Improved X-Ray Safety, Quality Control, and Resource Management in Medical Imaging Using QATrack+

Leading into the future: Revising nuclear medicine education across Canada to satisfy the newly imposed CAMRT leadership competency

INTRODUCTION

The adoption of the CanMEDS framework into the 2024 competency profiles established by the Canadian Association of Medical Radiation Technologists (CAMRT) requires an increased focus on leadership education and training of student medical radiation technologists (MRTs).

METHODS

A literature review was used to explore current MRT novice leadership and to identify content that should be incorporated into Canadian nuclear medicine curricula to meet the leadership component of the CanMEDS framework. A table, summarizing leadership education currently provided in English-speaking Canadian nuclear medicine programs, is provided.

RESULTS

A gap in leadership preparedness for medical radiation technologists as well as inconsistencies in leadership education within Canadian nuclear medicine curricula were identified. Content inclusions for leadership education is also provided.

DISCUSSION

Recommended content and strategies to revise MRT curricula are offered to enable effective healthcare leadership by MRTs. A specific focus on leadership areas such as technology adoption, radiation safety, and professional advocacy is recommended. The implications for nuclear medicine practice and education are described.

CONCLUSION

Standardized leadership education in nuclear medicine and medical radiation technology education will ensure MRTs are equipped with the skills required to lead effectively.

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed by combining three different sizes of convolution kernels, which are used to obtain multiple shallow features for fusion and increase the network's multiscale perception ability. Then, it combines batch normalization and residual learning technology to accelerate and optimize the deep network, while solving the problem of internal covariate transfer in deep learning. Finally, the joint loss function is defined by combining the perceptual loss and the traditional mean square error loss. When the network is trained, it can not only be compared at the pixel level but also be learned at a higher level of semantic features to generate a clearer target image. Based on the MATLAB simulation platform, the TCGA-GBM and CH-GBM datasets are used to experimentally demonstrate the proposed algorithm. The results show that when the image size is set to 190 × 215 and the optimization algorithm is Adam, the performance of the proposed algorithm is the best, and its denoising effect is significantly better than other comparison algorithms. Especially under high-intensity noise levels, the denoising advantage is more prominent.