Head Computed Tomography Scan Research Articles

Evaluation of Cancer Risk Induced by Radiation Exposure from Normal Head CT Scans

Purpose: Radiology examinations are growing significantly every year. Analysis of the CT scan reports can highlight defects and is a good way to develop safety in healthcare. This study aimed to evaluate the rate of normal head Computed Tomography (CT) scans at a hospital radiology department in Shahroud and estimate the cancer risk associated with these normal CT scans.
 Materials and Methods: In total, the data of 400 patients referred to the emergency radiology center of Imam Hossein hospital in Shahroud from November 23 to December 10, 2021, were collected. CT scan reports were categorized into three groups according to the interpretation of the radiologist. The BEIR VII model was used to estimate the radiation cancer risk.
 Results: Among the 400 patients, 248 (62%) were males and the average age of the patients was 49.05 ± 22.60 years. CT scans in 270 (67.5%) cases were reported normal. The average age of the patients with normal, and abnormal CT scans were 41.86 ± 20.27, and 63.03 ± 20.27 years, respectively and the difference was significant (p-value <0.001). The average effective dose was obtained 1.72±0.09, 1.31±0.11, and 0.87±0.09 mSv for different age groups of 1-5, 5-10, >10-year-old. The average risks of all solid cancers were 7.82 cases per 100,000 patients, while the average risk of leukemia was 0.71 cases per 100,000 patients.
 Conclusion: A large percentage of CT examinations are normal in our country which leads to many public health issues in the future years. Therefore, efforts should be made to establish predictor clinical factors to reduce unnecessary radiology examinations.

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

AbstractTraumatic brain injury (TBI) is not only an acute condition but also a chronic disease with long-term consequences. Intracranial hematomas are considered the primary consequences that occur in TBI and may have devastating effects that may lead to mass effect on the brain and eventually cause secondary brain injury. Emergent detection of hematoma in computed tomography (CT) scans and assessment of three major determinants, namely, location, volume, and size, is crucial for prognosis and decision-making, and artificial intelligence (AI) using deep learning techniques, such as convolutional neural networks (CNN) has received extended attention after demonstrations that it could perform at least as well as humans in imaging classification tasks. This article conducts a comparative review of medical and technological literature to update and establish evidence as to how technology can be utilized rightly for increasing the efficiency of the clinical workflow in emergency cases. A systematic and comprehensive literature search was conducted in the electronic database of PubMed and Google Scholar from 2013 to 2023 to identify studies related to the automated detection of intracranial hemorrhage (ICH). Inclusion and exclusion criteria were set to filter out the most relevant articles. We identified 15 studies on the development and validation of computer-assisted screening and analysis algorithms that used head CT scans. Our review shows that AI algorithms can prioritize radiology worklists to reduce time to screen for ICH in the head scans sufficiently and may also identify subtle ICH overlooked by radiologists, and that automated ICH detection tool holds promise for introduction into routine clinical practice.

Organ doses, effective dose, and cancer risk estimation from head and neck CT scans

PurposeThe goal of this study was to calculate the organ doses, effective doses, and cancer risk from head and neck computed tomography (CT) scans. Material and methodsAll patients underwent topogram, base, and cerebrum sequences during their head and neck examinations. Two techniques were used to determine the effective dose (ED). The first was based on the scanner derived dose length product, while the second involved using the software that calculated the organ and effective doses. Organ doses were computed using the tissue weighting factors from Report 103 by the International Commission on Radiation Protection (ICRP). The cancer risk values were calculated using web-based software that is based on the BEIR VII Phase 2 report. ResultsThere were 293 patients in this study (189 males, 104 females). The overall mean effective dose in the females was higher than that in the males (2.19 vs. 2.06 mSv). Male patients had a mean cancer risk of 0.011%, whereas female patients had a mean cancer risk of 0.015%. The younger females had much higher cancer risk values. ConclusionThe findings of this study will assist physicians in justifying and optimizing dose administration of head and neck CT examinations by weighing the benefits of diagnosis on the one hand and awareness of cancer risk on the other.

Rare presentation of BRASH syndrome with hypoglycemia and altered mental status

BackgroundBRASH syndrome (bradycardia, renal failure, atrioventricular nodal blockade, shock, and hyperkalemia) is a rare clinical condition with potentially severe outcomes. Patients with BRASH syndrome can present with diverse signs and symptoms and are usually in critical condition, but if recognized early, the syndrome is treatable and may have a favorable prognosis.Case presentationThis case study presents a 74-year-old patient with a history of multiple chronic conditions who was brought to the emergency department with a suspected cerebrovascular accident, altered mental status, and bradycardia. A head computed tomography scan was unremarkable but laboratory results showed hyperkalemia, acidosis, and renal failure with concomitant progressive hypoglycemia. The patient was diagnosed with a BRASH syndrome characterized by a vicious cycle of atrioventricular nodal blockade induced by the potentiated effect of beta-blockers or calcium channel blockers, in combination with progressive hypoglycemia due to the suspected accumulation of anti-diabetic medications, which influenced the presentation and initial triage in the emergency department. She was admitted to the intensive care unit for further management, where she continued to improve and was ultimately discharged in a relatively stable condition.ConclusionThis case study highlights the importance of considering rare and atypical presentations of medical conditions, particularly in elderly patients who may have multiple comorbidities. Early recognition and prompt management of such cases are crucial for improving patient outcomes.

Neurological features of Paget’s disease of bone

Abstract: Paget’s disease of bone is a chronic focal disorder characterized by disorganized bone remodeling. It is usually asymptomatic and the diagnosis is made incidentally on the basis of other medical problem investigations. Head bones are frequently affected and may cause deformities due to overgrowth of bone leading to neurological manifestations such as headaches, dizziness, and deafness. Here, we describe a case of a 72-year-old woman who complained of headaches, back pain, tinnitus, and hearing loss. Clinical examination showed spinal deformities. Laboratory tests revealed elevated bone remodeling markers. Head computed tomography scan showed bone lesions suggesting Paget’s disease. Isotope bone scan revealed uptake in skull and thoracic spine. The patient was treated with zoledronic acid and analgesic with significant improvement of headaches and back pain and persistence of hearing loss.

Ovarian non-Hodgkin lymphoma with central nervous system involvement: A case report

Background: Ovarian lymphoma is rare. Extranodal non-Hodgkin lymphoma (NHL) represents 0.5% of malignant genital diseases. Due to its rarity, there are no accepted treatment protocols. Diffuse large B cell lymphoma (DLBCL) has a central nervous system (CNS) involvement rate of 3-5%.
 Case presentation: A 38-year-old presented to the gynecological oncology clinic with abdominal pain and distension. Her tumor marker CA-125 levels were elevated. She developed right-sided facial nerve palsy and blindness. A diagnosis of aggressive NHL with a differential of DLBCL was made by histopathological and immunohistochemical examination of the excised ovarian tissue. A head computed tomography scan revealed subtle enhancing opacity suspicious of lymphomatous deposits. She was lost to follow-up after ten months of chemotherapy and cranial radiotherapy.
 Conclusion: Primary lymphoma of the ovary is rare, and the prognosis is often poorer when compounded with CNS involvement. Because of poor prognosis in the presence of CNS involvement, this case highlights the need for timely diagnosis and management.

Fahr’s Syndrome Secondary to Hypoparathyroidism Presenting with Paralysis and Recurrent Seizures: A Case Report

Aim: To describe a rare case of Fahr’s syndrome (FS) associated with chronic post-surgical hypoparathyroidism and hypocalcemia.
 Case Presentation: A 63-year-old female with a previous history of total thyroidectomy and hemiplegia presented to our hospital with altered mentation and recurrent generalized tonic-clonic seizures. Laboratory evaluation revealed hypoparathyroidism, hypocalcemia, and hypokalemia. Head computed tomography (CT) scan was consistent with FS, demonstrating extensive, bilateral, and symmetrical calcified deposits in the brain, predominantly in basal ganglia, corona radiata, and cerebellar hemispheres. The association with post-surgical hypoparathyroidism and relevant biochemical indices determined the suspected etiology of the pathologic process of ectopic calcification.
 Discussion: FS is a rare, neurodegenerative disorder characterized by abnormal bilateral ectopic calcified deposits in the basal ganglia and other brain structures. FS presents with a wide variety of neurological and psychiatric manifestations. The diagnosis is confirmed by neuroimaging studies such as a head CT scan or magnetic resonance imaging, which displays the calcification of BG and other structures in a bilateral and symmetrical pattern. Biochemical analysis may adjunctively identify the underlying risk factor of the disease.
 Conclusion: Our case represents a long-term severe consequence of untreated post-surgical hypoparathyroidism, which has consequently led to irreversible secondary FS. Maintenance of eucalcemic and euphosphatemic states is essential to prevent the progression of ectopic cerebral calcification.

