Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts

Characterization and Compensation of Hysteretic Cardiac Respiratory Motion in Myocardial Perfusion Studies Through MRI Investigations

Respiratory motion causes artifacts and blurring of cardiac structures in reconstructed images of SPECT and PET cardiac studies. Hysteresis in respiratory motion causes the organs to move in distinct paths during inspiration and expiration. Current respiratory motion correction methods use a signal generated by tracking the motion of the abdomen during respiration to bin list- mode data as a function of the magnitude of this respiratory signal. They thereby fail to account for hysteretic motion. The goal of this research was to demonstrate the effects of hysteretic respiratory motion and the importance of its correction for different medical imaging techniques particularly SPECT and PET. This study describes a novel approach for detecting and correcting hysteresis in clinical SPECT and PET studies. From the combined use of MRI and a synchronized Visual Tracking System (VTS) in volunteers we developed hysteretic modeling using the Bouc-Wen model with inputs from measurements of both chest and abdomen respiratory motion. With the MRI determined heart motion as the truth in the volunteer studies we determined the Bouc Wen model could match the behavior over a range of hysteretic cycles. The proposed approach was validated through phantom simulations and applied to clinical SPECT studies

Dynamic And Quantitative Radiomics Analysis In Interventional Radiology

Interventional Radiology (IR) is a subspecialty of radiology that performs invasive procedures driven by diagnostic imaging for predictive and therapeutic purpose. The development of artificial intelligence (AI) has revolutionized the industry of IR. Researchers have created sophisticated models backed by machine learning algorithms and optimization methodologies for image registration, cellular structure detection and computer-aided disease diagnosis and prognosis predictions. However, due to the incapacity of the human eye to detect tiny structural characteristics and inter-radiologist heterogeneity, conventional experience-based IR visual evaluations may have drawbacks. Radiomics, a technique that utilizes machine learning, offers a practical and quantifiable solution to this issue. This technology has been used to evaluate the heterogeneity of malignancies that are difficult to detect by the human eye by creating an automated pipeline for the extraction and analysis of high throughput computational imaging characteristics from radiological medical pictures. However, it is a demanding task to directly put radiomics into applications in IR because of the heterogeneity and complexity of medical imaging data. Furthermore, recent radiomics studies are based on static images, while many clinical applications (such as detecting the occurrence and development of tumors and assessing patient response to chemotherapy and immunotherapy) is a dynamic process. Merely incorporating static features cannot comprehensively reflect the metabolic characteristics and dynamic processes of tumors or soft tissues. To address these issues, we proposed a robust feature selection framework to manage the high-dimensional small-size data. Apart from that, we explore and propose a descriptor in the view of computer vision and physiology by integrating static radiomics features with time-varying information in tumor dynamics. The major contributions to this study include: Firstly, we construct a result-driven feature selection framework, which could efficiently reduce the dimension of the original feature set. The framework integrates different feature selection techniques to ensure the distinctiveness, uniqueness, and generalization ability of the output feature set. In the task of classification hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) in primary liver cancer, only three radiomics features (chosen from more than 1, 800 features of the proposed framework) can obtain an AUC of 0.83 in the independent dataset. Besides, we also analyze features’ pattern and contributions to the results, enhancing clinical interpretability of radiomics biomarkers. Secondly, we explore and build a pulmonary perfusion descriptor based on 18F-FDG whole-body dynamic PET images. Our major novelties include: 1) propose a physiology-and-computer-vision-interpretable descriptor construction framework by the decomposition of spatiotemporal information into three dimensions: shades of grey levels, textures, and dynamics. 2) The spatio-temporal comparison of pulmonary descriptor intra and inter patients is feasible, making it possible to be an auxiliary diagnostic tool in pulmonary function assessment. 3) Compared with traditional PET metabolic biomarker analysis, the proposed descriptor incorporates image’s temporal information, which enables a better understanding of the time-various mechanisms and detection of visual perfusion abnormalities among different patients. 4) The proposed descriptor eliminates the impact of vascular branching structure and gravity effect by utilizing time warping algorithms. Our experimental results showed that our proposed framework and descriptor are promising tools to medical imaging analysis

