Quantitative PET and SPECT

Since the introduction of personalized medicine, the primary focus of imaging has moved from detection and diagnosis to tissue characterization, the determination of prognosis, prediction of treatment efficacy, and measurement of treatment response. Precision (personalized) imaging heavily relies on the use of hybrid technologies and quantitative imaging biomarkers. The growing number of promising theragnostics require accurate quantification for pre- and post-treatment dosimetry. Furthermore, quantification is required in the pharmacokinetic analysis of new tracers and drugs and in the assessment of drug resistance. Positron Emission Tomography (PET) is, by nature, a quantitative imaging tool, relating the time–activity concentration in tissues and the basic functional parameters governing the biological processes being studied. Recent innovations in single photon emission computed tomography (SPECT) reconstruction techniques have allowed for SPECT to move from relative/semi-quantitative measures to absolute quantification. The strength of PET and SPECT is that they permit whole-body molecular imaging in a noninvasive way, evaluating multiple disease sites. Furthermore, serial scanning can be performed, allowing for the measurement of functional changes over time during therapeutic interventions. This Special Issue highlights the hot topics on quantitative PET and SPECT

Contrast-enhanced CT- and MRI-based perfusion assessment for pulmonary diseases: basics and clinical applications

Assessment of regional pulmonary perfusion as well as nodule and tumor perfusions in various pulmonary diseases are currently performed by means of nuclear medicine studies requiring radioactive macroaggregates, dual-energy computed tomography (CT), and dynamic first-pass contrast-enhanced perfusion CT techniques and unenhanced and dynamic first-pass contrast enhanced perfusion magnetic resonance imaging (MRI), as well as time-resolved three-dimensional or four-dimensional contrast-enhanced magnetic resonance angiography (MRA). Perfusion scintigraphy, single-photon emission tomography (SPECT) and SPECT fused with CT have been established as clinically available scintigraphic methods; however, they are limited by perfusion information with poor spatial resolution and other shortcomings. Although positron emission tomography with 15O water can measure absolute pulmonary perfusion, it requires a cyclotron for generation of a tracer with an extremely short half-life (2 min), and can only be performed for academic purposes. Therefore, clinicians are concentrating their efforts on the application of CT-based and MRI-based quantitative and qualitative perfusion assessment to various pulmonary diseases. This review article covers 1) the basics of dual-energy CT and dynamic first-pass contrast-enhanced perfusion CT techniques, 2) the basics of time-resolved contrast-enhanced MRA and dynamic first-pass contrast-enhanced perfusion MRI, and 3) clinical applications of contrast-enhanced CT- and MRI-based perfusion assessment for patients with pulmonary nodule, lung cancer, and pulmonary vascular diseases. We believe that these new techniques can be useful in routine clinical practice for not only thoracic oncology patients, but also patients with different pulmonary vascular diseases

