Black-blood Magnetic Resonance Imaging Research Articles

Fully automated contrast selection of joint bright- and black-blood late gadolinium enhancement imaging for robust myocardial scar assessment

PurposeJoint bright- and black-blood MRI techniques provide improved scar localization and contrast. Black-blood contrast is obtained after the visual selection of an optimal inversion time (TI) which often results in uncertainties, inter- and intra-observer variability and increased workload. In this work, we propose an artificial intelligence-based algorithm to enable fully automated TI selection and simplify myocardial scar imaging. MethodsThe proposed algorithm first localizes the left ventricle using a U-Net architecture. The localized left cavity centroid is extracted and a squared region of interest (“focus box”) is created around the resulting pixel. The focus box is then propagated on each image and the sum of the pixel intensity inside is computed. The smallest sum corresponds to the image with the lowest intensity signal within the blood pool and healthy myocardium, which will provide an ideal scar-to-blood contrast. The image's corresponding TI is considered optimal. The U-Net was trained to segment the epicardium in 177 patients with binary cross-entropy loss. The algorithm was validated retrospectively in 152 patients, and the agreement between the algorithm and two magnetic resonance (MR) operators' prediction of TI values was calculated using the Fleiss' kappa coefficient. Thirty focus box sizes, ranging from 2.3mm2 to 20.3cm2, were tested. Processing times were measured. ResultsThe U-Net's Dice score was 93.0 ± 0.1%. The proposed algorithm extracted TI values in 2.7 ± 0.1 s per patient (vs. 16.0 ± 8.5 s for the operator). An agreement between the algorithm's prediction and the MR operators' prediction was found in 137/152 patients (κ= 0.89), for an optimal focus box of size 2.3cm2. ConclusionThe proposed fully-automated algorithm has potential of reducing uncertainties, variability, and workload inherent to manual approaches with promise for future clinical implementation for joint bright- and black-blood MRI.

Expanded experience with cardiovascular black blood cinematic rendering.

Black blood cinematic rendering (BBCR) is a newly described preset for cinematic rendering, which creates photorealistic displays from volumetric data sets with the contrast-enhanced blood pool displayed as dark and transparent. That set of features potentially provides for enhanced visualization of endomyocardial and intraluminal pathology, as well as cardiac devices. The similarity of the images to black-blood magnetic resonance imaging (MRI) may allow for expansion of the evaluation of certain types of pathology into patient populations unable to undergo MRI. In the emergency setting, the rapid acquisition time and reasonable post-processing time make this technique clinically feasible. In this expanded experience, we demonstrate an expanded clinical experience with the BBCR technique, highlighting the applications for intraluminal cardiovascular evaluation, especially focused on current and potential emergency radiology applications.

Vessel density mapping of small cerebral vessels on 3D high resolution black blood MRI

Small cerebral blood vessels are largely inaccessible to existing clinical in vivo imaging technologies. This study aims to present a novel analysis pipeline for vessel density mapping of small cerebral blood vessels from high-resolution 3D black-blood MRI at 3T. Twenty-eight subjects (10 under 35 years old, 18 over 60 years old) were imaged with the T1-weighted turbo spin-echo with variable flip angles (T1w TSE-VFA) sequence optimized for black-blood small vessel imaging with iso-0.5 mm spatial resolution (interpolated from 0.51×0.51×0.64 mm3) at 3T. Hessian-based vessel segmentation methods (Jerman, Frangi and Sato filter) were evaluated by vessel landmarks and manual annotation of lenticulostriate arteries (LSAs). Using optimized vessel segmentation, large vessel pruning and non-linear registration, a semiautomatic pipeline was proposed for quantification of small vessel density across brain regions and further for localized detection of small vessel changes across populations. Voxel-level statistics was performed to compare vessel density between two age groups. Additionally, local vessel density of aged subjects was correlated with their corresponding gross cognitive and executive function (EF) scores using Montreal Cognitive Assessment (MoCA) and EF composite scores compiled with Item Response Theory (IRT). Jerman filter showed better performance for vessel segmentation than Frangi and Sato filter which was employed in our pipeline. Small cerebral blood vessels including small artery, arterioles, small veins, and venules on the order of a few hundred microns can be delineated using the proposed analysis pipeline on 3D black-blood MRI at 3T. The mean vessel density across brain regions was significantly higher in young subjects compared to aged subjects. In the aged subjects, localized vessel density was positively correlated with MoCA and IRT EF scores. The proposed pipeline is able to segment, quantify, and detect localized differences in vessel density of small cerebral blood vessels based on 3D high-resolution black-blood MRI. This framework may serve as a tool for localized detection of small vessel density changes in normal aging and cerebral small vessel disease.

