IntroductionThis article presents the outcome and recommendations following the second stage of a role development project conducted on behalf of the New Zealand Institute of Medical Radiation Technology (NZIMRT). The study sought to support the development of profiles and criteria that may be used to formulate Advanced Scopes of Practice for the profession. It commenced in 2011, following on from initial research that occurred between 2005 and 2008 investigating role development and a possible career structure for medical radiation technologists (MRTs) in New Zealand (NZ).MethodsThe study sought to support the development of profiles and criteria that could be used to develop Advanced Scopes of Practice for the profession through inviting 12 specialist medical imaging groups in NZ to participate in a survey.ResultsFindings showed strong agreement on potential profiles and on generic criteria within them; however, there was less agreement on specific skills criteria within specialist areas.ConclusionsThe authors recommend that one Advanced Scope of Practice be developed for Medical Imaging, with the establishment of generic and specialist criteria. Systems for approval of the overall criteria package for any individual Advanced Practitioner (AP) profile, audit and continuing professional development requirements need to be established by the Medical Radiation Technologists Board (MRTB) to meet the local needs of clinical departments. It is further recommended that the NZIMRT and MRTB promote and support the need for an AP pathway for medical imaging in NZ.
Magnetic resonance imaging (MRI) has been traditionally regarded as a safe imaging modality due to the absence of ionising radiation. However, MRI is a source of potential hazards with a variety of risks including, but not limited to, those associated with the various electromagnetic fields used for imaging. All MRI technologists (radiographers) require sound knowledge of the physical principles of the MRI scanner and must understand the associated safety risks and how to avoid adverse events from occurring. MRI technologists now assume more responsibility in clinical decision‐making, and their knowledge base has consequently had to expand significantly. In addition, rapid advancements in MRI technology and other correlated areas such as medical implant technology, and the associated increase in MRI safety issues, place increasing demands on the MRI technologist to constantly keep abreast of current and future developments. This article reviews current and emerging MRI safety issues relevant to the three MRI electromagnetic fields and highlights the key information that all MRI technologists should be fully cognisant of to ensure competent and safe practice within the MRI environment.
There is hope that the development of 2D shear wave elastography (2D‐SWE) technology will enhance clinical assessment of muscles. In addition to structural information obtained from traditional sonography, knowledge of muscle biomechanical properties may add diagnostic value in the investigation of pathological processes and functions of the musculoskeletal system. 2D‐SWE offers benefits over traditional biomechanical testing tools and may be adapted to assess muscle at rest, or in contracted or lengthened states. To date, disparate research methodologies and proprietary technological differences limit comparison of research findings. Consensus guidelines exist for clinical utilisation of elastography in fields such as liver, breast and thyroid imaging. Presently, no such consensus has been reached for 2D‐SWE muscle utilisation. This paper explores the current literature to describe the technical considerations and challenges of 2D‐SWE in the assessment of skeletal muscle.
Introduction Significant fatty infiltration of the supraspinatus muscle is an important prognostic indicator for the likelihood of supraspinatus tendon repair failure. Two‐dimensional shear wave elastography (2D‐SWE) has the ability to quantitatively assess muscle stiffness. Presented is a pilot study comparing 2D‐SWE with the gold standard of magnetic resonance imaging (MRI) fatty infiltration grading. Methods 2D‐SWE measurements were prospectively obtained in 152 shoulders of participants presenting for shoulder MRI examinations. 2D‐SWE measurements were obtained in the anterior and posterior aspects in the superficial half of the supraspinatus muscle and compared to MRI Goutallier grading of fatty infiltration as assessed by three observers. Region of interest measurements were placed within the elastogram (a) randomly, (b) at the highest colour‐coded region, and (c) within the most common colour code. Results t‐test reveals a statistically significant difference (P = .002) between the mean of eight randomly placed 2D‐SWE samples with Goutallier 1 (mean shear wave speed [SWS] = 2.49) and 2 (mean SWS = 2.24). Although the difference was statistically significant, the large overlap of the SWS distributions between Goutallier grades 1 and 2 indicates that SWS is a poor predictor of Goutallier grading. Conclusion Random SWS sampling throughout the superficial supraspinatus muscle highly correlates with MRI Goutallier grading but lacks accuracy.
Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Guala A. received funding from the Spanish Ministry of Science, Innovation and Universities Background Phase-contrast (PC) enhanced magnetic resonance (MR) angiography (MRA) is a class of angiogram exploiting velocity data to increase the signal-to-noise ratio, thus avoiding the administration of external contrast agent, normally used to segment 4D flow MR data. To train deep-learning algorithms to segment PC-MRA a large amount of manually annotated data is needed: however, the relatively novelty of the sequence, its rapid evolution and the extensive time needed to manually segment data limit its availability. Purpose The aim of this study was to test a deep learning algorithm in the segmentation of multi-center and multi-vendor PC-MRA and to test if transfer learning (TL) improves performance. Methods A large dataset (LD) of 262 and a small one (SD) of 22 PC-MRA, acquired without contrast agent at 1.5 T in a General Electric and a Siemens scanner, respectively, were manually annotated and divided into training (232 and 15 cases) and testing (30 and 7) sets. They both included PC-MRA of healthy subjects and patients with aortic diseases (excluding dissections) and native aorta. A convolutional neural networks (CNN) based on nnU-Net framework [1] was trained in the LD and another in the SD. The left ventricle was removed semi-automatically from the DL segmentations of the LD as it was not relevant for this application. Networks were then tested on the test sets of the dataset there were trained and the other dataset to assess generalizability. Finally, a fine-tuning transfer learning approach was applied to LD network and the performance on both test sets were tested. Dice score, Hausdorff distance, Jaccard score and Average Symmetrical Surface Distance were used as segmentation quality metrics. Results LD network achieved good performance in LD test set, with a DS of 0.904, ASSD of 1.47, J of 0.827 and HD of 6.35, which further improve after removing the left ventricle in the post-processing to a DS of 0.942, ASSD of 0.93, J of 0.892 and HD of 3.32. SD network results in an average DS of 0.895, ASSD of 0.59, J of 0.812 and HD of 2.05. Once tested on the testing set of the other dataset, LD network resulted in a DS of 0.612 while SD network in DS of 0.375, thus showing limited generalizability. However, the application of transfer learning to LD network resulted in the improvement of the evaluation metrics on the SD from a DS of 0.612 to 0.858, while slightly worsening in the first one without post-processing to 0.882. Conclusions nnU-net framework is effective for fast automatic segmentation of the aorta from multi-center and multi-vendor PC-MRA, showing performance comparable with the state of the art. The application of transfer learning allows for increased generalization to data from center not included in the original training. These results unlock the possibility for fully-automatic analysis of multi-vendor multi-center 4D flow MR.
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