Receptor Binding Techniques

This overview first discusses issues relating to the selection of radioligand for receptor binding assays, including the isotopic label and considerations pertaining to the pharmacological and chemical profile of the ligand. This is followed by a section on characterization of ligand‐binding assays, starting with tissue preparation methods, followed by detection of specific binding, determination of incubation and washing conditions and a discussion of saturation and competition assay formats. Quantification of the assay results can be accomplished by autoradiography or film densitometry. Finally, methods and considerations for analysis of the resulting data are presented.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143702/1/cpns0104.pd

Deep learning assisted MRI guided attenuation correction in PET

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological and functional details of the tissue at the molecular level. However, the acquired PET images have some limitations such as the attenuation. PET attenuation correction is an essential step to obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners, magnetic resonance (MR) images are used to address the problem of PET attenuation correction. The MR images segmentation is one simple and robust approach to create pseudo computed tomography (CT) images, which are used to generate attenuation coefficient maps to correct the PET attenuation. Recently, deep learning has been proposed and used as a promising technique to efficiently perform MR and various medical images segmentation. In this research work, deep learning guided segmentation approaches have been proposed to enhance the bone class segmentation of MR brain images in order to generate accurate pseudo-CT images. The first approach has introduced the combination of handcrafted features with deep learning features to enrich the set of features. Multiresolution analysis techniques, which generate multiscale and multidirectional coefficients of an image such as contourlet and shearlet transforms, are applied and combined with deep convolutional neural network (CNN) features. Different experiments have been conducted to investigate the number of selected coefficients and the insertion location of the handcrafted features. The second approach aims at reducing the segmentation algorithm’s complexity while maintaining the segmentation performance. An attention based convolutional encode-decoder network has been proposed to adaptively recalibrate the deep network features. This attention based network consists of two different squeeze and excitation blocks that excite the features spatially and channel wise. The two blocks are combined sequentially to decrease the number of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w images. Multiple fine tuning approaches and experiments have been conducted to study the best fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been used to carry an objective evaluation to measure the segmentation performance of the results obtained by the three proposed methods. The first and second approaches have been validated with other studies in the literature that applied deep network based segmentation technique to perform MR based attenuation correction for PET images. The proposed methods have shown an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC) from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%. The proposed excitation-based CNN has decreased the model complexity by decreasing the number of trainable parameters by more than 46% where less computing resources are required to train the model. The proposed hybrid transfer learning method has shown its superiority to build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied transfer learning methods especially with the bone class where the DSC is increased from 0.3841 to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than transfer learning using open and conservative fine tuning

Representing and Inferring Visual Perceptual Skills in Dermatological Image Understanding

Experts have a remarkable capability of locating, perceptually organizing, identifying, and categorizing objects in images specific to their domains of expertise. Eliciting and representing their visual strategies and some aspects of domain knowledge will benefit a wide range of studies and applications. For example, image understanding may be improved through active learning frameworks by transferring human domain knowledge into image-based computational procedures, intelligent user interfaces enhanced by inferring dynamic informational needs in real time, and cognitive processing analyzed via unveiling the engaged underlying cognitive processes. An eye tracking experiment was conducted to collect both eye movement and verbal narrative data from three groups of subjects with different medical training levels or no medical training in order to study perceptual skill. Each subject examined and described 50 photographical dermatological images. One group comprised 11 board-certified dermatologists (attendings), another group was 4 dermatologists in training (residents), and the third group 13 novices (undergraduate students with no medical training). We develop a novel hierarchical probabilistic framework to discover the stereotypical and idiosyncratic viewing behaviors exhibited by the three expertise-specific groups. A hidden Markov model is used to describe each subject\u27s eye movement sequence combined with hierarchical stochastic processes to capture and differentiate the discovered eye movement patterns shared by multiple subjects\u27 eye movement sequences within and among the three expertise-specific groups. Through these patterned eye movement behaviors we are able to elicit some aspects of the domain-specific knowledge and perceptual skill from the subjects whose eye movements are recorded during diagnostic reasoning processes on medical images. Analyzing experts\u27 eye movement patterns provides us insight into cognitive strategies exploited to solve complex perceptual reasoning tasks. Independent experts\u27 annotations of diagnostic conceptual units of thought in the transcribed verbal narratives are time-aligned with discovered eye movement patterns to help interpret the patterns\u27 meanings. By mapping eye movement patterns to thought units, we uncover the relationships between visual and linguistic elements of their reasoning and perceptual processes, and show the manner in which these subjects varied their behaviors while parsing the images

Development of a silicon photomultiplier based innovative and low cost positron emission tomography scanner.

