Background Amyloid‐beta and brain atrophy are hallmarks for Alzheimer's Disease that can be targeted with positron emission tomography (PET) MRI, respectively. MRI is cheaper, less‐invasive, more available than PET. There a known relationship between amyloid‐beta atrophy, meaning PET images could inferred from MRI. Purpose To build an image translation model using Conditional Generative Adversarial Network able to synthesize structural Study Type Retrospective. Population Eight hundred...

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure signal error. large databases, such as the Human Connectome Projects, over half of recordings may be unusable. As a result, direct removal low frequency variations from blood oxygen level-dependent (BOLD) time series is possible. This study proposes deep learning-based method for reconstruction variation...

Brain aging is a regional phenomenon, facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized estimates granular insights into processes. This essential to understand differences in trajectories healthy versus diseased subjects. In this work, deep learning-based multitask model proposed for voxel-level from T1-weighted magnetic resonance images. The outperforms models existing...

In this work, a denoising Cycle-GAN (Cycle Consistent Generative Adversarial Network) is implemented to yield high-field, high resolution, signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images from simulated low-field, low SNR MRI images. Resampling and additive Rician noise were used simulate low-field MRI. Images utilized train Denoising Autoencoder (DAE) Cycle-GAN, with paired unpaired cases. Both networks evaluated using SSIM PSNR image quality metrics. This work...

This paper describes our participation in the Clickbait challenge at SemEval 2023. In this work, we address classification task using transformers models an ensemble configuration. We tackle Spoiler Generation a two-level strategy of trained for extractive QA, and selecting best K candidates multi-part spoilers. test partitions, approaches obtained accuracy 0.716 BLEU-4 score 0.439 spoiler generation.

Among different physiological sources of noise in blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI), low-frequency fluctuation arterial carbon dioxide (CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) constitutes the strongest modulator BOLD signal. In this paper, performance respiration variation (RV) and respiratory volume per time (RVT) identifying abnormal but prominent patterns are studied. We...

Abstract Background Amyloid‐beta and structural brain atrophy are known to be hallmarks of Alzheimer’s Disease (AD), can quantified with Positron Emission Tomography (PET) Magnetic Resonance (MRI), respectively. PET scans use radiotracers that binds amyloid‐beta molecules, whereas MRI measures changes in morphology. difficult perform due cost (∼$5000/scan), invasiveness, ionizing radiation exposure, making them inaccessible for screening early‐onset AD. Conversely, is a cheaper (∼$500/scan),...

Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool extract variation (RV) waveforms directly from data without the need for peripheral recording devices. Goal(s): Investigate hypothesis that head motion parameters contain valuable information regarding patter, which can help machine learning algorithms estimate RV waveform. Approach: This study proposes CNN model reconstruction using and BOLD...

Motivation: Alzheimer's Disease hallmarks include amyloid-beta deposits and brain atrophy, detectable via PET MRI scans, respectively. is expensive, invasive exposes patients to ionizing radiation. cheaper, non-invasive, free from radiation but limited measuring atrophy. Goal: To develop an 3D image translation model that synthesizes images T1-weighted MRI, exploiting the known relationship between Approach: The was trained on 616 PET/MRI pairs validated with 264 pairs. Results: synthesized...

In this work, an image translation model is implemented to produce synthetic amyloid-beta PET images from structural MRI that are quantitatively accurate. Image pairs of and were used train the model. We found could be produced with a high degree similarity truth in terms shape, contrast overall SSIM PSNR. This work demonstrates performing quantitative feasible enable access information only MRI.

In many fMRI studies, respiratory signals are unavailable or do not have acceptable quality. Consequently, the direct removal of low-frequency variations from BOLD is possible. This study proposes a one-dimensional CNN model for reconstruction two measures including RV and RVT. Results show that can capture informative features reconstruct accurate RVT timeseries. It expected application proposed method will lower cost reduce complexity, decrease burden on participants because they be...

In this work, a denoising Cycle-GAN is implemented to yield high-field, high resolution, signal-to-noise ratio MRI images from simulated low-field, low images. Resampling and additive Rician noise were used simulate low-field MRI. Images utilized train DAE Cycle-GAN, with paired unpaired cases, respectively. Both networks evaluated using SSIM PSNR image quality metrics. This work demonstrates the use of advanced machine learning improve that can outperform classical autoencoders does not...

The precision of contouring target structures and organs-at-risk (OAR) in radiotherapy planning is crucial for ensuring treatment efficacy patient safety. Recent advancements deep learning (DL) have significantly improved OAR performance, yet the reliability these models, especially presence out-of-distribution (OOD) scenarios, remains a concern clinical settings. This application study explores integration epistemic uncertainty estimation within workflow to enable OOD detection clinically...

Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool extract variation (RV) waveforms directly from data without the need for peripheral recording devices. Goal(s): Investigate hypothesis that head motion parameters contain valuable information regarding patter, which can help machine learning algorithms estimate RV waveform. Approach: This study proposes CNN model reconstruction using and BOLD...

Motivation: Alzheimer&amp;rsquo;s Disease hallmarks include amyloid-beta deposits and brain atrophy, detectable via PET MRI scans, respectively. is expensive, invasive exposes patients to ionizing radiation. cheaper, non-invasive, free from radiation but limited measuring atrophy. Goal(s): To develop an 3D image translation model that synthesizes images T1-weighted MRI, exploiting the known relationship between Approach: The was trained on 616 PET/MRI pairs validated with 264 pairs. Results:...

Abstract Purpose External physiological monitoring is the primary approach to measure and remove effects of low‐frequency respiratory variation from BOLD‐fMRI signals. However, acquisition clean external data during fMRI not always possible, so recent research has proposed using machine learning directly estimate (RV), potentially obviating need for monitoring. In this study, we propose an extended method reconstructing RV waveforms resting state in healthy adult participants with inclusion...

In many fMRI studies, respiratory signals are unavailable or do not have acceptable quality. Consequently, the direct removal of low-frequency variations from BOLD is possible. This study proposes a one-dimensional CNN model for reconstruction two measures, RV and RVT. Results show that can capture informative features resting reconstruct realistic RVT timeseries. It expected application proposed method will lower cost reduce complexity, decrease burden on participants as they be required to...

In this work, the denoising autoencoder is applied to simulated low field, resolution, signal-to-noise images and used recover high paired image. Different types of noise, gaussian chi-squared, added in simulation. We found that worked slightly better for normally disturbed but not all cases. a linear trend between model performance with RMSE standard deviation noise. This work demonstrates use simple robust improve field MRI.

The influence of genetic predisposition on changes in brain morphology during aging remains largely unknown. This study explores the effects three key regions: total volume (TBV), lateral ventricular (LVV), and hippocampal (THV). age gap estimate (BrainAGE) biomarker is used as an input to a genome-wide association determine which single nucleotide polymorphisms (SNPs) genes are associated with accelerated aging. Six independent significant SNPs were found contribute morphological changes:...

In this work, an image translation model is implemented to produce synthetic amyloid-beta PET images from structural MRI that are quantitatively accurate. Image pairs of and were used train the model. We found could be produced with a high degree similarity truth in terms shape, contrast overall SSIM PSNR. This work demonstrates performing quantitative feasible enable access information only MRI.

Brain aging is a regional phenomenon, facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized estimates granular insights into processes. This essential to understand differences in trajectories healthy versus diseased subjects. In this work, deep learning-based multitask model proposed for voxel-level from T1-weighted magnetic resonance images. The outperforms models existing...