Stacking architecture for collimated backlight using cylindrical lens sheet with linear light sources or edge-lit/direct-lit BLU.

Highly collimated and directional backlights are essential for realizing advanced display technologies such as autostereoscopic 3D displays. Previously reported collimated backlights, either edge-lit or direct-lit, in general still suffer unsatisfactory form factors, directivity, uniformity, or crosstalk etc. In this work, we report a simple stacking architecture for the highly collimated and uniform backlights, by combining linear light source arrays and carefully designed cylindrical lens arrays. Experiments were conducted to validate the design and simulation, using the conventional edge-lit backlight or the direct-lit mini-LED (mLED) arrays as light sources, the NiFe (stainless steel) barrier sheets, and cylindrical lens arrays fabricated by molding. Highly collimated backlights with small angular divergence of ±1.45°âˆ¼±2.61°, decent uniformity of 93-96%, and minimal larger-angle sidelobes in emission patterns were achieved with controlled divergence of the light source and optimization of lens designs. The architecture reported here provides a convenient way to convert available backlight sources into a highly collimated backlight, and the use of optically reflective barrier also helps recycle light energy and enhance the luminance. The results of this work are believed to provide a facile approach for display technologies requiring highly collimated backlights.

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis.

BACKGROUND: Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images. METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number. RESULTS: Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively. CONCLUSIONS: The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these areas to advance the field. This study was funded by the Gen. & Mrs. M.C. Peng Fellowship and registered under PROSPERO (CRD42023427776).

Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography.

BACKGROUND: The delineation of brain arteriovenous malformations (bAVMs) is crucial for subsequent treatment planning. Manual segmentation is time-consuming and labor-intensive. Applying deep learning to automatically detect and segment bAVM might help to improve clinical practice efficiency. PURPOSE: To develop an approach for detecting bAVM and segmenting its nidus on Time-of-flight magnetic resonance angiography using deep learning methods. STUDY TYPE: Retrospective. SUBJECTS: 221 bAVM patients aged 7-79 underwent radiosurgery from 2003 to 2020. They were split into 177 training, 22 validation, and 22 test data. FIELD STRENGTH/SEQUENCE: 1.5 T, Time-of-flight magnetic resonance angiography based on 3D gradient echo. ASSESSMENT: The YOLOv5 and YOLOv8 algorithms were utilized to detect bAVM lesions and the U-Net and U-Net++ models to segment the nidus from the bounding boxes. The mean average precision, F1, precision, and recall were used to assess the model performance on the bAVM detection. To evaluate the model's performance on nidus segmentation, the Dice coefficient and balanced average Hausdorff distance (rbAHD) were employed. STATISTICAL TESTS: The Student's t-test was used to test the cross-validation results (P < 0.05). The Wilcoxon rank test was applied to compare the median for the reference values and the model inference results (P < 0.05). RESULTS: The detection results demonstrated that the model with pretraining and augmentation performed optimally. The U-Net++ with random dilation mechanism resulted in higher Dice and lower rbAHD, compared to that without that mechanism, across varying dilated bounding box conditions (P < 0.05). When combining detection and segmentation, the Dice and rbAHD were statistically different from the references calculated using the detected bounding boxes (P < 0.05). For the detected lesions in the test dataset, it showed the highest Dice of 0.82 and the lowest rbAHD of 5.3%. DATA CONCLUSION: This study showed that pretraining and data augmentation improved YOLO detection performance. Properly limiting lesion ranges allows for adequate bAVM segmentation. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.

BACKGROUND: Deep learning-based segmentation algorithms usually required large or multi-institute data sets to improve the performance and ability of generalization. However, protecting patient privacy is a key concern in the multi-institutional studies when conventional centralized learning (CL) is used. PURPOSE: To explores the feasibility of a proposed lesion delineation for stereotactic radiosurgery (SRS) scheme for federated learning (FL), which can solve decentralization and privacy protection concerns. STUDY TYPE: Retrospective. SUBJECTS: 506 and 118 vestibular schwannoma patients aged 15-88 and 22-85 from two institutes, respectively; 1069 and 256 meningioma patients aged 12-91 and 23-85, respectively; 574 and 705 brain metastasis patients aged 26-92 and 28-89, respectively. FIELD STRENGTH/SEQUENCE: 1.5T, spin-echo, and gradient-echo [Correction added after first online publication on 21 August 2023. Field Strength has been changed to "1.5T" from "5T" in this sentence.]. ASSESSMENT: The proposed lesion delineation method was integrated into an FL framework, and CL models were established as the baseline. The effect of image standardization strategies was also explored. The dice coefficient was used to evaluate the segmentation between the predicted delineation and the ground truth, which was manual delineated by neurosurgeons and a neuroradiologist. STATISTICAL TESTS: The paired t-test was applied to compare the mean for the evaluated dice scores (p < 0.05). RESULTS: FL performed the comparable mean dice coefficient to CL for the testing set of Taipei Veterans General Hospital regardless of standardization and parameter; for the Taichung Veterans General Hospital data, CL significantly (p < 0.05) outperformed FL while using bi-parameter, but comparable results while using single-parameter. For the non-SRS data, FL achieved the comparable applicability to CL with mean dice 0.78 versus 0.78 (without standardization), and outperformed to the baseline models of two institutes. DATA CONCLUSION: The proposed lesion delineation successfully implemented into an FL framework. The FL models were applicable on SRS data of each participating institute, and the FL exhibited comparable mean dice coefficient to CL on non-SRS dataset. Standardization strategies would be recommended when FL is used. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 1.

