Head Computed Tomography Imaging Research Articles

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.

Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored. To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion. Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies. The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48mm or degree). The resulting motion-corrected image, , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 33HU to 37 9HU, structural similarity index (SSIM) increases from 0.60 0.06 to 0.98 0.01) and the phantom study (image MAE drops from 117 17HU to 42 19HU, SSIM increases from 0.83 0.04 to 0.98 0.02). We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

Advancing pediatric head CT dose prediction: correlating patient size metrics with AP and LAT dimensions in Moroccan population

This study aims to establish comprehensive relationships between patient size, focusing on effective diameter (Deff) and water-equivalent diameter (Dw), in conjunction with anterior-posterior (AP) and lateral (LAT) dimensions. The primary goal is to refine precise patient dose estimations in pediatric head computed tomography (CT) examinations. Our examination involved a dataset of 105 pediatric head CT images sourced from a Moroccan pediatric university hospital. These images, selected from the central scanning range, underwent quantification for LAT, AP, Deff, and Dw, with measurements carried out using RadiAnt Dicom Viewer for lateral and anteroposterior dimensions. Our analytical approach encompassed modeling Deff and Dw as functions of LAT, AP, and combined AP + LAT dimensions. Additionally, Dw was modeled as a function of Deff and calculated Size Specific Dose Estimates (SSDE) based on Dw was compared to SSDE computed from one single dimension. The outcomes revealed strong correlations among Deff, Dw, and fundamental geometric dimensions (LAT, AP, and AP + LAT), as indicated by consistently high coefficients of determination (R² > 0.8). SSDE calculated from Dw shows impressive correlation with SSDE computed from a single dimension (R² > 0.9). This study offers insights into the relationships between patient size and critical dimensions, identifying high correlations that can represent a significant advancement in predicting doses for pediatric head CT examinations. This introduces the potential for streamlining the determination of head diameter and dose by relying on a single dimension, thereby enhancing the efficiency of dose predictions by facilitating the monitoring of radiation exposure of this population in clinical routine.

Motion Artifact Detection Based on Regional–Temporal Graph Attention Network from Head Computed Tomography Images

Artifacts are the main cause of degradation in CT image quality and diagnostic accuracy. Because of the complex texture of CT images, it is a challenging task to automatically detect artifacts from limited image samples. Recently, graph convolutional networks (GCNs) have achieved great success and shown promising results in medical imaging due to their powerful learning ability. However, GCNs do not take the attention mechanism into consideration. To overcome their limitations, we propose a novel Regional–Temporal Graph Attention Network for motion artifact detection from computed tomography images (RT-GAT). In this paper, head CT images are viewed as a heterogeneous graph by taking regional and temporal information into consideration, and the graph attention network is utilized to extract the features of the constructed graph. Then, the feature vector is input into the classifier to detect the motion artifacts. The experimental results demonstrate that our proposed RT-GAT method outperforms the state-of-the-art methods on a real-world CT dataset.

VOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images.

Accurate and consistent contouring of organs-at-risk (OARs) from medical images is a key step of radiotherapy (RT) cancer treatment planning. Most contouring approaches rely on computed tomography (CT) images, but the integration of complementary magnetic resonance (MR) modality is highly recommended, especially from the perspective of OAR contouring, synthetic CT and MR image generation for MR-only RT, and MR-guided RT. Although MR has been recognized as valuable for contouring OARs in the head and neck (HaN) region, the accuracy and consistency of the resulting contours have not been yet objectively evaluated. To analyze the interobserver and intermodality variability in contouring OARs in the HaN region, performed by observers with different level of experience from CT and MR images of the samepatients. In the final cohort of 27 CT and MR images of the same patients, contours of up to 31 OARs were obtained by a radiation oncology resident (junior observer, JO) and a board-certified radiation oncologist (senior observer, SO). The resulting contours were then evaluated in terms of interobserver variability, characterized as the agreement among different observers (JO and SO) when contouring OARs in a selected modality (CT or MR), and intermodality variability, characterized as the agreement among different modalities (CT and MR) when OARs were contoured by a selected observer (JO or SO), both by the Dice coefficient (DC) and 95-percentile Hausdorff distance (HD ). The mean (±standard deviation) interobserver variability was 69.0 ± 20.2% and 5.1 ± 4.1mm, while the mean intermodality variability was 61.6 ± 19.0% and 6.1 ± 4.3mm in terms of DC and HD , respectively, across all OARs. Statistically significant differences were only found for specific OARs. The performed MR to CT image registration resulted in a mean target registration error of 1.7 ± 0.5mm, which was considered as valid for the analysis of intermodalityvariability. The contouring variability was, in general, similar for both image modalities, and experience did not considerably affect the contouring performance. However, the results indicate that an OAR is difficult to contour regardless of whether it is contoured in the CT or MR image, and that observer experience may be an important factor for OARs that are deemed difficult to contour. Several of the differences in the resulting variability can be also attributed to adherence to guidelines, especially for OARs with poor visibility or without distinctive boundaries in either CT or MR images. Although considerable contouring differences were observed for specific OARs, it can be concluded that almost all OARs can be contoured with a similar degree of variability in either the CT or MR modality, which works in favor of MR images from the perspective of MR-only and MR-guidedRT.

