Simultaneous Estimation and Segmentation of T1 Map for Breast Parenchyma Measurement

Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T1 map estimation (E-Step) and the step of T1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T1 map can be noisy due to the complexity of T1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement

Toward quantitative limited-angle ultrasound reflection tomography to inform abdominal HIFU treatment planning

High-Intensity Focused Ultrasound (HIFU) is a treatment modality for solid cancers of the liver and pancreas which is non-invasive and free from many of the side-effects of radiotherapy and chemotherapy. The safety and efficacy of abdominal HIFU treatment is dependent on the ability to bring the therapeutic sound waves to a small focal ”lesion” of known and controllable location within the patient anatomy. To achieve this, pre-treatment planning typically includes a numerical simulation of the therapeutic ultrasound beam, in which anatomical compartment locations are derived from computed tomography or magnetic resonance images. In such planning simulations, acoustic properties such as density and speed-of-sound are assumed for the relevant tissues which are rarely, if ever, determined specifically for the patient. These properties are known to vary between patients and disease states of tissues, and to influence the intensity and location of the HIFU lesion. The subject of this thesis is the problem of non-invasive patient-specific measurement of acoustic tissue properties. The appropriate method, also, of establishing spatial correspondence between physical ultrasound transducers and modeled (imaged) anatomy via multimodal image reg-istration is also investigated; this is of relevance both to acoustic tissue property estimation and to the guidance of HIFU delivery itself. First, the principle of a method is demonstrated with which acoustic properties can be recovered for several tissues simultaneously using reflection ultrasound, given accurate knowledge of the physical locations of tissue compartments. Second, the method is developed to allow for some inaccuracy in this knowledge commensurate with the inaccuracy typical in abdominal multimodal image registration. Third, several current multimodal image registration techniques, and two novel modifications, are compared for accuracy and robustness. In conclusion, relevant acoustic tissue properties can, in principle, be estimated using reflected ultrasound data that could be acquired using diagnostic imaging transducers in a clinical setting

Treatment response assessment of breast masses on dynamic contrastâ enhanced magnetic resonance scans using fuzzy câ means clustering and level set segmentation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134958/1/mp8101.pd

Diffusionâ weighted imaging outside the brain: Consensus statement from an ISMRMâ sponsored workshop

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134160/1/jmri25196_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134160/2/jmri25196.pd

