Japanese encephalitis (JE) is a common infection caused by the Japanese encephalitis virus in Southeast Asia, which is transmitted to humans through Culex mosquitoes. Magnetic resonance imaging (MRI) is used to diagnose JE, which is often characterized by the presence of bilateral symmetric thalamic involvement. Here, we report a rare case of JE characterized by leptomeningeal enhancement without thalamic involvement. This leptomeningeal enhancement disappeared with the treatment; however, new non-specific multifocal and bilateral high signal intensities in the cerebral white matter were found on follow-up MRI.

Japanese encephalitis (JE) is a common infection caused by the Japanese encephalitis virus in Southeast Asia, which is transmitted to humans through Culex mosquitoes. Magnetic resonance imaging (MRI) is used to diagnose JE, which is often characterized by the presence of bilateral symmetric thalamic involvement. Here, we report a rare case of JE characterized by leptomeningeal enhancement without thalamic involvement. This leptomeningeal enhancement disappeared with the treatment; however, new non-specific multifocal and bilateral high signal intensities in the cerebral white matter were found on follow-up MRI.

Purpose: This study aims to enhance the performance of deep learning models for segmenting thin anatomical structures in medical images by introducing a label-preserving data-augmentation strategy. Materials and Methods: We developed a data-augmentation technique that applies geometric transformations and their inverses sequentially to input images while preserving the corresponding labels. This method was evaluated on inner ear magnetic resonance images for the automatic segmentation of semicircular canals characterized by thin and circular structures. The dataset included both internal and external samples. For the internal dataset, 70 subjects were used for model training and eight subjects for internal validation. Images were acquired using a 3 tesla magnetic resonance imaging scanner with a three-dimensional high-resolution T2 sequence, and ground-truth segmentations were manually annotated by an experienced radiologist. For external validation, four subjects from a public dataset (Vestibular-Schwannoma-SEG dataset, part of The Cancer Imaging Archive) with high-resolution T2 images for inner ear analysis were used. We performed quantitative evaluations using metrics such as Dice, intersection over union (IoU), 95% Hausdorff distance (HD), and average surface distance (ASD). A qualitative visual assessment was also performed. Results: The proposed model exhibited improved performance in semicircular canal segmentation in both quantitative and qualitative evaluations. Metrics such as Dice, IoU, 95% HD, and ASD indicated better performance than conventional methods. Conclusion The proposed label-preserving data augmentation method improves the segmentation of thin anatomical structures in medical images and offers a robust and efficient solution for enhancing deep learning models in medical imaging.

Purpose: This study aims to enhance the performance of deep learning models for segmenting thin anatomical structures in medical images by introducing a label-preserving data-augmentation strategy. Materials and Methods: We developed a data-augmentation technique that applies geometric transformations and their inverses sequentially to input images while preserving the corresponding labels. This method was evaluated on inner ear magnetic resonance images for the automatic segmentation of semicircular canals characterized by thin and circular structures. The dataset included both internal and external samples. For the internal dataset, 70 subjects were used for model training and eight subjects for internal validation. Images were acquired using a 3 tesla magnetic resonance imaging scanner with a three-dimensional high-resolution T2 sequence, and ground-truth segmentations were manually annotated by an experienced radiologist. For external validation, four subjects from a public dataset (Vestibular-Schwannoma-SEG dataset, part of The Cancer Imaging Archive) with high-resolution T2 images for inner ear analysis were used. We performed quantitative evaluations using metrics such as Dice, intersection over union (IoU), 95% Hausdorff distance (HD), and average surface distance (ASD). A qualitative visual assessment was also performed. Results: The proposed model exhibited improved performance in semicircular canal segmentation in both quantitative and qualitative evaluations. Metrics such as Dice, IoU, 95% HD, and ASD indicated better performance than conventional methods. Conclusion The proposed label-preserving data augmentation method improves the segmentation of thin anatomical structures in medical images and offers a robust and efficient solution for enhancing deep learning models in medical imaging.