Multimodal Data Fusion and Quantitative Analysis for Medical Applications

Medical big data is not only enormous in its size, but also heterogeneous and complex in its data structure, which makes conventional systems or algorithms difficult to process. These heterogeneous medical data include imaging data (e.g., Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI)), and non-imaging data (e.g., laboratory biomarkers, electronic medical records, and hand-written doctor notes). Multimodal data fusion is an emerging vital field to address this urgent challenge, aiming to process and analyze the complex, diverse and heterogeneous multimodal data. The fusion algorithms bring great potential in medical data analysis, by 1) taking advantage of complementary information from different sources (such as functional-structural complementarity of PET/CT images) and 2) exploiting consensus information that reflects the intrinsic essence (such as the genetic essence underlying medical imaging and clinical symptoms). Thus, multimodal data fusion benefits a wide range of quantitative medical applications, including personalized patient care, more optimal medical operation plan, and preventive public health. Though there has been extensive research on computational approaches for multimodal fusion, there are three major challenges of multimodal data fusion in quantitative medical applications, which are summarized as feature-level fusion, information-level fusion and knowledge-level fusion: • Feature-level fusion. The first challenge is to mine multimodal biomarkers from high-dimensional small-sample multimodal medical datasets, which hinders the effective discovery of informative multimodal biomarkers. Specifically, efficient dimension reduction algorithms are required to alleviate "curse of dimensionality" problem and address the criteria for discovering interpretable, relevant, non-redundant and generalizable multimodal biomarkers. • Information-level fusion. The second challenge is to exploit and interpret inter-modal and intra-modal information for precise clinical decisions. Although radiomics and multi-branch deep learning have been used for implicit information fusion guided with supervision of the labels, there is a lack of methods to explicitly explore inter-modal relationships in medical applications. Unsupervised multimodal learning is able to mine inter-modal relationship as well as reduce the usage of labor-intensive data and explore potential undiscovered biomarkers; however, mining discriminative information without label supervision is an upcoming challenge. Furthermore, the interpretation of complex non-linear cross-modal associations, especially in deep multimodal learning, is another critical challenge in information-level fusion, which hinders the exploration of multimodal interaction in disease mechanism. • Knowledge-level fusion. The third challenge is quantitative knowledge distillation from multi-focus regions on medical imaging. Although characterizing imaging features from single lesions using either feature engineering or deep learning methods have been investigated in recent years, both methods neglect the importance of inter-region spatial relationships. Thus, a topological profiling tool for multi-focus regions is in high demand, which is yet missing in current feature engineering and deep learning methods. Furthermore, incorporating domain knowledge with distilled knowledge from multi-focus regions is another challenge in knowledge-level fusion. To address the three challenges in multimodal data fusion, this thesis provides a multi-level fusion framework for multimodal biomarker mining, multimodal deep learning, and knowledge distillation from multi-focus regions. Specifically, our major contributions in this thesis include: • To address the challenges in feature-level fusion, we propose an Integrative Multimodal Biomarker Mining framework to select interpretable, relevant, non-redundant and generalizable multimodal biomarkers from high-dimensional small-sample imaging and non-imaging data for diagnostic and prognostic applications. The feature selection criteria including representativeness, robustness, discriminability, and non-redundancy are exploited by consensus clustering, Wilcoxon filter, sequential forward selection, and correlation analysis, respectively. SHapley Additive exPlanations (SHAP) method and nomogram are employed to further enhance feature interpretability in machine learning models. • To address the challenges in information-level fusion, we propose an Interpretable Deep Correlational Fusion framework, based on canonical correlation analysis (CCA) for 1) cohesive multimodal fusion of medical imaging and non-imaging data, and 2) interpretation of complex non-linear cross-modal associations. Specifically, two novel loss functions are proposed to optimize the discovery of informative multimodal representations in both supervised and unsupervised deep learning, by jointly learning inter-modal consensus and intra-modal discriminative information. An interpretation module is proposed to decipher the complex non-linear cross-modal association by leveraging interpretation methods in both deep learning and multimodal consensus learning. • To address the challenges in knowledge-level fusion, we proposed a Dynamic Topological Analysis framework, based on persistent homology, for knowledge distillation from inter-connected multi-focus regions in medical imaging and incorporation of domain knowledge. Different from conventional feature engineering and deep learning, our DTA framework is able to explicitly quantify inter-region topological relationships, including global-level geometric structure and community-level clusters. K-simplex Community Graph is proposed to construct the dynamic community graph for representing community-level multi-scale graph structure. The constructed dynamic graph is subsequently tracked with a novel Decomposed Persistence algorithm. Domain knowledge is incorporated into the Adaptive Community Profile, summarizing the tracked multi-scale community topology with additional customizable clinically important factors

