Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, K(trans), plasma volume fraction, v(p), and extravascular, extracellular space, v(e), directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, v(p), K(trans), and v(e), respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches

3D single breath-hold MR methodology for measuring cardiac parametric mapping at 3T

Mención Internacional en el título de doctorOne of the foremost and challenging subfields of MRI is cardiac magnetic resonance imaging (CMR). CMR is becoming an indispensable tool in cardiovascular medicine by acquiring data about anatomy and function simultaneously. For instance, it allows the non-invasive characterization of myocardial tissues via parametric mapping techniques. These mapping techniques provide a spatial visualization of quantitative changes in the myocardial parameters. Inspired by the need to develop novel high-quality parametric sequences for 3T, this thesis's primary goal is to introduce an accurate and efficient 3D single breath-hold MR methodology for measuring cardiac parametric mapping at 3T. This thesis is divided into two main parts: i) research and development of a new 3D T1 saturation recovery mapping technique (3D SACORA), together with a feasibility study regarding the possibility of adding a T2 mapping feature to 3D SACORA concepts, and ii) research and implementation of a deep learning-based post-processing method to improve the T1 maps obtained with 3D SACORA. In the first part of the thesis, 3D SACORA was developed as a new 3D T1 mapping sequence to speed up T1 mapping acquisition of the whole heart. The proposed sequence was validated in phantoms against the gold standard technique IR-SE and in-vivo against the reference sequence 3D SASHA. The 3D SACORA pulse sequence design was focused on acquiring the entire left ventricle in a single breath-hold while achieving good quality T1 mapping and stability over a wide range of heart rates (HRs). The precision and accuracy of 3D SACORA were assessed in phantom experiments. Reference T1 values were obtained using IR-SE. In order to further validate 3D SACORA T1 estimation accuracy and precision, T1 values were also estimated using an in-house version of 3D SASHA. For in-vivo validation, seven large healthy pigs were scanned with 3D SACORA and 3D SASHA. In all pigs, images were acquired before and after administration of MR contrast agent. The phantom results showed good agreement and no significant bias between methods. In the in-vivo experiments, all T1-weighted images showed good contrast and quality, and the T1 maps correctly represented the information contained in the T1-weighted images. Septal T1s and coefficients of variation did not considerably differ between the two sequences, confirming good accuracy and precision. 3D SACORA images showed good contrast, homogeneity and were comparable to corresponding 3D SASHA images, despite the shorter acquisition time (15s vs. 188s, for a heart rate of 60 bpm). In conclusion, the proposed 3D SACORA successfully acquired a whole-heart 3D T1 map in a single breath-hold at 3T, estimating T1 values in agreement with those obtained with the IR-SE and 3D SASHA sequences. Following the successful validation of 3D SACORA, a feasibility study was performed to assess the potential of modifying the acquisition scheme of 3D SACORA in order to obtain T1 and T2 maps simultaneously in a single breath-hold. This 3D T1/T2 sequence was named 3D dual saturation-recovery compressed SENSE rapid acquisition (3D dual-SACORA). A phantom of eight tubes was built to validate the proposed sequence. The phantom was scanned with 3D dual-SACORA with a simulated heart rate of 60 bpm. Reference T1 and T2 values were estimated using IR-SE and GraSE sequences, respectively. An in-vivo study was performed with a healthy volunteer to evaluate the parametric maps' image quality obtained with the 3D dual-SACORA sequence. T1 and T2 maps of the phantom were successfully obtained with the 3D dual-SACORA sequence. The results show that the proposed sequence achieved good precision and accuracy for most values. A volunteer was successfully scanned with the proposed sequence (acquisition duration of approximately 20s) in a single breath-hold. The saturation time images and the parametric maps obtained with the 3D dual-SACORA sequence showed good contrast and homogeneity. The septal T1 and T2 values are in good agreement with reference sequences and published work. In conclusion, this feasibility study's findings open the door to the possibility of using 3D SACORA concepts to develop a successful 3D T1/T2 sequence. In the second part of the thesis, a deep learning-based super-resolution model was implemented to improve the image quality of the T1 maps of 3D SACORA, and a comprehensive study of the performance of the model in different MR image datasets and sequences was performed. After careful consideration, the selected convolutional neural network to improve the image quality of the T1 maps was the Residual Dense Network (RDN). This network has shown outstanding performance against state-of-the-art methods on benchmark datasets; however, it has not been validated on MR datasets. In this way, the RDN model was initially validated on cardiac and brain benchmark datasets. After this validation, the model was validated on a self-acquired cardiac dataset and on improving T1 maps. The RDN model improved the images successfully for the two benchmark datasets, achieving better performance with the brain dataset than with the cardiac dataset. This result was expected as the brain images have more well-defined edges than the cardiac images, making the resolution enhancement more evident. On the self-acquired cardiac dataset, the model also obtained an enhanced performance on image quality assessment metrics and improved visual assessment, particularly on well-defined edges. Regarding the T1 mapping sequences, the model improved the image quality of the saturation time images and the T1 maps. The model was able to enhance the T1 maps analytically and visually. Analytically, the model did not considerably modify the T1 values while improving the standard deviation in both myocardium and blood. Visually, the model improved the T1 maps by removing noise and motion artifacts without losing resolution on the edges. In conclusion, the RDN model was validated on three different MR datasets and used to improve the image quality of the T1 maps obtained with 3D SACORA and 3D SASHA. In summary, a 3D single breath-hold MR methodology was introduced, including a ready to-go 3D single breath-hold T1 mapping sequence for 3T (3D SACORA), together with the ideas for a new 3D T1/T2 mapping sequence (3D dual-SACORA); and a deep learning-based post-processing implementation capable of improving the image quality of 3D SACORA T1 maps.This thesis has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N722427.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Carlos Alberola López.- Secretario: María Jesús Ledesma Carbayo.- Vocal: Nathan Mewto

