Medical imaging technology: Principles and systems

Medical Imaging has been playing one of the most important roles in medical diagnosis. As the technologies advance, so does medical imaging, and it gets an increasing role, not only in diagnosis, but also in treatment, such as image-guided therapies. Medical imaging also has a great impact on many research areas including neuroscience, drug development, phycology, psychiatry, cardiology, and so on. Advancement of the medical imaging technologies involves multidisciplinary research fields that encompass physics, medicine, electrical and mechanical engineering, biology and chemical engineering, etc. Considering the impact of medical imaging on clinics, research, and engineering, we select “Medical Imaging” as the theme of this Special Issue of Biomedical Engineering Letters. This special issue includes five review papers on recent advances of several medical imaging technologies and their applications. The first review paper entitled, “Model based filtered backprojection algorithm: a tutorial”, by Gengsheng L. Zeng, IEEE Fellow, USA, provides in-depth knowledge of very interesting new type of linear reconstruction algorithm with incorporation of noise models in it [1]. For many decades, the filtered backprojection (FBP) algorithm has been a popular choice in image reconstruction. However, recent developments of new medical imaging systems such as lowdose x-ray CT, nuclear medicine, and real-time MRI applications require more advanced reconstruction algorithms than the conventional FBP in order to incorporate measurement noise and system models. In recent years, many researchers have been developing various iterative algorithms to meet this demand and improve the final reconstructed images. However, the drawback of most iterative algorithms is the heavy computational cost. Therefore, it would be wonderful if we could have an FBP algorithm that performs comparable tasks to the iterative algorithms. In this first review paper, Dr. Zeng provides a way to develop such an FBP algorithm with incorporation of noise models, which may replace the state-of-the-art iterative algorithms. The second review paper, “Challenges and advances in MR imaging of the intervertebral disc could the cartilaginous endplate be a biomarker for the disc health?,” by Sung M. Moon, Ph.D., GE Healthcare, USA, provides an overview of new magnetic resonance imaging (MRI) techniques and findings on disc disease and cartilaginous endplate (CEP) [2]. A brief description on the anatomy of the CEP and intervertebral disc is followed by their roles in the disc Dosik Hwang ( ) School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea Tel : +82-2-2123-5771 E-mail : dosik.hwang@yonsei.ac.kr

Drawing on existing theoretical work, as well as field research, this dissertation examines the impact of medical imaging technologies on communication between physicians and older patients when diagnostics often privilege disembodied data over the patient voice. Current diagnostic trends are contextualized within the history of medicine, from Ancient Greece to the present, including the development of imaging. Since the 1970s, advanced medical imaging technologies (e.g., ultrasound, computed tomography, magnetic resonance imaging) have become the diagnostic norm in Western medicine. The rapidity of this shift, which renders the human body as flattened data, can outstrip considerations of the implications of applying such technologies to living patients. Focusing on older patients, who may be less technologically savvy than younger patients or medical professionals, the field research begins with semi-structured interviews of patients over age sixty-five, exploring their encounters with medical imaging equipment and professionals. This data is interrogated qualitatively using Foucauldian discourse analysis drawing on Andrea Doucet’s model of slow scholarship, and informed by Arthur Frank’s notion of letting stories breathe; themes were allowed to surface from the patients’ narratives, rather than imposed by the researcher. Information emerging from the data considers patients’ emotions, unexpected physical sensations, communicative strategies and rationalizations, as well as Foucauldian allusions to power. Observational research was also conducted during encounters between physicians and simulated patients in the presence of medical images; these encounters were followed by reflective exit interviews. Research indicates that although physicians are increasingly trained in patient-centred communication, it is not always optimally practised. Physicians are sometimes more comfortable with the medical discourse of disease than with the emotional, metaphoric language of the patient’s illness experience. Since the development of modern Western medicine in Europe of the late 1700s, physicians have been trained to seek pathology, with the increasing aid of medical technologies, rather than listening to their patients. For older patients, who may experience multiple co-morbidities, the lack of communication around advanced medical technologies can increase their sense of vulnerability and anxiety. The dissertation concludes with recommendations for both patients and practitioners to improve communication in the medical context.