The Silicon Photomultiplier (SiPM) is a state-of-the-art semiconductor photodetector consisting of a high density matrix (up to 104) of independent pixels of micro-metric dimension (from 10 μm to 100 μm) which form a macroscopic unit of 1 to 6 mm2 area. Each pixel is a single-photon avalanche diode operated with a bias voltage of a few volts above the breakdown voltage. When a charge carrier is generated in a pixel by an incoming photon or a thermal effect, a Geiger discharge confined to that pixel is initiated and an intrinsic gain of about 106 is obtained. The output signal of a pixel is the same regardless of the number of interacting photons and provide only a binary information. Since the pixels are arranged on a common Silicon substrate and are connected in parallel to the same readout line, the SiPM combined output response corresponds to the sum of all fired pixel currents. As a result, the SiPM as a whole is an analogue detector, which can measure the incoming light intensity. Nowadays a great number of companies are investing increasing efforts in SiPM detector performances and high quality mass production. SiPMs are in rapid evolution and benefit from the tremendous development of the Silicon technology in terms of cost production, design flexibility and performances. They have reached a high single photon detection sensitivity and photon detection efficiency, an excellent time resolution, an extended dynamic range. They require a low bias voltage and have a low power consumption, they are very compact, robust, flexible and cheap. Considering also their intrinsic insensitivity to magnetic field they result to have an extremely high potential in fundamental and applied science (particle and nuclear physics, astrophysics, biology, environmental science and nuclear medicine) and industry. The SiPM performances are influenced by some effects, as saturation, afterpulsing and crosstalk, which lead to an inherent non-proportional response with respect to the number of incident photons. Consequently, it is not trivial to relate the measured electronic signal to the corresponding light intensity. Since for most applications it is desirable to qualify the SiPM response (i.e in order to properly design a detector for a given application, perform corrections on measurements or on energy spectra, calibrate a SiPM for low light measurements, predict detector performance) the implementation of characterization procedures plays a key role. The SiPM field of application that has been considered in this thesis is the Positron Emission Tomography (PET). PET represents the most advanced in-vivo nuclear imaging modality: it provides functional information of the physiological and molecular processes of organs and tissues. Thanks to its diagnostic power, PET has a recognized superiority over all other imaging modalities in oncology, neurology and cardiology. SiPMs are usually successfully employed for the PET scanners because they allow the measurement of the Time Of Flight of the two coincidence photons to improve the signal to noise ratio of the reconstructed images. They also permit to perfectly combine the functional information with the anatomical one by inserting the PET scanner inside the Magnetic Resonance Imaging device. Recently, PET technology has also been applied to preclinical imaging to allow non invasive studies on small animals. The increasing demand for preclinical PET scanner is driven by the fact that small animals host a large number of human diseases. In-vivo imaging has the advantage to enable the measurement of the radiopharmaceutical distribution in the same animal for an extended period of time. As a result, PET represents a powerful research tool as it offers the possibility to study the abnormalities at the origin of a disease, understand its dynamics, evaluate the therapeutic response and develop new drugs and treatments. However, the cost and the complexity of the preclinical scanners are limiting factors for the spread of PET technology: 70-80% of small-animal PET is concentrated in academic or government research laboratories. The EasyPET concept proposed in this Thesis, protected under a patent filed by Aveiro University, aims to achieve a simple and affordable preclinical PET scanner. The innovative concept is based on a single pair of detector kept collinear during the whole data acquisition and a moving mechanism with two degrees of freedom to reproduce the functionalities of an entire PET ring. The main advantages are in terms of the reduction of the complexity and cost of the PET system. In addition the concept is bound to be robust against acollinear photoemission, scatter radiation and parallax error. The sensitivity is expected to represent a fragility due to the reduced geometrical acceptance. This drawback can be partially recovered by the possibility to accept Compton scattering events without introducing image degradation effects, thanks to the sensor alignment. A 2D imaging demonstrator has been realized in order to assess the EasyPET concept and its performance has been analyzed in this Thesis to verify the net balance between competing advantages and drawbacks. The demonstrator had a leading role in the outreach activity to promote the EasyPET concept and a significant outcome is represented by the new partners that recently joined the collaboration. The EasyPET has been licensed to Caen S.p.a. and, thanks to the participation of Nuclear Instruments to the electronic board re-designed, a new prototype has been realized with additional improvements concerning the mechanics and the control software. In this Thesis the prototype functionalities and performances are reported as a result of a commissioning procedure. The EasyPET will be commercialized by Caen S.p.a. as a product for the educational market and it will be addressed to high level didactic laboratories to show the operating principles and technology behind the PET imaging. The topics mentioned above will be examined in depth in the following Chapters according to the subsequent order. In Chapter 1 the Silicon Photomultiplier will be described in detail, from their operating principle to their main application fields passing through the advantages and the drawback effects connected with this type of sensor. Chapter 2 is dedicated to a SiPM standard characterization method based on the staircase and resolving power measurement. A more refined analysis involves the Multi-Photon spectrum, obtained by integrating the SiPM response to a light pulse. It exploits the SiPM single photon sensitivity and its photon number resolving capability to measure some of its properties of general interest for a multitude of potential applications, disentangling the features related to the statistics of the incident light. Chapter 3 reports another SiPM characterization method which implements a post-processing of the digitized SiPM waveforms with the aim of extracting a full picture of the sensor characteristics from a unique data-set. The procedure is very robust, effective and semi-automatic and suitable for sensors of various dimensions and produced by different vendors. Chapter 4 introduces the Positron Emission Tomography imaging: its principle, applications, related issues and state of the art of PET scanner will be explained. Chapter 5 deals with the preclinical PET, reporting the benefits and the technological challenges involved, the performance of the commercially available small animal PET scanners, the main applications and the frontier research in this field. In Chapter 6 the EasyPET concept is introduced. In particular, the basic idea behind the operating principle, the design layout and the image reconstruction will be illustrated and then assessed through the description and the performance analysis of the EasyPET proof of concept and demonstrator. The effect of the use of different sensor to improve the light collection and the coincidence detection efficiency, together with the analysis of the importance of the sensor and the crystal alignment will be reported in Chapter 7. The design, the functionalities and the commissioning of the EasyPET prototype addressed to the educational market will be defined in Chapter 8. Finally, Chapter 9 contains a summary of the conclusions and an outlook of the future research studies