Toward Head Computed Tomography Image Reconstruction Standardization With Deep-Learning-Assisted Automatic Detection

Toward Head Computed Tomography Image Reconstruction Standardization With Deep-Learning-Assisted Automatic Detection

Risk factors of prognosis in older patients with severe brain injury after surgical intervention

BackgroundOlder patients (aged ≥ 60 years) with severe brain injury have a high mortality and disability rate. The objective of this retrospective study was to assess the clinical risk factors of prognosis in older patients with severe brain injury after surgical intervention and to analyze the prognosis of the surviving group of patients 1 year after discharge.MethodsClinical data of older patients with severe brain injury who were admitted to two neurosurgical centers between January 2010 and December 2020 were collected. Patient age, sex, Glasgow Coma Scale (GCS) score at admission, underlying disease, mechanisms of injury, abnormal pupillary reflex, head computed tomography imaging findings (such as hematoma type),intraoperative brain swelling and other factors were reviewed. All the patients were categorized into a good prognosis (survival) group and a poor prognosis (death) group by the Glasgow Outcome Score (GOS); also, the related factors affecting the prognosis were screened and the independent risk factors were identified by the Binary logistic regression analysis. GOS was used to evaluate the prognosis of the surviving group of patients 1 year after discharge.ResultsOut of 269 patients, 171 (63.6%) survived, and 98 (36.4%) died during hospitalization. Univariate analysis showed that age, GCS score at admission, underlying diseases, abnormal pupillary reflex, the disappearance of ambient cistern, the midline structure shift, intraoperative brain swelling, oxygen saturation < 90%, and cerebral hernia were risk factors for the prognosis of older patients with severe brain injury after surgical intervention. Multivariate analysis showed that age, underlying diseases, disappearance of ambient cistern, Oxygen saturation < 90% and intraoperative brain swelling were independent risk factors of the prognosis in the population. The effect of surgical intervention differed among various age groups at 1-year follow-up after surgery.ConclusionsThe results of this retrospective study confirmed that age, underlying diseases, disappearance of ambient cistern, intraoperative brain swelling, and oxygen saturation < 90% are associated with poor prognosis in older postoperative patients with severe brain injury. Surgical intervention may improve prognosis and reduce mortality in older patients (age < 75 years). But for those patients (age ≥ 75 years), the prognosis was poor after surgical intervention.

Anatomic Variations of Foramen Ovale as a Predictor of Successful Cannulation in Percutaneous Trigeminal Rhizotomies.