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage

Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investigation and recognition by physicians. Computed tomography (CT) scanning is the investigation of choice for TBI and has become increasingly utilized under the shortage of trained radiology personnel. It is anticipated that deep learning models will be a promising solution for the generation of timely and accurate radiology reports. Our study examines the diagnostic performance of a deep learning model and compares the performance of that with detection, localization and classification of traumatic ICHs involving radiology, emergency medicine, and neurosurgery residents. Our results demonstrate that the high level of accuracy achieved by the deep learning model, (0.89), outperforms the residents with regard to sensitivity (0.82) but still lacks behind in specificity (0.90). Overall, our study suggests that the deep learning model may serve as a potential screening tool aiding the interpretation of head CT scans among traumatic brain injury patients.

The Loss of Temporal Muscle Volume is Associated with Poor Outcome in Patients with Subarachnoid Hemorrhage: An Observational Cohort Study

BackgroundIntensive care unit (ICU) acquired weakness is a major contributor to poor functional outcome of ICU patients. Quantification of temporal muscle volume assessed on routine computed tomography (CT) scans may serve as a biomarker for muscle wasting in patients suffering from acute brain injury.MethodsThis is a retrospective analysis of prospectively collected data. Temporal muscle volume was assessed on head CT scans of consecutive patients with spontaneous subarachnoid hemorrhage within prespecified time frames (on admission, then weekly ± 2 days). Whenever possible, temporal muscle volume was assessed bilaterally and averaged for the analysis. Poor functional outcome was defined as a 3-month modified Rankin Scale Score ≥ 3. Statistical analysis was performed using generalized estimating equations to handle repeated measurements within individuals.ResultsThe analysis comprised 110 patients with a median Hunt & Hess score of 4 (interquartile range 3–5). Median age was 61 (50–70) years, 73 patients (66%) were women. Baseline temporal muscle volume was 18.5 ± 0.78 cm3 and significantly decreased over time (p < 0.001) by a mean of 7.9% per week. Higher disease severity (p = 0.002), hydrocephalus (p = 0.020), pneumonia (p = 0.032), and bloodstream infection (p = 0.015) were associated with more pronounced muscle volume loss. Patients with poor functional outcome had smaller muscle volumes 2 and 3 weeks after subarachnoid hemorrhage compared with those with good outcome (p = 0.025). The maximum muscle volume loss during ICU stay was greater in patients with poor functional outcome (− 32.2% ± 2.5% vs. − 22.7% ± 2.5%, p = 0.008). The hazard ratio for poor functional outcome was 1.027 (95% confidence interval 1.003–1.051) per percent of maximum muscle volume loss.ConclusionsTemporal muscle volume, which is easily assessable on routine head CT scans, progressively decreases during the ICU stay after spontaneous subarachnoid hemorrhage. Because of its association with disease severity and functional outcome, it may serve as a biomarker for muscle wasting and outcome prognostication.

Adverse events with quetiapine and clarithromycin coprescription: A population-based retrospective cohort study.

