Faculty Bibliography

Recent successful trials of thrombectomy launched a shift to imaging-based patient selection for stroke intervention. Many centers have adopted CT perfusion imaging (CTP) as a routine part of stroke workflow, and the demand for emergent CTP interpretation is growing. Fully automated CTP postprocessing software that rapidly generates standardized color-coded CTP summary maps with minimal user input and with easy accessibility of the software output is increasingly being adopted. Such automated postprocessing greatly streamlines clinical workflow and CTP interpretation for radiologists and other frontline physicians. However, the straightforward interface overshadows the computational complexity of the underlying postprocessing workflow, which, if not carefully examined, predisposes the interpreting physician to diagnostic errors. Using case examples, this article aims to familiarize the general radiologist with interpreting automated CTP software data output in the context of contemporary stroke management, providing a discussion of CTP acquisition and postprocessing, a stepwise guide for CTP quality assurance and troubleshooting, and a framework for avoiding clinically significant pitfalls of CTP interpretation in commonly encountered clinical scenarios. Interpreting radiologists should apply the outlined approach for quality assurance and develop a comprehensive search pattern for the identified pitfalls, to ensure accurate CTP interpretation and optimize patient selection for reperfusion.

INTRODUCTION:Pressure gradients across venous stenosis are used as a marker for physiologically significant narrowing in idiopathic intracranial hypertension. Performing such measurements under conscious sedation (CS) more likely reflects physiologic conditions, but can be uncomfortable, leading some operators to perform measurement under general anesthesia (GA), though this may not be equivalent. METHODS:We performed a retrospective analysis of patients who received endovascular transverse sinus stenting due to idiopathic intracranial hypertension between August 2013 and May 2017. Patients' demographics and anesthetic parameters were collected along with venous pressure measurements. RESULTS:. After measurements during CS, GA was induced with propofol and maintained with a volatile anesthetic. The median [IQR; range] transverse sinus pressure gradient under CS was 18 [12, 25; 6-38] mmHg compared with 14 [8, 21; 3-26] mm Hg under GA. The median [IQR; range] pressure gradient change after initiation of GA was -3 [-12, 0; -22 to 9] mm Hg (P = 0.014). After correction for increases in internal jugular vein pressures associated with assumption of GA, the median [IQR; range] gradient change was -11 [-12.5, -5; -22 to 0] mm Hg (P < 0.001). CONCLUSIONS:The transition from CS to GA results in clinically meaningful reductions in transverse sinus gradients in idiopathic intracranial hypertension. Correction for increases in the internal jugular vein pressures reveals even more dramatic reductions in transverse sinus gradients.

OBJECTIVE:We describe our experience in implementing enterprise-wide standardized structured reporting for chest radiographs (CXRs) via change management strategies and assess the economic impact of structured template adoption. METHODS:Enterprise-wide standardized structured CXR reporting was implemented in a large urban health care enterprise in two phases from September 2016 to March 2019: initial implementation of division-specific structured templates followed by introduction of auto launching cross-divisional consensus structured templates. Usage was tracked over time, and potential radiologist time savings were estimated. Correct-to-bill (CTB) rates were collected between January 2018 and May 2019 for radiography. RESULTS:CXR structured template adoption increased from 46% to 92% in phase 1 and to 96.2% in phase 2, resulting in an estimated 8.5 hours per month of radiologist time saved. CTB rates for both radiographs and all radiology reports showed a linearly increasing trend postintervention with radiography CTB rate showing greater absolute values with an average difference of 20% throughout the sampling period. The CTB rate for all modalities increased by 12%, and the rate for radiography increased by 8%. DISCUSSION/CONCLUSIONS:Change management strategies prompted adoption of division-specific structured templates, and exposure via auto launching enforced widespread adoption of consensus templates. Standardized structured reporting resulted in both economic gains and projected radiologist time saved.

With continued technologic advances, it is not surprising that gaming techniques are increasingly being used in radiology residency programs. This comprehensive review on gaming in radiology education offers insight into the importance of gaming, types of games and principles utilized in gaming, as well as applications that are inherent in artificial intelligence and continued medical education. The advantages and disadvantages of gaming will be considered, as well as barriers to successful adoption of gaming.

The goal of this study was to investigate the application of machine learning models capable of capturing multiplica tive and temporal clinical risk factors for outcome prediction inpatients with aneurysmal subarachnoid hemorrhage (aSAH). We examined a cohort of 575 aSAH patients from Emory Healthcare, identified via digital subtraction angiog- raphy. The outcome measure was the modified Ranking Scale (mRS) after 90 days. Predictions were performed with longitudinal clinical and imaging risk factors as inputs into a regularized Logistic Regression, a feedforward Neural Network and a multivariate time-series prediction model known as the long short-term memory (LSTM) architecture. Through extraction of higher-order risk factors, the LSTM model achieved an AUC of 0.89 eight days into hospitaliza tion, outperforming other techniques. Our preliminary findings indicate the proposed model has the potential to aid treatment decisions and effective imaging resource utilization in high-risk patients by providing actionable predictions prior to the development of neurological deterioration.