The fields of medical imaging and medical imaging instrumentation are increasingly important. The state-of-the-art continues to advance at a very rapid pace. In fact, various medical imaging modalities are under development at the National Synchrotron Light Source (such as MECT and Transvenous Angiography.) It is important to understand how these techniques compare with today`s more conventional imaging modalities. The purpose of this report is to provide some basic information about the various medical imaging technologies currently in use and their potential developments as a basis for this comparison. This report is by no means an in-depth study of the physics and instrumentation of the various imaging modalities; instead, it is an attempt to provide an explanation of the physical bases of these techniques and their principal clinical and research capabilities.

This book places an emphasis on the basic principles and recent advances in medical imaging technologies. After an introduction, the rest of the text can be categorized into three main parts. Part 1 provides a comprehensive tutorial on the fundamentals of medical imaging modalities and image processing methodologies. Part 2 highlights the cutting-edge imaging modalities for radiography and the recent developments of information technology in biomedicine. Part 3 provides some case studies and applications in radiation therapy and computer-aided diagnosis along with a perspective on future trends in medical and molecular imaging. The book is well organized and well presented. A concise summary before each chapter highlights the most important points of the text. This book can be used as a textbook for graduate courses and as an excellent reference for radiologists, neurologists, biomedical engineers, and researchers who are interested in imaging processing and modeling.

In modern medicine, medical imaging technologies such as computed tomography (CT), X-ray, ultrasound, magnetic resonance imaging (MRI), nuclear medicine, etc., have been proven to provide useful diagnostic information by displaying areas of a lesion or tumor not visible to the human eye, and may also help provide additional recessive information by using modern data analysis methods. These methods, including Artificial Intelligence (AI) technologies, are based on deep learning architectures, and have shown remarkable results in recent studies. However, the lack of explanatory ability of connection-based, instead of algorithm-based, deep learning technologies is one of the main reasons for the delay in the acceptance of these technologies in the mainstream medical field. One of the recent methods that may offer the explanatory ability for the CNN classes of deep learning neural networks is the gradient-weighted class activation mapping (Grad-CAM) method, which produces heat-maps that may offer explanations of the classification results. There are already many studies in the literature that compare the objective metrics of Grad-CAM-generated heat-maps against other methods. However, the subjective evaluation of AI-based classification/prediction results using medical images by qualified personnel could potentially contribute more to the acceptance of AI than objective metrics. The purpose of this paper is to investigate whether and how the Grad-CAM heat-maps can help physicians and radiologists in making diagnoses by presenting the results from AI-based classifications as well as their associated Grad-CAM-generated heat-maps to a qualified radiologist. The results of this study show that the radiologist considers Grad-CAM-generated heat-maps to be generally helpful toward diagnosis.

BACKGROUND:Biomedical engineers in particular require fast and powerful data processing systems to process computerized tomography and magnetic resonance imaging scans and other medical imaging technologies. However, current computer data processing technologies are unable to satisfy such requirements. A promising approach to addressing these limitations is processing in memory (PIM). Unfortunately, several issues, such as the compatibility and interconnection of PIM with legacy systems, still remain.OBJECTIVE:This paper proposes a standard memory bus-based PIM interface for medical image processing and a PIM platform. The proposed PIM interface can overcome problems of compatibility with legacy systems.METHODS:We will adapt an embedded system based on a commercial application processor (AP) to a medical image system to verify the functions and the performance of the proposed PIM interface. Using the PIM platform, we apply the proposed PIM interface and the AP to execute an image processing program, measure the image processing times, and compare the results of the measurements.RESULTS: Experimental results show that while the functions of the proposed PIM interface are normal, the processing time of PIM is more than 81% faster than that of the AP.CONCLUSION:The experimental results prove that the proposed PIM interface is able to solve problems of compatibility with legacy systems. We foresee that not only the medical image processing field but also a number of academic fields and professional sectors will use PIM in their data-intensive applications.