Cardiovascular Magnetic Resonance Imaging for the Investigation of Patients with Coronary Heart Disease

Objectives To evaluate the role of stress perfusion cardiovascular magnetic resonance (CMR) in the investigation of stable coronary artery disease (CAD). Background Coronary artery disease remains the biggest cause of morbidity and mortality. The multi-parametric CMR examination is established as an investigative strategy for the investigation of CAD. Methods Study 1 & 2: Patients with stable coronary artery disease underwent a multi-parametric CMR protocol assessing 4 components: i) left ventricular function; ii) myocardial perfusion; iii) viability (late gadolinium enhancement (LGE)) and iv) coronary magnetic resonance angiography (MRA). The diagnostic accuracy of the individual components were assessed. The ischaemic burden of stress CMR Vs. Single Photon Emission Computed Tomography (SPECT) was determined. Study 3: Volunteers and patients were scanned with perfusion sequence which adapts the spatial resolution to the available scanning time and field-of-view. Study 4: A multi-centre pragmatic randomised controlled trial of patients with stable angina comparing CMR guided-care Vs. SPECT guided-care Vs. National Institute of Health and Care Excellence guided-care. Results Study 1 demonstrated the stress perfusion component of the multi-parametric CMR exam was the single most important component for overall diagnostic accuracy. However, the full combined multi-parametric protocol was the optimal approach for disease rule-out, and the LGE component best for rule-in. Study 2 showed that there was reasonable agreement of the summed stress scores between CMR and SPECT (a well established investigation with significant amounts of prognostic data). In study 3, a perfusion pulse sequence which automatically adapts the acquisition sequence to the available scanning time results in spatial resolution improvement and reduction in dark rim artefact. Finally in study 4 in patients with suspected angina using CMR as an initial investigative strategy produced a significantly lower probability of unnecessary angiography compared to NICE guidance. There were similar rates of CAD detection were comparable suggesting no penalty for using functional imaging as a gatekeeper for angiography. Conclusion CMR has high diagnostic accuracy for the detection of coronary artery disease; with similar detection of ischaemic burden to established tests and can be used safely and effectively as a gate keeper to invasive coronary angiography