T2 and T2⁎ mapping and weighted imaging in cardiac MRI

Cardiac imaging is progressing from simple imaging of heart structure and function to techniques visualizing and measuring underlying tissue biological changes that can potentially define disease and therapeutic options. These techniques exploit underlying tissue magnetic relaxation times: T1, T2 and T2*. Initial weighting methods showed myocardial heterogeneity, detecting regional disease. Current methods are now fully quantitative generating intuitive color maps that do not only expose regionality, but also diffuse changes – meaning that between-scan comparisons can be made to define disease (compared to normal) and to monitor interval change (compared to old scans). T1 is now familiar and used clinically in multiple scenarios, yet some technical challenges remain. T2 is elevated with increased tissue water – edema. Should there also be blood troponin elevation, this edema likely reflects inflammation, a key biological process. T2* falls in the presence of magnetic/paramagnetic materials – practically, this means it measures tissue iron, either after myocardial hemorrhage or in myocardial iron overload. This review discusses how T2 and T2⁎ imaging work (underlying physics, innovations, dependencies, performance), current and emerging use cases, quality assurance processes for global delivery and future research directions

Rapid quantitative magnetization transfer imaging: utilizing the hybrid state and the generalized Bloch model

Purpose: To improve spatial resolution and scan time of quantitative magnetization transfer (qMT) imaging without constraints on model parameters. Theory and Methods: We combine two recently-proposed models in a Bloch-McConnell equation: the dynamics of the free spin pool is confined to the hybrid state and the dynamics of the semi-solid spin pool is described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three marked qMT parameters while accounting for an 8-parameter model. We sparsely sample each time frame along this spin dynamics with a 3D radial koosh-ball trajectory, reconstruct the data with sub-space modeling, and fit the qMT model with a neural network for computational efficiency. Results: We were able to extract qMT parameter maps of the whole brain with a nominal resolution of 1mm isotropic and high SNR from a 12.6 minute scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi-solid spin pool and slower relaxation, consistent with previous reports. Conclusion: The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for highly efficient quantitative magnetization transfer imaging

Quantitative Magnetic Resonance Imaging and Analysis of Articular Cartilage and Osteoarthritis