Quetiapine is an atypical antipsychotic predominantly metabolized by the cytochrome P450 3A4 (CYP3A4) enzyme. We studied the risk of adverse events following coprescription of clarithromycin (a strong CYP3A4 inhibitor) versus azithromycin (not a CYP3A4 inhibitor) in quetiapine users. This was a population-based retrospective cohort study from 2004 to 2020 in Ontario, Canada in adult quetiapine users newly co-prescribed clarithromycin (n = 16,909) or azithromycin (n = 25,267). The primary outcome was the composite of hospital encounters with encephalopathy (defined as a diagnosis of delirium, disorientation, transient alteration of awareness, transient ischemic attack, or unspecified dementia), a fall, or a fracture within 30 days of new coprescription. Secondary outcomes were individual components of the composite outcome, hospital encounter with computed tomography(CT) head scan, and all-cause mortality. Coprescription of clarithromycin versus azithromycin with quetiapine was associated with a higher risk of the primary composite outcome (365 of 16,909 clarithromycin users [2.2%] vs. 309 of 16,929 azithromycin users [1.8%]; absolute risk increase, 0.34% [95% confidence interval,CI, 0.04-0.63]; relative risk [RR], 1.19 [95% CI,1.02-1.38]). This was primarily driven by an increase in fragility fractures (78 of 16,909 clarithromycin users [0.5%] vs. 45 of 16,923 azithromycin users [0.3%]; absolute risk increase, 0.20% [95% CI, 0.07-0.32]; RR, 1.74 [95% CI, 1.21-2.52]). Hospital encounters with a CT head scan were higher in clarithromycin users (220 of 16,909 [1.3%] vs. 175 of 16,923 azithromycin users [1.0%]; absolute risk increase, 0.27% [95% CI, 0.04-0.50]; RR, 1.26 [95% CI, 1.04-1.54]), but there was no difference in hospital encounters with encephalopathy, falls, or all-cause mortality between macrolide groups. Among adults taking quetiapine, concurrent use of clarithromycin compared with azithromycin was associated with a small but statistically greater 30-day risk of a hospital encounter for encephalopathy, falls, or fracture, which was predominantly related to a higher rate of fragility fractures.

GR.7 Artificial intelligence-based decision support predicts requirement for neurosurgical intervention in acute traumatic brain injury

Background: We aimed to develop an efficient and reliable artificial intelligence solution to automate prediction of neurosurgical intervention using acute traumatic brain injury computed tomography (CT) scans. Methods: TBI patients were identified from 2005 - 2022 at a Level 1 Canadian trauma center. Model training, validation, and testing was performed using head CT scans with patient-level labels corresponding to whether the patient received neurosurgical intervention. The finalized model was then deployed in a simulated prospective fashion on all TBI patients presenting to our center over an 18-month epoch. Results: 2,806 TBI scans were utilized for development of the Automated Surgical Intervention Support Tool (ASIST-TBI). 612 additional consecutive scans were used for simulated prospective model deployment. Prediction of neurosurgical intervention exhibited an area under receiver operating curve (AUC) of 0.92, accuracy of 0.87, sensitivity of 0.87, and specificity of 0.88 on the test dataset. On simulated prospective data, the results were: AUC 0.89, sensitivity 0.85, specificity 0.84 and accuracy of 0.84. Conclusions: We demonstrate the development and validation of ASIST-TBI, a machine learning model that accurately predicts whether TBI patients will need neurosurgical intervention. This model has potential application to optimize decision support and province-wide efficiency of inter-facility TBI triage to tertiary care centers.

Assessment of Unjustified Brain Ct Requests in The Emergency Room of The Public Health Care System in Palestine Case Study: Hebron Governmental Hospital

Computed tomography (CT), which gives doctors a precise cross-sectional image that clearly shows the features of the organs, is one of the most crucial pieces of medical imaging equipment. In contrast to other equipment, the CT scan may expose the patient to a very high dose of radiation. As a result of the hazards associated with imaging patients, the use of CT images must be restricted, especially when imaging the head. The aim of the study is to the evaluation of the percentage of unjustified brain CT requests at the emergency rooms of the governmental health sector in Palestine. The 1957-built Hebron Governmental Hospital (HGH) was chosen as a research study case with a clinical capacity of 237 beds and 596 staff members; it is one of the key Palestinian government hospitals currently operating in the Hebron Governorate of the West Bank-Palestine. Samples were taken from the hospital's emergency room between the first of the year and the end of the year in 2021. 6152 or so brain CT images were gathered. Following that, 500 instances were picked for the investigation. Radiologists were asked to assess 100 of these 500 cases to determine whether the patient needed a scan or not. According to the study, 78% of brain tomography images were for unjustifiable reasons. Additionally, it was revealed by the study's results that unnecessary head CT scans were more common than those from retrospective investigations. In particular, non-specialized doctors who request cross-sectional brain imaging for no apparent reason are to blame for this because of their lack of knowledge.

