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Positron-Emission Tomography

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Eliminating CT radiation for clinical PET examination using deep learning.

European journal of radiology
Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT significantly increases the risk of ionizing radiation exposure for patients. We propose a deep learning f...

A neural network-based algorithm for simultaneous event positioning and timestamping in monolithic scintillators.

Physics in medicine and biology
. Monolithic scintillator crystals coupled to silicon photomultiplier (SiPM) arrays are promising detectors for PET applications, offering spatial resolution around 1 mm and depth-of-interaction information. However, their timing resolution has alway...

Simulation study on 3D convolutional neural networks for time-of-flight prediction in monolithic PET detectors using digitized waveforms.

Physics in medicine and biology
We investigate the use of 3D convolutional neural networks for gamma arrival time estimation in monolithic scintillation detectors.The required data is obtained by Monte Carlo simulation in GATE v8.2, based on a 50 × 50 × 16 mmmonolithic LYSO crystal...

Deep-learning-based methods of attenuation correction for SPECT and PET.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to gene...

Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis.

Computer methods and programs in biomedicine
OBJECTIVES: Recent studies have shown that deep learning based on pre-treatment positron emission tomography (PET) or computed tomography (CT) is promising for distant metastasis (DM) and overall survival (OS) prognosis in head and neck cancer (HNC)....

Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [F]FDG PET imaging.

European radiology
OBJECTIVES: We proposed a novel deep learning-based radiomics (DLR) model to diagnose Parkinson's disease (PD) based on [F]fluorodeoxyglucose (FDG) PET images.

Validation of deep learning-based nonspecific estimates for amyloid burden quantification with longitudinal data.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To validate our previously proposed method of quantifying amyloid-beta (Aβ) load using nonspecific (NS) estimates generated with convolutional neural networks (CNNs) using [F]Florbetapir scans from longitudinal and multicenter ADNI data.

Computer-aided detection and segmentation of malignant melanoma lesions on whole-body F-FDG PET/CT using an interpretable deep learning approach.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In oncology, 18-fluorodeoxyglucose (F-FDG) positron emission tomography (PET) / computed tomography (CT) is widely used to identify and analyse metabolically-active tumours. The combination of the high sensitivity and specif...

Deep learning-based multimodal image analysis for cervical cancer detection.

Methods (San Diego, Calif.)
Cervical cancer is the fourth most common cancer in women, and its precise detection plays a critical role in disease treatment and prognosis prediction. Fluorodeoxyglucose positron emission tomography and computed tomography, i.e., FDG-PET/CT and PE...