Advanced signal processing methods in dynamic contrast enhanced magnetic resonance imaging

Tato dizertační práce představuje metodu zobrazování perfúze magnetickou rezonancí, jež je výkonným nástrojem v diagnostice, především v onkologii. Po ukončení sběru časové sekvence T1-váhovaných obrazů zaznamenávajících distribuci kontrastní látky v těle začíná fáze zpracování dat, která je předmětem této dizertace. Je zde představen teoretický základ fyziologických modelů a modelů akvizice pomocí magnetické rezonance a celý řetězec potřebný k vytvoření obrazů odhadu parametrů perfúze a mikrocirkulace v tkáni. Tato dizertační práce je souborem uveřejněných prací autora přispívajícím k rozvoji metodologie perfúzního zobrazování a zmíněného potřebného teoretického rozboru.This dissertation describes quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), which is a powerful tool in diagnostics, mainly in oncology. After a time series of T1-weighted images recording contrast-agent distribution in the body has been acquired, data processing phase follows. It is presented step by step in this dissertation. The theoretical background in physiological and MRI-acquisition modeling is described together with the estimation process leading to parametric maps describing perfusion and microcirculation properties of the investigated tissue on a voxel-by-voxel basis. The dissertation is divided into this theoretical analysis and a set of publications representing particular contributions of the author to DCE-MRI.

Improved quantification of perfusion in patients with cerebrovascular disease.

In recent years measurements of cerebral perfusion using bolus-tracking MRI have become common clinical practice in the diagnosis and management of patients with stroke and cerebrovascular disease. An active area of research is the development of methods to identify brain tissue that is at risk of irreversible damage, but amenable to salvage using reperfusion treatments, such as thrombolysis. However, the specificity and sensitivity of these methods are limited by the inaccuracies in the perfusion data. Accurate measurements of perfusion are difficult to obtain, especially in patients with cerebrovascular diseases. In particular, if the bolus of MR contrast is delayed and/or dispersed due to cerebral arterial abnormalities, perfusion is likely to be underestimated using the standard analysis techniques. The potential for such underestimation is often overlooked when using the perfusion maps to assess stroke patients. Since thrombolysis can increase the risk of haemorrhage, a misidentification of 'at-risk' tissue has potentially dangerous clinical implications. This thesis presents several methodologies which aim to improve the accuracy and interpretation of the analysed bolus-tracking data. Two novel data analysis techniques are proposed, which enable the identification of brain regions where delay and dispersion of the bolus are likely to bias the perfusion measurements. In this way true hypoperfusion can be distinguished from erroneously low perfusion estimates. The size of the perfusion measurement errors are investigated in vivo, and a parameterised characterisation of the bolus delay and dispersion is obtained. Such information is valuable for the interpretation of in vivo data, and for further investigation into the effects of abnormal vasculature on perfusion estimates. Finally, methodology is presented to minimise the perfusion measurement errors prevalent in patients with cerebrovascular diseases. The in vivo application of this method highlights the dangers of interpreting perfusion values independently of the bolus delay and dispersion

Quantitative Magnetic Resonance Imaging of Tissue Microvasculature and Microstructure in Selected Clinical Applications

This thesis is based on four papers and aims to establish perfusion and diffusion measurements with magnetic resonance imaging (MRI) in selected clinical applications. While structural imaging provides invaluable geometric and anatomical information, new disease relevant information can be obtained from measures of physiological processes inferred from advanced modelling. This study is motivated by clinical questions pertaining to diagnosis and treatment effects in particular patient groups where inflammatory processes are involved in the disease. Paper 1 investigates acquisition parameters in dynamic contrast enhanced (DCE)-MRI of the temporomandibular joint (TMJ) with possible involvement of juvenile idiopathic arthritis. High level elastic motion correction should be applied to DCE data from the TMJ, and the DCE data should be acquired with a sample rate of at least 4 s. Paper 2 investigates choices of arterial input functions (AIFs) in dynamic susceptibility contrast (DSC)-MRI in brain metastases. AIF shapes differed across patients. Relative cerebral blood volume estimates differentiated better between perfusion in white matter and grey matter when scan-specific AIFs were used than when patient-specific AIFs and population-based AIFs were used. Paper 3 investigates DSC-MRI perfusion parameters in relation to outcome after stereotactic radiosurgery (SRS) in brain metastases. Low perfusion prior to SRS may be related to unfavourable outcome. Paper 4 applies free water (FW) corrected diffusion MRI to characterise glioma. Fractional anisotropy maps of the tumour region were significantly impacted by FW correction. The estimated FW maps may also contribute to a better description of the tumour. Although there are challenges related to post-processing of MRI data, it was shown that the advanced MRI methods applied can add to a more accurate description of the TMJ and of brain lesions.Doktorgradsavhandlin