The discovery of X-ray in 1895 initiated the era of medical imaging diagnostics. Since then, medical imaging systems have realized unprecedented advancements. These systems have also turned out to be invaluable tools in the practice of diagnostic medicine. However, despite the significant development in medical imaging technologies, processing medical images still pose a substantial challenge especially when it comes to image segmentation. That problem is gradually being alleviated by the implementation of digital medical image processing, especially in the diagnosis and treatment of brain tumors. But the capability of most of the contemporary image delineating algorithms remains limited. Therefore, there is a need to come up with the new medical image segmentation programs to fully utilize the power of digital image processing. In light that, this article reviews some of the contemporary algorithmic protocols for brain tumor delineation systems and how effective they are.

In this work, the design and development of a 128-channel transceiver hardware for medical ultrasound imaging systems and research is presented. The proposed hardware solution integrates the analog front-end (AFE) sections, high voltage transmit pulser sections, field programmable gate array (FPGA)-based transmit beamforming and control logic, time gain compensation (TGC) and continuous (CW) Doppler functional circuits, and the necessary power supplies (high voltage (HV) and low voltage (LV)) into a single board. In addition, it integrates pervasive segments like power, clock tree sections, and power management and debugger logic. The developed transceiver solution helps to advance the research in medical ultrasound imaging techniques and technologies. To prototype an ultrasound imaging system, the developed hardware can be interfaced with a 128-channel ultrasound transducer array and an FPGA-based signal processing module. As the transceiver hardware is designed with commercially available chipsets, it provides the flexibility to programme the ultrasound AFE signal chain, transmit beamforming and the arbitrary transmit wave pattern. Besides, compared to the commercial open ultrasound research scanners, the flexibility to interface FPGA-based signal processing module helps to investigate the performance of hardware realisation of various ultrasound signal processing algorithms. Moreover, the work realises a single-board transceiver solution for multichannel ultrasound system fulfilment.

The practice of medicine has become extremely technology dependent, and this is especially the case for radiology [1]. Advances in medical imaging and information systems technologies have transformed radiology practice through the creation of new medical imaging techniques (e.g., molecular imaging), data delivery and presentation [e.g., picture archival and communication system (PACS)], and clinical decision support (e.g., CAD). Yet, despite the long-standing history of medical imaging and information technology (IT) innovation, the past few years have been relatively barren. This recent lack of innovation has led to a trend towards commoditization, which is particularly evident in information system technologies, such as PACS and RIS, which just a decade ago, were viewed as groundbreaking and innovative technologies [2]. The recent economic decline has served as a deterrent to R & D expenditures, further perpetuating the cycle of innovation decline [3]. In order to combat this trend in declining innovation, it is necessary to understand the principles of innovation, learn from past successes and failures, and proactively apply this knowledge to ongoing trends in medical practice and economics for guiding future innovation.

Introduction Advances in medical imaging technology have made it possible routinely to acquire high-resolution, three-dimensional images of human anatomy and function using a variety of imaging modalities. Today the number of acquired 2D images per exam varies from 150 images for screening up to 700 to 3000 for the diagnosis of complex cases. This large amount of images per case together with the growing importance of medical imaging in clinical practice, have continuously increased the workload of the radiologist, which explains the need for computer-assisted medical image analysis. Furthermore there is a quest for objective, quantitative information from medical images. In radiotherapy for instance, outlines of the irradiation volume and the neighboring organs at risk are delineated in 3D images and used to calculate a suitable radiation therapy. In neurology, degenerative diseases of the brain, such as multiple sclerosis, Alzheimer's, or schizophrenia, are studied by measuring brain shape and tissue changes in MR images. In cardiology, the health condition of the heart is assessed by studying the dynamics, the perfusion, and tissue characteristics of the heart muscle as revealed by MR or ultrasound images, and so forth. Traditionally, medical images are interpreted by visual inspection of the 2D images displayed slice by slice. Such radiological protocol is necessarily subjective , as it is based on the perception by a human observer and is usually restricted to mere qualitative statements and judgments. Moreover, the traditional 2D display of 3D images allows immediate inspection of anatomical structures in the two dimensions of the image plane only, whereas the third dimension has to be reconstructed mentally by the radiologist by looking at adjacent image slices.