Immune thrombocytopenia and risk of stroke: Evidence from a nationwide population-based cohort study.

BACKGROUND: Research investigating differences in the overall stroke risk between individuals with and without immune thrombocytopenia (ITP) is lacking. METHODS: This real-world study used the National Health Insurance Research Database (NHIRD). Risk of stroke was compared between 13,085 individuals with ITP enrolled between 1 January 2000 and 31 December 2015 and a control cohort of 52,340 individuals without ITP (1:4 ratio propensity score-matched by age, sex, index year, relevant comorbidities, and medications). Sub-distribution hazards models were used to estimate adjusted sub-distribution hazard ratio (SHR) and 95% confidence intervals (CIs), with the non-ITP group as the control group. RESULTS: Of the 65,425 participants, 13,085 had ITP, 63.3% were women, and the mean age was 52.59 years. The risk of both ischemic and hemorrhagic stroke was 1.14 times (adjusted SHR 1.14, 95% CI, 1.07-1.22) and 1.93 times (adjusted SHR 1.93, 95% CI, 1.70-2.20) higher in the ITP group than in controls. Patients with ITP in the 20- to 29-year subgroup had a higher risk of new-onset stroke (adjusted SHR, 4.06 (95% CI, 2.72-6.07), p value for interaction <0.01) than those aged 20-29 years without ITP. Individuals with severe ITP with splenectomy had a 1.79 times higher overall stroke risk than those without. CONCLUSIONS: ITP is associated with increased risk of both ischemic and hemorrhagic stroke.

Lesion delineation framework for vestibular schwannoma, meningioma and brain metastasis for gamma knife radiosurgery using stereotactic magnetic resonance images.

BACKGROUND AND OBJECTIVE: GKRS is an effective treatment for smaller intracranial tumors with a high control rate and low risk of complications. Target delineation in medical MR images is essential in the planning of GKRS and follow-up. A deep learning-based algorithm can effectively segment the targets from medical images and has been widely explored. However, state-of-the-art deep learning-based target delineation uses fixed sizes, and the isotropic voxel size may not be suitable for stereotactic MR images which use different anisotropic voxel sizes and numbers of slices according to the lesion size and location for clinical GKRS planning. This study developed an automatic deep learning-based segmentation scheme for stereotactic MR images. METHODS: We retrospectively collected stereotactic MR images from 506 patients with VS, 1,069 patients with meningioma and 574 patients with BM who had been treated using GKRS; the lesion contours and individual T1W+C and T2W MR images were extracted from the GammaPlan system. The three-dimensional patching-based training strategy and dual-pathway architecture were used to manage inconsistent FOVs and anisotropic voxel size. Furthermore, we used two-parametric MR image as training input to segment the regions with different image characteristics (e.g., cystic lesions) effectively. RESULTS: Our results for VS and BM demonstrated that the model trained using two-parametric MR images significantly outperformed the model trained using single-parametric images with median Dice coefficients (0.91, 0.05 versus 0.90, 0.06, and 0.82, 0.23 versus 0.78, 0.34, respectively), whereas predicted delineations in meningiomas using the dual-pathway model were dominated by single-parametric images (median Dice coefficients 0.83, 0.17 versus 0.84, 0.22). Finally, we combined three data sets to train the models, achieving the comparable or even higher testing median Dice (VS: 0.91, 0.07; meningioma: 0.83, 0.22; BM: 0.84, 0.23) in three diseases while using two-parametric as input. CONCLUSIONS: Our proposed deep learning-based tumor segmentation scheme was successfully applied to multiple types of intracranial tumor (VS, meningioma and BM) undergoing GKRS and for segmenting the tumor effectively from stereotactic MR image volumes for use in GKRS planning.

Using angle-selective optical film to enhance the light extraction of a thin-film encapsulated 3D reflective pixel for OLED displays.