Time-of-flight and black-blood MRI to study intracranial arteries in rats

Intracranial aneurysms (IAs) are usually incidentally discovered by magnetic resonance imaging (MRI). Once discovered, the risk associated with their treatment must be balanced with the risk of an unexpected rupture. Although clinical observations suggest that the detection of contrast agent in the aneurysm wall using a double-inversion recovery black-blood (BB) sequence may point to IA wall instability, the exact meaning of this observation is not understood. Validation of reliable diagnostic markers of IA (in)stability is of utmost importance to deciding whether to treat or not an IA. To longitudinally investigate IA progression and enhance our understanding of this devastating disease, animal models are of great help. The aim of our study was to improve a three-dimensional (3D)-time-of-flight (TOF) sequence and to develop a BB sequence on a standard preclinical 3-T MRI unit to investigate intracranial arterial diseases in rats. We showed that our 3D-TOF sequence allows reliable measurements of intracranial artery diameters, inter-artery distances, and angles between arteries and that our BB sequence enables us to visualize intracranial arteries. We report the first BB-MRI sequence to visualize intracranial arteries in rats using a preclinical 3-T MRI unit. This sequence could be useful for a large community of researchers working on intracranial arterial diseases.Relevance statement We developed a black-blood MRI sequence to study vessel wall enhancement in rats with possible application to understanding IAs instability and finding reliable markers for clinical decision-making.Key points• Reliable markers of aneurysm stability are needed for clinical decision.• Detection of contrast enhancement in the aneurysm wall may be associated with instability.• We developed a black-blood MRI sequence in rats to be used to study vessel wall enhancement of IAs.Graphical

Multimodality Imaging in Cranial Giant Cell Arteritis: First Experience with High-Resolution T1-Weighted 3D Black Blood without Contrast Enhancement Magnetic Resonance Imaging.

In order to support or refute the clinical suspicion of cranial giant cell arteritis (GCA), a supplemental imaging modality is often required. High-resolution black blood Magnetic Resonance Imaging (BB MRI) techniques with contrast enhancement can visualize artery wall inflammation in GCA. We compared findings on BB MRI without contrast enhancement with findings on 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/low-dose computed tomography (2-[18F]FDG PET/CT) in ten patients suspected of having GCA and in five control subjects who had a 2-[18F]FDG PET/CT performed as a routine control for malignant melanoma. BB MRI was consistent with 2-[18F]FDG PET/CT in 10 out of 10 cases in the group with suspected GCA. In four out of five cases in the control group, the BB MRI was consistent with 2-[18F]FDG PET/CT. In this small population, BB MRI without contrast enhancement shows promising performance in the diagnosis of GCA, and might be an applicable imaging modality in patients.

Black blood MRI sequences in the acute management of ruptured and unruptured intracranial aneurysms

We present an illustrative case series in which high spatial resolution black blood (BB) MRI sequences were used as an adjunct in the acute management of intracranial aneurysms with diagnostic uncertainty regarding rupture status. Several acute management dilemmas are discussed including the surveillance of previously treated ruptured intracranial aneurysms, identifying culprit lesion(s) amongst multiple ruptured intracranial aneurysms, and risk stratifying incidental unruptured intracranial aneurysms. We present our experience which supports the evaluation of this vessel wall imaging technique in larger multi-centre observational studies. MR imaging was performed on a 3.0 Tesla Siemens Somatom Vida system and sequences used included: Susceptibility Weighted Imaging, Diffusion Weighted Imaging & 3D T1 pre- and post-contrast-enhanced BB sequences.

