Dual-Energy CT: Balance Between Iodine Attenuation and Artifact Reduction for the Evaluation of Head and Neck Cancer.

OBJECTIVE: Dual-energy computed tomography high energy virtual monochromatic images (VMIs) can reduce artifact but suppress iodine attenuation in enhancing tumor. We investigated this trade-off to identify VMI(s) that strike the best balance between iodine detection and artifact reduction. METHODS: The study was performed using an Alderson radiation therapy phantom. Different iodine solutions (based on estimated tumor iodine content in situ using dual-energy computed tomography material decomposition) and different dental fillings were investigated. Spectral attenuation curves and quality index (QI: 1/SD) were evaluated. RESULTS: The relationship between iodine attenuation and QI depends on artifact severity and iodine concentration. For low to average concentration solutions degraded by mild to moderate artifact, the iodine attenuation and QI curves crossed at 95 keV. CONCLUSIONS: High energy VMIs less than 100 keV can achieve modest artifact reduction while preserving sufficient iodine attenuation and could represent a useful additional reconstruction for evaluation of head and neck cancer.

Central nervous system injury in utero: selected entities.

This report discusses the syndrome of amnionic bands, anencephaly, schizencephaly and hydranencephaly, four entities whose pathogenesis includes significant injury to the fetus in utero.

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients.

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model.

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

The Normal Adult Human Internal Auditory Canal: A Volumetric Multidetector Computed Tomography Study.

OBJECTIVE: The purpose of this study was to demonstrate that volumetric analysis of multidetector computed tomography (CT) images can be used to calculate the volume of the adult human internal auditory canal (IAC) reproducibly, and to describe the range of normal IAC volumes in the adult population with subgroup analysis of sex, age, and laterality. BACKGROUND: Previous studies of the IAC have typically used measurements in two dimensions or by using casts of cadavers to measure IAC volumes. This study is the first to report the normal ranges of IAC volumes measured by CT. METHODS: Two hundred eighty-one CT scans were assessed. Of the CT scans that met the inclusion criteria, a software package was used to manually contour the IACs in each subject to calculate the volumes in cubic millimeters. Subgroup analysis of laterality, sex, and age was evaluated. Interobserver agreement was calculated for the first 59 patients (118 canals). RESULTS: Two hundred fifty-nine scans (518 canals) met the inclusion criteria. The volumes ranged from 74 to 502 mm, with no statistically significant difference between left and right (p value = 0.69). In males, the range of volumes measured 74 to 502 mm while in females it ranged from 78 to 416 mm. Males had larger IAC volumes than females (Wilcoxon rank-sum test: S = 14,845.0, p value = 0.01 on the right, and S = 14,646, p value = 0.004 on the left). No correlation was found with age (Spearman: -0.10, p value = 0.09 on the right and -0.04, p value = 0.50 on the left). Excellent interobserver agreement was found. CONCLUSION: IAC volumes of normal adult subjects, measured by CT, were larger in males and not significantly different with respect to age or laterality.

Routine Dual-Energy Computed Tomography Scanning of the Neck in Clinical Practice: A Single-Institution Experience.

There is increasing use and popularity of dual-energy computed tomography (DECT) in many subspecialties in radiology. This article reviews the practical workflow implications of routine DECT scanning based on the experience at a single institution where a large percentage of elective neck CTs are acquired in DECT mode. The article reviews factors both on the production (technologist) and on the interpretation (radiologist) side, focusing on challenges posed and potential solutions for seamless workflow implementation.