Percutaneous trigeminal rhizotomies are common treatment modalities for medically refractory trigeminal neuralgia (TN). Failure of these procedures is frequently due to surgical inability to cannulate the foramen ovale (FO) and is thought to be due to variations in anatomy. The purpose of this study is to characterize the relationships between anatomic features surrounding FO and investigate the association between anatomic morphology and successful cannulation of FO in patients undergoing percutaneous trigeminal rhizotomy. A retrospective analysis was conducted of all patients undergoing percutaneous trigeminal rhizotomy for TN at our academic center between January 1, 2010, and July 31, 2022. Preoperative 1-mm thin-cut computed tomography head imaging was accessed to perform measurements surrounding the FO, including inlet width, outlet width, interforaminal distance (a representation of the lateral extent of FO along the middle fossa), and sella-sphenoid angle (a representation of the coronal slope of FO). Mann-Whitney U tests assessed the difference in measurements for patients who succeeded and failed cannulation. Among 37 patients who met inclusion criteria, 34 (91.9%) successfully underwent cannulation. Successful cannulation was associated with larger inlet widths (median = 5.87 vs 3.67 mm, U = 6.0, P = .006), larger outlet widths (median = 7.13 vs 5.10 mm, U = 14.0, P = .040), and smaller sella-sphenoid angles (median = 52.00° vs 111.00°, U = 0.0, P < .001). Interforaminal distances were not associated with the ability to cannulate FO surgically. We have identified morphological characteristics associated with successful cannulation in percutaneous rhizotomies for TN. Preoperative imaging may optimize surgical technique and predict cannulation failure.

Reproducibility of Facial Information in Three-Dimensional Reconstructed Head Images: An Exploratory Study.

Facial information acquired via three-dimensional reconstruction of head computed tomography (CT) data may be considered personal information, which can be problematic for neuroimaging studies. However, no study has verified the relationship between slice thickness and face reproducibility. This study determined the relationship and match rate between image slice thickness and face detection accuracy of face-recognition software in facial reconstructed models. Head CT data of 60 cases comprising entire faces obtained under conditions of non-contrast and 1-mm slice thickness were resampled to obtain 2-10-mm slice-thickness data. Facial models, reconstructed by image thresholding, were acquired from the data. We performed face detection tests per slice thickness on the models and calculated the face detection rate. The reconstructed facial models created from 1-mm slice-thickness data and other slice thicknesses were used as training and test data, respectively. Match confidence scores were obtained via three programs, match rates were calculated per slice thickness, and generalized estimating equations were used to evaluate the match rate trend. In general, the face detection rates for the 1-10-mm slice thicknesses were 100, 100, 98.3, 98.3, 95.0, 91.7, 86.7, 78.3, 68.3, and 61.7 %, respectively. The match rates for the 2-10-mm slice thicknesses were 100, 98.3, 98.3, 95.0, 85.0, 71.7, 53.3, 28.3, and 16.7 %, respectively. The reconstructed models tended to have higher match rates as the slice thickness decreased. Thus, thin-slice head CT imaging data may increase the possibility of the information becoming personally identifiable health information.

Association between Bilateral Selective Antegrade Cerebral Perfusion and Postoperative Ischemic Stroke in Patients with Emergency Surgery for Acute Type A Aortic Dissection-Single Centre Experience.

Acute type A aortic dissection (ATAAD) is a surgical emergency with a mortality of 1-2% per hour. Since its discovery over 200 years ago, surgical techniques for repairing a dissected aorta have evolved, and with the introduction of hypothermic circulatory arrest and cerebral perfusion, complex techniques for replacing the entire aortic arch were possible. However, postoperative neurological complications contribute significantly to mortality in this group of patients. The aim of this study was to determine the association between different bilateral selective antegrade cerebral perfusion (ACP) times and the incidence of postoperative ischemic stroke in patients with emergency surgery for ATAAD. Patients with documented hemorrhagic or ischemic stroke, clinical signs of stroke or neurological dysfunction prior to surgery, that died on the operating table or within 48 h after surgery, from whom the postoperative neurological status could not be assessed, and with incomplete medical records were excluded from this study. The diagnosis of postoperative stroke was made using head computed tomography imaging (CT) when clinical suspicion was raised by a neurologist in the immediate postoperative period. For selective bilateral antegrade cerebral perfusion, we used two balloon-tipped cannulas inserted under direct vision into the innominate artery and the left common carotid artery. Each cannula is connected to a separate pump with an independent pressure line. Near-infrared spectroscopy was used in all cases for cerebral oxygenation monitoring. The circulatory arrest was initiated after reaching a target core temperature of 25-28 °C. In total, 129 patients were included in this study. The incidence of postoperative ischemic stroke documented on a head CT was 24.8% (31 patients), and postoperative death was 20.9% (27 patients). The most common surgical technique performed was supravalvular ascending aorta and Hemiarch replacement with a Dacron graft in 69.8% (90 patients). The mean cardiopulmonary bypass time was 210 +/- 56.874 min, the mean aortic cross-clamp time was 114.775 +/- 34.602 min, and the mean cerebral perfusion time was 37.837 +/- 18.243 min. Using logistic regression, selective ACP of more than 40 min was independently associated with postoperative ischemic stroke (OR = 3.589; 95%CI = 1.418-9.085; p = 0.007). Considering the high incidence of postoperative stroke in our study population, we concluded that bilateral selective ACP should be used with caution, especially in patients with severely calcified ascending aorta and/or aortic arch and supra-aortic vessels. All efforts should be made to minimize the duration of circulatory arrest when using bilateral selective ACP with a target of less than 30 min, in hypothermia, at a body temperature of 25-28 °C.