Morphometric analysis of foramen ovale, foramen spinosum, and foramen rotundum of human skull using computed tomography scan: a cross-sectional study.

A cross-sectional study was carried out in the Department of Radiodiagnosis and Imaging at BP Koirala Institute of Health Sciences (BPKIHS), Nepal using a purposive sampling method. We included 96 adult patients (≥18 years) who underwent CT scan of the head for any clinical indications. All those participants below 18 years, inadequate visualization or erosions of skull base foramina, and/or not consenting were excluded. Appropriate statistical calculations were done using the statistical package for social sciences (SPSS), version 21. The P-value of less than 0.05 was considered statistically significant. The mean length, width, and area of FO was 7.79±1.10mm, 3.68±0.64mm, and 22.80±6.18mm2, respectively. The mean length, width, and area of FS was 2.38±0.36mm, 1.94±0.30mm, and 3.69±0.95mm2, respectively. Similarly, the mean height, width, and area of FR was 2.41±0.49mm, 2.40±0.55mm, and 4.58±1.49mm2, respectively. The male participants had statistically significant higher mean dimensions of FO and FS (P<0.05) than the female participants. There were statistically insignificant correlations of dimensions of these foramina with age and between the left and right side of each foraminal dimensions (P>0.05). The sex-based difference in dimensions of FO and FS should be clinically considered in evaluating the pathology of these foramina. However, further studies using objective assessment of foraminal dimensions are required to draw obvious inferences.

Computed tomographic evaluation of head injury patients in Umuahia, Abia State Nigeria

Background: Trauma to the scalp, skull and brain is defined as head injury. Head injury is a silent killer, especially in patients that have intracranial hemorrhage with mass effect on the brain parenchyma. It is a major health challenge worldwide especially in places where there is lack of medical insurance, ignorance and non availability of the right imaging modality. Before the advent of Computed Tomography (CT), plain radiographs had been the imaging modality available for the evaluation of head injury. Plain radiographs give limited information about head injury because apart from skull fractures, plain radiographs cannot confirm diagnosis of intracranial collections, brain contusions and others. CT scan was chosen because its four dimensional images can be reconstructed with volume rendering, bone details can be evaluated, and with the Hounsfield unit, measurements of collections, haematomas can be evaluated. There are not much reports on head injury findings in Umuahia and hence this study is set to contribute to the data on head injury in and around Umuahia.&#x0D; Materials and methods: This is a retrospective study of patients with head injury that came for head CT scan examination in Umuahia from January 2022 to December 2022. Patients were scanned with 160 slides Canon Aquilion CT Scanner; patients were made to lie in supine position. 3mm axial slices were taken from the base of the skull to the vertex with reformatted images. No contrast was used because it is contraindicated in traumatic brain injury. Patients gender, age, aetiology of injury and findings of radiologists were collected as data and entered into Microsoft Excel data base and statistically analyzed using statistical package for social sciences for windows (SPSS inc., USA) version 21.0, and results tabulated.&#x0D; Results: Out of the total population studied (108), 82.4% were males while 17.6% were females. Majority of the cases fell within the age group of 51yrs- 60yrs(20.4%), followed by 21yrs-30yrs(19.4%) and the least being below1yr (1.9%). Road traffic accident was the commonest cause of head injury (64%), followed by fall from height (19.4%), trauma to the head with weapons (13.9%) while glass bottle trauma and unknown causes were the least having (0.9%) and (1%) respectively. CT findings revealed that 52.1% of the cases resulted in hemorrhages, 18.6% resulted in hematoma formations, 6.5% were soft tissue injuries, 5.4% were pneumo-cephalus and 1.6% resulted in cerebral edema.&#x0D; Conclusions: Computed Tomography (CT) scan is a very useful tool in the evaluation of patients with head injury. The study showed that more males were involved with head injuries than females and that the most common cause of head injury in Umuahia, Abia State, is road traffic accident with intra-cerebral haemorrhage being the commonest CT finding.

EVALUATION OF RADIATION DOSE IN MULTI-SLICE COMPUTED TOMOGRAPHY PROTOCOLS OF HEAD AND NECK REGIONS.