Joint 2D–3D cross-pseudo supervision for carotid vessel wall segmentation

IntroductionThe segmentation of the carotid vessel wall using black-blood magnetic resonance images was a crucial step in the diagnosis of atherosclerosis. The objective was to accurately isolate the region between the artery lumen and outer wall. Although supervised learning methods achieved remarkable accuracy in vessel segmentation, their effectiveness remained limited due to their reliance on extensive labeled data and human intervention. Furthermore, when confronted with three-dimensional datasets featuring insufficient and discontinuous label data, these learning-based approaches could lose their efficacy. In this paper, we proposed a novel Joint 2D–3D Cross-Pseudo Supervision (JCPS) method for accurate carotid vessel wall segmentation.MethodsIn this study, a vascular center-of-gravity positioning module was developed to automatically estimate the region of blood vessels. To achieve accurate segmentation, we proposed a joint 2D–3D semi-supervised network to model the three-dimensional continuity of vascular structure. In addition, a novel loss function tailored for vessel segmentation was introduced, consisting of four components: supervision loss, cross-pseudo supervision loss, pseudo label supervision loss, and continuous supervision loss, all aimed at ensuring the accuracy and continuity of the vessel structure. In what followed, we also built up a user-friendly Graphical User Interface based on our JCPS method for end-users.ResultsOur proposed JCPS method was evaluated using the Carotid Artery Vessel Wall Segmentation Challenge dataset to assess its performance. The experimental results clearly indicated that our approach surpassed the top 10 methods on the leaderboard, resulting in a significant enhancement in segmentation accuracy. Specifically, we achieved an average Dice similarity coefficient increase from 0.775 to 0.806 and an average quantitative score improvement from 0.837 to 0.850, demonstrating the effectiveness of our proposed JCPS method for carotid artery vessel wall segmentation.ConclusionThe experimental results suggested that the JCPS method had a high level of generalization performance by producing pseudo labels that were comparable with software annotations for data-imbalanced segmentation tasks.

Association between Carotid Bifurcation Angle and Vulnerable Plaque Volume Using Black Blood Magnetic Resonance Imaging.

The morphology of the internal carotid artery (ICA) bifurcation is increasingly being recognized as the cause of atherosclerosis and vulnerable plaque leading to cerebral infarction. In this study, we investigated the relationship between carotid bifurcation angle and carotid plaque volume evaluated using black blood magnetic resonance imaging (BB-MRI). Among the 90 patients who underwent revascularization for atherosclerotic symptomatic carotid stenosis between April 2016 and October 2022 using BB-MRI, carotid plaque was evaluated in 57 patients. Relative overall signal intensity (roSI) was defined as the signal intensity of the plaque on T1-weighted images relative to the signal intensity of the sternocleidomastoid muscle in the same slice as the common carotid bifurcation. Regions showing roSI ≥ 1.0 were defined as plaque, and the plaque volume and relative plaque volume were measured from roSI ≥1.0 to ≥2.0 in 0.1 increments. We calculated the angles between the common carotid artery (CCA) and the ICA and between the CCA and the external carotid artery (ECA) on magnetic resonance angiography. We classified two groups according to carotid bifurcation angles based on the ICA angle: Group A = <35° and Group B = ≥35°. Compared with Group A (n = 42), Group B (n = 15) showed a greater relative plaque volume between roSI ≥ 1.3 and roSI ≥ 1.5. A significant correlation was identified between relative plaque volume with roSI ≥ 1.4 and ICA angle (p = 0.049). Vulnerable plaque was significantly more frequent in the group with an ICA angle of ≥35. Moreover, the ICA angle was significantly greater in patients with a roSI of ≥1.4.

Segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black blood magnetic resonance imaging with multi-task learning.