Trade-off between the radiation parameters and image quality using iterative reconstruction techniques in head computed tomography: a phantom study.

Iterative reconstruction techniques (IRTs) are commonly used in computed tomography (CT) and help to reduce image noise. To determine the minimum radiation dose while preserving image quality in head CT using IRTs. The anthropomorphic phantom was used to scan nine head CT image series with varied radiation parameters. CT dose parameters, including volume CT dose index (CTDIvol [in mGy]) and dose length product (DLP [in mGy/cm]), were recorded for each scan series. Different noise levels (iDoseL1-6) were used in IRT reconstructions for soft and bone tissues. In total, 15 measurements were taken from five regions of interest (ROI) with an area of 10 mm2. The signal-to-noise ratio (SNR) and noise values obtained at different ROIs were compared among various reconstruction methods with repeated measures of statistical analysis. In the head CT scan, applying IRT iDoseL5 had the lowest noise and highest SNR for soft tissue (P < 0.05), and increased iDose can decrease CT dose by 54.6% without compromising image quality. While for bone tissue reconstruction, no clear association was found between the level of iDose and noise. However, when CTDIvol is >20 mGy, iDoseL4 is slightly superior to other reconstruction methods (P < 0.065). Using IRTs in head CTs reduces radiation dose while maintaining image quality. IDoseL5 provided optimal balance for soft tissue.

Should Infants with Blunt Traumatic Brain Injuries and Intracranial Hemorrhage Have Routine Repeat Imaging?

The practice of repeat head CT imaging in infants as a distinct population is poorly studied. The purpose of this study was to evaluate the incidence and utility of repeat head CT in the infant population. A 10-year retrospective review was conducted of infants with blunt traumatic head injuries (N = 50) that presented to a trauma center. Information from the hospital trauma registry and patient medical records were extracted regarding the size and type of injury, number and results of computed tomography (CT) imaging, changes in neurological exams, and any interventions that were required. Most patients (68%) had at least one repeat CT, with 26% showing progression of hemorrhage. Decreased Glasgow Coma Scale was associated with having repeat CT scans. Nearly one in four infants had a change in management associated with repeat imaging. Repeat CT scans resulted in operative interventions in 11.8% of cases and longer intensive care unit (ICU) stays in 8.8% of cases. Repeat CT scans were associated with increased hospital length of stay, but not with increased ventilator days, ICU length of stay, or mortality. Worsening bleeds were associated with mortality, but not with other hospital outcomes. Changes in management following repeat CT appeared to be more common in this population than in older children or adults. Findings from this study supported repeat CT imaging in infants, however, further research is needed to validate results of this study.

Artificial Intelligence Model Trained with Sparse Data to Detect Facial and Cranial Bone Fractures from Head CT.