Heterogeneidad tumoral en imágenes PET-CT

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Estructura de la Materia, Física Térmica y Electrónica, leída el 28/01/2021Cancer is a leading cause of morbidity and mortality [1]. The most frequent cancers worldwide are non–small cell lung carcinoma (NSCLC) and breast cancer [2], being their management a challenging task [3]. Tumor diagnosis is usually made through biopsy [4]. However, medical imaging also plays an important role in diagnosis, staging, response to treatment, and recurrence assessment [5]. Tumor heterogeneity is recognized to be involved in cancer treatment failure, with worse clinical outcomes for highly heterogeneous tumors [6,7]. This leads to the existence of tumor sub-regions with different biological behavior (some more aggressive and treatment-resistant than others) [8-10]. Which are characterized by a different pattern of vascularization, vessel permeability, metabolism, cell proliferation, cell death, and other features, that can be measured by modern medical imaging techniques, including positron emission tomography/computed tomography (PET/CT) [10-12]. Thus, the assessment of tumor heterogeneity through medical images could allow the prediction of therapy response and long-term outcomes of patients with cancer [13]. PET/CT has become essential in oncology [14,15] and is usually evaluated through semiquantitative metabolic parameters, such as maximum/mean standard uptake value (SUVmax, SUVmean) or metabolic tumor volume (MTV), which are valuables as prognostic image-based biomarkers in several tumors [16-17], but these do not assess tumor heterogeneity. Likewise, fluorodeoxyglucose (18F-FDG) PET/CT is important to differentiate malignant from benign solitary pulmonary nodules (SPN), reducing so the number of patients who undergo unnecessary surgical biopsies. Several publications have shown that some quantitative image features, extracted from medical images, are suitable for diagnosis, tumor staging, the prognosis of treatment response, and long-term evolution of cancer patients [18-20]. The process of extracting and relating image features with clinical or biological variables is called “Radiomics” [9,20-24]. Radiomic parameters, such as textural features have been related directly to tumor heterogeneity [25]. This thesis investigated the relationships of the tumor heterogeneity, assessed by 18F-FDG-PET/CT texture analysis, with metabolic parameters and pathologic staging in patients with NSCLC, and explored the diagnostic performance of different metabolic, morphologic, and clinical criteria for classifying (malignant or not) of solitary pulmonary nodules (SPN). Furthermore, 18F-FDG-PET/CT radiomic features of patients with recurrent/metastatic breast cancer were used for constructing predictive models of response to the chemotherapy, based on an optimal combination of several feature selection and machine learning (ML) methods...El cáncer es una de las principales causas de morbilidad y mortalidad. Los más frecuentes son el carcinoma de pulmón de células no pequeñas (NSCLC) y el cáncer de mama, siendo su tratamiento un reto. El diagnóstico se suele realizar mediante biopsia. La heterogeneidad tumoral (HT) está implicada en el fracaso del tratamiento del cáncer, con peores resultados clínicos para tumores muy heterogéneos. Esta conduce a la existencia de subregiones tumorales con diferente comportamiento biológico (algunas más agresivas y resistentes al tratamiento); las cuales se caracterizan por diferentes patrones de vascularización, permeabilidad de los vasos sanguíneos, metabolismo, proliferación y muerte celular, que se pueden medir mediante imágenes médicas, incluida la tomografía por emisión de positrones/tomografía computarizada con fluorodesoxiglucosa (18F-FDG-PET/CT). La evaluación de la HT a través de imágenes médicas, podría mejorar la predicción de la respuesta al tratamiento y de los resultados a largo plazo, en pacientes con cáncer. La 18F-FDG-PET/CT es esencial en oncología, generalmente se evalúa con parámetros metabólicos semicuantitativos, como el valor de captación estándar máximo/medio (SUVmáx, SUVmedio) o el volumen tumoral metabólico (MTV), que tienen un gran valor pronóstico en varios tumores, pero no evalúan la HT. Asimismo, es importante para diferenciar los nódulos pulmonares solitarios (NPS) malignos de los benignos, reduciendo el número de pacientes que van a biopsias quirúrgicas innecesarias. Publicaciones recientes muestran que algunas características cuantitativas, extraídas de las imágenes médicas, son robustas para diagnóstico, estadificación, pronóstico de la respuesta al tratamiento y la evolución, de pacientes con cáncer. El proceso de extraer y relacionar estas características con variables clínicas o biológicas se denomina “Radiomica”. Algunos parámetros radiómicos, como la textura, se han relacionado directamente con la HT. Esta tesis investigó las relaciones entre HT, evaluada mediante análisis de textura (AT) de imágenes 18F-FDG-PET/CT, con parámetros metabólicos y estadificación patológica en pacientes con NSCLC, y exploró el rendimiento diagnóstico de diferentes criterios metabólicos, morfológicos y clínicos para la clasificación de NPS. Además, se usaron características radiómicas de imágenes 18F-FDG-PET/CT de pacientes con cáncer de mama recurrente/metastásico, para construir modelos predictivos de la respuesta a la quimioterapia, combinándose varios métodos de selección de características y aprendizaje automático (ML)...Fac. de Ciencias FísicasTRUEunpu

IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION

Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good hard-tissue contrast. In this context, Positron Emission Tomography (PET) is a non-invasive imaging technique which has the advantage, over morphological imaging techniques, of providing functional information about the patient’s disease. In the last few years, several criteria to assess therapy response in oncological patients have been proposed, ranging from anatomical to functional assessments. Changes in tumour size are not necessarily correlated with changes in tumour viability and outcome. In addition, morphological changes resulting from therapy occur slower than functional changes. Inclusion of PET images in radiotherapy protocols is desirable because it is predictive of treatment response and provides crucial information to accurately target the oncological lesion and to escalate the radiation dose without increasing normal tissue injury. For this reason, PET may be used for improving the Planning Treatment Volume (PTV). Nevertheless, due to the nature of PET images (low spatial resolution, high noise and weak boundary), metabolic image processing is a critical task. The aim of this Ph.D thesis is to develope smart methodologies applied to the medical imaging field to analyse different kind of problematic related to medical images and data analysis, working closely to radiologist physicians. Various issues in clinical environment have been addressed and a certain amount of improvements has been produced in various fields, such as organs and tissues segmentation and classification to delineate tumors volume using meshing learning techniques to support medical decision. In particular, the following topics have been object of this study: • Technique for Crohn’s Disease Classification using Kernel Support Vector Machine Based; • Automatic Multi-Seed Detection For MR Breast Image Segmentation; • Tissue Classification in PET Oncological Studies; • KSVM-Based System for the Definition, Validation and Identification of the Incisinal Hernia Reccurence Risk Factors; • A smart and operator independent system to delineate tumours in Positron Emission Tomography scans; 3 • Active Contour Algorithm with Discriminant Analysis for Delineating Tumors in Positron Emission Tomography; • K-Nearest Neighbor driving Active Contours to Delineate Biological Tumor Volumes; • Tissue Classification to Support Local Active Delineation of Brain Tumors; • A fully automatic system of Positron Emission Tomography Study segmentation. This work has been developed in collaboration with the medical staff and colleagues at the: • Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi (DIBIMED), University of Palermo • Cannizzaro Hospital of Catania • Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) Centro Nazionale delle Ricerche (CNR) of Cefalù • School of Electrical and Computer Engineering at Georgia Institute of Technology The proposed contributions have produced scientific publications in indexed computer science and medical journals and conferences. They are very useful in terms of PET and MRI image segmentation and may be used daily as a Medical Decision Support Systems to enhance the current methodology performed by healthcare operators in radiotherapy treatments. The future developments of this research concern the integration of data acquired by image analysis with the managing and processing of big data coming from a wide kind of heterogeneous sources

Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data

The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, ¹⁸F-FDG and ⁶⁸Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions

Selected Topics on Computed Tomography

This book is a research publication that covers developments within the Diagnostics field of study. The book is a collection of reviewed scholarly contributions written by different authors and edited by an expert with specific expertise. Each scholarly contribution represents a chapter which is complete in itself but related to the major topics and objectives. The target audience comprises scholars and specialists in the field