Light extraction improvement is still an important issue for active-matrix organic light-emitting diode displays (AMOLEDs). In our previous work, a three-dimensional (3D) reflective pixel configuration embedding the OLED in the concave 3D reflector and patterned high-index filler had been proposed for significant enhancement of the pixel light extraction. In this work, influences of thin film encapsulation (TFE) on light extraction of such reflective 3D OLED pixels are considered as well by simulation studies. Unfortunately, the optical simulation reveals strong reduction of the light extraction efficiency induced by TFE layers. As such, an additional angle-selective optical film structure between the pixel and the encapsulation layers is introduced to control the angular distribution of the light coupled into the encapsulation layers and to solve TFE-induced optical losses. As a result, TFE-induced losses can be substantially reduced to retain much of light extraction efficiency. The results of this study are believed to provide useful insights and guides for developing even more efficient and power-saving AMOLEDs.

Acceleration of Magnetic Resonance Fingerprinting Reconstruction Using Denoising and Self-Attention Pyramidal Convolutional Neural Network.

Magnetic resonance fingerprinting (MRF) based on echo-planar imaging (EPI) enables whole-brain imaging to rapidly obtain T1 and T2* relaxation time maps. Reconstructing parametric maps from the MRF scanned baselines by the inner-product method is computationally expensive. We aimed to accelerate the reconstruction of parametric maps for MRF-EPI by using a deep learning model. The proposed approach uses a two-stage model that first eliminates noise and then regresses the parametric maps. Parametric maps obtained by dictionary matching were used as a reference and compared with the prediction results of the two-stage model. MRF-EPI scans were collected from 32 subjects. The signal-to-noise ratio increased significantly after the noise removal by the denoising model. For prediction with scans in the testing dataset, the mean absolute percentage errors between the standard and the final two-stage model were 3.1%, 3.2%, and 1.9% for T1, and 2.6%, 2.3%, and 2.8% for T2* in gray matter, white matter, and lesion locations, respectively. Our proposed two-stage deep learning model can effectively remove noise and accurately reconstruct MRF-EPI parametric maps, increasing the speed of reconstruction and reducing the storage space required by dictionaries.

An unsymmetrical thermally activated delayed fluorescence emitter enables orange-red electroluminescence with 31.7% external quantum efficiency.

The thermally activated delayed fluorescence (TADF) emitters based on donor-acceptor (D-A) configuration were continuously developed in the past few years, whereas an unsymmetrical TADF emitter with A-D-A' configuration has never been reported. Herein, an A-D-A' type TADF emitter of TRZ-SBA-NAI was firstly developed by simultaneously integrating 2-phenyl-1H-benzo[de]isoquinoline-1,3(2H)-dione and 2,4,6-triphenyl-1,3,5-triazine acceptors into a spirobiacridine donor core. Due to the coexistence of double charge-transfer excited states, TRZ-SBA-NAI displayed dual emission containing a dominant orange-red emission and an anti-Kasha's rule sky-blue emission shoulder in solution. As doped into the host matrix, TRZ-SBA-NAI only exhibited an orange-red emission, together with a high photoluminescence quantum yield of 87%. The linear molecular shape imparted TRZ-SBA-NAI with a high horizontal dipole ratio of 88%. As a result, the TRZ-SBA-NAI based devices achieved a record-high external quantum efficiency of 31.7% with an electroluminescence peak at 593 nm. This finding not only enriches the diversity in TADF molecular design, but also unlocks the huge potential of A-D-A' type TADF emitters for excellent device performance.

Integrating molecular rigidity and chirality into thermally activated delayed fluorescence emitters for highly efficient sky-blue and orange circularly polarized electroluminescence.

By integrating high molecular rigidity and stable chirality, two pairs of D*-A type circularly polarized thermally activated delayed fluorescence (CP-TADF) emitters with an almost absolute quasi-equatorial conformer geometry and excellent photoluminescence quantum efficiencies (PLQYs) are developed, achieving state-of-the-art electroluminescence performance among blue and orange circularly polarized organic light-emitting diodes (CP-OLEDs).

High-Efficiency Red Electroluminescence Based on a Carbene-Cu(I)-Acridine Complex.

How to develop efficient red-emitting organometallics of earth-abundant copper(I) is a formidable challenge in the field of organic light-emitting diodes (OLEDs) because Cu(I) complexes have weak spin-orbit coupling and a serious excited-state reorganization effect. Here, a red Cu(I) complex, MAC*-Cu-DPAC, was developed using a rigid 9,9-diphenyl-9,10-dihydroacridine donor ligand in a carbene-metal-amide motif. The Cu(I) complex achieved satisfactory red emission, a high photoluminescence quantum yield of up to 70%, and a sub-microsecond lifetime. Thanks to a linear geometry and the acceptor and donor ligands in a coplanar conformation, the complex exhibited a high horizontal dipole ratio of 77% in the host matrix, first demonstrated for coinage metal(I) complexes. The resulting OLEDs delivered high external quantum efficiencies of 21.1% at a maximum and 20.1% at 1000 nits, together with a red emission peak at ∼630 nm. These values represent the state-of-the-art performance for red-emitting OLEDs based on coinage metal complexes.