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The emergence of foundation models, including language and vision models, has reshaped AI's landscape, offering capabilities across various applications. Deploying and fine-tuning these large models, like GPT-3 and BERT, presents…
Learning harmful shortcuts such as spurious correlations and biases prevents deep neural networks from learning the meaningful and useful representations, thus jeopardizing the generalizability and interpretability of the learned…
Organ-specific, targeted Field-of-View (FoV) Positron Emission Tomography (PET)/Magnetic Resonance Imaging (MRI) inserts are viable solutions for a number of imaging tasks where whole-body PET/MRI systems lack the necessary sensitivity and…
In this work, we present modeling and imaging performance of a dual panel limited-angle TOF-PET system for intraoperative surgical applications using GATE monte carlo toolkit. Several detector parameters such as detector pixel dimensions,…
We present first results from a Positron Emission Tomography (PET) scanner demonstrator based on Resistive Plate Chambers and specialized for brain imaging. The device features a 30 cm wide cubic field-of-view and each detector comprises 40…
In the present work we describe the design, construction, and testing of the optical prototype developed for the BOLDPET project, with the objective of creating a PET detection module with high spatial and time resolution. The BOLDPET…
For long time non-pure beta emitters are avoided from PET imaging due to extra dose and increase in background from Compton scattering. But advent of high-resolution Compton camera system opens up new domain of imaging. Various non-pure…
In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT). VPIT is the first method that uses voxel pseudo images for 3D SOT. The input point cloud is structured by…
Simulations of optical quantum systems are essential for the development of quantum technologies. However, these simulations are often computationally intensive, especially when repeated evaluations are required for data fitting, parameter…
Positron Emission Tomography (PET) is a medical imaging modality that utilizes positron-emitting isotopes, such as Ga-68 and F-18, for many diagnostic purposes. The positron annihilates with an electron from the surrounding area, creating…
Objective: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes…
Position Emission Tomography (PET) is an advanced clinical diagnostic imaging technique for nuclear medicine. Small animal PET is increasingly uesd for studying the animal model of disease, new drugs and new therapies. A prototype of…
In this study we address the feasibility and main properties of a positron emission tomograph (PET) based on RPCs. The concept, making use of the converter-plate principle, takes advantage of the intrinsic layered structure of RPCs and its…
To address the challenges of sensor fusion and safety risk prediction, contemporary closed-loop autonomous driving neural networks leveraging imitation learning typically require a substantial volume of parameters and computational…
Positron emission tomography (PET) is a critical tool for diagnosing tumors and neurological disorders but poses radiation risks to patients, particularly to sensitive populations. While reducing injected radiation dose mitigates this risk,…
Computationally expensive Radiative Transfer Models (RTMs) are widely used} to realistically reproduce the light interaction with the Earth surface and atmosphere. Because these models take long processing time, the common practice is to…
The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PET) is important to oncologic imaging. However, existing deep learning methods for PET/CT…
Running AI models on smart edge devices can unlock versatile user experiences, but presents challenges due to limited compute and the need to handle multiple tasks simultaneously. This requires a vision encoder with small size but powerful…
This work presents AEQUAM (Area Efficient QUAntum eMulation), a toolchain that enables faster and more accessible quantum circuit verification. It consists of a compiler that translates OpenQASM 2.0 into RISC-like instructions, Cython…
Rotational symmetry is a defining feature of many tomography systems, including computed tomography (CT) and emission computed tomography (ECT), where detectors are arranged in a circular or periodically rotating configuration. This study…