Advanced perfusion quantification methods for dynamic PET and MRI data modelling

The functionality of tissues is guaranteed by the capillaries, which supply the microvascular network providing a considerable surface area for exchanges between blood and tissues. Microcirculation is affected by any pathological condition and any change in the blood supply can be used as a biomarker for the diagnosis of lesions and the optimization of the treatment. Nowadays, a number of techniques for the study of perfusion in vivo and in vitro are available. Among the several imaging modalities developed for the study of microcirculation, the analysis of the tissue kinetics of intravenously injected contrast agents or tracers is the most widely used technique. Tissue kinetics can be studied using different modalities: the positive enhancement of the signal in the computed tomography and in the ultrasound dynamic contrast enhancement imaging; T1-weighted MRI or the negative enhancement of T2* weighted MRI signal for the dynamic susceptibility contrast imaging or, finally, the uptake of radiolabelled tracers in dynamic PET imaging. Here we will focus on the perfusion quantification of dynamic PET and MRI data. The kinetics of the contrast agent (or the tracer) can be analysed visually, to define qualitative criteria but, traditionally, quantitative physiological parameters are extracted with the implementation of mathematical models. Serial measurements of the concentration of the tracer (or of the contrast agent) in the tissue of interest, together with the knowledge of an arterial input function, are necessary for the calculation of blood flow or perfusion rates from the wash-in and/or wash-out kinetic rate constants. The results depend on the acquisition conditions (type of imaging device, imaging mode, frequency and total duration of the acquisition), the type of contrast agent or tracer used, the data pre-processing (motion correction, attenuation correction, correction of the signal into concentration) and the data analysis method. As for the MRI, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a non-invasive imaging technique that can be used to measure properties of tissue microvasculature. It is sensitive to differences in blood volume and vascular permeability that can be associated with tumour angiogenesis. DCE-MRI has been investigated for a range of clinical oncologic applications (breast, prostate, cervix, liver, lung, and rectum) including cancer detection, diagnosis, staging, and assessment of treatment response. Tumour microvascular measurements by DCE-MRI have been found to correlate with prognostic factors (such as tumour grade, microvessel density, and vascular endothelial growth factor expression) and with recurrence and survival outcomes. Furthermore, DCE-MRI changes measured during treatment have been shown to correlate with outcome, suggesting a role as a predictive marker. The accuracy of DCE-MRI relies on the ability to model the pharmacokinetics of an injected contrast agent using the signal intensity changes on sequential magnetic resonance images. DCE-MRI data are usually quantified with the application of the pharmacokinetic two-compartment Tofts model (also known as the standard model), which represents the system with the plasma and tissue (extravascular extracellular space) compartments and with the contrast reagent exchange rates between them. This model assumes a negligible contribution from the vascular space and considers the system in, what-is-known as, the fast exchange limit, assuming infinitely fast transcytolemmal water exchange kinetics. In general, the number, as well as any assumption about the compartments, depends on the properties of the contrast agent used (mainly gadolinium) together with the tissue physiology or pathology studied. For this reason, the choice of the model is crucial in the analysis of DCE-MRI data. The value of PET in clinical oncology has been demonstrated with studies in a variety of cancers including colorectal carcinomas, lung tumours, head and neck tumours, primary and metastatic brain tumours, breast carcinoma, lymphoma, melanoma, bone cancers, and other soft-tissue cancers. PET studies of tumours can be performed for several reasons including the quantification of tumour perfusion, the evaluation of tumour metabolism, the tracing of radiolabelled cytostatic agents. In particular, the kinetic analysis of PET imaging has showed, in the past few years, an increasing value in tumour diagnosis, as well as in tumour therapy, through providing additional indicative parameters. Many authors have showed the benefit of kinetic analysis of anticancer drugs after labelling with radionuclide in measuring the specific therapeutic effect bringing to light the feasibility of applying the kinetic analysis to the dynamic acquisition. Quantification methods can involve visual analysis together with compartmental modelling and can be applied to a wide range of different tracers. The increased glycolysis in the most malignancies makes 18F-FDG-PET the most common diagnostic method used in tumour imaging. But, PET metabolic alteration in the target tissue can depend by many other factors. For example, most types of cancer are characterized by increased choline transport and by the overexpression of choline kinase in highly proliferating cells in response to enhanced demand of phosphatidylcholine (prostate, breast, lung, ovarian and colon cancers). This effect can be diagnosed with choline-based tracers as the 18Ffluoromethylcholine (18F-FCH), or the even more stable 18F-D4-Choline. Cellular proliferation is also imaged with 18F-fluorothymidine (FLT), which is trapped within the cytosol after being mono phosphorylated by thymidine kinase-1 (TK1), a principal enzyme in the salvage pathway of DNA synthesis. 18F-FLT has been found to be useful for noninvasive assessment of the proliferation rate of several types of cancer and showed high reproducibility and accuracy in breast and lung cancer tumours. The aim of this thesis is the perfusion quantification of dynamic PET and MRI data of patients with lung, brain, liver, prostate and breast lesions with the application of advanced models. This study covers a wide range of imaging methods and applications, presenting a novel combination of MRI-based perfusion measures with PET kinetic modelling parameters in oncology. It assesses the applicability and stability of perfusion quantification methods, which are not currently used in the routine clinical practice. The main achievements of this work include: 1) the assessment of the stability of perfusion quantification of D4-Choline and 18F-FLT dynamic PET data in lung and liver lesions, respectively (first applications in the literature); 2) the development of a model selection in the analysis of DCE-MRI data of primary brain tumours (first application of the extended shutter speed model); 3) the multiparametric analysis of PET and MRI derived perfusion measurements of primary brain tumour and breast cancer together with the integration of immuohistochemical markers in the prediction of breast cancer subtype (analysis of data acquired on the hybrid PET/MRI scanner). The thesis is structured as follows: - Chapter 1 is an introductive chapter on cancer biology. Basic concepts, including the causes of cancer, cancer hallmarks, available cancer treatments, are described in this first chapter. Furthermore, there are basic concepts of brain, breast, prostate and lung cancers (which are the lesions that have been analysed in this work). - Chapter 2 is about Positron Emission Tomography. After a brief introduction on the basics of PET imaging, together with data acquisition and reconstruction methods, the chapter focuses on PET in the clinical settings. In particular, it shows the quantification techniques of static and dynamic PET data and my results of the application of graphical methods, spectral analysis and compartmental models on dynamic 18F-FDG, 18F-FLT and 18F-D4- Choline PET data of patients with breast, lung cancer and hepatocellular carcinoma. - Chapter 3 is about Magnetic Resonance Imaging. After a brief introduction on the basics of MRI, the chapter focuses on the quantification of perfusion weighted MRI data. In particular, it shows the pharmacokinetic models for the quantification of dynamic contrast enhanced MRI data and my results of the application of the Tofts, the extended Tofts, the shutter speed and the extended shutter speed models on a dataset of patients with brain glioma. - Chapter 4 introduces the multiparametric imaging techniques, in particular the combined PET/CT and the hybrid PET/MRI systems. The last part of the chapter shows the applications of perfusion quantification techniques on a multiparametric study of breast tumour patients, who simultaneously underwent DCE-MRI and 18F-FDG PET on a hybrid PET/MRI scanner. Then the results of a predictive study on the same dataset of breast tumour patients integrated with immunohistochemical markers. Furthermore, the results of a multiparametric study on DCE-MRI and 18F-FCM brain data acquired both on a PET/CT scanner and on an MR scanner, separately. Finally, it will show the application of kinetic analysis in a radiomic study of patients with prostate cancer

