Chapter 12 - Modeling People and Populations: Exploring Medical Visualization Through Immersive Interactive Virtual Environments

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.

The similarities and crossovers between geophysical and medical imaging are well known and documented (see President's Pages by D. Lumley and J. Zhang in the March 2021 and June 2022 issues, respectively, for two recent examples from this journal). Each of the two industries has introduced technology of tremendous value to the other, and one can only imagine what future advancements in medical imaging may bring to exploration geophysics — and vice versa. Welcome to the wonderland of the intersection of geophysical imaging and medical imaging.

Medical imaging is becoming indispensable for patients' healthcare. Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation, and image database retrieval. With advances in medical imaging, new imaging modalities and methodologies such as cone-beam/multislice CT, 3D ultrasound imaging, tomosynthesis, diffusion-weighted magnetic resonance imaging (MRI), positron-emission tomography (PET)/CT, electrical impedance tomography, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Because of large variations and complexityit is generally difficult to derive analytic solutions or simple equations to represent objects such as lesions and anatomy in medical images. Therefore, tasks in medical imaging require “learning from examples” for accurate representation of data and prior knowledge. Because of its essential needs, machine learning in medical imaging is one of the most promising, growing fields. The main aim of this special issue is to help advance the scientific research within the broad field of machine learning in medical imaging. The special issue was planned in conjunction with the International Workshop on Machine Learning in Medical Imaging (MLMI 2010) [1], which was the first workshop on this topic, held at the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010) in September, 2010, in Beijing, China. This special issue is one in a series of special issues of journals on this topic [2]; it focuses on major trends and challenges in this area, and it presents work aimed at identifying new cutting-edge techniques and their use in medical imaging. The quality level of the submissions for this special issue was very high. A total of 17 papers were submitted to this issue in response to the call for papers. Based on a rigorous review process, 10 papers (59%) were accepted for publication in the special issue. The special issue starts by a review of studies on a class of machine-learning techniques, called pixel/voxel-based machine learning, in medical imaging by K. Suzuki. A series of medical imaging applications of machine-learning techniques are presented. A large variety of applications are well represented here, including organ modeling by D. Wang et al. and X. Qiao and Y.-W. Chen, brain function estimation by V. Michel et al., image reconstruction by H. Shouno et al., lesion classification by P. Wighton et al., modality classification by X.-H. Han and Y.-W. Chen, lesion segmentation by M. Zortea et al., organ segmentation by S. Alzubi et al., and visualization of molecular signals by F. Mattoli et al. Also, the issue covers various biomedical imaging modalities, including MRI by D. Wang et al., CT by X. Qiao and Y.-W. Chen and H. Shouno et al., functional MRI by V. Michel et al., dermoscopy by P. Wighton et al. and M. Zortea et al., scintigraphy by X.-H. Han and Y.-W. Chen, ultrasound imaging by X.-H. Han and Y.-W. Chen, radiography by X.-H. Han and Y.-W. Chen, MR angiography by S. Alzubi et al., and microscopy by F. Mattoli et al. as well as a variety of organs, including the kidneys by D. Wang et al., liver by X. Qiao and Y.-W. Chen, brain by V. Michel et al. and F. Mattoli et al., chest by S. Alzubi et al., skin by P. Wighton et al. and M. Zortea et al., and heart by F. Mattoli et al. Various machine-learning techniques were developed/used to solve the respective problems, including structured dictionary learning by D. Wang et al., generalized N-dimensional principal component analysis by X. Qiao et al., multiclass sparse Bayesian regression by V. Michel et al., Bayesian hyperparameter inference by H. Shouno et al., supervised learning of probabilistic models based on maximum aposteriori estimation and conditional random fields by P. Wighton et al., joint kernel equal contribution in support vector classification by X.-H. Han and Y.-W. Chen, and iterative hybrid classification by M. Zortea et al. We are grateful to all authors for their excellent contributions to this special issue and to all reviewers for their reviews and constructive suggestions. We hope that this special issue will inspire further ideas for creative research, advance the field of machine learning in medical imaging, and facilitate the translation of the research from bench to bedside. Kenji Suzuki Pingkun Yan Fei Wang Dinggang Shen

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.

