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- ItemCompact Monocular Video-Ophthalmoscope to Measure Retinal Reflectance Changes to Flicker Light Stimuli(WILEY-V C H VERLAG GMBH, 2025-03-11) Kolář, Radim; Vičar, Tomáš; Chmelík, Jiří; Jakubíček, Roman; Odstrčilík, Jan; Nohel, Michal; Skorkovská, Karolína; Tornow, Ralf-PeterThis paper describes a compact video-ophthalmoscope (VO) designed for capturing retinal video sequences of the optic nerve head (ONH) under flicker light stimulation. The device uses an OLED display and a fiber optic-coupled LED light source, enabling high-frame-rate video at low illumination intensity (12 mu W/cm2). Retinal responses were recorded in 10 healthy subjects during flicker light exposure with a pupil irradiance of 2 mu W/cm2. Following 20 s of stimulation, all subjects displayed changes in retinal reflectance and pulsation attenuation, linked to blood flow and volume variations. These findings suggest that increased blood volume leads to decreased retinal reflectance. Temporal analysis confirmed the ability to capture flicker-induced retinal reflectance changes, indicating its potential for spatial and temporal analysis. Overall, this device offers a portable approach for investigating dynamic retinal responses to light stimuli, which can aid the diagnosis of retinal diseases like diabetic retinopathy, glaucoma, or neurodegenerative diseases affecting retinal blood circulation.
- ItemDeep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors Using Segmentation and Feature Extraction(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2025-05-09) Hamza, Ali; Mézl, MartinBreast cancer remains a significant global health challenge, requiring accurate and effective diagnostic methods for timely treatment. Ultrasound imaging is a valuable diagnostic tool for breast cancer because of its affordability, accessibility, and non-ionizing radiation properties. This study proposes a classification method for breast ultrasound images that integrates segmentation and feature extraction. Initially, ultrasound images are pre-processed to enhance quality and reduce noise, followed by segmentation using the U-Net++ architecture. Feature extraction is then performed using MobileNetV2, and these features are used to train and validate classification models to differentiate between malignant and benign breast masses. The model's performance is assessed using accuracy, precision, recall, Mean IoU, and Dice Score metrics. The U-Net++ model achieved superior segmentation performance with a Dice Score of 0.911 and a Mean IoU of 0.838, outperforming related methods such as U-Net (0.888 Dice, 0.79 IoU) and Efficient U-Net (0.904 Dice, 0.80 IoU). In the classification task, MobileNetV2 when paired with the ANN classifier, produced the highest test accuracy at 96.58%, with a precision of 97% and recall of 96%. Our approach demonstrates superior performance compared to other models, such as RMTL-Net, which achieved 91.02% accuracy, and hybrid CAD models with 94% accuracy. This highlights the benefits of combining advanced segmentation and feature extraction techniques, with MobileNetV2 proving to be the better model, offering superior accuracy and robustness in classification tasks. This approach has the potential to support promise for supporting radiologists, enhance diagnostic accuracy, and ultimately improve outcomes for breast cancer patients. In the future, we will use comprehensive datasets to validate our methodology.
- ItemShadows of very high-frequency oscillations can be detected in lower frequency bands of routine stereoelectroencephalography(NATURE PORTFOLIO, 2024-01-19) Vašíčková, Zuzana; Klimeš, Petr; Cimbálník, Jan; Trávníček, Vojtěch; Pail, Martin; Halámek, Josef; Jurák, Pavel; Brázdil, MilanVery high-frequency oscillations (VHFOs, > 500 Hz) are more specific in localizing the epileptogenic zone (EZ) than high-frequency oscillations (HFOs, < 500 Hz). Unfortunately, VHFOs are not visible in standard clinical stereo-EEG (SEEG) recordings with sampling rates of 1 kHz or lower. Here we show that "shadows" of VHFOs can be found in frequencies below 500 Hz and can help us to identify SEEG channels with a higher probability of increased VHFO rates. Subsequent analysis of Logistic regression models on 141 SEEG channels from thirteen patients shows that VHFO "shadows" provide additional information to gold standard HFO analysis and can potentially help in precise EZ delineation in standard clinical recordings.
- ItemDeep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time(AME PUBLISHING COMPANY, 2024-04-11) Jurka, Martin; Macová, Iva; Wagnerová, Monika; Čapoun, Otakar; Jakubíček, Roman; Ouředníček, Petr; Lambert, Lukáš; Burgetová, AndreaBackground: Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics. Methods: In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS). The images were reconstructed with and without DLR in super-resolution mode. The image quality was rated in six domains. Signal-to-noise ratio (SNR), and image sharpness were measured. Results: The mean acquisition time was 281 +/- 23 s for the standard and 140 +/- 12 s for the short acquisition (P<0.0001). DLR images had higher sharpness compared to non-DLR (P<0.001). Short and short-DLR had lower SNR than the standard and standard-DLR (P<0.001). The perceived image quality of short-DLR was rated better in all categories compared to the standard sequence (P<0.001 to P=0.004). All domains of subjective evaluation were correlated with measured image sharpness (P<0.001). Conclusions: T2-TSE acquisition of the prostate accelerated using CS combined with DLR reconstruction provides images with increased sharpness that have a superior quality as perceived by human readers compared to standard T2-TSE. The perceived image quality is correlated with measured image contrast.
- ItemMethod for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity(PUBLIC LIBRARY SCIENCE, 2024-04-25) Balzekas, Irena; Trzasko, Joshua; Yu, Grace; Richner, Thomas; Mívalt, Filip; Sladký, Vladimír; Gregg, Nicholas M.; Van Gompel, Jamie J.; Miller, Kai J.; Croarkin, Paul E.; Křemen, Václav; Worrell, GregoryNumerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals.Trial Registration: NCT03946618. Circadian and multiday cycles are an important part of many long-term neuro-behavioral phenomena such as pathological inter-ictal epileptiform discharges (IEDs) and seizures in epilepsy. Long-term, ambulatory, neuro-behavioral monitoring in human patients involves complex recording systems that can be subject to intermittent, irregular data loss and storage limitations, resulting in sparse, irregularly sampled data. Cycle identification in sparse data or irregular data using traditional frequency decomposition techniques typically requires interpolation to create a regular timeseries. Using unique, long-term recordings of pathological brain activity in people with epilepsy implanted with an investigational device, we developed a method to identify cycles in sparse, irregular neuro-behavioral data without interpolation. We anticipate this approach will enable retrospective cycle identification in sparse neuro-behavioral timeseries and support prospective sparse sampling in monitoring systems to enable long-term monitoring of patients and to extend storage capacity in a variety of ambulatory monitoring applications.