Atherosclerotic cardiovascular disease is the leading cause of death worldwide. Early detection of carotid atherosclerosis can prevent the progression of cardiovascular disease. Many (semi-) automatic methods have been designed for the segmentation of carotid vessel wall and the diagnosis of carotid atherosclerosis (i.e., the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis) on black blood magnetic resonance imaging (BB-MRI). However, most of these methods ignore the intrinsic correlation among different tasks on BB-MRI, leading to limited performance. Thus, we model the intrinsic correlation among the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis tasks on BB-MRI by using the multi-task learning technique and propose a gated multi-task network (GMT-Net) to perform three related tasks in a neural network (i.e., carotid artery lumen segmentation, outer wall segmentation, and carotid atherosclerosis diagnosis). In the proposed method, the GMT-Net is composed of three modules, including the sharing module, the segmentation module, and the diagnosis module, which interact with each other to achieve better learning performance. At the same time, two new adaptive layers, namely, the gated exchange layer and the gated fusion layer, are presented to exchange and merge branch features. The proposed method is applied to the CAREII dataset (i.e., 1057 scans) for the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis. The proposed method can achieve promising segmentation performances (0.9677 Dice for the lumen and 0.9669 Dice for the outer wall) and better diagnosis accuracy of carotid atherosclerosis (0.9516 AUC and 0.9024 Accuracy) in the "CAREII test" dataset (i.e., 106 scans). The results show that the proposed method has statistically significant accuracy and efficiency. Even without the intervention of reviewers required for the previous works, the proposed method automatically segments the lumen and outer wall together and diagnoses carotid atherosclerosis with high performance. The proposed method can be used in clinical trials to help radiologists get rid of tedious reading tasks, such as screening review to separate normal carotid arteries from atherosclerotic arteries and to outline vessel wall contours.

Association of Dental Infections with Intracranial Atherosclerotic Stenosis

Introduction: Periodontal disease (PD) and dental caries are oral infections leading to tooth loss that are associated with atherosclerosis and cerebrovascular disease. We assessed the hypothesis that PD and caries are associated with asymptomatic intracranial atherosclerosis (ICAS) in the Atherosclerosis Risk in Communities (ARIC) study. Methods: Full-mouth clinical periodontal measurements (7 indices) collected at 6 sites per tooth from 6,155 subjects from the Dental Atherosclerosis Risk in Communities Study (DARIC) without prior stroke were used to differentiate seven PD stages (Periodontal Profile Class [PPC]-I to -VII) and dental caries on coronal dental surface (DS) and dental root surface (DRS). A stratified subset underwent 3D time-of-flight MR angiogram and 3D high isotropic-resolution black blood MRI. ICAS was graded according to the criteria established by the Warfarin-Aspirin Symptomatic Intracranial Disease (WASID) trial. We evaluated the relationship between PD stage and dental caries with asymptomatic ICAS, graded as no ICAS, <50% ICAS, and ≥50% ICAS. Results: Among dentate subjects who underwent vascular imaging, 801 (70%) had no ICAS, 232 (20%) had <50% ICAS, and 112 (10%) had ≥50% ICAS. Compared to participants without gum disease (PPC-I), participants with mild-moderate tooth loss (PPC-VI), severe tooth loss (PPC-VII), and severe PD (PPC-IV) had higher odds of having <50% ICAS. Participants with extensive gingivitis (PPC-V) had significantly higher odds of having ≥50% ICAS. This association remained significant after adjusting for confounding variables: age, gender, race, hypertension, diabetes, dyslipidemia, 3-level education, and smoking status. There was no association between dental caries (DS and DRS) and ICAS <50% and ≥50%. Conclusion: We report significant associations between mild-moderate tooth loss, severe tooth loss, and severe PD with <50% ICAS as well as an association between extensive gingivitis and ≥50% ICAS. We did not find an association between dental caries and ICAS.

Quantitative evaluation for intravascular structures of vertebral artery dissection with a novel zoomed high-resolution black-blood MR imaging.

