Focal Spot, Fall/Winter 2002/2003

https://digitalcommons.wustl.edu/focal_spot_archives/1092/thumbnail.jp

Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT

In this paper, we aimed to provide a review and tutorial for researchers in the field of medical imaging using language models to improve their tasks at hand. We began by providing an overview of the history and concepts of language models, with a special focus on large language models. We then reviewed the current literature on how language models are being used to improve medical imaging, emphasizing different applications such as image captioning, report generation, report classification, finding extraction, visual question answering, interpretable diagnosis, and more for various modalities and organs. The ChatGPT was specially highlighted for researchers to explore more potential applications. We covered the potential benefits of accurate and efficient language models for medical imaging analysis, including improving clinical workflow efficiency, reducing diagnostic errors, and assisting healthcare professionals in providing timely and accurate diagnoses. Overall, our goal was to bridge the gap between language models and medical imaging and inspire new ideas and innovations in this exciting area of research. We hope that this review paper will serve as a useful resource for researchers in this field and encourage further exploration of the possibilities of language models in medical imaging

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Positron emission tomography (PET) images are widely used in many clinical applications such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both standard-dose and low-dose PET data into a common space and then performing patch based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level Canonical Correlation Analysis (mCCA) scheme to solve this problem. Specifically, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. Additionally, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain datasets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value

The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

UK Biobank is a population-based cohort of half a million participants aged 40–69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world’s largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future direction

Organizational responses to technological discontinuities: the case of the American College of Radiology (ACR)

A long history of organizational research has shown that organizations are affected significantly by changes in technology. Scholars have given particular attention to the effects of so-called disruptive or discontinuous technological changes. Studies have repeatedly shown that established, incumbent organizations tend to suffer deep performance declines (and even complete demise) in the face of such changes, and researchers have devoted much attention to identifying the organizational conditions and processes that are responsible for this persistent and widespread pattern of adaptation failure. This dissertation, which examines the response of the American College of Radiology (ACR) to the emergence of nuclear magnetic resonance imaging technology (NMR), aims to contribute to this well-established research tradition in three distinct and important ways. First, it focuses on a fundamentally different type of organization, a professional association, rather than the technology producers examined in most prior research. Although technologies are well known to be embedded in “communities” that include technology producers, suppliers, customers, governmental entities, professional societies, and other entities, most prior research has focused on the responses and ultimate fate of producers alone. Little if any research has explored the responses of professional organizations in particular. Second, the study employs a sophisticated process methodology that identifies the individual events that make up the organization’s response to technological change, as well as the overall sequence through which these events unfold. This process approach contrasts sharply with the variance models used in most previous studies and offers the promise of developing knowledge about how adaptation ultimately unfolds (or fails to). Finally, the project also contributes significantly through its exploration of an apparently successful case of adaptation to technological change. Though nuclear magnetic resonance imaging posed a serious threat to the ACR and its members, this threat appears to have been successfully managed and overcome. Although the unique nature of the organization and the technology under study place some important limits on the generalizablity of this research, its findings nonetheless provide some important basic insights about the process through which social organizations can successfully adapt to discontinuous technological changes. These insights, which may also be of substantial relevance to technology producer organizations, will also be elaborated

A push for change: a review of the sue of advanced neuroimaging in the urgent evaluation of acute stroke, and the impact on clinical guidelines

In 1996, the United States Food and Drug Administration officially approved the use of intravenous recombinant tissue-type plasminogen activator for treatment of acute ischemic stroke, with the requirement that a baseline computed tomography (CT) scan be performed to rule out acute intracerebral or subarachnoid hemorrhage. Today, the American Heart Association (AHA) Stroke Council acknowledges magnetic resonance imaging (MRI) as more sensitive to the detection of ischemia, and yet, guidelines released by the group suggest that either CT or MRI may serve as the primary, hyperacute imaging modality. The AHA recommends that for most cases, non-contrast-enhanced CT scans provide sufficient information for medical management decisions. A systematic review of published literature was conducted to compare current capabilities of CT and MRI in an effort to determine which imaging modality should be used in the setting of acute ischemic stroke. Current research indicates that MRI is comparable to CT in the detection of acute hemorrhage, but superior in the detection of acute ischemia. In addition, MRI has demonstrated the ability to not only identify suitable patients for treatment, but also identify patients whose treatment would be unnecessary and potentially dangerous. Therefore, the hope is that clinical guidelines, like those released by the AHA Stroke Council, will be modified to promote MRI as the primary imaging modality. A modification to the major clinical guidelines will initiate a change in the approach of acute stroke evaluation across all clinical stroke centers

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

Design and Mining of Health Information Systems for Process and Patient Care Improvement

abstract: In healthcare facilities, health information systems (HISs) are used to serve different purposes. The radiology department adopts multiple HISs in managing their operations and patient care. In general, the HISs that touch radiology fall into two categories: tracking HISs and archive HISs. Electronic Health Records (EHR) is a typical tracking HIS, which tracks the care each patient receives at multiple encounters and facilities. Archive HISs are typically specialized databases to store large-size data collected as part of the patient care. A typical example of an archive HIS is the Picture Archive and Communication System (PACS), which provides economical storage and convenient access to diagnostic images from multiple modalities. How to integrate such HISs and best utilize their data remains a challenging problem due to the disparity of HISs as well as high-dimensionality and heterogeneity of the data. My PhD dissertation research includes three inter-connected and integrated topics and focuses on designing integrated HISs and further developing statistical models and machine learning algorithms for process and patient care improvement. Topic 1: Design of super-HIS and tracking of quality of care (QoC). My research developed an information technology that integrates multiple HISs in radiology, and proposed QoC metrics defined upon the data that measure various dimensions of care. The DDD assisted the clinical practices and enabled an effective intervention for reducing lengthy radiologist turnaround times for patients. Topic 2: Monitoring and change detection of QoC data streams for process improvement. With the super-HIS in place, high-dimensional data streams of QoC metrics are generated. I developed a statistical model for monitoring high- dimensional data streams that integrated Singular Vector Decomposition (SVD) and process control. The algorithm was applied to QoC metrics data, and additionally extended to another application of monitoring traffic data in communication networks. Topic 3: Deep transfer learning of archive HIS data for computer-aided diagnosis (CAD). The novelty of the CAD system is the development of a deep transfer learning algorithm that combines the ideas of transfer learning and multi- modality image integration under the deep learning framework. Our system achieved high accuracy in breast cancer diagnosis compared with conventional machine learning algorithms.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201