Imágenes digitales del cerebro en la década de 1970: la construcción de una tecnología híbrida / Digital brain imaging in the 1970s building a hybrid technology

La tomografía computarizada (TC), una tecnología basada en la combinación de radiología y computación, fue utilizada para la exploración de un tumor cerebral en una paciente de un hospital de Londres en 1971. La cabeza de la persona se introdujo en un escáner, fabricado por la empresa discográfica británica EMI, para medir la cantidad de radiación absorbida por los diferentes puntos del cerebro. La imagen confeccionada constaba de una matriz tonal digital que se materializó en papel prensa, en tubo de rayos catódicos y en una fotografía Polaroid. En este artículo se mostrará el proceso de producción de la imagen TC, una representación visual que se convertiría, a partir de la década de 1970, en una tecnología habitual para las neurociencias contemporáneas, además de paradigma de representación visual en el tránsito de lo analógico a lo digital. Para ello analizaremos las implicaciones de la imagen radiográfica, inventada en un momento de auge de la imagen-movimiento, donde se enmarcan las cronofotografías, las secuencias de imágenes y la cinematografía. En este análisis haremos hincapié en los diferentes regímenes escópicos donde se encuentran cada una de las imágenes. El conocimiento, que llevó a la construcción del dispositivo, circuló entre la clínica, la industria y el laboratorio. En la construcción de las TC participaron una diversidad de agentes, incluida la computadora, la radiación por rayos X, un equipo de ingenieros electrónicos, un grupo de neurorradiólogos del Hospital Atkinson Marley y el escáner de rayos X, entre otros. Además, veremos la repercusión de varios factores vinculados a la epistemología de la imagen TC, como lo concerniente al reforzamiento clínico en el diagnóstico clínico, la vinculación de lo morfológico a lo psíquico en relación al cerebro y el tránsito de la imagen-movimiento a la imagen-tiempo. (AU)

Computed tomography (CT), a technology based on the combination of radiology and computation, was used to scan a patient’s brain tumor in a London hospital in 1972. The patient’s head was introduced into a scanner, manufactured by the British record company EMI, to measure the amount of radiation absorbed by different points in the brain. The resulting image consisted of a digital tonal matrix that was materialized on newsprint, a cathode ray tube, and a Polaroid photograph. This article describes the production process of the CT image, a visual representation that would become, from the 1970s onwards, a common technology for contemporary neurosciences and a paradigm of visual representation in the transition from analog to digital. To this end, we analyze the implications of the radiographic image, invented at a time of the rise of theimage-movement in which chronophotographs, image sequences, and cinematography are framed. In this analysis, we focus on the scopic regime in which each image is found. The knowledge that led to construction of the device circulated between the clinic, industry, and laboratory. A variety of agents were involved in the construction of CT scans, including the computer, X-ray radiation, a team of electronic engineers, a group of neuroradiologists from Atkinson Marley Hospital, and the X-ray scanner, among others. We also report on the impact of several factors associated with the epistemology of CT imaging, such as the reinforcement of clinical diagnoses, the linking of the morphological to the psychic in relation to the brain, and the transition from image-motion to image-time. (AU)

[Dr. Eiichi Tanaka's Achievement in the Research of Image Reconstruction].

In memoriam of Dr. Eiichi Tanaka who passed away on August 21, 2021, I review his achievement in the research of tomographic image reconstruction. Tomographic image reconstruction is an important research area which has wide applications including X-ray CT, nuclear medicine imaging such as PET and SPECT, and electron microscopy. Since 1970's, Dr. Tanaka has worked on numerous important topics in tomographic reconstruction fields aiming at using them in image reconstruction for PET and SPECT. Among them, in this paper, I will introduce his research on Filtered BackProjection (FBP) method, analytical attenuation correction in SPECT, image reconstruction in Time-of-Flight PET, image reconstruction for 3-D PET imaging, and iterative image reconstruction method called Dynamic Row-Action Maximum Likelihood Algorithm (DRAMA).

Global evolution of research on pulmonary nodules: a bibliometric analysis.

Aim: To provide a historical and global picture of research concerning lung nodules, compare the contributions of major countries and explore research trends over the past 10 years. Methods: A bibliometric analysis of publications from Scopus (1970-2020) and Web of Science (2011-2020). Results: Publications about pulmonary nodules showed an enormous growth trend from 1970 to 2020. There is a high level of collaboration among the 20 most productive countries and regions, with the USA located at the center of the collaboration network. The keywords 'deep learning', 'artificial intelligence' and 'machine learning' are current hotspots. Conclusions: Abundant research has focused on pulmonary nodules. Deep learning is emerging as a promising tool for lung cancer diagnosis and management.

