A Novel Estimation Method for Physiological Parameters in Dynamic Contrast-Enhanced MRI: Application of a Distributed Parameter Model Using Fourier-Domain Calculations

Bisdas

Magnetic Resonance Imaging

et al. 2011

114

130

Tracer kinetic methods employed for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial-venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods for T 1 -weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrastenhanced MRI data.

Section: Distributed Parameter (Dp) Models and Variationsmentioning

confidence: 99%

“…However, recent studies have raised concerns about the presence of discontinuities in the impulse residue functions of DP models which could result in convergence problems during fitting, leading to reduced parameter precision (75,(77)(78)(79). It is explained in Ref.…”

Section: Distributed Parameter (Dp) Models and Variationsmentioning

confidence: 99%

Fundamentals of tracer kinetics for dynamic contrast‐enhanced MRI

Bisdas

Magnetic Resonance Imaging

et al. 2011

114

130

“…The closed-form model solution exists only in the Laplace space [205]. According to Garpebring et al [206], the TH solution could be found with the fast Fourier transform. This approach removes many practical obstacles to using the TH in DCE-MRI analysis [70].…”

Section: B Tissue Homogeneity (Th) Modelmentioning

confidence: 99%

“…However, the TH estimation is somewhat difficult and slow even with the AATH, due to a large number of initial guesses in order to avoid too large parameter values [207]. Also, the increased computational cost if voxel-wise parametric maps are needed hinders the wider use of the model [206].…”

Section: B Tissue Homogeneity (Th) Modelmentioning

confidence: 99%

“…In contrast to the DP, the TH has not been widely used for DCE-MRI analysis due to the lack of a closed-form solution in the time domain. Because the closed-form solution exists only in the Laplace space [205], the fast Fourier transform was used by Garpebring et al [206] to apply the TH for studying brain tumors.…”

Section: Clinical Applications Of Parametric Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of contrast-enhanced medical images.

Khalifa¹

DEDICATIONThis dissertation is dedicated to my mother. Words cannot express how much I wish that she was still with me to see the completion of this endeavor.iii ACKNOWLEDGMENTSIn the name of Allah the most merciful, the most compassionate. All deepest thanks are due to Almighty Allah for the uncountable gifts given to me.I would like to thank my dissertation advisor, Dr. Ayman El-Baz, for his continuous encouragement, guidance, and support over the course of my Ph.D. study. I thank him for giving me the opportunity to work in his research group and providing me the opportunity to work on promising applications and meet interesting people. Without his expertise, guidance, and commitment, my research would not have been possible. I truly appreciate his optimism whenever things looked impossible.I also express my deepest gratitude to Dr. Karla Conn Welch, my dissertation coadvisor, and Dr. Tamer Inanc, Dr. Cindy Harnett, Dr. Olfa Nasraoui, and Dr. Jake Wildstrom for being on my dissertation committee with enthusiasm and taking interest in my research in the midst of many other responsibilities and commitments.I also have to thank Dr. Georgy Gimel'farb for his useful discussions and valuable comments and feedback. He has never hesitated to share his experience in Markov-Gibbs random field and the field of image processing. I also would like to thank Dr. Mohamed Abou El-Ghar of the radiology department, Urology and Nephrology Center, University of Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection,(ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer.However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfuvi sion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building n...

Two‐compartment modeling of tissue microcirculation revisited

2017

Physiologically well-defined tissue parameters are structurally identifiable and accurately estimable from DCE data by the conceptually modified two-compartment model in combination with the bias correction. The accuracy of the bias-corrected flow is nearly comparable to that of the three other (theoretically unbiased) model parameters. As compared to conventional two-compartment modeling, this feature constitutes a major advantage for tracer kinetic analysis of both preclinical and clinical DCE imaging studies.