MRI plays an important role in the continuing search for a sensitive osteoarthritis (OA) imaging biomarker able to detect early, pre-morphological alterations in cartilage composition. Determining the compositional recovery pattern of cartilage following acute joint loading could potentially present a more sensitive biomarker for defining cartilage health [1]. However, only a limited amount of studies have assessed both the immediate effect of joint loading on cartilage, as well as its post-loading recovery. In addition, when assessing the compositional responses of cartilage to joint loading, previous studies usually did not incorporate the measurement error of the used quantitative MRI technique into their analysis. Therefore, an uncertainty persists whether or not compositional MRI techniques are sensitive enough to measure changes in water and macromolecular content of cartilage, or if previous studies were merely measuring noise. Consequently, an objective of this thesis is to increase our understanding of and reliability in quantitative T2 and T1ρ relaxation time mapping to detect compositional responses of cartilage following a joint loading activity. Furthermore, to obtain the quantitative morphological and compositional measures of cartilage, detailed region-specific delineation of cartilage is required. This delineation (or segmentation) of cartilage is laborious and time-consuming as it is usually performed manually by an expert observer. Many new advances in image analysis, particularly those in convolutional neural networks (CNNs) and deep learning, have enabled a time-efficient semi- or fully-automated alternative to this process [2, 3]. This thesis explores the utility of deep CNNs generated segmentations for accurate surface-based analysis of cartilage morphology and composition from knee MRIs as well as of cortical bone thickness from knee CTs. Chapter 1 will provide an introduction into the structure and biomechanics of articular cartilage and the role of MRI in imaging the degenerative joint disorder, osteoarthritis as well as the effects of different joint loading activities on cartilage morphology and composition. Chapter 2 explains the principle of MRI and the pulse sequences used in the following chapter for the morphometric and compositional assessment of articular cartilage. Chapter 3 describes the use of 3D Cartilage Surface Mapping (3D-CaSM) [3] to assess variations in cartilage T1ρ and T2 relaxation times of young, healthy participants following a mild, unilateral stepping activity. By evaluating and incorporating the intrasessional repeatability of the T1ρ and T2 mapping techniques, I aim to highlight those cartilage areas experiencing exercise-induced compositional changes greater than measurement error. A significant amount of time is needed to manually segment the regions-of-interest required to perform the 3D-CaSM used in Chapter 3. Therefore, in Chapter 4, I assessed the use of deep convolutional neural networks for automating the segmentation process for multiple knee joint tissues simultaneous and increase the time-efficiency for evaluating knee MR datasets. I evaluated the use of a conditional Generative Adversarial Network (cGAN) as a potentially improved method for automated segmentation compared to the widely used convolutional neural network, U-Net. In Chapter 5 I combined the 3D-CaSM and automated segmentation methods presented in Chapters 3 and 4, respectively to assess the use of fully automatic segmentations of femoral and tibial bone-cartilage structures for accurate surface-based analysis of cartilage morphology and composition on knee MR images. This was performed on publicly available data from the Osteoarthritis Initiative, a multicentre observational study with expert manual segmentations provided by the Zuse Institute in Berlin. Chapter 6 describes an automated pipeline for subchondral cortical bone thickness mapping from knee CT data. I developed a method of using automated segmentations of articular cartilage and bone from knee MRI data to determine the periarticular bone surface which is covered by cartilage. This surface was then used to perform cortical bone thickness measurements on corresponding CT data. I validated this pipeline using data from the EU-funded, multi-centre observational study called Applied Private-Public partneRship enabling OsteoArthritis Clinical Headway (APPROACH). Chapter 7 summarises the main conclusions and contributions of the works presented in this thesis as well as providing directions for future work.PhD Studentship funded by GlaxoSmithKlin

HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting

Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on dictio-nary matching to map the temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent discretization errors, as well as high computational complexity as the dictionary size grows. To alleviate these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting approach, referred to as HYDRA. Methods: HYDRA involves two stages: a model-based signature restoration phase and a learning-based parameter restoration phase. Signal restoration is implemented using low-rank based de-aliasing techniques while parameter restoration is performed using a deep nonlocal residual convolutional neural network. The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences. In test mode, it takes a temporal MRF signal as input and produces the corresponding tissue parameters. Results: We validated our approach on both synthetic data and anatomical data generated from a healthy subject. The results demonstrate that, in contrast to conventional dictionary-matching based MRF techniques, our approach significantly improves inference speed by eliminating the time-consuming dictionary matching operation, and alleviates discretization errors by outputting continuous-valued parameters. We further avoid the need to store a large dictionary, thus reducing memory requirements. Conclusions: Our approach demonstrates advantages in terms of inference speed, accuracy and storage requirements over competing MRF method

PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation

With the advent of convolutional neural networks~(CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as $T_1$-weighted and $T_2$-weighted contrasts with only $T_1$-weighted training data. The segmentations generated are highly accurate with state-of-the-art results~(overall Dice overlap$=0.94$), with a fast run time~($\approx$ 45 seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev

An Information Theory Model for Optimizing Quantitative Magnetic Resonance Imaging Acquisitions

Quantitative magnetic resonance imaging (qMRI) is a powerful group of imaging techniques with a growing number of clinical applications, including synthetic image generation in post-processing, automatic segmentation, and diagnosis of disease from quantitative parameter values. Currently, acquisition parameter selection is performed empirically for quantitative MRI. Tuning parameters for different scan times, tissues, and resolutions requires some measure of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to maximize image quality and the reliability of the previously mentioned methods which follow image acquisition. The objective of this work is to introduce and evaluate a quantitative method for selecting parameters that minimize image variability. An information theory framework was developed for this purpose and applied to a 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) signal model for synthetic MRI. In this framework, mutual information is used to measure the information gained by a measurement as a function of acquisition parameters, quantifying the information content of the acquisition parameters and allowing informed parameter selection. The information theory framework was tested on synthetic data generated from a representative mathematical phantom, measurements acquired on a qMRI multiparametric imaging standard phantom, and in vivo measurements in a human brain. The application of this information theory framework resulted in successful parameter optimization with respect to mutual information. Both the phantom and in vivo measurements showed that higher mutual information calculated by the model correlated with smaller standard deviation in the reconstructed parametric maps. With this framework, optimal acquisition parameters can be selected to improve image quality, image repeatability, or scan time. This method could reduce the time and labor necessary to achieve images of the desired quality. Making an informed acquisition parameter selection reduces uncertainty in the imaging output and optimizes information gain within the bounds of clinical constraints

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

Quantitative MRI and machine learning for the diagnosis and prognosis of Multiple Sclerosis

Multiple sclerosis (MS) is an immune-mediated, inflammatory, neurological disease affecting myelin in the central nervous system, whose driving mechanisms are not yet fully understood. Conventional magnetic resonance imaging (MRI) is largely used in the MS diagnostic process, but because of its lack of specificity, it cannot reliably detect microscopic damage. Quantitative MRI provides instead feature maps that can be exploited to improve prognosis and treatment monitoring, at the cost of prolonged acquisition times and specialised MR-protocols. In this study, two converging approaches were followed to investigate how to best use the available MRI data for the diagnosis and prognosis of MS. On one hand, qualitative data commonly used in clinical research for lesion and anatomical purposes were shown to carry quantitative information that could be used to conduct myelin and relaxometry analyses on cohorts devoid of dedicated quantitative acquisitions. In this study arm, named bottom-up, qualitative information was up-converted to quantitative surrogate: traditional model-fitting and deep-learning frameworks were proposed and tested on MS patients to extract relaxometry and indirect-myelin quantitative data from qualitative scans. On the other hand, when using multi-modal MRI data to classify MS patients with different clinical status, different MR-features contribute to specific classification tasks. The top-down study arm consisted in using machine learning to reduce the multi-modal dataset dimensionality only to those MR-features that are more likely to be biophysically meaningful with respect to each MS phenotype pathophysiology. Results show that there is much more potential to qualitative data than lesion and tissue segmentation, and that specific MRI modalities might be better suited for investigating individual MS phenotypes. Efficient multi-modal acquisitions informed by biophysical findings, whilst being able to extract quantitative information from qualitative data, would provide huge statistical power through the use of large, historical datasets, as well as constitute a significant step forward in the direction of sustainable research