Improving your four-dimensional image: traveling through a decade of light-sheet-based fluorescence microscopy research.

Light-sheet-based fluorescence microscopy features optical sectioning in the excitation process. This reduces phototoxicity and photobleaching by up to four orders of magnitude compared with that caused by confocal fluorescence microscopy, simplifies segmentation and quantification for three-dimensional cell biology, and supports the transition from on-demand to systematic data acquisition in developmental biology applications.

The Quest for the FFA and Where It Led.

This article tells the story behind our first paper on the fusiform face area (FFA): how we chose the question, developed the methods, and followed the data to find the FFA and subsequently many other functionally specialized cortical regions. The paper's impact had less to do with the particular findings in the paper itself and more to do with the method that it promoted and the picture of the human mind and brain that it led to. The use of a functional localizer to define a candidate region in each subject individually enabled us not just to make pictures of brain activation, but also to ask principled, hypothesis-driven questions about a thing in nature. This method enabled stronger and more extensive tests of the function of each cortical region than had been possible before in humans and, as a result, has produced a large body of evidence that the human cortex contains numerous regions that are specifically engaged in particular mental processes. The growing inventory of cortical regions with distinctive and often very specific functions can be seen as an initial sketch of the basic components of the human mind. This sketch also serves as a roadmap into the vast and exciting new landscape of questions about the computations, structural connections, time course, development, plasticity, and evolution of each of these regions, as well as the hardest question of all: how do these regions work together to produce human intelligence?

A 20 years of progress and future of quantitative magnetic resonance imaging (qMRI) of cartilage and articular tissues-personal perspective.

OBJECTIVE: In 1994, the first article on quantitative magnetic resonance imaging (qMRI) of articular cartilage was published, and tremendous progress in image acquisition, image analysis, and applications has since been made. The objective of this personal perspective is to highlight milestones in the field of qMRI of cartilage and other articular tissues over these past 20 years. METHODS: Based on a Pubmed search of original articles, the authors selected 30 articles which they deemed to be among the first to provide an important technological step forward in qMRI of cartilage, provided a first application in a particular context, or provided mechanistic insight into articular cartilage physiology, pathology, or treatment. RESULTS: This personal perspective summarizes results from these 30 articles. Further, the authors provide examples of how qMRI of cartilage has translated to quantitative analysis approaches of other articular tissues, including bone, meniscus, and synovium/edema. Eventually, the report provides a summary of how the lessons learned might be applied to future clinical trials and clinical practice. CONCLUSIONS: Over the past 20 years, quantitative imaging of articular tissues has emerged from a method to a dynamic field of research by its own. Continuing the qMRI biomarker qualification process will be crucial in convincing regulatory agencies to accept these as primary outcomes in phase 3 intervention trials. Once successful structural intervention will actually become available in OA, qMRI biomarkers may play an essential role in monitoring response to therapy in the clinic, and in stratifying disease phenotypes that respond differently to treatment.

Microscopic Image Photography Techniques of the Past, Present, and Future.

CONTEXT: The field of pathology is driven by microscopic images. Educational activities for trainees and practicing pathologists alike are conducted through exposure to images of a variety of pathologic entities in textbooks, publications, online tutorials, national and international conferences, and interdepartmental conferences. During the past century and a half, photographic technology has progressed from primitive and bulky, glass-lantern projector slides to static and/or whole slide digital-image formats that can now be transferred around the world in a matter of moments via the Internet. OBJECTIVE: To provide a historic and technologic overview of the evolution of microscopic-image photographic tools and techniques. DATA SOURCES: Primary historic methods of microscopic image capture were delineated through interviews conducted with senior staff members in the Emory University Department of Pathology. Searches for the historic image-capturing methods were conducted using the Google search engine. Google Scholar and PubMed databases were used to research methods of digital photography, whole slide scanning, and smart phone cameras for microscopic image capture in a pathology practice setting. CONCLUSIONS: Although film-based cameras dominated for much of the time, the rise of digital cameras outside of pathology generated a shift toward digital-image capturing methods, including mounted digital cameras and whole slide digital-slide scanning. Digital image capture techniques have ushered in new applications for slide sharing and second-opinion consultations of unusual or difficult cases in pathology. With their recent surge in popularity, we suspect that smart phone cameras are poised to become a widespread, cost-effective method for pathology image acquisition.

