Paul Babyn

Bio: Paul Babyn is an academic researcher from University of Saskatchewan. The author has contributed to research in topics: Iterative reconstruction & Magnetic resonance imaging. The author has an hindex of 54, co-authored 307 publications receiving 11466 citations. Previous affiliations of Paul Babyn include Saskatchewan Health & University of Toronto.

An autoinflammatory disease with deficiency of the interleukin-1-receptor antagonist

Abstract: BACKGROUND Autoinflammatory diseases manifest inflammation without evidence of infection, high-titer autoantibodies, or autoreactive T cells. We report a disorder caused by mutations of IL1RN, which encodes the interleukin-1-receptor antagonist, with prominent involvement of skin and bone. METHODS We studied nine children from six families who had neonatal onset of sterile multifocal osteomyelitis, periostitis, and pustulosis. Response to empirical treatment with the recombinant interleukin-1-receptor antagonist anakinra in the first patient prompted us to test for the presence of mutations and changes in proteins and their function in interleukin-1-pathway genes including IL1RN. RESULTS We identified homozygous mutations of IL1RN in nine affected children, from one family from Newfoundland, Canada, three families from the Netherlands, and one consanguineous family from Lebanon. A nonconsanguineous patient from Puerto Rico was homozygous for a genomic deletion that includes IL1RN and five other interleukin-1-family members. At least three of the mutations are founder mutations; heterozygous carriers were asymptomatic, with no cytokine abnormalities in vitro. The IL1RN mutations resulted in a truncated protein that is not secreted, thereby rendering cells hyperresponsive to interleukin-1 beta stimulation. Patients treated with anakinra responded rapidly. CONCLUSIONS We propose the term deficiency of the interleukin-1-receptor antagonist, or DIRA, to denote this autosomal recessive autoinflammatory disease caused by mutations affecting IL1RN. The absence of interleukin-1-receptor antagonist allows unopposed action of interleukin-1, resulting in life-threatening systemic inflammation with skin and bone involvement. (ClinicalTrials.gov number, NCT00059748.)

US or CT for Diagnosis of Appendicitis in Children and Adults? A Meta-Analysis

Abstract: Purpose: To perform a meta-analysis to evaluate the diagnostic performance of ultrasonography (US) and computed tomography (CT) for the diagnosis of appendicitis in pediatric and adult populations. Materials and Methods: Medical literature (from 1986 to 2004) was searched for articles on studies that used US, CT, or both as diagnostic tests for appendicitis in children (26 studies, 9356 patients) or adults (31 studies, 4341 patients). Prospective and retrospective studies were included if they separately reported the rate of true-positive, true-negative, false-positive, and false-negative diagnoses of appendicitis from US and CT findings compared with the positive and negative rates of appendicitis at surgery or follow-up. Clinical variables, technical factors, and test performance were extracted. Three readers assessed the quality of studies. Results: Pooled sensitivity and specificity for diagnosis of appendicitis in children were 88% (95% confidence interval [CI]: 86%, 90%) and 94% (95% CI: 92%, 95%), ...

Principles, Techniques, and Applications of T2*-based MR Imaging and Its Special Applications

Abstract: T2* relaxation refers to decay of transverse magnetization caused by a combination of spin-spin relaxation and magnetic fi eld inhomogeneity. T2* relaxation is seen only with gradient-echo (GRE) imaging because transverse relaxation caused by magnetic fi eld inhomogeneities is eliminated by the 180° pulse at spin-echo imaging. T2* relaxation is one of the main determinants of image contrast with GRE sequences and forms the basis for many magnetic resonance (MR) applications, such as susceptibility-weighted (SW) imaging, perfusion MR imaging, and functional MR imaging. GRE sequences can be made predominantly T2* weighted by using a low fl ip angle, long echo time, and long repetition time. GRE sequences with T2*-based contrast are used to depict hemorrhage, calcifi cation, and iron deposition in various tissues and lesions. SW imaging uses phase information in addition to T2*-based contrast to exploit the magnetic susceptibility differences of the blood and of iron and calcifi cation in various tissues. Perfusion MR imaging exploits the signal intensity decrease that occurs with the passage of a high concentration of gadopentetate dimeglumine through the microvasculature. Change in oxygen saturation during specifi c tasks changes the local T2*, which leads to the blood oxygen level–dependent effect seen at functional MR imaging. The basics of T2* relaxation, T2*-weighted sequences, and their clinical applications are presented, followed by the principles, techniques, and clinical uses of four T2*-based applications, including SW imaging, perfusion MR imaging, functional MR imaging, and iron overload imaging.

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.

Abstract: Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.

Computed Tomography — An Increasing Source of Radiation Exposure

Abstract: The number of computed tomographic (CT) studies performed is increasing rapidly. Because CT scans involve much higher doses of radiation than plain films, we are seeing a marked increase in radiation exposure in the general population. Epidemiologic studies indicate that the radiation dose from even two or three CT scans results in a detectable increase in the risk of cancer, especially in children. This article summarizes the facts about this form of radiation exposure and the implications for public health.

A survey on Image Data Augmentation for Deep Learning

Abstract: Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.