Purpose: This study aims to enhance the performance of deep learning models for segmenting thin anatomical structures in medical images by introducing a label-preserving data-augmentation strategy. Materials and Methods: We developed a data-augmentation technique that applies geometric transformations and their inverses sequentially to input images while preserving the corresponding labels. This method was evaluated on inner ear magnetic resonance images for the automatic segmentation of semicircular canals characterized by thin and circular structures. The dataset included both internal and external samples. For the internal dataset, 70 subjects were used for model training and eight subjects for internal validation. Images were acquired using a 3 tesla magnetic resonance imaging scanner with a three-dimensional high-resolution T2 sequence, and ground-truth segmentations were manually annotated by an experienced radiologist. For external validation, four subjects from a public dataset (Vestibular-Schwannoma-SEG dataset, part of The Cancer Imaging Archive) with high-resolution T2 images for inner ear analysis were used. We performed quantitative evaluations using metrics such as Dice, intersection over union (IoU), 95% Hausdorff distance (HD), and average surface distance (ASD). A qualitative visual assessment was also performed. Results: The proposed model exhibited improved performance in semicircular canal segmentation in both quantitative and qualitative evaluations. Metrics such as Dice, IoU, 95% HD, and ASD indicated better performance than conventional methods. Conclusion The proposed label-preserving data augmentation method improves the segmentation of thin anatomical structures in medical images and offers a robust and efficient solution for enhancing deep learning models in medical imaging.

Purpose: This study aims to enhance the performance of deep learning models for segmenting thin anatomical structures in medical images by introducing a label-preserving data-augmentation strategy. Materials and Methods: We developed a data-augmentation technique that applies geometric transformations and their inverses sequentially to input images while preserving the corresponding labels. This method was evaluated on inner ear magnetic resonance images for the automatic segmentation of semicircular canals characterized by thin and circular structures. The dataset included both internal and external samples. For the internal dataset, 70 subjects were used for model training and eight subjects for internal validation. Images were acquired using a 3 tesla magnetic resonance imaging scanner with a three-dimensional high-resolution T2 sequence, and ground-truth segmentations were manually annotated by an experienced radiologist. For external validation, four subjects from a public dataset (Vestibular-Schwannoma-SEG dataset, part of The Cancer Imaging Archive) with high-resolution T2 images for inner ear analysis were used. We performed quantitative evaluations using metrics such as Dice, intersection over union (IoU), 95% Hausdorff distance (HD), and average surface distance (ASD). A qualitative visual assessment was also performed. Results: The proposed model exhibited improved performance in semicircular canal segmentation in both quantitative and qualitative evaluations. Metrics such as Dice, IoU, 95% HD, and ASD indicated better performance than conventional methods. Conclusion The proposed label-preserving data augmentation method improves the segmentation of thin anatomical structures in medical images and offers a robust and efficient solution for enhancing deep learning models in medical imaging.

BACKGROUND: The purpose of this study was to qualitatively and quantitatively evaluate the effects of a metal artifact reduction for orthopedic implants (O-MAR) for brain computed tomographic angiography (CTA) in patients with aneurysm clips and coils. METHODS: The study included 36 consecutive patients with 47 intracranial metallic implants (42 aneurysm clips, 5 coils) who underwent brain CTA. The computed tomographic images with and without the O-MAR were independently reviewed both quantitatively and qualitatively by two reviewers. For quantitative analysis, image noises near the metallic implants of non-O-MAR and O-MAR images were compared. For qualitative analysis, image quality improvement and the presence of new streak artifacts were assessed. RESULTS: Image noise was significantly reduced near metallic implants (P < 0.01). Improvement of implant-induced streak artifacts was observed in eight objects (17.0%). However, streak artifacts were aggravated in 11 objects (23.4%), and adjacent vessel depiction was worsened in eight objects (17.0%). In addition, new O-MAR-related streak artifacts were observed in 32 objects (68.1%). New streak artifacts were more prevalent in cases with overlapping metallic implants on the same axial plane than in those without (P = 0.018). Qualitative assessment revealed that the overall image quality was not significantly improved in O-MAR images. CONCLUSION: In conclusion, the use of the O-MAR in patients with metallic implants significantly reduces image noise. However, the degree of the streak artifacts and surrounding vessel depiction were not significantly improved on O-MAR images.
Aneurysm ; Angiography* ; Artifacts* ; Brain* ; Humans ; Noise ; Orthopedics* ; Quality Improvement