Progresses in the areas of artificial intelligence, machine learning, and medical imaging technologies have allowed the development of the medical image processing field with some astonishing results in the last two decades. These innovations enabled the clinicians to view the human body in high-resolution or three-dimensional cross-sectional slices, which resulted in an increase in the accuracy of the diagnosis and the examination of patients in a non-invasive manner. The fundamental step for magnetic resonance imaging (MRI) brain scans classifiers is their ability to extract meaningful features. As a result, many works have proposed different methods for features extraction to classify the abnormal growths in the brain MRI scans. More recently, the application of deep learning algorithms to medical imaging leads to impressive performance enhancements in classifying and diagnosing complicated pathologies, such as brain tumors. In this paper, a deep learning feature extraction algorithm is proposed to extract the relevant features from MRI brain scans. In parallel, handcrafted features are extracted using the modified gray level co-occurrence matrix (MGLCM) method. Subsequently, the extracted relevant features are combined with handcrafted features to improve the classification process of MRI brain scans with support vector machine (SVM) used as the classifier. The obtained results proved that the combination of the deep learning approach and the handcrafted features extracted by MGLCM improves the accuracy of classification of the SVM classifier up to 99.30%.

National cancer institute initiative for development of novel imaging technologies

Medical imaging technologies such as magnetic resonance imaging (MRI) and computerized tomographic (CT) are used to diagnose a wide range of medical diseases. Medical images are generated by detecting density differences between different tissues in the body. Multiple medical image visualization is of critical importance to diagnosis. This paper introduces a dual-view medical image visualization prototype based on spatial-temporal psychovisual modulation (STPVM). Temporal psychovisual modulation (TPVM) enables a single display to generate multiple visual content for different viewers. Spatial psychovisual modulation (SPVM) extends the idea of TPVM to spatial domain. STPVM combines TPVM and SPVM by exploiting both temporal and spatial redundancy of modern displays. Based on STPVM technology, one display can present even more images simultaneously. In this demo, two kinds of medical images e.g. T1 and T2 weighted MRI images, are presented simultaneously. Physicians can switch between either image by just moving the eye fixations. Since T1 and T2 are shown simultaneously and are aligned on the screen, it is more convenient for the physicians to get different information of the same spot from the T1 and T2 images. The developed demo is useful for physicians during surgery navigation and effectively reduces the burden of mental transfer.

Cancer remains a major global health challenge, characterized by high incidence and mortality rates. Accurate early diagnosis and treatment are essential for improving patient survival. Medical imaging technologies, such as X‐ray, computed tomography (CT), positron emission tomography (PET), ultrasound (US), magnetic resonance imaging (MRI), and mammography, play a key role in identifying the location, size, shape, density, and structure of tumors, as well as assessing their spread. The diverse types and large volume of medical images place a heavy workload on radiologists, potentially leading to increased misdiagnosis rates and prolonged diagnostic periods. This situation underscores the growing need for computer‐aided diagnostic systems in clinical practice. Deep learning, a subfield of artificial intelligence (AI), demonstrates significant potential in analyzing and interpreting complex imaging data, enabling precise tumor characterization and improved diagnostic outcomes. This review examines the integration of AI techniques—such as machine learning, deep learning and convolutional neural networks in medical imaging, which have notably enhanced diagnostic accuracy, early detection rates, and workflow efficiency in cancer diagnostics. By applying automated feature extraction and analysis across various imaging modalities, AI systems assist in clinical decision‐making and reduce human oversight. However, challenges remain in areas like data quality, privacy concerns, and model generalizability, which constrain the broader application of AI. Future research should address these challenges to support more robust, reliable, and accessible AI‐driven imaging solutions for cancer diagnosis.

Applying Quantitative Benefit–Risk Analysis to Aid Regulatory Decision Making in Diagnostic Imaging: Methods, Challenges, and Opportunities