Investigation of time-resolved volumetric MRI to enhance MR-guided radiotherapy of moving lung tumors

In photon radiotherapy of lung cancer, respiratory-induced motion introduces systematic and statistical uncertainties in treatment planning and dose delivery. By integrating magnetic resonance imaging (MRI) in the treatment planning process in MR-guided radiotherapy (MRgRT), uncertainties in target volume definition can be reduced with respect to state-of-the-art X-ray-based approaches. Furthermore, MR-guided linear accelerators (MR-Linacs) offer dose delivery with enhanced accuracy and precision through daily treatment plan adaptation and gated beam delivery based on real-time MRI. Today, the potential of MRgRT of moving targets is, however, not fully exploited due to the lack of time-resolved four-dimensional MRI (4D-MRI) in clinical practice. Therefore, the aim of this thesis was to develop and experimentally validate new methods for motion characterization and estimation with 4D-MRI for MRgRT of lung cancer. Different concepts were investigated for all phases of the clinical workflow - treatment planning, beam delivery, and post-treatment analysis. Firstly, a novel internal target volume (ITV) definition method based on the probability-of-presence of moving tumors derived from real-time 4D-MRI was developed. The ability of the ITVs to prospectively account for changes occurring over the course of several weeks was assessed in retrospective geometric analyses of lung cancer patient data. Higher robustness of the probabilistic 4D-MRI-based ITVs against interfractional changes was observed compared to conventional target volumes defined with four-dimensional computed tomography (4D-CT). The study demonstrated that motion characterization over extended times enabled by real-time 4D-MRI can reduce systematic and statistical uncertainties associated with today’s standard workflow. Secondly, experimental validation of a published motion estimation method - the propagation method - was conducted with a porcine lung phantom under realistic patient-like conditions. Estimated 4D-MRIs with a temporal resolution of 3.65 Hz were created based on orthogonal 2D cine MRI acquired at the scanner unit of an MR-Linac. A comparison of these datasets with ground truth respiratory-correlated 4D-MRIs in geometric analyses showed that the propagation method can generate geometrically accurate estimated 4D-MRIs. These could decrease target localization errors and enable 3D motion monitoring during beam delivery at the MR-Linac in the future. Lastly, the propagation method was extended to create continuous time-resolved estimated synthetic CTs (tresCTs). The proposed method was experimentally tested with the porcine lung phantom, successively imaged at a CT scanner and an MR-Linac. A high agreement of the images and corresponding dose distributions of the tresCTs and measured ground truth 4D-CTs was found in geometric and dosimetric analyses. The tresCTs could be used for post-treatment time-resolved reconstruction of the delivered dose to guide treatment adaptations in the future. These studies represent important steps towards a clinical application of time-resolved 4D-MRI methods for enhanced MRgRT of lung tumors in the near future

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community

Shedding light into the brain: Methodological innovations in optical neuroimaging

Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are non-invasive techniques used to infer stimulus-locked variations in human cortical activity from optical variations of near-infrared light injected and subsequently detected at specified scalp locations. Relative to functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), these optical techniques are more portable, less invasive and less sensitive to motion artifacts, making them ideal to explore brain activity in a variety of cognitive situations, and in a range of populations, including newborns and children. FNIRS and DOT measure stimulus-locked hemodynamic response in the form of changes in oxy- (HbO) and deoxy- (HbR) hemoglobin concentration taking place in specific areas. This signal is however structurally intertwined with physiological noise owing to cardiac pulsations, respiratory oscillations and vasopressure wave. Furthermore, the absolute magnitude of hemodynamic responses is substantially smaller than these non-informative components of the measured optical signal, and has a frequency which largely overlaps with that of the vasopressure wave. Thus, recovering the hemodynamic response is a challenging task. Several methods have been proposed in the literature to try to reduce physiological noise oscillations and recover the hemodynamic response, but none of them has become a common standard in the optical signal processing pipeline. In this thesis, a novel algorithm, devised to overcome a large subset of drawbacks associated with the use of these literature techniques, is presented and validated. Reduced sensitivity to motion artifacts notwithstanding, the optical signal must always be assumed as contaminated by some form of mechanical instability, most prominently during signal acquisitions from pathological (e.g., stroke patients) or difficult (e.g., newborns) populations. Several techniques have been proposed to correct for motion artifacts with the specific aim of preserving contaminated measures as opposed to rejecting them. However, none of them has become the gold standard in the optical signal processing pipeline, and there are currently no objective approaches to choose the most appropriate filtering technique based on objective parameters. In fact, due to the extreme variability in shape, frequency content and amplitude of the motion artifacts, it is likely that the best technique to apply is data-dependent and, in this vein, it is essential to provide users with objective tools able to select the best motion correction technique for the data set under examination. In this thesis, a novel objective approach to perform this selection is proposed and validated on a data-set containing a very challenging type of motion artifacts. While fNIRS allows only spectroscopic measurements of hemoglobin concentration changes, DOT allows to obtain 3D reconstructed images of HbO and HbR concentration changes. To increase the accuracy and interpretability of DOT reconstructed images, valuable anatomical information should be provided. While several adult head models have been proposed and validated in this context, only few single-ages head models have been presented for the neonatal population. However, due to the rapid growth and maturation of the infant's brain, single-age models fail to capture precise information about the correct anatomy of every infant's head under examination. In this thesis, a novel 4D head model, ranging from the preterm to the term age, is proposed, allowing developmental neuroscientists to make finer-grained choices about the age-matched head model and perform image reconstruction with an anatomy as similar as possible to the real one. The outline of the thesis will be as follows. In the first two chapters of this thesis, the state of the art of optical techniques will be reviewed. Particularly, in chapter 1, a brief introduction on the physical principles of optical techniques and a comparison with other more common neuroimaging techniques will be presented. In chapter 2, the components of the measured optical signal will be described and a brief review of state of the art of the algorithms that perform physiological noise removal will be presented. The theory on which optical image reconstruction is based will be reviewed afterwards. In the final part of the chapter, some of the studies and achievements of optical techniques in the adult and infants populations will be reviewed and the open issues and aims of the thesis will be presented. In chapters 3, 4 and 5, new methodologies and tools for signal processing and image reconstruction will be presented. Particularly, in chapter 3, a novel algorithm to reduce physiological noise contamination and recover the hemodynamic response will be introduced. The proposed methodology will be validated against two literature methods and results and consequent discussion will be reported. In chapter 4, instead, a novel objective approach for the selection of the best motion correction technique will be proposed. The main literature algorithms for motion correction will be reviewed and the proposed approach will be validated using these motion correction techniques on real cognitive data. In chapter 5, instead, a novel 4D neonatal optical head model will be presented. All the steps performed for its creation will be explained and discussed and a demonstration of the head model in use will also be exhibited. The last part of the thesis (chapters 6, 7 and 8) will be dedicated to illustrate three distinct examples of application of the proposed methodologies and tools on neural empirical data. In chapter 6, the physiological noise removal algorithm proposed in chapter 3 will be applied to recover subtle temporal differences between hemodynamic responses measured in two different areas of the motor cortex in short- vs. long- duration tapping. In chapter 7, the same algorithm will be applied to reduce physiological noise and recover hemodynamic responses measured during a visual short-term memory paradigm. In both chapters, cognitive results and a brief discussion will be reported. In chapter 8, instead, the neonatal optical head model proposed in chapter 5 will be applied to perform image reconstruction with data acquired on a healthy full term baby. In the same chapter, the importance of motion artifact correction will be highlighted, reconstructing HbO concentration changes images before and after the correction took place

MRI-Based Attenuation Correction in Emission Computed Tomography

The hybridization of magnetic resonance imaging (MRI) with positron emission tomography (PET) or single photon emission computed tomography (SPECT) enables the collection of an assortment of biological data in spatial and temporal register. However, both PET and SPECT are subject to photon attenuation, a process that degrades image quality and precludes quantification. To correct for the effects of attenuation, the spatial distribution of linear attenuation coefficients (μ-coefficients) within and about the patient must be available. Unfortunately, extracting μ-coefficients from MRI is non-trivial. In this thesis, I explore the problem of MRI-based attenuation correction (AC) in emission tomography. In particular, I began by asking whether MRI-based AC would be more reliable in PET or in SPECT. To this end, I implemented an MRI-based AC algorithm relying on image segmentation and applied it to phantom and canine emission data. The subsequent analysis revealed that MRI-based AC performed better in SPECT than PET, which is interesting since AC is more challenging in SPECT than PET. Given this result, I endeavoured to improve MRI-based AC in PET. One problem that required addressing was that the lungs yield very little signal in MRI, making it difficult to infer their μ-coefficients. By using a pulse sequence capable of visualizing lung parenchyma, I established a linear relationship between MRI signal and the lungs’ μ-coefficients. I showed that applying this mapping on a voxel-by-voxel basis improved quantification in PET reconstructions compared to conventional MRI-based AC techniques. Finally, I envisaged that a framework for MRI-based AC methods would potentiate further improvements. Accordingly, I identified three ways an MRI can be converted to μ-coefficients: 1) segmentation, wherein the MRI is divided into tissue types and each is assigned an μ-coefficient, 2) registration, wherein a template of μ-coefficients is aligned with the MRI, and 3) mapping, wherein a function maps MRI voxels to μ-coefficients. I constructed an algorithm for each method and catalogued their strengths and weaknesses. I concluded that a combination of approaches is desirable for MRI-based AC. Specifically, segmentation is appropriate for air, fat, and water, mapping is appropriate for lung, and registration is appropriate for bone

Diffeomorphic image registration with applications to deformation modelling between multiple data sets

