AIMC Journal:
EJNMMI physics

Showing 1 to 10 of 25 articles

Image optimization for low-dose 18F-FDG breast PET/MRI using deep learning: a pilot study.

EJNMMI physics Jul 15, 2026
BACKGROUND: Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer and has the potential to improve diagnostic accuracy, staging, treatment response assessment, and guid...
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MLPET: a localized neural network approach for probabilistic post-reconstruction PET image analysis using informed priors.

EJNMMI physics Jul 15, 2026
PURPOSE: PET images often make small lesions difficult to identify because of noise and system blur. We address this by developing and evaluating MLPET , a fast localized machine-learning method that approximates a computationally expensive probabili...
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Evaluation of deep learning-based reconstruction models on non-TOF BGO PET/CT: impact of acquisition times and BSREM penalization factors on lesion detectability and SNR.

EJNMMI physics Jul 12, 2026
BACKGROUND: New long field-of-view (FOV) PET scanners using bismuth germanate (BGO) detectors without time-of-flight (TOF) capability are now available. These systems incorporate deep learning-based TOF (DLb-TOF) models to compensate for the absence ...
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Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).

EJNMMI physics Jun 19, 2026
PURPOSE: High-quality 4D dynamic PET imaging is often compromised by noise, especially in low-count frames, which limits clinical utility and quantitative accuracy. This study proposes a novel spatiotemporal denoising method (SPRINTER) that integrate...
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A deep learning framework for lesion-level treatment response prediction in hodgkin lymphoma using PET/CT tensor radiomics.

EJNMMI physics Jun 15, 2026
BACKGROUND: Accurate prediction of treatment response in Hodgkin lymphoma (HL) is crucial for personalized therapy. The Tensor Radiomics (TR) paradigm advances traditional radiomics by producing and analyzing diverse feature variations, employing ten...
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A tissue-informed deep learning-based method for positron range correction in preclinical [Formula: see text]Ga PET imaging.

EJNMMI physics Jun 7, 2026
PURPOSE: Positron range (PR) limits spatial resolution and quantitative accuracy in PET imaging, particularly for high-energy positron-emitting radionuclides such as [Formula: see text]Ga. This study proposes a deep learning-based approach using 3D r...
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The impact of scan time on dynamic [Formula: see text]-FAPI-04 total-body PET parametric imaging generated by deep learning models.

EJNMMI physics Jun 3, 2026
PURPOSE: To date, some studies have employed deep learning techniques to directly generate dynamic positron emission tomography (PET) parametric images from static PET. Compared with traditional methods, this approach requires only a single PET/compu...
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Evaluation of segmentation accuracy and the improvement of time effectiveness using deep learning-based segmentation in 177Lu-DOTATATE dosimetry : The type of article: original research article.

EJNMMI physics May 24, 2026
BACKGROUND: The efficacy of deep learning-based artificial intelligence segmentation (AI-seg) in 177Lu-DOTATATE dosimetry remains underexplored. This study evaluates AI-seg's contouring accuracy, dosimetric reliability, and time efficiency. METHODS: ...
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A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.

EJNMMI physics May 22, 2026
BACKGROUND: Pediatric lymphoma patients undergo multiple 18F-FDG PET/CT examinations for staging and response assessment, raising concerns about cumulative radiation dose, particularly from the CT component. We propose SnapPET, a CT-sparing deep lear...
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Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.

EJNMMI physics May 21, 2026
BACKGROUND: Deep learning (DL)-based denoising methods have shown promise for reducing radiation dose and/or acquisition time in pediatric PET imaging. However, conventional DL approaches typically require large and diverse training datasets to achie...
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