Quantification of perfusion abnormalities using dynamic contrast-enhanced magnetic resonance imaging in muco-obstructive lung diseases

Pulmonary perfusion is regionally impaired in muco-obstructive lung diseases such as cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) due to the destruction of the alveolar-capillary bed and hypoxic pulmonary vasoconstriction in response to alveolar hypoxia. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an established technique for assessing regional perfusion abnormalities by exploiting contrast enhancement in the lung parenchyma during the first pass of an intravenously injected contrast agent bolus. Typically, perfusion abnormalities are assessed in clinical studies by visual scoring or by quantifying pulmonary blood flow (PBF) and pulmonary blood volume (PBV). Automated quantification can help to address inter-reader variability issues with human reader, facilitate detailed perfusion analyses and is time efficient. However, currently used absolute quantification of PBF and PBV is highly variable. For this reason, an algorithm was developed to quantify the extent of pulmonary perfusion in percent (QDP) using unsupervised clustering algorithms, which leads to an intrinsic normalisation and can reduce variability compared to absolute perfusion quantification. The aims of this work were to develop a robust algorithm for quantifying QDP, to investigate the midterm reproducibility of QDP, and to validate QDP using MRI perfusion scoring, quantitative computed tomography (CT) parameters, and pulmonary function testing (PFT) parameters. Furthermore, the performance of QDP was compared to the performance of PBF and PBV. The development of QDP and its technical and clinical validation were performed using data from two studies, which utilise DCE-MRI. First, the algorithm was developed using data of 83 COPD subjects from the ‘COSYCONET’ COPD cohort by comparing different unsupervised clustering approaches including Otsu´s method, k-means clustering, and 80th percentile threshold. Second, the reproducibility of QDP was investigated using data from a study of 15 CF and 20 COPD patients who underwent DCE-MRI at baseline and one month later (reproducibility study). According to the indicator dilution theory, impulse response function maps were calculated from DCE-MRI data, which formed the basis for the quantification of QDP, PBF and PBV. Overall, QDP based on Otsu´s method showed the highest agreement with the MRI perfusion score, quantitative CT parameters and PFT parameters in the COSYCONET study and was therefore selected for further evaluations. QDP correlated moderately with the MRI perfusion score in CF (r=0.46, p<0.05) and moderately to strongly in COPD (r=0.66 and r=0.72, p<0.001) in both studies. PBF and PBV correlated poorly with the MRI perfusion score in CF (r=-0.29, p=0.132 and r=-0.35, p=0.067, respectively) and moderately in COPD (r=-0.49 to -0.57, p<0.001). QDP correlated strongly with the CT parameter for emphysema (r=0.74, p<0.001) and weakly with the CT parameter for functional small airway disease (r=0.35, p<0.001) in COPD. The extent of perfusion defects from DCE-MRI corresponded to extent of abnormal lung (emphysema+functional small airway disease) from CT, with a mean difference of 6.03±16.94. QDP correlated moderately with PFT parameters in both studies and patient groups, with one exception in the reproducibility study where no correlation was observed in the COPD group. The use of unsupervised clustering approaches increased the reproducibility (±1.96SD related to the median) of QDP (CF: ±38%, COPD: ±37%) compared to PBF(CF: ±89%, COPD: ±55%) and PBV(CF: ±55%, COPD: ±51%) and reduced outliers. These results demonstrate that the quantification of pulmonary perfusion using unsupervised clustering approaches in combination with the mathematical models of the indicator dilution theory improves the reproducibility and the correlations with visual MRI perfusion scoring, quantitative CT parameters and PFT parameters. QDP based on Otsu´s method showed high agreement with the MRI perfusion score, suggesting that in future clinical studies pulmonary perfusion can be assessed objectively by computer algorithms replacing the time-consuming visual scoring. Concordance between the extent of QDP from MRI and the extent of abnormal lung from CT indicates that pulmonary perfusion abnormalities themselves may contribute to, or at least precede, the development of irreversible emphysema. The findings of both studies show that QDP is clinically meaningful in muco-obstructive lung diseases as it is significantly associated with the MRI perfusion score, quantitative CT parameters, and PFT parameters