Over last years, the diffeomorphic image registration algorithms have been successfully introduced into the field of the medical image analysis. At the same time, the particular usability of these techniques, in majority derived from the solid mathematical background, has been only quantitatively explored for the limited applications such as longitudinal studies on treatment quality, or diseases progression. The thesis considers the deformable image registration algorithms, seeking out those that maintain the medical correctness of the estimated dense deformation fields in terms of the preservation of the object and its neighbourhood topology, offer the reasonable computational complexity to satisfy time restrictions coming from the potential applications, and are able to cope with low quality data typically encountered in Adaptive Radiotherapy (ART). The research has led to the main emphasis being laid on the diffeomorphic image registration to achieve one-to-one mapping between images. This involves introduction of the log-domain parameterisation of the deformation field by its approximation via a stationary velocity field. A quantitative and qualitative examination of existing and newly proposed algorithms for pairwise deformable image registration presented in this thesis, shows that the log-Euclidean parameterisation can be successfully utilised in the biomedical applications. Although algorithms utilising the log-domain parameterisation have theoretical justification for maintaining diffeomorphism, in general, the deformation fields produced by them have similar properties as these estimated by classical methods. Having this in mind, the best compromise in terms of the quality of the deformation fields has been found for the consistent image registration framework. The experimental results suggest also that the image registration with the symmetrical warping of the input images outperforms the classical approaches, and simultaneously can be easily introduced to most known algorithms. Furthermore, the log-domain implicit group-wise image registration is proposed. By linking the various sets of images related to the different subjects, the proposed image registration approach establishes a common subject space and between-subject correspondences therein. Although the correspondences between groups of images can be found by performing the classic image registration, the reference image selection (not required in the proposed implementation), may lead to a biased mean image being estimated and the corresponding common subject space not adequate to represent the general properties of the data sets. The approaches to diffeomorphic image registration have been also utilised as the principal elements for estimating the movements of the organs in the pelvic area based on the dense deformation field prediction system driven by the partial information coming from the specific type of the measurements parameterised using the implicit surface representation, and recognising facial expressions where the stationary velocity fields are used as the facial expression descriptors. Both applications have been extensively evaluated based on the real representative data sets of three-dimensional volumes and two-dimensional images, and the obtained results indicate the practical usability of the proposed techniques

Issues in the processing and analysis of functional NIRS imaging and a contrast with fMRI findings in a study of sensorimotor deactivation and connectivity

Includes abstract.~Includes bibliographical references.The first part of this thesis examines issues in the processing and analysis of continuous wave functional linear infrared spectroscopy (fNIRS) of the brain usung the DYNOT system. In the second part, the same sensorimotor experiment is carried out using functional magnetic resonance imaging (fMRI) and near infrared spectroscopy in eleven of the same subjects, to establish whether similar results can be obtained at the group level with each modality. Various techniques for motion artefact removal in fNIRS are compared. Imaging channels with negligible distance between source and detector are used to detect subject motion, and in data sets containing deliberate motion artefacts, independent component analysis and multiple-channel regression are found to improve the signal-to-noise ratio

Non-Invasive Imaging for the Assessment of Cardiac Dose and Function Following Focused External Beam Irradiation

Technological advances in imaging and radiotherapy have led to significant improvement in the survival rate of breast cancer patients. However, a larger proportion of patients are now exhibiting the less understood, latent effects of incidental cardiac irradiation that occurs during left-sided breast radiotherapy. Here, we examine the utility of four-dimensional computed tomography (4D-CT) for the accurate assessment of cardiac dose; and a hybrid positron emission tomography (PET) magnetic resonance imaging (MRI) system to longitudinally study radiation-induced cardiac effects in a canine model. Using 4D-CT and deformable dose accumulation, we assessed the variation caused by breathing motion in the estimated dose to the heart, left-ventricle, and left anterior descending artery (LAD) of left-sided breast cancer patients. The LAD showed substantial variation in dose due to breathing. In light of this, we suggest the use of 4D-CT and dose accumulation for future clinical studies looking at the relationship between LAD dose and cardiac toxicity. Although symptoms of cardiac dysfunction may not manifest clinically for 10-15 years post radiation, PET-MRI can potentially identify earlier changes in cardiac inflammation and perfusion that are typically asymptomatic. Using PET-MRI, the progression of radiation-induced cardiac toxicity was assessed in a large animal model. Five canines were imaged using 13N-ammonia and 18F-fluorodeoxyglucose (FDG) PET-MRI to assess changes in myocardial perfusion and inflammation, respectively. All subjects were imaged at baseline, 1 week, 4 weeks, 3 months, 6 months, and 12 months after focused cardiac irradiation. To the best of our knowledge PET has not been previously used to assess cardiac perfusion following irradiation. The delivered dose to the heart, left ventricle, LAD, and left circumflex artery were comparable to what has been observed during breast radiotherapy. Relative to baseline, a transient increase in myocardial perfusion was observed followed by a gradual return to baseline. However, a persistent increase in FDG uptake was observed throughout the entire left ventricle, including both irradiated and less-irradiated portions of the heart. In light of these findings, we suggest the use of this imaging approach for future human studies to assess mitigation strategies aimed at minimizing cardiac exposure and long-term toxicity subsequent to left-sided breast irradiation