Video-Rate Fluorescence Molecular Tomography for Hand-held and Multimodal Molecular Imaging

In the United States, cancer is the second leading cause of death following heart disease. Although, a variety of treatment regimens are available, cancer management is complicated by the complexity of the disease and the variability, between people, of disease progression and response to therapy. Therefore, advancements in the methods and technologies for cancer diagnosis, prognosis and therapeutic monitoring are critical to improving the treatment of cancer patients. The development of improved imaging methods for early diagnosis of cancer and of near real-time monitoring of tumor response to therapy may improve outcomes as well as the quality of life of cancer patients. In the last decade, imaging methods including ultrasound, computed tomography: CT), magnetic resonance imaging: MRI), single photon emission computed tomography: SPECT), and positron emission tomography: PET), have revolutionized oncology. More recently optical techniques, that have access to unique molecular reporting strategies and functional contrasts, show promise for oncologic imaging This dissertation focuses on the development and optimization of a fiber-based, video-rate fluorescence molecular tomography: FMT) instrument. Concurrent acquisition of fluorescence and reference signals allowed the efficient generation of ratio-metric data for 3D image reconstruction. Accurate depth localization and high sensitivity to fluorescent targets were established to depths of \u3e10 mm. In vivo accumulation of indocyanine green dye was imaged in the region of the sentinel lymph node: SLN) following intradermal injection into the forepaw of rats. These results suggest that video-rate FMT has potential as a clinical tool for noninvasive mapping of SLN. Spatial and temporal co-registration of nuclear and optical images can enable the fusion of the information from these complementary molecular imaging modalities. A critical challenge is in integrating the optical and nuclear imaging hardware. Flexible fiber-based FMT systems provide a viable solution. The various imaging bore sizes of small animal nuclear imaging systems can potentially accommodate the FMT fiber imaging arrays. In addition FMT imaging facilitates co-registering the nuclear and optical contrasts in time. In this dissertation, the feasibility of integrating the fiber-based, video-rate FMT system with a commercial preclinical NanoSPECT/CT platform was established. Feasibility of in vivo imaging is demonstrated by tracking a monomolecular multimodal-imaging agent: MOMIA) during transport from the forepaw to the axillary lymph nodes region of a rat. These co-registered FMT/SPECT/CT imaging results with MOMIAs may facilitate the development of the next generation preclinical and clinical multimodal optical-nuclear platforms for a broad array of imaging applications, and help elucidate the underlying biological processes relevant to cancer diagnosis and therapy monitoring. Finally, I demonstrated that video-rate FMT is sufficiently fast to enable imaging of cardiac, respiratory and pharmacokinetic induced dynamic fluorescent signals. From these measurements, the image-derived input function and the real-time uptake of injected agents can be deduced for pharmacokinetic analysis of fluorescing agents. In a study comparing normal mice against mice liver disease, we developed anatomically guided dynamic FMT in conjunction with tracer kinetic modeling to quantify uptake rates of fluorescing agents. This work establishes fiber-based, video-rate FMT system as a practical and powerful tool that is well suited to a broad array of potential imaging applications, ranging from early disease detection, quantifying physiology and monitoring progression of disease and therapies