Quantitative pathology in virtual microscopy: history, applications, perspectives.

With the emerging success of commercially available personal computers and the rapid progress in the development of information technologies, morphometric analyses of static histological images have been introduced to improve our understanding of the biology of diseases such as cancer. First applications have been quantifications of immunohistochemical expression patterns. In addition to object counting and feature extraction, laws of thermodynamics have been applied in morphometric calculations termed syntactic structure analysis. Here, one has to consider that the information of an image can be calculated for separate hierarchical layers such as single pixels, cluster of pixels, segmented small objects, clusters of small objects, objects of higher order composed of several small objects. Using syntactic structure analysis in histological images, functional states can be extracted and efficiency of labor in tissues can be quantified. Image standardization procedures, such as shading correction and color normalization, can overcome artifacts blurring clear thresholds. Morphometric techniques are not only useful to learn more about biological features of growth patterns, they can also be helpful in routine diagnostic pathology. In such cases, entropy calculations are applied in analogy to theoretical considerations concerning information content. Thus, regions with high information content can automatically be highlighted. Analysis of the "regions of high diagnostic value" can deliver in the context of clinical information, site of involvement and patient data (e.g. age, sex), support in histopathological differential diagnoses. It can be expected that quantitative virtual microscopy will open new possibilities for automated histological support. Automated integrated quantification of histological slides also serves for quality assurance. The development and theoretical background of morphometric analyses in histopathology are reviewed, as well as their application and potential future implementation in virtual microscopy.

Multiple testing corrections, nonparametric methods, and random field theory.

I provide a selective review of the literature on the multiple testing problem in fMRI. By drawing connections with the older modalities, PET in particular, and how software implementations have tracked (or lagged behind) theoretical developments, my narrative aims to give the methodological researcher a historical perspective on this important aspect of fMRI data analysis.

Multivariate pattern analysis of fMRI: the early beginnings.

In 2001, we published a paper on the representation of faces and objects in ventral temporal cortex that introduced a new method for fMRI analysis, which subsequently came to be called multivariate pattern analysis (MVPA). MVPA now refers to a diverse set of methods that analyze neural responses as patterns of activity that reflect the varying brain states that a cortical field or system can produce. This paper recounts the circumstances and events that led to the original study and later developments and innovations that have greatly expanded this approach to fMRI data analysis, leading to its widespread application.

The future of acquisition speed, coverage, sensitivity, and resolution.

Two decades of technology development has continually improved the image quality, spatial-temporal resolution, and sensitivity of the fMRI acquisition. In this article, I assess our current acquisition needs, briefly examine the technological breakthroughs that have benefited fMRI in the past, and look at some promising technologies that are currently under development to try to envision what the fMRI acquisition protocol of the future might look like.

The story of the initial dip in fMRI.

Over the past 20 years much attention has been given to characterizing the spatial accuracy of fMRI based signals and to techniques that improve on its co-localization with neuronal activity. While the vast majority of fMRI studies have always used the conventional positive BOLD signal, alternative contrast options have demonstrated superior spatial specificity. One of these options surfaced shortly after the initial BOLD fMRI demonstrations and was motivated by optical imaging studies which revealed an early signal change that was much smaller but spatially more specific than the delayed positive response. This early signal change was attributed to oxygenation changes prior to any subsequent blood flow increases. After observation of this biphasic hemodynamic response in fMRI, because this early response resulted in a small MR signal decrease prior to the onset of the large signal increase, it became known as the "initial dip". While the initial dip in fMRI was subsequently reported by many studies, including those in humans, monkeys, and cats, there were conflicting views about the associated mechanisms and whether it could be generalized across brain regions or species, in addition to whether or not it would prove fruitful for neuroscience. These discrepancies, along with the implications that the initial dip might increase the spatial specificity of BOLD fMRI from 2 to 3mm to something more closely associated with neural activity, resulted in lot of buzz and controversy in the community for many years. In this review, the authors provide an account of the story of the initial dip in MR based functional imaging from the Minnesota perspective, where the first demonstrations, characterizations, and applications of the initial dip commenced.

Modelling with independent components.