Although non-stroke vertebral artery dissection (VAD) is diagnosed using MRI, detecting the subtle intravascular structure remains challenging. This study aimed to evaluate the validity of quantitative intravascular scanning based on novel zoomed high-resolution black blood (Z-HB) MRI for distinguishing VAD from other vessel pathologies. Twenty-one patients with non-stroke VAD and 18 with symptomatic atherosclerotic plaques in their vertebral artery underwent Z-HB MRI and subsequent profile curve processing. Axial Z-HB imaging was obtained from dissected and normal segments in patients with VAD and atherosclerotic plaque in patients with ischemia. We investigated the qualitative categorization of the scanning patterns of the intravascular signals. We also evaluated the quantitative ability of each profile curve to discriminate multiple vessel pathologies by analyzing the receiver operating characteristics curves. Profile curve processing of 140 Z-HB images categorized the intravascular signal patterns into luminal, asymmetrical, and omega types. The asymmetrical type included both dissecting and atherosclerotic vessels, and the omega type included dissecting and normal vessels. In the asymmetrical type, quantitative evaluation successfully distinguished intramural hematomas of VAD from atherosclerotic plaque with an area under the curve of 0.80. The intimal flap of the VAD was distinguished from the blood flow artifact of the normal vessel with an area under the curve of 0.93 in the omega type. A combination of novel Z-HB MRI and profile curve processing provided an ultra-high-resolution analysis of the intravascular structure of non-stroke VAD and successfully distinguished VAD from normal vessels or atherosclerotic plaques.

The Role of Advanced Imaging in Neurosurgical Diagnosis.

Neurosurgery as a specialty has developed at a rapid pace as a result of the continual advancements in neuroimaging modalities. With more sophisticated imaging options available to the modern neurosurgeon, diagnoses become more accurate and at a faster rate, allowing for greater surgical planning and precision. Herein, the authors review the current heavily used imaging modalities within neurosurgery, weighing their strengths and weaknesses, and provide a look into new advances and imaging options within the field. Of the many imaging modalities currently available to the practicing neurosurgeon, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasonography (US) are used most heavily within the field for appropriate diagnosis of neuropathologies in question. For each, their strengths are weighed regarding appropriate capabilities in accurate diagnosis of cranial or spinal lesions. Reasoning for choosing one over the other for various pathologies is also reviewed. Current limitations of each is also assessed, providing insight for possible improvement for each. New advancements in imaging options are subsequently reviewed for best uses within neurosurgery, including the new utilization of FIESTA sequencing, glymphatic mapping, black-blood MRI, and functional MRI. The specialty of neurosurgery will continue to heavily rely on improvements within imaging options available for improved diagnosis and greater surgical outcomes for the patients treated. The synthesis of techniques provided herein may provide meaningful guidance for neurosurgeons in effectively diagnosing neurological pathologies while also helping guide future efforts in neuroimaging developments.

UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images

We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation.

Evaluation of Intra-Aneurysmal Residual Blood Flow with the iMSDE T1-Black Blood Imaging after Flow Diverter Treatment.

We aimed to evaluate the efficacy of the "improved motion-sensitized driven-equilibrium (iMSDE)"-prepared T1-weighted black blood (T1-BB) MRI for monitoring treatment effect with a flow diverter (FD) for cerebral aneurysms. Following the exclusion of concomitant coiling and retreatment cases from 60 consecutive cases of cerebral aneurysms treated with FDs at our institution, 32 with imaging data were included in the analysis. Detectability of residual blood flow within the aneurysms was validated as follows: 1) comparison of MRI sequences (iMSDE-prepared T1-BB images, T1-weighted images [ T1WI], and time-of-flight [ TOF]-MRA) in cases of incompletely occluded aneurysms and 2) comparison of angiography and MRI sequences in the same period. 1) The probability of diagnosing intra-aneurysmal blood flow was significantly higher with iMSDE-prepared T1-BB (iMSDE-prepared T1-BB vs. T1WI, p <0.001; iMSDE-prepared T1-BB vs. TOF-MRA, p <0.001). 2) The diagnostic accuracy of residual aneurysmal blood flow was significantly higher with iMSDE-prepared T1-BB than that with T1WI (p = 0.032). Furthermore, in cases of incomplete occlusion, the probability of detecting intra-aneurysmal blood flow was significantly higher with iMSDE-prepared T1-BB (iMSDE-prepared T1-BB vs. T1WI, p <0.001; iMSDE-prepared T1-BB vs. TOF-MRA, p = 0.023). Our results demonstrated that iMSDE-prepared T1-BB could help distinguish between blood flow and thrombus within the aneurysms after FD treatment, especially in the early stages of FD treatment.