In head and neck computed tomography (CT) imaging, the optimisation of radiation dose is crucial due to the presence of radio-sensitive organs. This study aimed to evaluate the radiation dose in multi-slice CT for head and neck examinations. Volume CT dose index, dose length product and effective dose (E) were assessed for 10 head and neck CT scans performed on 292 adult patients (mean age 49.2±15.9y). The study resulted in median E values of 0.82, 1.62, 2.43, 0.93, 1.70, 0.83, 3.55, 6.25, 2.19 and 5.26mSv, respectively, for sinuses (non-contrast (NC)), sinuses (NC) and contrast-enhanced (CE), petrous bone (PTB)/internal auditory meatus (IAM) (NC+CE), PTB/IAM (NC), orbit (NC+CE), orbit (NC), brain with the orbit (NC), brain CT angiography (CTA) subtraction, neck (NC) and brain/neck (NC). Furthermore, the overall radiation doses of this institution were found to be below the values suggested by similar studies. However, optimisation of the dose is required for brain CTA.

Artificial intelligence for detection of intracranial haemorrhage on head computed tomography scans: diagnostic accuracy in Hong Kong.

The use of artificial intelligence (AI) to identify acute intracranial haemorrhage (ICH) on computed tomography (CT) scans may facilitate initial imaging interpretation in the accident and emergency department. However, AI model construction requires a large amount of annotated data for training, and validation with real-world data has been limited. We developed an algorithm using an open-access dataset of CT slices, then assessed its utility in clinical practice by validating its performance on CT scans from our institution. Using a publicly available international dataset of >750 000 expert-labelled CT slices, we developed an AI model which determines ICH probability for each CT scan and nominates five potential ICH-positive CT slices for review. We validated the model using retrospective data from 1372 non-contrast head CT scans (84 [6.1%] with ICH) collected at our institution. The model achieved an area under the curve of 0.842 (95% confidence interval=0.791-0.894; P<0.001) for scan-based detection of ICH. A pre-specified probability threshold of ≥50% for the presence of ICH yielded 78.6% accuracy, 73% sensitivity, 79% specificity, 18.6% positive predictive value, and 97.8% negative predictive value. There were 62 true-positive scans and 22 false-negative scans, which could be reduced to six false-negative scans by manual review of model-nominated CT slices. Our model exhibited good accuracy in the CT scan-based detection of ICH, considering the low prevalence of ICH in Hong Kong. Model refinement to allow direct localisation of ICH will facilitate the use of AI solutions in clinical practice.

Siam-VAE: A hybrid deep learning based anomaly detection framework for automated quality control of head CT scans.

An automated quality control (QC) system is essential to ensure streamlined head computed tomography (CT) scan interpretations that do not affect subsequent image analysis. Such a system is advantageous compared to current human QC protocols, which are subjective and time-consuming. In this work, we aim to develop a deep learning-based framework to classify a scan to be of usable or unusable quality. Supervised deep learning models have been highly effective in classification tasks, but they are highly complex and require large, annotated data for effective training. Additional challenges with QC datasets include - 1) class-imbalance - usable cases far exceed the unusable ones and 2) weak-labels - scan level labels may not match slice level labels. The proposed framework utilizes these weak labels to augment a standard anomaly detection technique. Specifically, we proposed a hybrid model that consists of a variational autoencoder (VAE) and a Siamese Neural Network (SNN). While the VAE is trained to learn how usable scans appear and reconstruct an input scan, the SNN compares how similar this input scan is to its reconstruction and flags the ones that are less similar than a threshold. The proposed method is more suited to capture the differences in non-linear feature structure between the two classes of data than typical anomaly detection methods that depend on intensity-based metrics like root mean square error (RMSE). Comparison with state-of-the-art anomaly detection methods using multiple classification metrics establishes superiority of the proposed framework in flagging inferior quality scans for review by radiologists, thus reducing their workload and establishing a reliable and consistent dataflow.