The development of PACS image databases has long been thought of as a major technological challenge, due to the amount of data to be managed. On the contrary, the authors think that despite major improvements in storage technology, new data management techniques must be proposed to make image databases medically useful in PACS environments. More precisely, image databases must contain not only images per se, but also the description of all objects used in medical imaging, in order to permit the remote processing, analysis, and interpretation of images. In several other papers, the authors explain why they adopted an object-oriented approach to model information in medical imaging. In this paper, the focus is on the inventory of objects manipulated in medical imaging from a qualitative viewpoint. For this purpose, a large number of representative imaging procedures were selected. The authors characterized how they are asked for by clinicians, realized in imaging departments, and consumed by requiring physicians and surgeons, in three French university hospitals. On the basis of this inventory, a set of image data -- i.e. of objects used in medical imaging -- was defined to show that this set must evolve with advances in medical imaging, and to point out that relational DBMS concepts cannot represent all image data.

Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and—through patient-specific modelling—derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechanical analysis.

Medical imaging uses various types of electromagnetic radiation to produce images which reveal exquisite detail about the structures and functions of the human body. As medical imaging evolves, it takes on new roles beyond the detection and assessment of disease. Advances in medical imaging are stimulating medical scientists to develop new devices and techniques which characterize and treat diseases. This is often achieved by incorporating imaging in new roles for guiding, delivering, monitoring and assessing interventional procedures, frequently in real time during the procedure. This presentation will illustrate how this medical imaging “vision” is stimulating new advances in cardiology, tumour detection and biopsy, cancer treatment, and assessment and treatment of diseases of the brain.

This chapter discusses the importance of machine learning in the field of medical imaging for reconstructing medical images from the measured raw data. Besides discussing the use of machine learning for medical image reconstruction, a general overview is also provided on all the existing techniques of medical imaging. Mathematical models are provided in order to understand better the use of machine learning for reconstruction purposes. We discuss both unsupervised techniques like dictionary learning, auto-encoders, and supervised techniques which include learning of hyperparameters and various regularization methods used in deep learning models that replace various steps in iterative algorithms used for image reconstruction such as for image enhancement.

A huge amount of medical images is generated through advanced medical imaging in hospitals. Medical image compression plays a key role in telemedicine to have high image quality for medical images with relevant diagnostic information and with less storage requirements, so that it can be easily transmitted over a network and can be accessed within a limited time. This paper discusses about the performance analysis of different image compression for the medical images their applications in telemedicine. The performance variations of compression algorithm can be determined using objective parameters such as MSE, PSNR and SNR at different bit rates.

For efficient collaboration between participants, eye gaze is seen as being critical for interaction. Teleconferencing systems such as the AcessGrid allow users to meet across geographically disparate rooms but as of now there seems no substitute for face to face meetings. This paper gives an overview of some preliminary work that looks towards integrating eye gaze into an immersive Collaborative Virtual Environment and assessing the impact that this would have on interaction between the users of such a system. An experiment was conducted to assess the difference between users abilities to judge what objects an avatar is looking at with only head gaze being viewed and also with eye and head gaze data being displayed. The results from the experiment show that eye gaze is of vital importance to the subjects correctly identifying what a person is looking at in an immersive virtual environment. This is followed by a description of how the eye tracking system has been integrated into an immersive collaborative virtual environment and some preliminary results from the use of such a system.