OBJECTIVE: Poly(lactide-co-glycolide) (PLGA) nanoparticles are promising materials for the development of new drug-releasing systems. The purpose of this study was to evaluate the in vivo retention time of materials loaded in nanoparticles as compared with that of the material alone by in vivo imaging in nude mice. MATERIALS AND METHODS: Mice (n = 20) were injected with 0.1 mL fluorescent material 1,1′-dioctadecyl-3,3,3′,3′ tetramethylindotricarbocyanine iodide (DiR)-loaded PLGA nanoparticles (200 nm) into the right paraspinal muscle, and the same volume of pure DiR solution was injected into the left paraspinal muscle. Fluorescence images were obtained using an in vivo optical imaging system. Fluorescent images were taken 1 day after the injection, and seven more images were taken at 1-week intervals. Image analysis was done with ImageJ program, and one region of interest was chosen manually, which corresponded to the highest signal-intensity area of fluorescence signal intensity. RESULTS: After 7 weeks, 12 mice showed a right-sided dominant signal, representing the DiR loaded PLGA nanoparticles; 5 mice showed a left-side dominant signal, representing the free DiR solution; and 3 mice showed no signal at all beginning 1 day after the injection. During the 7-week period, the mean signal intensities of the free DiR solution and DiR-loaded PLGA nanoparticles diverged gradually. On day 1, the mean signal intensity of free DiR solution was significantly higher than that of DiR-loaded PLGA (p < 0.001). Finally, by week 7, DiR-loaded PLGA express significantly high signal intensity compared with free DiR solution (p = 0.031). CONCLUSION: The results of the current study suggested that therapeutic agents bound to PLGA nanoparticles may exhibit prolonged retention times.
Animals ; Fluorescence ; Mice* ; Mice, Nude ; Nanoparticles* ; Optical Imaging ; Paraspinal Muscles ; Polyglactin 910*

OBJECTIVE: To evaluate texture data of the torn supraspinatus tendon (SST) on preoperative T2-weighted magnetic resonance arthrography (MRA) using the gray-level co-occurrence matrix (GLCM) for prediction of post-operative tendon state. MATERIALS AND METHODS: Fifty patients who underwent arthroscopic rotator cuff repair for full-thickness tears of the SST were included in this retrospective study. Based on 1-year follow-up, magnetic resonance imaging showed that 30 patients had intact SSTs, and 20 had rotator cuff retears. Using GLCM, two radiologists measured independantly the highest signal intensity area of the distal end of the torn SST on preoperative T2-weighted MRA, which were compared between two groups.The relationships with other well-known prognostic factors, including age, tear size (anteroposterior dimension), retraction size (mediolateral tear length), grade of fatty degeneration of the SST and infraspinatus tendon, and arthroscopic fixation technique (single or double row), also were evaluated. RESULTS: Of all the GLCM features, the retear group showed significantly higher entropy (p < 0.001 and p = 0.001), variance (p = 0.030 and 0.011), and contrast (p = 0.033 and 0.012), but lower angular second moment (p < 0.001 and p = 0.002) and inverse difference moment (p = 0.027 and 0.027), as well as larger tear size (p = 0.001) and retraction size (p = 0.002) than the intact group. Retraction size (odds ratio [OR] = 3.053) and entropy (OR = 17.095) were significant predictors. CONCLUSION: Texture analysis of torn SSTs on preoperative T2-weighted MRA using the GLCM may be helpful to predict postoperative tendon state after rotator cuff repair.
Arthrography* ; Data Interpretation, Statistical ; Entropy ; Follow-Up Studies ; Humans ; Magnetic Resonance Imaging ; Retrospective Studies ; Rotator Cuff* ; Shoulder Joint ; Tears ; Tendons*