Data scarcity and data imbalance are two major challenges in training deep learning models on medical images, such as brain tumor MRI data. The recent advancements in generative artificial intelligence have opened new possibilities for synthetically generating MRI data, including brain tumor MRI scans. This approach can be a potential solution to mitigate the data scarcity problem and enhance training data availability. This work focused on adapting the 2D latent diffusion models to generate 3D multi-contrast brain tumor MRI data with a tumor mask as the condition. The framework comprises two components: a 3D autoencoder model for perceptual compression and a conditional 3D Diffusion Probabilistic Model (DPM) for generating high-quality and diverse multi-contrast brain tumor MRI samples, guided by a conditional tumor mask. Unlike existing works that focused on generating either 2D multi-contrast or 3D single-contrast MRI samples, our models generate multi-contrast 3D MRI samples. We also integrated a conditional module within the UNet backbone of the DPM to capture the semantic class-dependent data distribution driven by the provided tumor mask to generate MRI brain tumor samples based on a specific brain tumor mask. We trained our models using two brain tumor datasets: The Cancer Genome Atlas (TCGA) public dataset and an internal dataset from the University of Texas Southwestern Medical Center (UTSW). The models were able to generate high-quality 3D multi-contrast brain tumor MRI samples with the tumor location aligned by the input condition mask. The quality of the generated images was evaluated using the Fréchet Inception Distance (FID) score. This work has the potential to mitigate the scarcity of brain tumor data and improve the performance of deep learning models involving brain tumor MRI data.
Transcranial infrared laser stimulation (TILS) has shown effectiveness in improving human cognition and was investigated using broadband near-infrared spectroscopy (bb-NIRS) in our previous study, but the effect of laser heating on the actual bb-NIRS measurements was not investigated. To address this potential confounding factor, 11 human participants were studied. First, we measured time-dependent temperature increases on forehead skin using clinical-grade thermometers following the TILS experimental protocol used in our previous study. Second, a subject-averaged, time-dependent temperature alteration curve was obtained, based on which a heat generator was controlled to induce the same temperature increase at the same forehead location that TILS was delivered on each participant. Third, the same bb-NIRS system was employed to monitor hemodynamic and metabolic changes of forehead tissue near the thermal stimulation site before, during, and after the heat stimulation. The results showed that cytochrome-c-oxidase of forehead tissue was not significantly modified by this heat stimulation. Significant differences in oxyhemoglobin, total hemoglobin, and differential hemoglobin concentrations were observed during the heat stimulation period versus the laser stimulation. The study demonstrated a transient hemodynamic effect of heat-based stimulation distinct to that of TILS. We concluded that the observed effects of TILS on cerebral hemodynamics and metabolism are not induced by heating the skin.
Transcranial infrared laser stimulation (TILS) is a non-destructive and non-thermal photobiomodulation therapy or process on the human brain; TILS uses infrared light from lasers or LEDs and has gained increased recognition for its beneficial effects on a variety of neurological and psychological conditions. While the mechanism of TILS has been assumed to stem from cytochrome-c-oxidase (CCO), which is the last enzyme in the electron transportation chain and is the primary photoacceptor, no literature is found to report electrophysiological response to TILS. In this study, a 64-channel electroencephalography (EEG) system was employed to monitor electrophysiological activities from 15 healthy human participants before, during and after TILS. A placebo experimental protocol was also applied for rigorous comparison. After recording a 3-minute baseline, we applied a 1064-nm laser with a power of 3.5W on the right forehead of each human participant for 8 minutes, followed by a 5-minute recovery period. In 64-channel EEG data analysis, we utilized several methods (root mean square, principal component analysis followed by independent component analysis, permutation conditional mutual information, and time-frequency wavelet analysis) to reveal differences in electrophysiological response to TILS between the stimulated versus placebo group. The analyzed results were further investigated using general linear model and paired t-test to reveal statistically meaningful responses induced by TILS. Moreover, this study will provide spatial mapping of human electrophysiological and possibly neural network responses to TILS for first time, indicating the potential of EEG to be an effective method for monitoring neurological improvement induced by TILS.
KEYWORDS: Oxygen, Infrared lasers, Hemodynamics, Near infrared spectroscopy, In vivo imaging, Infrared radiation, Light emitting diodes, Nondestructive evaluation, Brain, Electron transport
Transcranial infrared laser stimulation (TILS) uses infrared light (lasers or LEDs) for nondestructive and non-thermal photobiomodulation on the human brain. Although TILS has shown its beneficial effects to a variety of neurological and psychological conditions, its physiological mechanism remains unknown. Cytochrome-c-oxidase (CCO), the last enzyme in the electron transportation chain, is proposed to be the primary photoacceptor of this infrared laser. In this study, we wish to validate this proposed mechanism. We applied 8 minutes in vivo TILS on the right forehead of 11 human participants with a 1064-nm laser. Broad-band near infrared spectroscopy (bb-NIRS) from 740-900nm was also employed near the TILS site to monitor hemodynamic and metabolic responses during the stimulation and 5-minute recovery period. For rigorous comparison, we also performed similar 8-min bb-NIR measurements under placebo conditions. A multi-linear regression analysis based on the modified Beer-Lambert law was performed to estimate concentration changes of oxy-hemoglobin (Δ[HbO]), deoxy-hemoglobin (Δ[Hb]), and cytochrome-c-oxidase (Δ[CCO]). We found that TILS induced significant increases of [CCO], [HbO] and a decrease of [Hb] with dose-dependent manner as compared with placebo treatments. Furthermore, strong linear relationships or interplays between [CCO] versus [HbO] and [CCO] versus [Hb] induced by TILS were observed in vivo for the first time. These relationships have clearly revealed close coupling/relationship between the hemodynamic oxygen supply and blood volume versus up-regulation of CCO induced by photobiomodulation. Our results demonstrate the tremendous potential of bb-NIRS as a non-invasive in vivo means to study photobiomodulation mechanisms and perform treatment evaluations of TILS.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.