Diffusion and perfusion MRI and applications in cerebral ischaemia

Two MRI techniques, namely diffusion and perfusion imaging, are becoming increasingly used for evaluation of the pathophysiology of stroke. This work describes the use of these techniques, together with more conventional MRI modalities (such as T1, and T2 imaging) in the investigation of cerebral ischaemia. The work was performed both in a paediatric population in a whole-body clinical MR system (1.5 T) and in an animal model of focal ischaemia at high magnetic field strength (8.5 T). For the paediatric studies, a single shot echo planar imaging (EPI) sequence was developed to enable the on-line calculation of maps of the trace of the diffusion tensor. In the process of this development, it was necessary to address two different imaging artefacts in these maps: eddy current induced image shifts, and residual Nyquist ghost artefacts. Perfusion imaging was implemented using an EPI sequence to follow the passage through the brain of a bolus of a paramagnetic contrast agent. Computer simulations were performed to evaluate the limitations of this technique in the quantification of cerebral blood flow when delay in the arrival and dispersion of the bolus of contrast agent are not accounted for. These MRI techniques were applied to paediatric patients to identify acute ischaemic events, as well as to differentiate between multiple acute events, or between acute and chronic events. Furthermore, the diffusion and perfusion findings were shown to contribute significantly to the management of patients with high risk of stroke, and in the evaluation of treatment outcome. In the animal experiments, permanent middle cerebral artery occlusion was performed in rats to investigate longitudinally the acute MRI changes (first 4-6 hours) following an ischaemic event. This longitudinal analysis contributed to the understanding of the evolution of the ischaemic lesion. Furthermore, the findings allowed the acute identification of tissue 'at risk' of infarction

Semiautomated Skeletonization of the Pulmonary Arterial Tree in Micro-CT Images

We present a simple and robust approach that utilizes planar images at different angular rotations combined with unfiltered back-projection to locate the central axes of the pulmonary arterial tree. Three-dimensional points are selected interactively by the user. The computer calculates a sub- volume unfiltered back-projection orthogonal to the vector connecting the two points and centered on the first point. Because more x-rays are absorbed at the thickest portion of the vessel, in the unfiltered back-projection, the darkest pixel is assumed to be the center of the vessel. The computer replaces this point with the newly computer-calculated point. A second back-projection is calculated around the original point orthogonal to a vector connecting the newly-calculated first point and user-determined second point. The darkest pixel within the reconstruction is determined. The computer then replaces the second point with the XYZ coordinates of the darkest pixel within this second reconstruction. Following a vector based on a moving average of previously determined 3- dimensional points along the vessel\u27s axis, the computer continues this skeletonization process until stopped by the user. The computer estimates the vessel diameter along the set of previously determined points using a method similar to the full width-half max algorithm. On all subsequent vessels, the process works the same way except that at each point, distances between the current point and all previously determined points along different vessels are determined. If the difference is less than the previously estimated diameter, the vessels are assumed to branch. This user/computer interaction continues until the vascular tree has been skeletonized

Methods for assisting the automation of Dynamic Susceptibility Contrast Magnetic Resonance Imaging Analysis

Purpose Dynamic susceptibility-contrast magnetic resonance imaging (DSC-MRI) is widely used for cerebral perfusion measurement, but dependence on operator input leads to a time-consuming, subjective, and poorly-reproducible analysis. Although automation can overcome these limitations, investigations are required to further simplify and accelerate the analysis. This research focuses on automating arterial voxel (AV) and brain tissue segmentation, and model-dependent deconvolution steps of DSC-MRI analysis. Methods Several features were extracted from DSC-MRI data; their AV- and tissue voxel- discriminatory powers were evaluated by the area-under-the-receiver-operating-characteristic-curve (AUCROC). Thresholds for discarding non-arterial voxels were identified using ROC cut-offs. The applicability of DSC-MRI time-series data for brain segmentation was explored. Two segmentation approaches that clustered the dimensionality-reduced raw data were compared with two raw−data-based approaches, and an approach using principal component analysis (PCA) for dimension-reduction. Computation time and Dice coefficients (DCs) were compared. For model-dependent deconvolution, four parametric transit time distribution (TTD) models were compared in terms of goodness- and stability-of-fit, consistency of perfusion estimates, and computation time. Results Four criteria were effective in distinguishing AVs, forming the basis of a framework that can determine optimal thresholds for effective criteria to discard tissue voxels with high sensitivity and specificity. Compared to raw−data-based approaches, one of the proposed segmentation approaches identified GM with higher (>0.7, p<0.005), and WM with similar DC. The approach outperformed the PCA-based approach for all tissue regions (p<0.005), and clustered similar regions faster than other approaches (p<0.005). For model-dependent deconvolution, all TTD models gave similar perfusion estimates and goodness-of-fit. The gamma distribution was most suitable for perfusion analysis, showing significantly higher fit stability and lower computation time. Conclusion The proposed methods were able to simplify and accelerate automatic DSC-MRI analysis while maintaining performance. They will particularly help clinicians in rapid diagnosis and characterisation of tumour or stroke lesions, and subsequent treatment planning and monitoring