The presence of cranial and facial bone fractures is an important finding on non-enhanced head computed tomography (CT) scans from patients who have sustained head trauma. Some prior studies have proposed automatic cranial fracture detections, but studies on facial fractures are lacking. We propose a deep learning system to automatically detect both cranial and facial bone fractures. Our system incorporated models consisting of YOLOv4 for one-stage fracture detection and improved ResUNet (ResUNet++) for the segmentation of cranial and facial bones. The results from the two models mapped together provided the location of the fracture and the name of the fractured bone as the final output. The training data for the detection model were the soft tissue algorithm images from a total of 1,447 head CT studies (a total of 16,985 images), and the training data for the segmentation model included 1,538 selected head CT images. The trained models were tested on a test dataset consisting of 192 head CT studies (a total of 5,890 images). The overall performance achieved a sensitivity of 88.66%, a precision of 94.51%, and an F1 score of 0.9149. Specifically, the cranial and facial regions were evaluated and resulted in a sensitivity of 84.78% and 80.77%, a precision of 92.86% and 87.50%, and F1 scores of 0.8864 and 0.8400, respectively. The average accuracy for the segmentation labels concerning all predicted fracture bounding boxes was 80.90%. Our deep learning system could accurately detect cranial and facial bone fractures and identify the fractured bone region simultaneously.

Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase

Predicting poor neurological outcomes after resuscitation is important for planning treatment strategies. We constructed an explainable artificial intelligence-based prognostic model using head computed tomography (CT) scans taken immediately within 3 h of resuscitation from cardiac arrest and compared its predictive accuracy with that of previous methods using gray-to-white matter ratio (GWR). We included 321 consecutive patients admitted to our institution after resuscitation for out-of-hospital cardiopulmonary arrest with circulation resumption over 6 years. A machine learning model using head CT images with transfer learning was used to predict the neurological outcomes at 1 month. These predictions were compared with the predictions of GWR for multiple regions of interest in head CT using receiver operating characteristic (ROC)-area under curve (AUC) and precision recall (PR)-AUC. The regions of focus were visualized using a heatmap. Both methods had similar ROC-AUCs, but the machine learning model had a higher PR-AUC (0.73 vs. 0.58). The machine learning-focused area of interest for classification was the boundary between gray and white matter, which overlapped with the area of focus when diagnosing hypoxic– ischemic brain injury. The machine learning model for predicting poor outcomes had superior accuracy to conventional methods and could help optimize treatment.

Physical Examination Sensitivity for Skull Fracture in Pediatric Patients With Blunt Head Trauma: A Secondary Analysis of the National Emergency X-Radiography Utilization Study II Head Computed Tomography Validation Study.

We evaluated the emergency department (ED) providers' ability to detect skull fractures in pediatric patients presenting with blunt head trauma. This was a secondary analysis of the National Emergency X-Radiography Utilization Study (NEXUS) Head computed tomography (CT) validation study. Demographics and clinical characteristics were analyzed for pediatric patients. Radiologist interpretations of head CT imaging were abstracted and cataloged. Detection of skull fractures was evaluated through provider response to specific clinical decision instrument criteria (NEXUS or Canadian head CT rules) at the time of initial patient evaluation. The presence of skull fracture was determined by formal radiologist interpretation of CT imaging. Between April 2006, and December 2015, 1,018 pediatric patients were enrolled. One hundred twenty-eight (12.5%) children had a notable injury reported on CT head. Skull fracture was present in most (66.4%) children with intracranial injuries. The sensitivity and specificity of provider physical examination to detect skull fractures was 18.5% (95% confidence interval 10.5% to 28.7%) and 96.6% (95.3% to 97.7%), respectively. The most common injuries associated with skull fractures were subarachnoid hemorrhage (27%) and subdural hematoma (22.3%). Skull fracture is common in children with intracranial injury after blunt head trauma. Despite this, providers were found to have poor sensitivity for skull fractures in this population, and these injuries may be missed on initial emergency department assessment.