Intraorbital findings in giant cell arteritis on black blood MRI

ObjectiveBlindness is a feared complication of giant cell arteritis (GCA). However, the spectrum of pathologic orbital imaging findings on magnetic resonance imaging (MRI) in GCA is not well understood. In this study, we assess inflammatory changes of intraorbital structures on black blood MRI (BB-MRI) in patients with GCA compared to age-matched controls.MethodsIn this multicenter case-control study, 106 subjects underwent BB-MRI. Fifty-six patients with clinically or histologically diagnosed GCA and 50 age-matched controls without clinical or laboratory evidence of vasculitis were included. All individuals were imaged on a 3-T MR scanner with a post-contrast compressed-sensing (CS) T1-weighted sampling perfection with application-optimized contrasts using different flip angle evolution (SPACE) BB-MRI sequence. Imaging results were correlated with available clinical symptoms.ResultsEighteen of 56 GCA patients (32%) showed inflammatory changes of at least one of the intraorbital structures. The most common finding was enhancement of at least one of the optic nerve sheaths (N = 13, 72%). Vessel wall enhancement of the ophthalmic artery was unilateral in 8 and bilateral in 3 patients. Enhancement of the optic nerve was observed in one patient. There was no significant correlation between imaging features of inflammation and clinically reported orbital symptoms (p = 0.10). None of the age-matched control patients showed any inflammatory changes of intraorbital structures.ConclusionsBB-MRI revealed inflammatory findings in the orbits in up to 32% of patients with GCA. Optic nerve sheath enhancement was the most common intraorbital inflammatory change on BB-MRI. MRI findings were independent of clinically reported orbital symptoms.Key Points• Up to 32% of GCA patients shows signs of inflammation of intraorbital structures on BB-MRI.• Enhancement of the optic nerve sheath is the most common intraorbital finding in GCA patients on BB-MRI.• Features of inflammation of intraorbital structures are independent of clinically reported symptoms.

NIMG-13. DEEP LEARNING-BASED AUTOMATIC DETECTION AND SEGMENTATION OF BRAIN METASTASES FOR STEREOTACTIC ABLATIVE RADIOTHERAPY USING BLACK-BLOOD MAGNETIC RESONANCE IMAGING

Abstract PURPOSE/OBJECTIVES Stereotactic ablative radiotherapy (SABR) is becoming popular in the treatment of Brain metastases (BM). However, detection and manual delineation of BM are labor-intense processes, and normal blood vessels are occasionally mistaken for metastatic lesions. In this study, we investigated the efficacy and accuracy of a deep learning (DL) model for the detection and segmentation of BM using black-blood (BB) magnetic resonance imaging (MRI). MATERIALS/METHODS The BB MRI data of 48 patients with 806 BMs were collected to train and validate the DL model. Since MRI data have an inconsistent intensity scale across the patients, we applied piecewise linear histogram matching algorithm, also called the Nyul normalization. To deal with bias corruption in the patient, N4 bias field correction was applied. The modified U-Net was implemented to automatically detect and segment BMs. The detection performance was measured with sensitivity and average false positives and the segmentation performance was measured with the dice score (DSC). RESULTS Twelve patients with 132 BMs were randomly selected as the test-set for evaluating our trained model. In the test-set, 19.2% BMs had a volume of &amp;lt; 0.02 cc, max volume of 16.642 cc, min volume of 0.009 cc, median volume of 0.071 cc, and mean volume of 1.158 cc. The sensitivity was 96.87% and average false positives were 0.2. We consider &amp;lt; 0.02 cc volume as the small volume. 100% sensitivity for BMs with volumes ≥ 0.02 cc, and 84.6% sensitivity for BMs with volumes &amp;lt; 0.02 cc volume. The DSC was 86.27 (range, 77-91.2). CONCLUSIONS Our model can detect and segment BMs on BB MRI data with good detection and segmentation performance. Further study is on a way to generate a synthetic BB image from the T1-Gd image by applying this model.