Throughout the past 15 years, medical schools around the world have interrogated the value of cadaveric dissection in anatomy courses during undergraduate medical education (UME) in light of advances in medical imaging, increasing costs of UME, decreased time allotted to anatomy education, and the availability of high‐quality prosections and models.Proponents of dissection argue that in addition to gaining knowledge of anatomy at the basic science level, medical students develop qualities vital to the practice of medicine such as developing clinician habits‐of‐mind, understanding 3D anatomy as it relates to medical imaging, and reflecting on death and dying. Furthermore, medical students themselves feel as though dissection enhances their ability to work as a team, undergo professional development, and come to terms with death. Proponents also note that deep learning of any subject is cultivated by direct experiential learning, which may describe dissection to a greater degree than other learning modalities.Detractors, however, argue that such qualities can be instilled in students at other times throughout UME, and that because physicians primarily interact with anatomy through medical imaging and living models, the time spent dissecting may be better spent gaining proficiency with these modalities. Other detractors have claimed that many of the benefits of dissection such as touch‐mediated learning and exposure to death and dying can be obtained through prosection. Detractors also note that dissection has been shown to be no better than other modalities in impacting student performance on anatomy exams.This presentation will review the existing literature regarding the dissection vs prosection debate and provide perceptional feedback on both instructional methodologies from the perspective of a current medical student who has experience teaching and learning anatomy through various modalities at the undergraduate, graduate, and professional school levels.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Use of imaging has become widespread in almost every field of Science and Engineering. Imaging tools and image processing techniques are playing very vital role in the advancements of technology. Especially in the medical field where examination, diagnostics and analysis is always needed for better treatment of diseases by using effective imaging techniques e.g. X- ray imaging, ultrasounds, radiology, CT scans etc. For the detection of cancerous cells, these imaging devices are highly appreciable. The medical imaging devices used by the doctors are very costly due to which the cost of diagnosis is also increased. This paper reviews the techniques and methodologies adopted in the diagnosis of malfunctioning body parts and surgeries. The capabilities of smart sensing electronic gadget “Kinect” are presented over the expensive medical imaging equipment. It provides us two forms of outputs, RGB data and depth data of the under examination object or person. The study validates better identification of objects using kinect sensor as compared to earlier image capturing devices.

Advancements in medical imaging, nuclear medicine, and radiotherapy have significantly improved patient care, while also requiring responsive education and training programmes and continuing professional development (CPD) for radiographers who work across medical imaging, nuclear medicine, and radiotherapy. This article presents findings from the EU-REST (European Union Radiation, Education, Staffing and Training) project with a focus on the evaluation of education and training requirements for radiographers across EU Member States. Evidence-based guidelines to harmonise radiographer education and training and improve safety and quality in medical settings are proposed. The findings highlight the need for standardised, competency-based curricula that align with evolving technologies, safety regulations, and professional responsibilities, together with the importance of integrating radiation safety, quality management, and patient-centred care into curricula. To address accessibility and workforce needs, diverse entry pathways, flexible learning models, and equitable financial support for student radiographers are recommended. Harmonisation of training content, structured clinical placements, and mandatory CPD are also proposed to ensure radiographers remain proficient in emerging technologies such as AI and automation. The findings also underscore the necessity of national accreditation, certification, and licensing systems to maintain high professional standards. Establishing a unified core curriculum at the European level would enhance education quality and ensure compliance with the basic safety standards directive (BSSD). Additionally, postgraduate training opportunities should be expanded to support specialisation and career advancement. By adopting these recommendations, the radiographer profession can cultivate a highly skilled workforce capable of delivering safe, effective, and innovative patient care, ensuring alignment with the future demands of healthcare and technological progress.Critical relevance statementThe radiographer education and training recommendations developed by the EU-REST project propose a framework to ensure a highly skilled radiographer workforce capable of delivering safe, effective, and innovative patient care, ensuring alignment with the future demands of healthcare and technological progress.Key PointsThe EU-REST project explored the education and training requirements for radiographers across the EU with a focus on patient safety and quality.The development of standardised education and training guidelines for radiographers is essential to ensuring a highly skilled, safe, and effective workforce.These recommendations will support the development of competent, adaptable, and research-driven professionals who contribute to the advancement of patient care.

Generative adversarial network in medical imaging: A review.

Handbook of Medical Imaging, Processing and Analysis: Edited by Isaac N. Bankman, PhD, Academic Press, 2000, ISBN 0-12-077790-8, 901 pp.