Measurement of Foramen Magnum Dimensions in Computed Tomography of Head

Introduction: Dimensions of the foramen magnum are clinically important because of the vital structures passing through it and for sex determination of the skull. The objective of this study was to measure various dimensions of the foramen magnum in Computed Tomography (CT) and correlate them with sex. Methods: Descriptive cross-sectional study was done on 120 CT head images in the Department of Radiology and Imaging, from July to October 2019. The shape of the foramen magnum (FM), anteroposterior diameter (APD), transverse diameter (TD), FM area and FM index were calculated. Statistical analysis was done with SPSS 25 and Excel 2016. Discriminant function analysis and binary logistic regression analysis were used for gender verification. RESULTS: The mean values of the APD, TD, area and FM index in males and females were 33.56 ± 4.30 mm, 29.76 ± 4.10 mm, 792.94 ± 191.60 mm2, 85.01 ± 8.67 and 30.01 ± 2.04 mm, 25.33 ± 4.6 mm, 599.63± 128.13 mm2, 83.17 ± 7.78 respectively. All dimensions were significantly greater in males (r = 0.906, P&lt;0.001). The most common shape of the foramen magnum was oval (40.8%). The accuracy of sex identification was 68.3% using dimensions of the foramen magnum. Conclusion: There was sexual dimorphism in the diameter and area of the foramen magnum which can be used for sex determination.

Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans.

In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid hemorrhage (SAH) on head computed tomography (CT) scans and that the trained model performs satisfactorily when tested using external and real-world data. We used noncontrast head CT images of patients admitted to Helsinki University Hospital between 2012 and 2017. We manually segmented (i.e., delineated) SAH on 90 head CT scans and used the segmented CT scans together with 22 negative (no SAH) control CT scans in training an open-source convolutional neural network (U-Net) to identify and localize SAH. We then tested the performance of the trained algorithm by using external data sets (137 SAH and 1,242 control cases) collected in 2 foreign countries and also by creating a data set of consecutive emergency head CT scans (8 SAH and 511 control cases) performed during on-call hours in 5 different domestic hospitals in September 2021. We assessed the algorithm's capability to identify SAH by calculating patient- and slice-level performance metrics, such as sensitivity and specificity. In the external validation set of 1,379 cases, the algorithm identified 136 of 137 SAH cases correctly (sensitivity 99.3% and specificity 63.2%). Of the 49,064 axial head CT slices, the algorithm identified and localized SAH in 1845 of 2,110 slices with SAH (sensitivity 87.4% and specificity 95.3%). Of 519 consecutive emergency head CT scans imaged in September 2021, the algorithm identified all 8 SAH cases correctly (sensitivity 100.0% and specificity 75.3%). The slice-level (27,167 axial slices in total) sensitivity and specificity were 87.3% and 98.8%, respectively, as the algorithm identified and localized SAH in 58 of 77 slices with SAH. The performance of the algorithm can be tested on through a web service. We show that the shared algorithm identifies SAH cases with a high sensitivity and that the slice-level specificity is high. In addition to openly sharing a high-performing deep learning algorithm, our work presents infrequently used approaches in designing, training, testing, and reporting deep learning algorithms developed for medical imaging diagnostics. This study provides Class III evidence that a deep learning algorithm correctly identifies the presence of subarachnoid hemorrhage on CT scan.

EYE RADIATION DOSE SAVING IN HEAD CT EXAMINATIONS USING COPPER-BISMUTH RADIATION SHIELD.

The aim of the present study is to fabricate a new shield with an optimal combination of copper and bismuth to protect the eyes in the head computed tomography (CT) examinations without compromising image quality. Radiation shields with different compositions were constructed. Computed Tomography Dose Index phantom was used to evaluate the effectiveness of shields in dose reduction and their impact on image quality quantitatively. The shield that caused the least noise in the phantom study was selected for human study. The 10%Bi-90%Cu shield had the least effect on increasing the image noise, and also no remarkable artifact was seen in the CT image of the phantom. The patient study showed that only in 25% of the study group the artifact was observed so that it did not distort the interpretation of the image. It can be concluded that the 10%Bi-90%Cu shield is flexible and durable and would be safely used in the clinic to reduce the eye radiation dose in head CT imaging without compromising image quality.