Deep Learning-Based Automatic Detection and Segmentation of Brain Metastases for Stereotactic Ablative Radiotherapy Using Black-Blood Magnetic Resonance Imaging

<h3>Purpose/Objective(s)</h3> Brain metastases (BM) are the most common intracranial tumors in adults. Benefiting from efficient local control, stereotactic ablative radiotherapy (SABR) is becoming popular in the treatment of BM. Detection and manual delineation of BM are labor-intense processes, and normal blood vessels, which demonstrate high signal intensity on contrast enhanced magnetic resonance imaging (CE-MRI), are occasionally mistaken for metastatic lesions. Recently, several efforts have been made to develop automatic detection and segmentation for BM using advanced deep learning (DL) algorithms. In this study, we investigated the efficacy and accuracy of a DL model for the detection and segmentation of BM using black-blood (BB) MRI. <h3>Materials/Methods</h3> The BB MRI data of 48 patients with 806 BMs were collected to train and validate the DL model. BMs was contoured by a radiation oncologist. Since MRI data have an inconsistent intensity scale across the patients, we applied piecewise linear histogram matching algorithm, also called the Nyul normalization. To deal with bias corruption in the patient, N4 bias field correction was applied. The modified U-Net was implemented to automatically detect and segment BMs. One of the main strengths of modified U-Net is preserving the edge that occurs during the down-sampling process. The evaluation was conducted in both detection and segmentation. The detection performance was measured with detection sensitivity and average false positives and the segmentation performance was measured with the dice score (DSC). <h3>Results</h3> Twelve patients with 132 BMs were randomly selected as the test-set for evaluating our trained model. In the test-set, 19.2% BMs had a volume of <0.02 cc, max volume of 16.642 cc, min volume of 0.009 cc, median volume of 0.071 cc, and mean volume of 1.158 cc. The detection sensitivity was 96.87% by finding 128 among 132 BMs and average false positives were 0.2. When the results were analyzed, we consider <0.02 cc volume as the small volume. As the results, 100% sensitivity for BMs with volumes ≥0.02 cc, and 84.6% sensitivity for BMs with volumes <0.02 cc volume. The DSC of modified U-Net was 86.27 (range, 77-91.2), which is a significant improvement over DSC of U-Net of 81 (range, 64.71-89.2). Due to the edge preservation characteristics of the modified U-Net, the contour is well segmented along the boundary in the image even for a BM of a very small volume. <h3>Conclusion</h3> In this study, our model can detect and segment BMs on BB MRI data with good detection and segmentation performance, suggesting its considerable benefit for SABR.

Feasibility of Preoperative Magnetic Resonance Angiography/Black-Blood Magnetic Resonance Imaging/Computed Tomography Fusion Imaging Without Contrast Agent for Carotid Endarterectomy

Preoperative identification of the carotid bifurcation (CB) location and plaque and stenosis distal end of the cervical internal carotid artery in relation to bony structures is essential for carotid endarterectomy (CEA). However, for patients with contrast contraindications, cervical 3-dimensional computed tomography angiography (3D-CTA) is unavailable. In this study, we created fusion images of magnetic resonance angiography (MRA), black-blood (BB) magnetic resonance imaging (MRI), and CT to determine if these noncontrast agent images are superior to 3D-CTA for preoperative CEA planning. The fusion images showed vascular structures obtained by MRA, plaque observed by BB-MRI, and bone structures shown by CT. Spatial localization accuracy was verified by directly overlaying contrast-enhanced 3D-CTA images on the fusion images. We validated this technique in 50 patients with unilateral ICA stenosis, 28 of whom underwent CEA. The 2D-distance CB MRA-CTA (the 2D distance difference between CB MRA and CB CTA perpendicular to the long axis of the carotid artery) was measured. We also compared the findings of the fusion image regarding the CB location and plaque distal end with the operative findings. The median 2D distance CB MRA-CTA was 1mm. CB MRA was located distal and proximal to CB CTA in 21and 29 patients, respectively. The CB location and fusion-image plaque were consistent with the intraoperative findings in all CEA patients. Fusion images created from MRA, BB-MRI, and noncontrast CT were feasible as an alternative to 3D-CTA for patients with contrast contraindications.

