Quality initiatives: blind spots at brain imaging

Fibrous Dysplasia of the Clivus: Case Report and Literature Review

Fibrous dysplasia is a benign, developmental bone disorder that causes fibrous replacement of normal skeletal tissue. This may lead to weakness, distortion, and tissue expansion. Fibrous dysplasia can occur anywhere in the body, including the craniofacial area. The clivus is a central skull bone formed by the bases of the sphenoid and occiput, respectively. The clivus is a rare, usually unrecognized, and seldom reported location for the development of fibrous dysplasia. Although fibrous dysplasia of the clivus (FDC) is usually discovered by incidental findings, it can sometimes present with clinical symptoms. In this case, we discuss a 30-year-old male who presents to the emergency room with headaches, altered mental status, and a prior presentation of location-related symptomatic epilepsy. Magnetic resonance imaging depicted a mass in the clivus, low in signal on T1 and mildly hypointense on T2 imaging. Follow-up computed tomography (CT) imaging, as recommended, revealed the classic presentation of FDC. In this paper, we discuss the significance of this condition and the importance of thorough investigation to rule out differential diagnoses that may present with similar acute symptoms as this patient.

A Deep Learning Approach for Automated Bone Removal from Computed Tomography Angiography of the Brain

Advanced visualization techniques such as maximum intensity projection (MIP) and volume rendering (VR) are useful for evaluating neurovascular anatomy on CT angiography (CTA) of the brain; however, interference from surrounding osseous anatomy is common. Existing methods for removing bone from CTA images are limited in scope and/or accuracy, particularly at the skull base. We present a new brain CTA bone removal tool, which addresses many of these limitations. A deep convolutional neural network was designed and trained for bone removal using 72 brain CTAs. The model was tested on 15 CTAs from the same data source and 17 CTAs from an independent external dataset. Bone removal accuracy was assessed quantitatively, by comparing automated segmentation results to manual segmentations, and qualitatively by evaluating VR visualization of the carotid siphons compared to an existing method for automated bone removal. Average Dice overlap between automated and manual segmentations from the internal and external test datasets were 0.986 and 0.979 respectively. This was superior compared to a publicly available noncontrast head CT bone removal algorithm which had a Dice overlap of 0.947 (internal dataset) and 0.938 (external dataset). Our algorithm yielded better VR visualization of the carotid siphons than the publicly available bone removal tool in 14 out of 15 CTAs (93%, chi-square statistic of 22.5, p-value of < 0.00001) from the internal test dataset and 15 out of 17 CTAs (88%, chi-square statistic of 23.1, p-value of < 0.00001) from the external test dataset. Bone removal allowed subjectively superior MIP and VR visualization of vascular anatomy/pathology. The proposed brain CTA bone removal algorithm is rapid, accurate, and allows superior visualization of vascular anatomy and pathology compared to other available techniques and was validated on an independent external dataset.

AS. Clival and Paraclival Lesions: A Pictorial Review

The clivus is a midline anatomical structure in the central skull base. It is affected by a wide range of non-neoplastic, benign and malignant pathologies, some of which typically affect the clivus because of its strategic location and embryological origins. Clival lesions may often be asymptomatic with occasional complaints like headache or cranial neuropathy in few. Cross-sectional imaging techniques, namely, computed tomographic scan and magnetic resonance imaging, thus, play a key role in approximating to the final diagnosis and estimating the disease extent. In this article, we highlight the important imaging features of various clival and paraclival pathologies to facilitate effective diagnosis, therapeutic planning, and management.

Diagnostic Errors in Cerebrovascular Pathology: Retrospective Analysis of a Neuroradiology Database at a Large Tertiary Academic Medical Center

BACKGROUND AND PURPOSE

Diagnostic errors affect 2%-8% of neuroradiology studies, resulting in significant potential morbidity and mortality. This retrospective analysis of a large database at a single tertiary academic institution focuses on diagnostic misses in cerebrovascular pathology and suggests error-reduction strategies.

MATERIALS AND METHODS

CT and MR imaging reports from a consecutive database spanning 2015-2020 were searched for errors of attending physicians in cerebrovascular pathology. Data were collected on missed findings, study types, and interpretation settings. Errors were categorized as ischemic, arterial, venous, hemorrhagic, and "other."