Independent Component Analysis (ICA) is a computational technique for identifying hidden statistically independent sources from multivariate data. In its basic form, ICA decomposes a 2D data matrix (e.g. time × voxels) into separate components that have distinct characteristics. In FMRI it is used to identify hidden FMRI signals (such as activations). Since the first application of ICA to Functional Magnetic Resonance Imaging (FMRI) in 1998, this technique has developed into a powerful tool for data exploration in cognitive and clinical neurosciences. In this contribution to the commemorative issue 20 years of FMRI I will briefly describe the basic principles behind ICA, discuss the probabilistic extension to ICA and touch on what I think are some of the most notorious loose ends. Further, I will describe some of the most powerful 'killer' applications and finally share some thoughts on where I believe the most promising future developments will lie.

The continuing challenge of understanding and modeling hemodynamic variation in fMRI.

Interpretation of fMRI data depends on our ability to understand or model the shape of the hemodynamic response (HR) to a neural event. Although the HR has been studied almost since the beginning of fMRI, we are still far from having robust methods to account for the full range of known HR variation in typical fMRI analyses. This paper reviews how the authors and others contributed to our understanding of HR variation. We present an overview of studies that describe HR variation across voxels, healthy volunteers, populations, and dietary or pharmaceutical modulations. We also describe efforts to minimize the effects of HR variation in intrasubject, group, population, and connectivity analyses and the limits of these methods.

The intravascular susceptibility effect and the underlying physiology of fMRI.

In this article, I will first give a brief account of my work at MGH on characterizing the intravascular susceptibility effect. Then I will describe studies into the underlying physiology of BOLD-fMRI which has become of interest to my group in the following decade. I will touch issues such as signal source of BOLD fMRI, capillary recruitment, the elusive initial dip and others.

The road to functional imaging and ultrahigh fields.

The Center for Magnetic Resonance (CMRR) at the University of Minnesota was one of the laboratories where the work that simultaneously and independently introduced functional magnetic resonance imaging (fMRI) of human brain activity was carried out. However, unlike other laboratories pursuing fMRI at the time, our work was performed at 4T magnetic field and coincided with the effort to push human magnetic resonance imaging to field strength significantly beyond 1.5T which was the high-end standard of the time. The human fMRI experiments performed in CMRR were planned between two colleagues who had known each other and had worked together previously in Bell Laboratories, namely Seiji Ogawa and myself, immediately after the Blood Oxygenation Level Dependent (BOLD) contrast was developed by Seiji. We were waiting for our first human system, a 4T system, to arrive in order to attempt at imaging brain activity in the human brain and these were the first experiments we performed on the 4T instrument in CMRR when it became marginally operational. This was a prelude to a subsequent systematic push we initiated for exploiting higher magnetic fields to improve the accuracy and sensitivity of fMRI maps, first going to 9.4T for animal model studies and subsequently developing a 7T human system for the first time. Steady improvements in high field instrumentation and ever expanding armamentarium of image acquisition and engineering solutions to challenges posed by ultrahigh fields have brought fMRI to submillimeter resolution in the whole brain at 7T, the scale necessary to reach cortical columns and laminar differentiation in the whole brain. The solutions that emerged in response to technological challenges posed by 7T also propagated and continues to propagate to lower field clinical systems, a major advantage of the ultrahigh fields effort that is underappreciated. Further improvements at 7T are inevitable. Further translation of these improvements to lower field clinical systems to achieve new capabilities and to magnetic fields significantly higher than 7T to enable human imaging is inescapable.

Linear systems analysis of the fMRI signal.

In 1995 when we began our investigations of the human visual system using fMRI, little was known about the temporal properties of the fMRI signal. Before we felt comfortable making quantitative estimates of neuronal responses with this new technique, we decided to first conduct a basic study of how the time-course of the fMRI response varied with stimulus timing and strength. The results ended up showing strong evidence that to a first approximation the hemodynamic transformation was linear in time. This was both important and remarkable: important because nearly all fMRI data analysis techniques assume or require linearity, and remarkable because the physiological basis of the hemodynamic transformation is so complex that we still have a far from complete understanding of it. In this paper, we provide highlights of the results of our original paper supporting the linear transform hypothesis. A reanalysis of the original data provides some interesting new insights into the published results. We also provide a detailed appendix describing of the properties and predictions of a linear system in time in the context of the transformation between neuronal responses and the BOLD signal.

The general linear model and fMRI: does love last forever?

In this review, we first set out the general linear model (GLM) for the non technical reader, as a tool able to do both linear regression and ANOVA within the same flexible framework. We present a short history of its development in the fMRI community, and describe some interesting examples of its early use. We offer a few warnings, as the GLM relies on assumptions that may not hold in all situations. We conclude with a few wishes for the future of fMRI analyses, with or without the GLM. The appendix develops some aspects of use of contrasts for testing for the more technical reader.