Abstract 314: Changes In The Hounsfield Unit Proportion Of Head Computed Tomography With Time And Neurological Outcomes In Out-of-hospital Cardiac Arrest Survivors

Changes in the Hounsfield Unit proportion of head computed tomography with time and neurological outcomes in out-of-hospital cardiac arrest survivors Aim: We compared changes in Hounsfield Unit proportion (HUp) with neurological outcomes using head computed tomography (HCT) obtained before and after targeted temperature management of out-of-hospital cardiac arrest (OHCA) survivors. Methods: This retrospective observational study included adult comatose OHCA survivors who underwent HCT scans within 6 h (first HCT) and 72-96 h (second HCT) after the return of spontaneous circulation (ROSC). Automated quantitative analysis using non-enhanced HCT images was applied to identify the difference in HU value distribution across the intracranial area between neurological outcomes. Each pixel in an HCT image has a certain HU level, λ, which is between 1 and 79 in this study. The proportion (%) of λ in a CT image is 0 to 100, with the sum of every λp as 100, as per the definition of λp. The primary outcome was the 6-month poor (CPC 3-5) outcome. Results: Of the 41 included patients (30 males; 73%), 14 (34%) had poor outcomes. In the first HCT, only HU = 29-30 showed a difference according to the neurological outcome; however, in the second HCT, HU = 1-12, 22-33, 39-50 showed a difference ( P &lt; 0.05) (Fig. 1). Among them, the greatest difference between the good and poor neurological outcome groups was the proportion of pixels with HU = 22-33, and the average values were 36.0 ± 6.0 and 43.6 ± 8.1, respectively ( P &lt; 0.01). In addition, receiver operating characteristic analysis showed that the of the second HCT was predictive of prognostic performance from poor neurological outcomes six months after ROSC to moderate to good (0.76; 95% confidence interval, 0.60-0.88). Conclusions: The proportion of in total cerebral regions in HCT scans obtained between 72-96 hours after ROSC in OHCA survivors was associated with poor neurological outcomes six months after ROSC.

Risk factors for development of cerebral edema following cardiac arrest

BackgroundCerebral edema following cardiac arrest is a well-known complication of resuscitation and portends a poor outcome. We identified predictors of post-cardiac arrest cerebral edema and tested the association of cerebral edema with discharge outcome. MethodsWe performed a retrospective chart review including patients admitted at a single center between January 2015–March 2020 following resuscitation from in-hospital and out-of-hospital cardiac arrest who had head computed tomography imaging. Our primary outcome was moderate-to-severe cerebral edema, which we defined as loss of grey-white differentiation with effacement of the basal and ambient cisterns and radiographic evidence of uncal herniation. We used logistic regression to test associations of demographic information, clinical predictors and comorbidities with moderate-severe cerebral edema. ResultsWe identified 727 patients who met the inclusion criteria, of whom 102 had moderate-to-severe cerebral edema. We identified six independent predictors of moderate-to-severe cerebral edema: younger age, prolonged arrest duration, pulseless electrical activity/asystole as initial rhythm, unwitnessed cardiac arrest, hyperglycemia on admission, and lower Glasgow coma score on presentation. Of patients with moderate-to-severe cerebral edema, 2% survived to discharge, 56% had withdrawal of life-sustaining therapies and 42% progressed to death by neurological criteria. ConclusionsOur study identified several risk factors associated with the development of cerebral edema following cardiac arrest. Further studies are needed to determine the benefits of early interventions in these high-risk patients.

Estimation of patient's angle from skull radiographs using deep learning.

Skull radiography, an assessment method for initial diagnosis and post-operative follow-up, requires substantial retaking of various types of radiographs. During retaking, a radiologic technologist estimates a patient's rotation angle from the radiograph by comprehending the relationship between the radiograph and the patient's angle for adequate assessment, which requires extensive experience. To develop and test a new deep learning model or method to automatically estimate patient's angle from radiographs. The patient's position is assessed using deep learning to estimate their angle from skull radiographs. Skull radiographs are simulated using two-dimensional projections from head computed tomography images and used as input data to estimate the patient's angle, using deep learning under supervised training. A residual neural network model is used where the rectified linear unit is changed to a parametric rectified linear unit, and dropout is added. The patient's angle is estimated in the lateral and superior-inferior directions. Applying this new deep learning model, the estimation errors are 0.56±0.36° and 0.72±0.52° in the lateral and superior-inferior angles, respectively. These findings suggest that a patient's angle can be accurately estimated from a radiograph using a deep learning model leading to reduce retaking time, and then used to facilitate skull radiography.