Vestibular schwannoma showing a dural tail on contrastenhanced magnetic resonance images

1997 ◽  
Vol 111 (9) ◽  
pp. 877-879 ◽  
Author(s):  
Yoshihiro Noguchi ◽  
Atsushi Komatsuzaki ◽  
Ichiro Yamada ◽  
Hideji Okuno ◽  
Hidetoshi Haraguchi
Keyword(s):  
Magnetic Resonance ◽  
Histological Examination ◽  
Dura Mater ◽  
Vestibular Schwannoma ◽  
Cerebellopontine Angle ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
Collagen Fibres ◽  
Contrast Enhanced ◽  
Dural Tail

AbstractThe dural tail on contrast-enhanced magnetic resonance (MR) images, frequently observed in meningiomas, has been used to distinguish between cerebellopontine angle meningiomas and vestibular schwannomas. We report on a 66-year-old female with vestibular schwannoma showing the dural tail on contrast-enhanced MR images. Histological examination revealed that the dural tail corresponded to the thickened dura mater comprising of collagen fibres and scattered hyalinization with no tumoral invasion.

scholarly journals Effusion attenuates the effect of synovitis on radiographic progression in patients with hand osteoarthritis: a longitudinal magnetic resonance imaging study

2020 ◽  
Vol 40 (1) ◽  
pp. 315-319
Author(s):  
W. Damman ◽  
R. Liu ◽  
M. Reijnierse ◽  
F. R. Rosendaal ◽  
J. L. Bloem ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Radiographic Progression ◽  
Imaging Study ◽  
Magnetic Resonance Images ◽  
High Signal ◽  
Hand Osteoarthritis ◽  
Mr Images ◽  
Contrast Enhanced ◽  
Key Points

AbstractAn exploratory study to determine the role of effusion, i.e., fluid in the joint, in pain, and radiographic progression in patients with hand osteoarthritis. Distal and proximal interphalangeal joints (87 patients, 82% women, mean age 59 years) were assessed for pain. T2-weighted and Gd-chelate contrast-enhanced T1-weighted magnetic resonance images were scored for enhanced synovial thickening (EST, i.e., synovitis), effusion (EST and T2-high signal intensity [hsi]) and bone marrow lesions (BMLs). Effusion was defined as follows: (1) T2-hsi > 0 and EST = 0; or 2) T2-hsi = EST but in different joint locations. Baseline and 2-year follow-up radiographs were scored following Kellgren-Lawrence, increase ≥ 1 defined progression. Associations between the presence of effusion and pain and radiographic progression, taking into account EST and BML presence, were explored on the joint level. Effusion was present in 17% (120/691) of joints, with (63/120) and without (57/120) EST. Effusion on itself was not associated with pain or progression. The association with pain and progression, taking in account other known risk factors, was stronger in the absence of effusion (OR [95% CI] 1.7 [1.0–2.9] and 3.2 [1.7–5.8]) than in its presence (1.6 [0.8–3.0] and 1.3 [0.5–3.1]). Effusion can be assessed on MR images and seems not to be associated with pain or radiographic progression but attenuates the association between synovitis and progression. Key Points• Effusion is present apart from synovitis in interphalangeal joints in patients with hand OA.• Effusion in finger joints can be assessed as a separate feature on MR images.• Effusion seems to be of importance for its attenuating effect on the association between synovitis and radiographic progression.

Magnetic resonance imaging/magnetic resonance angiography fusion technique for intraoperative navigation during microsurgical resection of cerebral arteriovenous malformations

2012 ◽  
Vol 32 (5) ◽  
pp. E7 ◽  
Author(s):  
Kimon Bekelis ◽  
Symeon Missios ◽  
Atman Desai ◽  
Clifford Eskey ◽  
Kadir Erkmen
Keyword(s):  
Magnetic Resonance ◽  
Arteriovenous Malformations ◽  
Setup Time ◽  
Magnetic Resonance Images ◽  
Intraoperative Navigation ◽  
Mr Images ◽  
Intraoperative Angiography ◽  
Fusion Technique ◽  
Contrast Enhanced ◽  
Microsurgical Resection

