Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast-enhanced MRI.

Purpose: Fitting tracer kinetic models using linear methods is much faster than using their nonlinear counterparts, although this comes often at the expense of reduced accuracy and precision. The aim of this study was to derive and compare the performance of the linear compartmental tissue uptake (CTU) model with its nonlinear version with respect to their percentage error and precision. Theory and Methods: The linear and nonlinear CTU models were initially compared using simulations with varying noise and temporal sampling. Subsequently, the clinical applicability of the linear model was demonstrated on 14 patients with locally advanced cervical cancer examined with dynamic contrast-enhanced magnetic resonance imaging. Results: Simulations revealed equal percentage error and precision when noise was within clinical achievable ranges (contrast-to-noise ratio >10). The linear method was significantly faster than the nonlinear method, with a minimum speedup of around 230 across all tested sampling rates. Clinical analysis revealed that parameters estimated using the linear and nonlinear CTU model were highly correlated (ρ ≥ 0.95). Conclusion: The linear CTU model is computationally more efficient and more stable against temporal downsampling, whereas the nonlinear method is more robust to variations in noise. The two methods may be used interchangeably within clinical achievable ranges of temporal sampling and noise

An extended vascular model for less biased estimation of permeability parameters in DCEâ T1 images

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137317/1/nbm3698_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137317/2/nbm3698.pd

Quantification of myocardial blood flow with cardiovascular magnetic resonance throughout the cardiac cycle

BACKGROUND: Myocardial blood flow (MBF) varies throughout the cardiac cycle in response to phasic changes in myocardial tension. The aim of this study was to determine if quantitative myocardial perfusion imaging with cardiovascular magnetic resonance (CMR) can accurately track physiological variations in MBF throughout the cardiac cycle. METHODS: 30 healthy volunteers underwent a single stress/rest perfusion CMR study with data acquisition at 5 different time points in the cardiac cycle (early-systole, mid-systole, end-systole, early-diastole and end-diastole). MBF was estimated on a per-subject basis by Fermi-constrained deconvolution. Interval variations in MBF between successive time points were expressed as percentage change. Maximal cyclic variation (MCV) was calculated as the percentage difference between maximum and minimum MBF values in a cardiac cycle. RESULTS: At stress, there was significant variation in MBF across the cardiac cycle with successive reductions in MBF from end-diastole to early-, mid- and end-systole, and an increase from early- to end-diastole (end-diastole: 4.50 ± 0.91 vs. early-systole: 4.03 ± 0.76 vs. mid-systole: 3.68 ± 0.67 vs. end-systole 3.31 ± 0.70 vs. early-diastole: 4.11 ± 0.83 ml/g/min; all p values <0.0001). In all cases, the maximum and minimum stress MBF values occurred at end-diastole and end-systole respectively (mean MCV = 26 ± 5%). There was a strong negative correlation between MCV and peak heart rate at stress (r = -0.88, p < 0.001). The largest interval variation in stress MBF occurred between end-systole and early-diastole (24 ± 9% increase). At rest, there was no significant cyclic variation in MBF (end-diastole: 1.24 ± 0.19 vs. early-systole: 1.28 ± 0.17 vs.mid-systole: 1.28 ± 0.17 vs. end-systole: 1.27 ± 0.19 vs. early-diastole: 1.29 ± 0.19 ml/g/min; p = 0.71). CONCLUSION: Quantitative perfusion CMR can be used to non-invasively assess cyclic variations in MBF throughout the cardiac cycle. In this study, estimates of stress MBF followed the expected physiological trend, peaking at end-diastole and falling steadily through to end-systole. This technique may be useful in future pathophysiological studies of coronary blood flow and microvascular function