RESULTS

A total of 245,762 CT and MR imaging neuroradiology examinations were interpreted during the study period. Vascular diagnostic errors were present in 165 reports, with a mean of 49.6 (SD, 23.3) studies on the shifts when an error was made, compared with 34.9 (SD, 19.2) on shifts without detected errors (P < .0001). Seventy percent of examinations occurred in the hospital setting; 93.3% of errors were perceptual; 6.7% were interpretive; and 93.9% (n = 155) were clinically significant (RADPEER 2B or 3B). The distribution of errors was arterial and ischemic each with 33.3%, hemorrhagic with 21.8%, and venous with 7.5%. Most errors involved brain MR imaging (30.3%) followed by head CTA (27.9%) and noncontrast head CT (26.1%). The most common misses were acute/subacute infarcts (25.1%), followed by aneurysms (13.7%) and subdural hematomas (9.7%).

CONCLUSIONS

Most cerebrovascular diagnostic errors were perceptual and clinically significant, occurred in the emergency/inpatient setting, and were associated with higher-volume shifts. Diagnostic errors could be minimized by adjusting search patterns to ensure vigilance on the sites of the frequently missed pathologies.

Checklists to reduce diagnostic error: a systematic review of the literature using a human factors framework

OBJECTIVES

To apply a human factors framework to understand whether checklists reduce clinical diagnostic error have (1) gaps in composition; and (2) components that may be more likely to reduce errors.

DESIGN

Systematic review.

DATA SOURCES

PubMed, EMBASE, Scopus and Web of Science were searched through 15 February 2022.

ELIGIBILITY CRITERIA

Any article that included a clinical checklist aimed at improving the diagnostic process. Checklists were defined as any structured guide intended to elicit additional thinking regarding diagnosis.

DATA EXTRACTION AND SYNTHESIS

Two authors independently reviewed and selected articles based on eligibility criteria. Each extracted unique checklist was independently characterised according to the well-established human factors framework: Systems Engineering Initiative for Patient Safety 2.0 (SEIPS 2.0). If reported, checklist efficacy in reducing diagnostic error (eg, diagnostic accuracy, number of errors or any patient-related outcomes) was outlined. Risk of study bias was independently evaluated using standardised quality assessment tools in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

RESULTS

A total of 30 articles containing 25 unique checklists were included. Checklists were characterised within the SEIPS 2.0 framework as follows: Work Systems subcomponents of Tasks (n=13), Persons (n=2) and Internal Environment (n=3); Processes subcomponents of Cognitive (n=20) and Social and Behavioural (n=2); and Outcomes subcomponents of Professional (n=2). Other subcomponents, such as External Environment or Patient outcomes, were not addressed. Fourteen checklists examined effect on diagnostic outcomes: seven demonstrated improvement, six were without improvement and one demonstrated mixed results. Importantly, Tasks-oriented studies more often demonstrated error reduction (n=5/7) than those addressing the Cognitive process (n=4/10).

CONCLUSIONS

Most diagnostic checklists incorporated few human factors components. Checklists addressing the SEIPS 2.0 Tasks subcomponent were more often associated with a reduction in diagnostic errors. Studies examining less explored subcomponents and emphasis on Tasks, rather than the Cognitive subcomponents, may be warranted to prevent diagnostic errors.

Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model

The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for craniotomy is dependent on the injury type and the patient's neurologic exam. The potential subtypes of intracranial hemorrhage that may necessitate surgical intervention include subdural hemorrhage, epidural hemorrhage, and intraparenchymal hemorrhage. We proposed a novel automatic method for segmenting the hemorrhage subtypes on a CT scan by integrated CT scan with bone window as input of a deep learning model. Brain CT scans were collected from adult patients and annotated regions of subdural hemorrhage, epidural hemorrhage, and intraparenchymal hemorrhage by neuroradiologists. Their raw DICOM images were preprocessed by two different window settings i.e., subdural and bone windows. The collected CT scans were divided into two datasets namely training and test datasets. A deep-learning model was modified to segment regions of each hemorrhage subtype. The model is a three-dimensional convolutional neural network including four parallel pathways that process the input at different resolutions. It was trained by a training dataset. After the segmentation result was produced by the deep-learning model, it was then improved in the post-processing step. The size of the segmented lesion was considered, and a region-growing algorithm was applied. We evaluated the performance of the proposed method on the test dataset. The method reached the median Dice similarity coefficients higher than 0.37 for each hemorrhage subtype. The proposed method demonstrates higher Dice similarity coefficients and improved segmentation performance compared to previously published literature.

Missed cervical spine injuries: aim for the top

PURPOSE

To determine the incidence of missed cervical spine injuries by radiology registrars in a major trauma centre and to identify any common blind spots.

MATERIALS AND METHODS

All patients with an acute traumatic injury who underwent a CT scan of the cervical spine in our unit, which serves a population of approximately 900,000, between September 2016 and December 2017 and whom had a separate radiology trainee report and final neuroradiology consultant report available were included in the study. We recorded the date and time of the scan, the registrar error and the registrar grade. An error was defined as follows: (1) a missed fracture; (2) a missed ligamentous injury; (3) overcall of a fracture (e.g. degenerative calcification or nutrient vessel). Groups were compared with the chi-square test.

RESULTS

Five hundred seventy-three CT scans of the cervical spine fitted the inclusion criteria and were analysed. There were a total of 149 injuries over eight levels in 96 patients. There were 12 registrar errors (2.1% discrepancy rate), of which 11 were missed acute injuries (9 fractures and 2 disco-ligamentous injuries). The grade of the registrar was not significant (p = 0.603). Seventy-three percent (8/11) missed injuries were disproportionately at the cranio-cervical junction, where only 11.6% of traumatic cervical spine injuries occur p < 0.0001. Forty-five percent of the missed injuries included occipital condyle fractures, which occurred in only 12/149 injuries (8%).

CONCLUSIONS

Radiology registrars safely report emergency CT scans of the cervical spine performed following trauma with a low discrepancy rate. Missed cervical spine injuries commonly occur at the cranio-cervical junction, which should become a standard review area.

Artificial Intelligence Trained by Deep Learning Can Improve Computed Tomography Diagnosis of Nontraumatic Subarachnoid Hemorrhage by Nonspecialists

Subarachnoid hemorrhage (SAH) is a serious cerebrovascular disease with a high mortality rate and is known as a disease that is hard to diagnose because it may be overlooked by noncontrast computed tomography (NCCT) examinations that are most frequently used for diagnosis. To create a system preventing this oversight of SAH, we trained artificial intelligence (AI) with NCCT images obtained from 419 patients with nontraumatic SAH and 338 healthy subjects and created an AI system capable of diagnosing the presence and location of SAH. Then, we conducted experiments in which five neurosurgery specialists, five nonspecialists, and the AI system interpreted NCCT images obtained from 135 patients with SAH and 196 normal subjects. The AI system was capable of performing a diagnosis of SAH with equal accuracy to that of five neurosurgery specialists, and the accuracy was higher than that of nonspecialists. Furthermore, the diagnostic accuracy of four out of five nonspecialists improved by interpreting NCCT images using the diagnostic results of the AI system as a reference, and the number of oversight cases was significantly reduced by the support of the AI system. This is the first report demonstrating that an AI system improved the diagnostic accuracy of SAH by nonspecialists.

Analysis of misses in imaging of head and neck pathology by attending neuroradiologists at a single tertiary academic medical centre

AIM

To analyse errors in head and neck (H&N) pathology made by attending neuroradiologists at a single tertiary-care centre.

MATERIALS AND METHODS

A neuroradiology quality assurance (QA) database of radiological errors was searched for attending physician errors in H&N pathology from 2014-2020. Data were limited to computed tomography (CT) and magnetic resonance imaging (MRI) reports. Data were collected on missed pathologies and study types. Misses were grouped into three categories: central neck (thyroid gland, aerodigestive tract), lateral neck (salivary glands, lymph nodes, soft tissues), and face/orbits (orbits, sinuses, masticator space).

RESULTS

During the study period, a total of 283,248 CT and MRI neuroradiology examinations were interpreted (all indications). Seventy-four H&N misses were identified comprising 85.1% perceptual and 14.9% interpretive errors. The distribution of errors was face/orbits (37.8%), central neck (36.5%), and lateral neck (25.7%). Clinically significant errors were found most commonly in the aerodigestive tract (21%), orbits (17.7%), masticator space, and parotid glands (14.5% each). The majority (67.6%) of the misses were detected on examinations that were not performed for a primary H&N indication; MRI brain was the most common examination (27%). Clearly malignant or potentially malignant masses accounted for 48.6% of all misses.

CONCLUSION

The majority of H&N misses were perceptual and were detected on examinations not performed for a H&N indication. Clearly malignant or potentially malignant masses represented half of all misses.

Rare case of atypical Dejerine syndrome in a child

Medial medullary syndrome (aka Dejerine syndrome) is a rare condition that develops following infarction of the medial medulla and is classically defined by the presence of Dejerine’s triad of contralateral weakness in upper and lower extremities, contralateral hemisensory loss of vibration and proprioception, and ipsilateral tongue weakness. It is typically caused by occlusion of the vertebral artery or one of its branches. We report the case of a 6-year-old girl who suffered a medial medullary infarction, and she was diagnosed with atypical Dejerine syndrome. Medial medullary infarct leading to atypical Dejerine syndrome has not been reported in this young of a patient in the literature to date.

Theragnostic Use of Radiolabelled Dota-Peptides in Meningioma: From Clinical Demand to Future Applications

Meningiomas account for approximately 30% of all new diagnoses of intracranial masses. The 2016 World Health Organization's (WHO) classification currently represents the clinical standard for meningioma's grading and prognostic stratification. However, watchful waiting is frequently the chosen treatment option, although this means the absence of a certain histological diagnosis. Consequently, MRI (or less frequently CT) brain imaging currently represents the unique available tool to define diagnosis, grading, and treatment planning in many cases. Nonetheless, these neuroimaging modalities show some limitations, particularly in the evaluation of skull base lesions. The emerging evidence supporting the use of radiolabelled somatostatin receptor analogues (such as dota-peptides) to provide molecular imaging of meningiomas might at least partially overcome these limitations. Moreover, their potential therapeutic usage might enrich the current clinical offering for these patients. Starting from the strengths and weaknesses of structural and functional neuroimaging in meningiomas, in the present article we systematically reviewed the published studies regarding the use of radiolabelled dota-peptides in surgery and radiotherapy planning, in the restaging of treated patients, as well as in peptide-receptor radionuclide therapy of meningioma.

Brain computerized tomography reading in suspected acute ischemic stroke patients: what are essentials for medical students?

BACKGROUND

Few systematic methods prioritize the image education in medical students (MS). We hope to develop a checklist of brain computerized tomography (CT) reading in patients with suspected acute ischemic stroke (AIS) for MS and primary care (PC) physicians.

METHODS

Our pilot group generated the items indicating specific structures or signs for the checklist of brain CT reading in suspected AIS patients for MS and PC physicians. These items were used in a modified web-based Delphi process using the online software "SurveyMonkey". In total 15 panelists including neurologists, neurosurgeons, neuroradiologists, and emergency department physicians participated in the modified Delphi process. Each panelist was encouraged to express feedback, agreement or disagreement on the inclusion of each item using a 9-point Likert scale. Items with median scores of 7-9 were included in our final checklist.

RESULTS

Fifty-two items were initially provided for the first round of the Delphi process. Of these, 35 achieved general agreement of being an essential item for the MS and PC physicians. The other 17 of the 52 items in this round and another two added items suggested by the panelists were further rated in the next round. Finally, 38 items were included in the essential checklist items of brain CT reading in suspected AIS patients for MS and PC physicians.

CONCLUSIONS

We established a reference regarding the essential items of brain CT reading in suspected AIS patients. We hope this helps to minimize malpractice and a delayed diagnosis, and to improve competency-based medical education for MS and PC physicians.

Risk Factors for Perceptual-versus-Interpretative Errors in Diagnostic Neuroradiology

BACKGROUND AND PURPOSE

Diagnostic errors in radiology are classified as perception or interpretation errors. This study determined whether specific conditions differed when perception or interpretation errors occurred during neuroradiology image interpretation.

MATERIALS AND METHODS

In a sample of 254 clinical error cases in diagnostic neuroradiology, we classified errors as perception or interpretation errors, then characterized imaging technique, interpreting radiologist's experience, anatomic location of the abnormality, disease etiology, time of day, and day of the week. Interpretation and perception errors were compared with hours worked per shift, cases read per shift, average cases read per shift hour, and the order of case during the shift when the error occurred.

RESULTS

Perception and interpretation errors were 74.8% (n = 190) and 25.2% (n = 64) of errors, respectively. Logistic regression analyses showed that the odds of an interpretation error were 2 times greater (OR, 2.09; 95% CI, 1.05-4.15; P = .04) for neuroradiology attending physicians with ≤5 years of experience. Interpretation errors were more likely with MR imaging compared with CT (OR, 2.10; 95% CI, 1.09-4.01; P = .03). Infectious/inflammatory/autoimmune diseases were more frequently associated with interpretation errors (P = .04). Perception errors were associated with faster reading rates (6.01 versus 5.03 cases read per hour; P = .004) and occurred later during the shift (24th-versus-18th case; P = .04).

CONCLUSIONS

Among diagnostic neuroradiology error cases, interpretation-versus-perception errors are affected by the neuroradiologist's experience, technique, and the volume and rate of cases read. Recognition of these risk factors may help guide programs for error reduction in clinical neuroradiology services.

Acute ocular traumatic imaging: what the radiologist should know [corrected]

Acute ocular trauma accounts for a substantial number of emergency department visits in the USA, and represents a significant source of disability to patients; however, the orbits remain a potential blind spot for radiologists. The goal of this article is to review the relevant anatomy of the orbit and imaging findings associated with commonly encountered acute ocular traumatic pathology, while highlighting the salient information which should be reported to the ordering clinician. Topics discussed include trauma to the anterior and posterior chamber, lens dislocations, intraocular foreign bodies, and open and contained globe injuries.

Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms

BACKGROUND AND OBJECTIVE

Accurate vessel segmentation of magnetic resonance angiography (MRA) images is essential for computer-aided diagnosis of cerebrovascular diseases such as stenosis or aneurysm. The ability of a segmentation algorithm to correctly reproduce the geometry of the arterial system should be expressed quantitatively and observer-independently to ensure objectivism of the evaluation.

METHODS

This paper introduces a methodology for validating vessel segmentation algorithms using a custom-designed MRA simulation framework. For this purpose, a realistic reference model of an intracranial arterial tree was developed based on a real Time-of-Flight (TOF) MRA data set. With this specific geometry blood flow was simulated and a series of TOF images was synthesized using various acquisition protocol parameters and signal-to-noise ratios. The synthesized arterial tree was then reconstructed using a level-set segmentation algorithm available in the Vascular Modeling Toolkit (VMTK). Moreover, to present versatile application of the proposed methodology, validation was also performed for two alternative techniques: a multi-scale vessel enhancement filter and the Chan-Vese variant of the level-set-based approach, as implemented in the Insight Segmentation and Registration Toolkit (ITK). The segmentation results were compared against the reference model.

RESULTS

The accuracy in determining the vessels centerline courses was very high for each tested segmentation algorithm (mean error rate = 5.6% if using VMTK). However, the estimated radii exhibited deviations from ground truth values with mean error rates ranging from 7% up to 79%, depending on the vessel size, image acquisition and segmentation method.

CONCLUSIONS

We demonstrated the practical application of the designed MRA simulator as a reliable tool for quantitative validation of MRA image processing algorithms that provides objective, reproducible results and is observer independent.

Head computed tomography in the emergency department: a collection of easily missed findings that are life-threatening or life-changing

BACKGROUND

The use of noncontrast head computed tomography (CT) has become commonplace in the emergency department (ED) as a means of screening for a wide variety of pathologies. Approximately 1 in 14 ED patients receives a head CT scan, and analyzing and interpreting this high volume of images in a timely manner is a daily challenge.

OBJECTIVES

Minimizing interpretation error is of paramount importance in the context of life-threatening and time-sensitive diagnoses. Therefore, it is prudent for the physician to recognize particular pitfalls in head CT interpretation and establish search patterns and practices that minimize such errors. In this article, we discuss a collection of common ED cases with easily missed findings, and identify time-effective practices and patterns to minimize interpretation error.

DISCUSSION

There are numerous reasons for false-negative interpretations, including, but not limited to, incomplete or misleading clinical history, failure to review prior studies, suboptimal windowing and leveling, and failure to utilize multiple anatomic views via multi-planar reconstructions and scout views. We illustrate this in four specific clinical scenarios: stroke, trauma, headache, and altered mental status.

CONCLUSION

Accurate and timely interpretation in the emergent setting is a daily challenge for emergency physicians. Knowledge of easily overlooked yet critical findings is a first step in minimizing interpretation error.

Potential pitfalls in imaging of paediatric cerebral sinovenous thrombosis

Paediatric cerebral sinovenous thrombosis (CSVT) is a rare but serious condition. The imaging signs may be subtle with a number of potential pitfalls. We present a pictorial essay of the pitfalls of diagnosis of CSVT on CT and MRI. We describe, using examples, potential pitfalls on both imaging modalities including anatomical variants of the cerebral venous system, extra-axial fluid collections and enhancement of chronic thrombus. Pitfalls particular to CT are discussed including beam-hardening artefact, image windowing and neonatal physiological intravascular hyperdensity. We review the potential variability in the appearance of thrombus on MRI, dependent largely on the stage of thrombus evolution and the pulse sequence. The signal intensity of thrombi, although described as evolving in a typical pattern on T1- and T2-weighted MRI, may be affected by variability in the degree of oxygenation of red cells in the forming thrombus, dilution and secondary propagation of thrombosis. Individual MRI sequences should not be interpreted in isolation, but as a set, and compared with CT images if available.