Patient-specific Monte Carlo-based organ dose estimates in spiral CT via optical 3D body scanning and adaptation of a voxelized phantom dataset: proof-of-principle

Objective. We present a method for personalized organ dose estimates obtained before the computed tomography (CT) exam, via 3D optical body scanning and Monte Carlo (MC) simulations. Approach. A voxelized phantom is derived by adapting a reference phantom to the body size and shape measured with a portable 3D optical scanner, which returns the 3D silhouette of the patient. This was used as an external rigid envelope for incorporating a tailored version of the internal body anatomy derived from a phantom dataset (National Cancer Institute, NIH, USA) matched for gender, age, weight, and height. The proof-of-principle was conducted on adult head phantoms. The Geant4 MC code provided estimates of the organ doses from 3D absorbed dose maps in the voxelized body phantom. Main results. We applied this approach for head CT scanning using an anthropomorphic voxelized head phantom derived from 3D optical scans of manikins. We compared the estimates of head organ doses with those provided by the NCICT 3.0 software (NCI, NIH, USA). Head organ doses differed up to 38% using the proposed personalized estimate and MC code, with respect to corresponding estimates calculated for the standard (non-personalized) reference head phantom. Preliminary application of the MC code to chest CT scans is shown. Real-time pre-exam personalized CT dosimetry is envisaged with adoption of a Graphics Processing Unit-based fast MC code. Significance. The developed procedure for personalized organ dose estimates before the CT exam, introduces a new approach for realistic description of size and shape of patients via voxelized phantoms specific for each patient.

Accurate segmentation of head and neck radiotherapy CT scans with 3D CNNs: consistency is key

Objective. Automatic segmentation of organs-at-risk in radiotherapy planning computed tomography (CT) scans using convolutional neural networks (CNNs) is an active research area. Very large datasets are usually required to train such CNN models. In radiotherapy, large, high-quality datasets are scarce and combining data from several sources can reduce the consistency of training segmentations. It is therefore important to understand the impact of training data quality on the performance of auto-segmentation models for radiotherapy. Approach. In this study, we took an existing 3D CNN architecture for head and neck CT auto-segmentation and compare the performance of models trained with a small, well-curated dataset (n = 34) and then a far larger dataset (n = 185) containing less consistent training segmentations. We performed 5-fold cross-validations in each dataset and tested segmentation performance using the 95th percentile Hausdorff distance and mean distance-to-agreement metrics. Finally, we validated the generalisability of our models with an external cohort of patient data (n = 12) with five expert annotators. Main results. The models trained with a large dataset were greatly outperformed by models (of identical architecture) trained with a smaller, but higher consistency set of training samples. Our models trained with a small dataset produce segmentations of similar accuracy as expert human observers and generalised well to new data, performing within inter-observer variation. Significance. We empirically demonstrate the importance of highly consistent training samples when training a 3D auto-segmentation model for use in radiotherapy. Crucially, it is the consistency of the training segmentations which had a greater impact on model performance rather than the size of the dataset used.

Headache, vision changes and cranial nerve palsy after head trauma.

A 43-year-old female was brought to the hospital after a ground level fall and her Glasgow Coma Scale was 15. Non-contrast head computed tomography (CT) scan showed small bilateral subdural hematomas, left traumatic subarachnoid hemorrhage, left occipital bone and left longitudinal temporal bone fractures. She was managed conservatively. Interval repeat non-contrast head CT scans showed resolution of the hemorrhage. Two and a half months later she presented to the hospital with headaches, intermittent diplopia, blurry vision and a right lateral gaze palsy. Non-contrast head CT scan showed mild hyperattenuation in the cavernous sinus. Subsequently, a CT angiogram of the head was obtained that showed opacification of the bilateral cavernous sinus and distention of the orbital veins (Figure 1A,B). Evaluation with diagnostic cerebral angiogram showed direct bilateral carotid-cavernous fistulas (CCFs) (Figures 2 and 3). She underwent staged embolization of the CCFs. Direct CCF is the abnormal connection between the internal carotid artery and the cavernous sinus.1, 2 CCFs can present with the classic triad of chemosis, pulsatile proptosis and ocular bruit.3, 4 Other symptoms include orbital pain, abducens nerve palsy, deterioration of visual acuity and ophthalmoplegia.4, 5 Causes of CCF formation are traumatic, iatrogenic, connective tissue disorders and spontaneous.2, 6-9 Traumatic CCFs formation occurs in up to 1.25% of patients with traumatic brain injury or facial and skull base fractures.10, 11 Bilateral CCF formation is rare after trauma and symptoms develop in a delayed fashion.10-15 Treatment options include conservative management, open surgery, endovascular procedures and radiosurgery.16-18