Novel approaches to MRI of glioma

Gliomas are extremely heterogeneous, both morphologically and biologically, which contributes to a very poor prognosis. Current imaging of glioma is insufficient for a thorough diagnosis, therapy assessment and prognosis prediction. Moreover, refined and more sophisticated imaging technique could help in furthering our knowledge of gliomas. In order to facilitate proliferation, cancer cells undergo a change in structure and an increase in metabolism that results in distortion and disruption of tissue architecture. Gliomas are characterised by an increase in cells of variable sizes, as well as changes in the tissue microstructure. Diffusion-Weighted Imaging (DWI) and the apparent diffusion coefficient (ADC), have been extensively studied as potential imaging biomarkers for cellularity and tissue architecture. However, several studies have shown partial overlap in the measured values between tumour subtypes. Moreover, ADC is influenced by several factors and does not provide detailed information on the tissue microstructure. The Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours (VERDICT) is a novel diffusion model that infers tissue microstructure compartment from conventional DWI measurements. This model derives metrics for the intracellular, intravascular and extracellular– extravascular spaces providing a more detailed interpretation of the tissue microstructure. To date, VERDICT has been applied to xenograft models of colorectal cancer, patient studies of prostate cancer and recently its feasibility in glioma has been shown. In this PhD I have applied a shortened version of the VERDICT method to image intratumoral and intertumoral heterogeneity in glioma. The results have also been validated with histology as part of a prospective study. Gliomas also exhibit a significant increase in mitotic activity within the tumour. The increased number of mitosis alters cell density which, in turn, affects the total concentration of tissue sodium as the concentration of tissue sodium is approximately ten-fold higher in the extracellular compared to the intracellular space. In addition, there is a decrease in Na+/K+-ATPase activity in tumours due to ATP depletion, which contributes to disturb sodium homeostasis. Non-invasive detection of 23Na with MRI has the potential to quantify sodium concentration and therefore could be an imaging probe of cell morphology and membrane function within the tumour microenvironment, as well as a method of probing tissue heterogeneity. During my PhD, a novel 23Na-MRI technique has been used to evaluate sodium distribution within glioma and in the surrounding tissue. Metabolic reprogramming is one of the major driving forces for determining glioma growth and invasion. Therefore, the non-invasive characterization of metabolic intratumoral, peritumoral and intertumoral heterogeneity in vivo could help to better stratify patients and to develop novel therapeutic strategies targeting cancer-specific metabolic pathways. 13C magnetic resonance imaging (MRI) using dynamic nuclear polarization (DNP) is a novel technique that allows non-invasive assessment of the metabolism of hyperpolarized (HP) 13C-labelled molecules in vivo, such as the exchange of [1-13C]pyruvate to [1-13C]lactate in tumours (Warburg effect). Part of my PhD has focused on developing and translating HP [1-13C]pyruvate MRI to explore metabolic reprogramming in glioma and the surrounding microenvironment. The overall aim of my PhD has been to develop novel approaches to imaging glioma with MRI to probe both the architectural and metabolic changes of Glioma. The preliminary evidence suggests that these tools can more deeply phenotype tumours than conventional imaging approaches. Although the main focus of this work has been gliomas, the techniques developed and presented here may be applied to study other pathological conditions within the brain, which raises the possibility of other potential clinical applications for this work

New Segmentation Models for the Radiologic Characterization of Polycystic Kidney Disease Patients from MR and CT Images

Recent advances in genomics have contributed to a better understanding of the pathogenesis of the polycystic kidney disease (PKD), suggesting new treatment strategies to inhibit or delay cyst formation and expansion. The efficacy of these therapies is evaluated by estimation of cystic burden measured by magnetic resonange imaging (MRI) as total kidney volume (TKV). In this Thesis, different imaging approaches are proposed for a correct characterization of the PKD patient by the estimation of renal and cyst volume from magnetic resonance and computed tomography (CT) images. TKV estimation method from MRI relies on a previously validated method developed for axial images that has been adapted and validated to work on coronal images. The results have been compared with the ones obtained from axial images and validated with volume estimation obtained from manual tracing. The performace of the semi-automated method in terms of misclassification of the PKD patient was also evaluated in comparison with other radiologic approaches currently usedfor TKV assessment such as the ellipsoid method and the mid-slice method. A novel method for TKV computation from CT images is proposed. This multi- step approach is completely automated and includes the use of a level set approach to identify the renal contour and so extrapolate the renal volume. The segmented kidneys obtained with the developed methods where used for the segmentation of the cysts. A similar strategy was used for cyst segmentation and counting from MR images. Every cyst agglomerate underwent a voting mechanism based on the curvature of the object interface to distinguish the single cysts. The results of this approach for TCV computation was validated through comparison with TCV obtained by manual segmentation. The last chapter is dedicated to the research activity conducted in the area of diffussion weighted imaging

Real-Time Magnetic Resonance Imaging

Real‐time magnetic resonance imaging (RT‐MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast‐switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady‐state free precession, and single‐shot rapid acquisition with relaxation enhancement. RT‐MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft‐tissue contrast, as well as flow information. In this review, we discuss the history of RT‐MRI, fundamental tradeoffs, enabling technology, established applications, and current trends

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