Multimodal phantoms for clinical PET/MRI

Phantoms are commonly used throughout medical imaging and medical physics for a multitude of applications, the designs of which vary between modalities and clinical or research requirements. Within positron emission tomography (PET) and nuclear medicine, phantoms have a well-established role in the validation of imaging protocols so as to reduce the administration of radioisotope to volunteers. Similarly, phantoms are used within magnetic resonance imaging (MRI) to perform quality assurance on clinical scanners, and gel-based phantoms have a longstanding use within the MRI research community as tissue equivalent phantoms. In recent years, combined PET/MRI scanners for simultaneous acquisition have entered both research and clinical use. This review explores the designs and applications of phantom work within the field of simultaneous acquisition PET/MRI as published over the period of a decade. Common themes in the design, manufacture and materials used within phantoms are identified and the solutions they provided to research in PET/MRI are summarised. Finally, the challenges remaining in creating multimodal phantoms for use with simultaneous acquisition PET/MRI are discussed. No phantoms currently exist commercially that have been designed and optimised for simultaneous PET/MRI acquisition. Subsequently, commercially available PET and nuclear medicine phantoms are often utilised, with CT-based attenuation maps substituted for MR-based attenuation maps due to the lack of MR visibility in phantom housing. Tissue equivalent and anthropomorphic phantoms are often developed by research groups in-house and provide customisable alternatives to overcome barriers such as MR-based attenuation correction, or to address specific areas of study such as motion correction. Further work to characterise materials and manufacture methods used in phantom design would facilitate the ability to reproduce phantoms across sites

Spatial two tissue compartment model for DCE-MRI

In the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) compartment models allow to describe the uptake of contrast medium with biological meaningful kinetic parameters. As simple models often fail to adequately describe the observed uptake behavior, more complex compartment models have been proposed. However, the nonlinear regression problem arising from more complex compartment models often suffers from parameter redundancy. In this paper, we incorporate spatial smoothness on the kinetic parameters of a two tissue compartment model by imposing Gaussian Markov random field priors on them. We analyse to what extent this spatial regularisation helps to avoid parameter redundancy and to obtain stable parameter estimates. Choosing a full Bayesian approach, we obtain posteriors and point estimates running Markov Chain Monte Carlo simulations. The proposed approach is evaluated for simulated concentration time curves as well as for in vivo data from a breast cancer study

An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited

Background: Perfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology. Results: Five compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft’s model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters. Conclusion: We developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control

Comparison of Model Predictive Control performance using grey-box and white box controller models

Model predictive control (MPC) for building climate control has received increasing attention the last decade. Its large scale implementation is, however, still hampered by the difficulty of obtaining accurate but computationally efficient multi-zone building controller models. This paper compares an existing grey-box approach with a novel white-box approach to obtain a controller model of the building envelope and it compares the performance achieved by using these two approaches. The comparison is made for an existing office building, which is currently controlled using a grey-box MPC [1].  The building envelope and its heating, cooling and air conditioning systems  (HVAC) are modelled using the Modelica building energy simulation library IDEAS. The model is validated using measurement data from the real building. This detailed simulation model is composed of discretised partial differential equations, ordinary differential equations and algebraic equations. The model is therefore too complex to be used as controller model for MPC. Two MPC approaches are compared. On the one hand, the white-box controller model is obtained by linearizing the building envelope part of the simulation model and by pre-computing model inputs such as solar gains through each window [2]. The method generates a linear state space model, which produces very similar temperatures as the original non-linear model. On the other hand, the grey-box identification method that was used to obtain the current controller model, is also applied to the detailed simulation model. Both white-box and grey-box MPC are applied to the detailed simulation model. The dynamics of the HVAC systems are not included in the MPC model but the efficiencies, constraints, cost function and boundary conditions are included. The energy use, the achieved thermal zone comfort and the prediction performance are compared. Finally, a new grey-box model is identified with operation data of the real building and the multi-step ahead prediction performance of the white-box and of both the grey-box models obtained with the simulation data and obtained with the measured data is computed for the real building using the measurement data and the weather forecast, which are used by the current MPC implementation.  [1] Zdenek Vana, Jiri Cigler, Jan Siroky, Eva Zacekova, Lukas Ferkl. Model-based energy efficient control applied to an office building. J. Process Control (2014).  [2] Picard, D., Jorissen, F., and Helsen, L. 2015. Methodology for Obtaining Linear State Space Building Energy Simulation Models. In 11th International Modelica Conference, pages 51–58, Paris