Update on sports imaging

Sports Imaging has dramatically increased in the past decade with increasing number of adolescents, young and middle-aged adults participating in non-competitive/hobby sports. Therefore, sports injuries are no longer confined to elite athletes. Furthermore, newer forms of sports such as mountain climbing, pickle ball and curling etc. are gaining popularity. Majority of the injuries in sports medicine are from musculoskeletal trauma. Therefore, it is imperative that the musculoskeletal radiologist becomes familiar with various sports related injury patterns as these are commonly encountered in daily practice. This update aims to briefly encapsulate the major aspects of sports imaging. It includes the imaging manifestations of various types of musculoskeletal injuries on different modalities (commonly US and MRI) and briefly mentions the various image guided interventions, performed both on the sports field and in the hospital setting.     

Comparing the rates of low-value back images ordered by physicians and nurse practitioners for Medicare beneficiaries in primary care

BackgroundLittle is known about the extent of ordering low-value services by.PurposeTo compare the rates of low-value back images ordered by primary care physicians (PCMDs) and primary care nurse practitioners (PCNPs).MethodWe used 2012 and 2013 Medicare Part B claims for all beneficiaries in 18 hospital referral ?regions (HRRs) and a measure of low-value back imaging from Choosing Wisely. Models included random clinician effect and fixed effects for beneficiary age, disability, Elixhauser comorbidities, clinician sex, the emergency department setting, back pain visit volume, organization, and region (HRR).FindingsPCNPs (N = 231) and PCMDs (N = 4,779) order low-value back images at similar rates (NP: all images: 26.5%; MRI/CT: 8.4%; MD: all images: 24.5%; MRI/CT: 7.7%), with no detectable significant difference when controlling for covariates.DiscussionPCNPs and PCMDs order low-value back images at an effectively similar rate.     

Utility of Tissue Similarity Maps Based on Relative Cerebral Blood Volume for Grading Gliomas as Validated by Histological Results

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On the Relationship between Dynamic Contrast-Enhanced Ultrasound Parameters and the Underlying Vascular Architecture Extracted from Acoustic Angiography

Dynamic contrast-enhanced ultrasound (DCE-US) has been proposed as a powerful tool for cancer diagnosis by estimation of perfusion and dispersion parameters reflecting angiogenic vascular changes. This work was aimed at identifying which vascular features are reflected by the estimated perfusion and dispersion parameters through comparison with acoustic angiography (AA). AA is a high-resolution technique that allows quantification of vascular morphology. Three-dimensional AA and 2-D DCE-US bolus acquisitions were used to monitor the growth of fibrosarcoma tumors in nine rats. AA-derived vascular properties were analyzed along with DCE-US perfusion and dispersion to investigate the differences between tumor and control and their evolution in time. AA-derived microvascular density and DCE-US perfusion exhibited good agreement, confirmed by their spatial distributions. No vascular feature was correlated with dispersion. Yet, dispersion provided better cancer classification than perfusion. We therefore hypothesize that dispersion characterizes vessels that are smaller than those visible with AA.     

The effect of different statistical approaches on image quality data obtained from radiological examinations

IntroductionSelection of optimal image acquisition protocols in medical imaging remains a grey area, the superimposed use of the Likert scale in radiological image quality evaluations creates an additional challenge for the statistical analysis of image quality data.Using a simulation study, we have trialled a novel approach to analysing radiological image quality Likert scale data.MethodsA simulation study was undertaken where simulated datasets were generated based on the distribution of Likert scale values according to varying image acquisition protocols from a real dataset. Simulated Likert scale values were pooled in four different ways; the mean, median, mode and the summation of patient Likert scale values of which the total was assigned a categorical Likert scale value. Estimates of bias, MAPE and RMSPE were then calculated for all four pooling approaches to determine which method most accurately represented an expert's opinion.ResultsWhen compared to an expert's opinion, the method of summation and categorisation of Likert scale values was most accurate 49 times out of the 114 (43.0%) tests. The mean 28 times out of 114 (24.6%), the median 23 times out of 114 (20.2%) and the mode 17 times out of 114 (14.9%).ConclusionWe conclude that our method of summation and categorisation of Likert scale values is most often the best representation of the simulated data compared to the expert's opinion.Implications for practiceThere is scope to reproduce this simulation study with multiple observers to reflect clinical reality more accurately with the dynamic nature of multiple observers. This also prompts future investigation into other anatomical areas, to see if the same methods produce similar results.