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

Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.

T2* relaxation refers to decay of transverse magnetization caused by a combination of spin-spin relaxation and magnetic field inhomogeneity. T2* relaxation is seen only with gradient-echo (GRE) imaging because transverse relaxation caused by magnetic field inhomogeneities is eliminated by the 180 degrees 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 flip angle, long echo time, and long repetition time. GRE sequences with T2*-based contrast are used to depict hemorrhage, calcification, 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 calcification 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 specific 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.

From the diagnostic performance perspective, CT had a significantly higher sensitivity than did US in studies of children and adults; from the safety perspective, however, one should consider the radiation associated with CT, especially in children.

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.

Summary. Background: Prophylactic treatment for severe hemophilia A is likely to be more effective than treatment when bleeding occurs, however, prophylaxis is costly. We studied an inception cohort of 25 boys using a tailored prophylaxis approach to see if clotting factor use could be reduced with acceptable outcomes. Methods: Ten Canadian centers enrolled subjects in this 5-year study. Children were followed every 3 months at a comprehensive care hemophilia clinic. They were initially treated with once-weekly clotting factor; the frequency was escalated in a stepwise fashion if unacceptable bleeding occurred. Bleeding frequency, target joint development, physiotherapy and radiographic outcomes, as well as resource utilization, were determined prospectively. Results: The median follow-up time was 4.1 years (total 96.9 person-years). The median time to escalate to twice-weekly therapy was 3.42 years (lower 95% confidence limit 2.05 years).Nine subjects developed target joints at a rate of 0.09 per person-year. There was an average of 1.2 joint bleeds per person-year. The cohort consumed on average 3656 IU kg )1 year )1 of factor (F) VIII. Ten subjects required central venous catheters (three while on study); no complications of these devices were seen. One subject developed a transient FVIII inhibitor. End-of-study joint examination scores -both clinically and radiographically -were normal or near-normal. Conclusions: Most boys with severe hemophilia A will probably have little bleeding and good joint function with tailored prophylaxis, while infusing less FVIII than usually required for traditional prophylaxis.

Color Doppler US is more accurate than abdominal radiography in depicting bowel necrosis in NEC.

Steady-state sequences are a class of rapid magnetic resonance (MR) imaging techniques based on fast gradient-echo acquisitions in which both longitudinal magnetization (LM) and transverse magnetization (TM) are kept constant. Both LM and TM reach a nonzero steady state through the use of a repetition time that is shorter than the T2 relaxation time of tissue. When TM is maintained as multiple radiofrequency excitation pulses are applied, two types of signal are formed once steady state is reached: preexcitation signal (S-) from echo reformation; and postexcitation signal (S+), which consists of free induction decay. Depending on the signal sampled and used to form an image, steady-state sequences can be classified as (a) postexcitation refocused (only S+ is sampled), (b) preexcitation refocused (only S- is sampled), and (c) fully refocused (both S+ and S- are sampled) sequences. All tissues with a reasonably long T2 relaxation time will show additional signals due to various refocused echo paths. Steady-state sequences have revolutionized cardiac imaging and have become the standard for anatomic functional cardiac imaging and for the assessment of myocardial viability because of their good signal-to-noise ratio and contrast-to-noise ratio and increased speed of acquisition. They are also useful in abdominal and fetal imaging and hold promise for interventional MR imaging. Because steady-state sequences are now commonly used in MR imaging, radiologists will benefit from understanding the underlying physics, classification, and clinical applications of these sequences.