Traumatic Cervical Cerebrovascular Injury and the Role of CTA: AJR Expert Panel Narrative Review.

Traumatic cerebrovascular injury (CVI) involving the cervical carotid and vertebral arteries is rare but can lead to stroke, hemodynamic compromise, and mortality in the absence of early diagnosis and treatment. The diagnosis of both blunt CVI (BCVI) and penetrating CVI is based on cerebrovascular imaging. The most commonly used screening criteria for BCVI include the expanded Denver criteria and the Memphis criteria, each providing varying thresholds for subsequent imaging. Neck CTA has supplanted catheter-based digital subtraction angiography as the preferred screening modality for CVI in patients with trauma. This AJR Expert Panel Narrative Review describes the current state of CTA-based cervical imaging in trauma. We review the most common screening criteria for BCVI, discuss BCVI grading scales that are based on neck CTA, describe the diagnostic performance of CTA in the context of other imaging modalities and evolving treatment strategies, and provide a practical guide for neck CTA implementation.

Head and Neck Sarcoma Tumor Board Survival Guide for Neuroradiologists: Imaging Findings, History, and Pathology.

Sarcomas of the head and neck carry a poor prognosis as diagnosis is often delayed until a late stage of the disease. Accordingly, it is essential to be familiar with the clinical and imaging features of sarcomas to suggest an appropriate differential diagnosis for collaborating surgeons and pathologists. However, as there are only 1000-1500 cases in the United States annually, many radiologists lack experience with pertinent imaging findings of sarcoma and lack knowledge of both treatment and necessary follow-up. In this review, a brief discussion of WHO definitions and histopathology is included to decode information provided by pathologists. Finally, staging and treatments are illuminated to aid the radiologist with initial imaging staging and follow-up care. This review aims to increase the comprehensive knowledge of a neuroradiologist and further their value to the multidisciplinary tumor board.

Continuing to Thrive in Academic Radiology Despite Decreasing Reimbursement.

Decreasing radiology reimbursement is a major challenge faced by academic radiology practices in the United States. The consequent increased workload from reading more radiological studies can lead to job dissatisfaction, burnout and adverse impact on research, innovation, and education. Thriving successfully in an academic practice despite low reimbursement requires modification of radiology business models and culture of the practice. In this article, we review the financial and operational strategies to mitigate low reimbursement and strategies for thriving in academic radiology without burnout.

Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation.

OBJECTIVES: Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method-Heterogeneity Diffusion Imaging (HDI)-to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan. METHODS: Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction. RESULTS: The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV. CONCLUSIONS: Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI's clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading.

Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI.

Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i.e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i.e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography.