AJNR. American journal of neuroradiology最新文献

Background and purpose: The novel MR imaging technique of vascular architecture mapping allows in vivo characterization of local changes in cerebral microvasculature, but reference ranges for vascular architecture mapping parameters in healthy brain tissue are lacking, limiting its potential applicability as an MR imaging biomarker in clinical practice. We conducted whole-brain vascular architecture mapping in a large cohort to establish vascular architecture mapping parameter references ranges and identify region-specific cortical and subcortical microvascular profiles.

Materials and methods: This was a single-center examination of adult patients with unifocal, stable low-grade gliomas with multiband spin- and gradient-echo EPI sequence at 3T using parallel imaging. Voxelwise plotting of resulting values for gradient-echo (R₂*) versus spin-echo (R₂) relaxation rates during contrast agent bolus administration generates vessel vortex curves that allow the extraction of vascular architecture mapping parameters representative of, eg, vessel type, vessel radius, or CBV in the underlying voxel. Averaged whole-brain parametric maps were calculated for 9 parameters, and VOI analysis was conducted on the basis of a standardized brain atlas and individual cortical GM and WM segmentation.

Results: Prevalence of vascular risk factors among subjects (n = 106; mean age, 39.2 [SD, 12.5] years; 56 women) was similar to those in the German population. Compared with WM, we found cortical GM to have larger mean vascular calibers (5.80 [SD, 0.59] versus 4.25 [SD, 0.62] P < .001), increased blood volume fraction (20.40 [SD, 4.49] s^-1 versus 11.05 [SD, 2.44] s^-1; P < .001), and a dominance of venous vessels. Distinct microvascular profiles emerged for cortical GM, where vascular architecture mapping vessel type indicator differed, eg, between the thalamus and cortical GM (mean, -2.47 [SD, 4.02] s^-2 versus -5.41 [SD, 2.84] s^-2; P < .001). Intraclass correlation coefficient values indicated overall high test-retest reliability for vascular architecture mapping parameter mean values when comparing multiple scans per subject.

Conclusions: Whole-brain vascular architecture mapping in the adult brain reveals region-specific microvascular profiles. The obtained parameter reference ranges for distinct anatomic and functional brain areas may be used for future vascular architecture mapping studies on cerebrovascular pathologies and might facilitate early discovery of microvascular changes, in, eg, neurodegeneration and neuro-oncology.

Background and purpose: Menière disease (MD) manifests in 2 major endotypes: one with a hypoplastic, underdeveloped endolymphatic sac (MD-hp) and the other with a normally developed sac that degenerates over time (MD-dg). Determining the specific endotype in patients is important for predicting disease progression, tailoring patient counseling, and optimizing treatment strategies. Endotype diagnosis involves measuring an angular trajectory of the vestibular aqueduct (ATVA), with an ATVA ≥140° indicative of MD-hp and an ATVA ≤120° of MD-dg. However, assessing the ATVA can be challenging. This study aimed to explore the link between ATVA and the thickness of the retrolabyrinthine bone as an alternative diagnostic measure that could provide differentiation between MD endotypes using CT and MR imaging.

Materials and methods: Retrospective review of CT temporal bone imaging from 32 adult patients with definite MD (60 ears) and 33 age-matched controls without MD or other inner ear symptoms (61 ears) was performed. The ATVA and retrolabyrinthine bone thickness were measured using uniform methodology on standardized axial CT images. Comparative analyses were performed to determine the correlation between ATVA and retrolabyrinthine bone thickness. Additionally, from a separate cohort of 11 patients (22 ears), CT and MR examinations of the temporal bone were retrospectively reviewed for retrolabyrinthine bone thickness measurements, to verify the correlation across the 2 modalities.

Results: The average retrolabyrinthine bone thickness was statistically significantly different between MD endotypes, being a mean of 0.8 (SD, 0.3) mm in patients with MD-hp (ATVA ≥140°) and 2.0 (SD, 0.9) mm in patients with MD-dg (ATVA ≤120°), with a consistent pattern of thin retrolabyrinthine bone in MD-hp and variable thickness in MD-dg. Receiver operating characteristic curve analysis within the MD cohort revealed that a retrolabyrinthine bone thickness ≥1.2 mm effectively rules out MD-hp. Excellent interrater reliability was noted for the retrolabyrinthine measurement, and there was near-perfect correlation between CT and MR measurements.

Conclusions: Retrolabyrinthine bone thickness proved to be a useful and straightforward alternative marker for distinguishing MD endotypes, being particularly useful for excluding MD-hp. Including information on retrolabyrinthine bone thickness should be considered a routine part of reporting in the context of MD imaging.

Temporal lobe epilepsy is a common form of epilepsy that is often associated with hippocampal sclerosis (HS). Although HS is commonly considered a binary assessment in radiologic evaluation, it is known that histopathologic changes occur in distinct clusters. Some subtypes of HS only affect certain subfields, resulting in minimal changes to the overall volume of the hippocampus. This is likely a major reason why whole hippocampal volumetrics have underperformed versus expert readers in the diagnosis of HS. With recent advancements in MRI technology, it is now possible to characterize the substructure of the hippocampus more accurately. However, this is not consistently addressed in radiographic evaluations. The histologic subtype of HS is critical for prognosis and treatment decision-making, necessitating improved radiologic classification of HS. The International League Against Epilepsy (ILAE) has issued a consensus classification scheme for subtyping HS histopathologic changes. This review aims to explore how the ILAE subtypes of HS correlate with radiographic findings, introduce a grading system that integrates radiologic and pathologic reporting in HS, and outline an approach to detecting HS subtypes by using MRI. This framework will not only benefit current clinical evaluations, but also enhance future studies involving high-resolution MRI in temporal lobe epilepsy.

Background and purpose: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence (AI)-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an AI model for detecting steno-occlusive lesions in the intracranial arteries.

Materials and methods: Overall, 138 TOF-MRA images were collected from 2 institutions, which served as internal (n = 62) and external (n = 76) test sets, respectively. Each study was reviewed by 5 radiologists (2 neuroradiologists and 3 radiology residents) to compare the usage and nonusage of our proposed AI model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed by using the area under the jackknife free-response receiver operating characteristic curve (AUFROC) and reading time for comparison.

Results: The average AUFROC for the 5 radiologists demonstrated an improvement from 0.70 without AI to 0.76 with AI (P = .027). Notably, this improvement was most pronounced among the 3 radiology residents, whose performance metrics increased from 0.68 to 0.76 (P = .002). Despite an increased reading time by using AI, there was no significant change among the readings by radiology residents. Moreover, the use of AI resulted in improved interobserver agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752).

Conclusions: Our proposed AI model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less experienced readers may benefit the most from this model.

Background: Hereditary hemorrhagic telangiectasia is an autosomal dominant vascular dysplasia characterized by mucocutaneous telangiectasias, recurrent epistaxis, and organ vascular malformations including in the brain, which occur in about 10% of patients. These brain vascular malformations include high-flow AVMs and AVFs as well as low-flow capillary malformations. High-flow lesions can rupture, causing neurologic morbidity and mortality.

State of practice: International guidelines for the diagnosis and management of hereditary hemorrhagic telangiectasia recommend screening children for brain vascular malformations with contrast enhanced MR imaging at hereditary hemorrhagic telangiectasia diagnosis. Screening has not been uniformly adopted by some practitioners who contend that screening is not justified. Arguments against screening include application of short-term data from the adult A Randomized Trial of Unruptured Brain Arteriovenous Malformations (ARUBA) trial of unruptured sporadic brain AVMs to children with hereditary hemorrhagic telangiectasia as well as concerns about administration of sedation or IV contrast and causing patients or families increased anxiety.

Analysis: In this article, a multidisciplinary group of experts on hereditary hemorrhagic telangiectasia reviewed data that support screening guidelines and counter arguments against screening. Children with hereditary hemorrhagic telangiectasia have a preponderance of high-flow lesions including AVFs, which have the highest rupture risk. The rupture risk among children is estimated at about 0.7% per lesion per year and is additive across lesions and during a lifetime. ARUBA, an adult clinical trial of expectant medical management versus treatment of unruptured brain AVMs, favored medical management at 5 years but is not applicable to pediatric patients with hereditary hemorrhagic telangiectasia given the life expectancy of a child. Additionally, interventional, radiosurgical, and surgical techniques have improved with time. Experienced neurovascular experts can prospectively determine the best treatment for each child on the basis of local resources. The "watch and wait" approach to imaging means that children with brain vascular malformations will not be identified until a potentially life-threatening and deficit-producing intracerebral hemorrhage occurs. This expert group does not deem this to be an acceptable trade-off.

Basi-parallel anatomic scanning has been widely used for assessing the vascular morphology of vertebral basilar arteries. Previous studies have demonstrated its efficacy in evaluating the morphology of the MCA, which we refer to as MCA parallel anatomic scanning MR imaging (MCPAS). In this study, we present our experience with the application of MCPAS in patients with MCA occlusion. Endovascular treatment was performed on the patients with intact MCA morphology visible in on MCPAS, with no intracranial hemorrhage, occlusion, or other complications observed. No severe stenosis or re-occlusion was observed at the 12-month postoperative follow-up. In conclusion, MCPAS is an effective method for assessing the outer contour of an occlusive MCA. Endovascular treatment can be considered a safe and efficient option for patients who show a favorable MCA through MCPAS assessment.

Background and purpose: Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and characteristics. This study aims to characterize the emergence and evolution of AI/ML articles within neuroradiology and provide a comprehensive overview of the trends, challenges, and future directions of the field.

Materials and methods: We performed a bibliometric analysis of the American Journal of Neuroradiology; the journal was queried for original research articles published since inception (January 1, 1980) to December 3, 2022 that contained any of the following key terms: "machine learning," "artificial intelligence," "radiomics," "deep learning," "neural network," "generative adversarial network," "object detection," or "natural language processing." Articles were screened by 2 independent reviewers, and categorized into statistical modeling (type 1), AI/ML development (type 2), both representing developmental research work but without a direct clinical integration, or end-user application (type 3), which is the closest surrogate of potential AI/ML integration into day-to-day practice. To better understand the limiting factors to type 3 articles being published, we analyzed type 2 articles as they should represent the precursor work leading to type 3.

Results: A total of 182 articles were identified with 79% being nonintegration focused (type 1 n = 53, type 2 n = 90) and 21% (n = 39) being type 3. The total number of articles published grew roughly 5-fold in the last 5 years, with the nonintegration focused articles mainly driving this growth. Additionally, a minority of type 2 articles addressed bias (22%) and explainability (16%). These articles were primarily led by radiologists (63%), with most (60%) having additional postgraduate degrees.

Conclusions: AI/ML publications have been rapidly increasing in neuroradiology with only a minority of this growth being attributable to end-user application. Areas identified for improvement include enhancing the quality of type 2 articles, namely external validation, and addressing both bias and explainability. These results ultimately provide authors, editors, clinicians, and policymakers important insights to promote a shift toward integrating practical AI/ML solutions in neuroradiology.