Object Microsurgical resection of arteriovenous malformations (AVMs) is facilitated by real-time image guidance that demonstrates the precise size and location of the AVM nidus. Magnetic resonance images have routinely been used for intraoperative navigation, but there is no single MRI sequence that can provide all the details needed for characterization of the AVM. Additional information detailing the specific location of the feeding arteries and draining veins would be valuable during surgery, and this detail may be provided by fusing MR images and MR angiography (MRA) sequences. The current study describes the use of a technique that fuses contrast-enhanced MR images and 3D time-of-flight MR angiograms for intraoperative navigation in AVM resection. Methods All patients undergoing microsurgical resection of AVMs at the Dartmouth Cerebrovascular Surgery Program were evaluated from the surgical database. Between 2009 and 2011, 15 patients underwent surgery in which this contrast-enhanced MRI and MRA fusion technique was used, and these patient form the population of the present study. Results Image fusion was successful in all 15 cases. The additional data manipulation required to fuse the image sets was performed on the morning of surgery with minimal added setup time. The navigation system accurately identified feeding arteries and draining veins during resection in all cases. There was minimal imaging-related artifact produced by embolic materials in AVMs that had been preoperatively embolized. Complete AVM obliteration was demonstrated on intraoperative angiography in all cases. Conclusions Precise anatomical localization, as well as the ability to differentiate between arteries and veins during AVM microsurgery, is feasible with the aforementioned MRI/MRA fusion technique. The technique provides important information that is beneficial to preoperative planning, intraoperative navigation, and successful AVM resection.

scholarly journals Detection of Vestibular Schwannoma on Triple-parametric Magnetic Resonance Images Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Tzu-Hsuan Huang ◽  
Wei-Kai Lee ◽  
Chih-Chun Wu ◽  
Cheng-Chia Lee ◽  
Chia-Feng Lu ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Vestibular Schwannoma ◽  
Network Architecture ◽  
Confidence Score ◽  
Tumor Detection ◽  
Magnetic Resonance Images ◽  
Image Size ◽  
Mr Images ◽  
Residual Network ◽  
Average Precision

Abstract Purpose The first step in typical treatment of vestibular schwannoma (VS) is to localize the tumor region, which is time-consuming and subjective because it relies on repeatedly reviewing different parametric magnetic resonance (MR) images. A reliable, automatic VS detection method can streamline the process. Methods A convolutional neural network architecture, namely YOLO-v2 with a residual network as a backbone, was used to detect VS tumors from MR images. To heighten performance, T1-weighted–contrast-enhanced, T2-weighted, and T1-weighted images were combined into triple-channel images for feature learning. The triple-channel images were cropped into three sizes to serve as input images of YOLO-v2. The VS detection effectiveness levels were evaluated for two backbone residual networks that downsampled the inputs by 16 and 32. Results The results demonstrated the VS detection capability of YOLO-v2 with a residual network as a backbone model. The average precision was 0.7953 for a model with 416 × 416-pixel input images and 16 instances of downsampling, when both the thresholds of confidence score and intersection-over-union were set to 0.5. In addition, under an appropriate threshold of confidence score, a high average precision, namely 0.8171, was attained by using a model with 448 × 448-pixel input images and 16 instances of downsampling. Conclusion We demonstrated successful VS tumor detection by using a YOLO-v2 with a residual network as a backbone model on resized triple-parametric MR images. The results indicated the influence of image size, downsampling strategy, and confidence score threshold on VS tumor detection.

scholarly journals Differential Diagnosis of Uterine Leiomyoma and Uterine Sarcoma Using Magnetic Resonance Images: A Literature Review

Healthcare ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 158 ◽  
Author(s):  
Ayako Suzuki ◽  
Masato Aoki ◽  
Chiho Miyagawa ◽  
Kosuke Murakami ◽  
Hisamitsu Takaya ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Diffusion Weighted Imaging ◽  
Uterine Leiomyoma ◽  
Essential Role ◽  
Uterine Sarcoma ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Contrast Enhanced Mri

MRI plays an essential role in patients before treatment for uterine mesenchymal malignancies. Although MRI includes methods such as diffusion-weighted imaging and dynamic contrast-enhanced MRI, the differentiation between uterine myoma and sarcoma always becomes problematic. The present paper discusses important findings to ensure that sarcomas are not overlooked in magnetic resonance (MR) images, and we describe the update in the differentiation between uterine leiomyoma and sarcoma with recent reports.

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

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