Discussionmentioning

confidence: 99%

DCE‐MRI pixel‐by‐pixel quantitative curve pattern analysis and its application to osteosarcoma

Guo

Reddick

2009

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Purpose: To present a novel curve pattern analysis (CPA) method to characterize and quantify signal curves from the dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) data without any prerequisites such as arterial input function (AIF) or T 1 measurement. Materials and Methods:CPA parameters represent characteristics of the scaled DCE signal curve. Simulations were performed to investigate the dependence of CPA parameters on T 1 , TR, and flip angle. In vivo studies were performed on five pediatric patients with osteosarcoma. Parametric maps were generated using the CPA method and a pharmacokinetic model-based method for comparison.Results: Simulations show that CPA parameters varied less than 2% when T 1 changed from 300 msec to 1500 msec, and less than 10% when the flip angle changed from 30°to 40°. Various curve patterns can be qualitatively identified and recognized from CPA parameter maps. Simulation and in vivo studies showed that the CPA parameter had a strong correlation with k ep , with correlation coefficients of 0.9983 in the simulation and 0.95 in the in vivo studies. Conclusion:A novel CPA method is presented. Simulations and in vivo studies showed that the CPA method provides a feasible alternative to quantifying DCE-MRI studies with possibly higher repeatability by minimizing variations potentially induced by AIF and T 1 estimations and model dependence.

show abstract

Section: Discussionmentioning

confidence: 99%

DCE‐MRI pixel‐by‐pixel quantitative curve pattern analysis and its application to osteosarcoma

Guo

Reddick

2009

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…A detailed description of the various steps involved in the method can be found in Ref. 11. Using this methodology, the local AIF generated in patients with various cerebrovascular abnormalities was found to display regional heterogeneity, with areas having delayed bolus arrival and wider peaks that were consistent with the patients' vascular abnormalities (e.g., see Fig.…”

Section: Minimization Of the Dispersion Processmentioning

confidence: 99%

Bolus dispersion issues related to the quantification of perfusion MRI data

Calamante

2005

Magnetic Resonance Imaging

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Alternatively, it has been suggested that local or regional AIF measurements can improve determination of the perfusion parameters (5)(6)(7)(8)(9)(10)(11). These local AIFs are measured closer to the capillaries of the brain tissue compared to global AIF measurements.…”

mentioning

confidence: 99%

“…However, blood sampling cannot be performed at that point in the brain-vasculature. Therefore, previous validations of local AIF measurements have been performed by comparing the perfusion maps (and sometimes the CTCs) from local AIF measurements to those obtained from manually selected global AIF measurements (8)(9)(10)20,21). A single report showed additional comparison to PET perfusion maps (20).…”

mentioning

confidence: 99%

Evaluation of signal formation in local arterial input function measurements of dynamic susceptibility contrast MRI

Bleeker

Webb

Walderveen

et al. 2011

Magnetic Resonance in Med

View full text Add to dashboard Cite

Correct arterial input function (AIF) measurements in dynamic susceptibility contrast-MRI are crucial for quantification of the hemodynamic parameters. Often a single global AIF is selected near a large brain-feeding artery. Alternatively, local AIF measurements aim for voxel-specific AIFs from smaller arteries. Because local AIFs are measured higher in the arterial-tree, it is assumed that these will reflect the true input of the microvasculature much better. However, do the measured local AIFs reflect the true concentration-time curves of small arteries? To answer this question, in vivo data were used to evaluate local AIF candidates selected based on two different types of angiograms. For interpretation purposes, a 3D numerical model that simulated partial-volume effects in local AIF measurements was created and the simulated local AIFs were compared to the ground truth. The findings are 2-fold. First, the in vivo data showed that the shape-characteristics of local AIFs are similar to the shape-characteristics of gray matter concentration-time curves. Second, these findings are supported by the simulations showing broadening of the measured local AIFs compared to the ground truth. These findings are suggesting that local AIF measurements do not necessarily reflect the true concentration-time curve in small arteries. Magn Reson Med 67:1324-1331,

show abstract

Defining a local arterial input function for perfusion MRI using independent component analysis

Cited by 162 publications

References 20 publications

DCE‐MRI pixel‐by‐pixel quantitative curve pattern analysis and its application to osteosarcoma

DCE‐MRI pixel‐by‐pixel quantitative curve pattern analysis and its application to osteosarcoma

Bolus dispersion issues related to the quantification of perfusion MRI data

Evaluation of signal formation in local arterial input function measurements of dynamic susceptibility contrast MRI

Contact Info

Product

Resources

About