Factors contributing to restenosis after carotid artery stenting and the usefulness of magnetic resonance plaque imaging: A study of 308 consecutive patients

PurposeCarotid artery stenting (CAS) is an alternative treatment strategy to carotid endarterectomy. However, post-intervention in-stent restenosis (ISR) remains a complication. The timing, frequency, and prognostic factors of ISR are unclear. The incidence and timing of post-CAS ISR were investigated, including plaque imaging to investigate the prognostic factors. MethodsWe retrospectively investigated 308 patients diagnosed with carotid stenosis who underwent CAS between April 2008 and April 2020. The patients also underwent carotid ultrasonography (US) at 1, 3, 6, and 12 months postinterventionally and every 6 months thereafter. Contributory factors in patients with ISR were also investigated. ResultsThe percentage value of ISR was 4.9% (15/308). The median time from the procedure to ISR was 7 months. The ISR rate was higher for patients with a plaque/muscle ratio of ≤ 2.30 on T2-weighted imaging (T2WI) on black-blood magnetic resonance imaging (BB MRI) (p = 0.022). A plaque/muscle ratio of ≤ 2.30 on BB MRI T2WI was identified as an independent risk factor for ISR. A comparison of the incidence of ISR and the plaque/muscle ratio level on MRI T2WI was also significant (p < 0.001). In T2WI imaging of BB MRI, the cutoff value for detecting ISR from the Plaque/Muscle ratio was 1.310 [area under the curve (AUC) = 0.935, Sensitivity = 100%, specificity = 85.2%]. ConclusionsThe incidence of post-CAS ISR was 4.9%, at a median time of 7 months after stenting. Plaque/muscle ratio on T2WI in pre-intervention MRI plaque imaging may be a useful predictor of post-intervention ISR.

Abstract TMP76: Association Of Adiposity With Intracranial Atherosclerosis

Introduction: Adiposity, as measured by Body Mass Index (BMI) and Waist-to-Hip Ratio (WHR), is associated with ischemic stroke. However, association with Intercranial Atherosclerosis has not been well-established in the United States. We assessed the hypothesis that adiposity is associated with asymptomatic intracranial atherosclerosis (ICAS). Methods: BMI was calculated as weight (kg)/height (m) 2 . The ratio of waist (umbilical level) to hip (maximum buttocks) circumference (WHR) was calculated as a measure of fat distribution. In the ARIC cohort, a stratified subset (N=1145) underwent 3D time-of-flight MR angiogram and 3D high-isotropic resolution black blood MRI. ICAS was graded according to the criteria established by the Warfarin-Aspirin Symptomatic Intracranial Disease trial. In this study, we evaluated the relationship between BMI as well as WHR, and significant asymptomatic ICAS, defined as ≥50% stenosis. Student t-tests were performed to test continuous variables, and X 2 test was used to compare categorical variables. Analysis of covariance (ANCOVA) was used for multivariate testing. Results: Among subjects who underwent vascular imaging, 1033 (90%) had &lt;50% ICAS and 112 (10%) had ≥50% ICAS. At the time of assessment, those with ≥50% ICAS were older (age 79±5 vs. 76±5, p&lt;0.001), and had higher systolic blood pressure or SBP (134±19 vs. 130±18, p=0.02), compared with those with &lt;50% ICAS. The BMI was higher (29.1±6.7 vs. 28.1±5.2), although the difference was statistically borderline significant (p=0.06). The WHR was higher (0.95±0.07 vs. 0.93±0,08), the difference being statistically significant (p=0.01). The difference remained significant after adjustment for age and SBP (p=0.02). Conclusion: We report a significant association between WHR, a measure of central adiposity, and ≥50% ICAS. This may be one of the first reported associations between central adiposity and ICAS. These results emphasize the importance of adiposity distribution in addition to overall adiposity, as a risk factor for significant asymptomatic ICAS.