Advancing Positron Emission Tomography Image Quantification: Artificial Intelligence-Driven Methods, Clinical Challenges, and Emerging Opportunities in Long-Axial Field-of-View Positron Emission Tomography/Computed Tomography Imaging

Fereshteh Yousefirizi; Movindu Dassanayake; Alejandro Lopez; Andrew Reader; Gary J. R. Cook; Clemens Mingels; Arman Rahmim; Robert Seifert; Ian Alberts

doi:10.1016/j.cpet.2025.07.010

arXiv · 2509.03751

Advancing Positron Emission Tomography Image Quantification: Artificial Intelligence-Driven Methods, Clinical Challenges, and Emerging Opportunities in Long-Axial Field-of-View Positron Emission Tomography/Computed Tomography Imaging

Fereshteh Yousefirizi·Movindu Dassanayake·Alejandro Lopez·Andrew Reader·Gary J. R. Cook·Clemens Mingels·Arman Rahmim·Robert Seifert·Ian Alberts

View original source ↗Read source document ↗Export metadata ↓

Abstract

MTV is increasingly recognized as an accurate estimate of disease burden, which has prognostic value, but its implementation has been hindered by the time-consuming need for manual segmentation of images. Automated quantitation using AI-driven approaches is promising. AI-driven automated quantification significantly reduces labor-intensive manual segmentation, improving consistency, reproducibility, and feasibility for routine clinical practice. AI-enhanced radiomics provides comprehensive characterization of tumor biology, capturing intratumoral and intertumoral heterogeneity beyond what conventional volumetric metrics alone offer, supporting improved patient stratification and therapy planning. AI-driven segmentation of normal organs improves radioligand therapy planning by enabling accurate dose predictions and comprehensive organ-based radiomics analysis, further refining personalized patient management.

Explore related subjects

physics.med-ph

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Fereshteh Yousefirizi, Movindu Dassanayake, Alejandro Lopez, Andrew Reader, Gary J. R. Cook, Clemens Mingels, Arman Rahmim, Robert Seifert, Ian Alberts. 2025-09-03. Advancing Positron Emission Tomography Image Quantification: Artificial Intelligence-Driven Methods, Clinical Challenges, and Emerging Opportunities in Long-Axial Field-of-View Positron Emission Tomography/Computed Tomography Imaging. https://doi.org/10.1016/j.cpet.2025.07.010

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Voxel-Matching NORDIC: Non-local patch formation by time-series similarity increases tSNR in high-resolution BOLD fMRI

Submillimeter functional magnetic resonance imaging (fMRI) based on blood-oxygenation-level-dependent (BOLD) signal enables the study of brain function at the submillimeter level, uncovering insights into fine-scale organisations like cortical layers and columns. However, its inherently low contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) often limit its reliability and applicability. Noise Reduction with Distribution Corrected Principal Components Analysis (NORDIC PCA) is a locally low-rank denoising algorithm that reduces thermal noise levels in BOLD fMRI in a local patch manner. However, local patches often contain a mixture of signals from multiple tissues that negatively affects the low-rank structure of the patches, which limits the denoising capabilities of the algorithm. We propose an alternative approach for patch formation by gathering similar non-local voxels, dubbed voxel-matching (VM) NORDIC. The results on submillimeter-resolution BOLD fMRI data indicate that VM-NORDIC effectively promotes the low rank of the patches by boosting signal redundancy, allowing for more efficient noise attenuation. Moreover, the method barely affects spatial smoothness due to the non-local voxel selection based on time-series similarity. In particular, VM-NORDIC outperforms standard NORDIC with default local patching (Standard-NORDIC) in terms of temporal SNR (tSNR) (~9-90% larger than Standard-NORDIC; ~23-250% larger than the original) and spatial smoothness estimates (~20% of the smoothness induced by Standard-NORDIC). These improvements are fundamental to improving the validity and precision of fMRI studies at submillimeter resolutions.

physics.med-ph↗

arXiv2026

Computed Tomography Reconstruction Algorithm Using Markov Random Field Model

X-ray computed tomography (CT) reveals the materials' internal structures non-destructively from a tilt series of projected images. Filtered back projection (FBP) is a widely-adopted reconstruction algorithm in CT owing to its small computational cost. Under low-dose or sparse-view conditions, however, FBP often amplifies noise, severely degrading the reconstructed images. In this study, we evaluated the performance of a Bayesian CT reconstruction algorithm based on the Markov random field model under such adverse conditions. Through simulations, we demonstrated that the proposed algorithm shows higher reconstruction performance than FBP under both low-dose and sparse-view conditions. The hyperparameters are estimated by minimizing the Bayesian free energy, enabling adaptive reconstruction that reflects the noise characteristics of the observed projection data. These results suggest that the proposed algorithm can broaden the applicability of CT to dose-sensitive applications and time-constrained measurements, where only limited observed projection data are available.

physics.med-ph↗

arXiv2026

Charge Collection Efficiency in Air-Vented Plane-Parallel Ionisation Chambers at Ultra-High Dose Rates: A Self-Consistent Garfield++ Monte Carlo Model Including Space-Charge Effects and Ion Recombination

Ultra-high dose rate (UHDR) irradiation used in FLASH radiotherapy can lead to significant reductions in charge collection efficiency (CCE) in plane-parallel ionisation chambers (PPICs) due to enhanced ion recombination, while the high charge densities involved can also induce strong space-charge-induced electric-field distortions. To investigate these coupled effects, we extended the Garfield++ Monte Carlo particle-transport framework by implementing ion--ion recombination and self-consistent space-charge electric field calculations. The developed Monte Carlo model couples particle transport, electron attachment, recombination processes, and dynamic electric-field distortions. The implementation was validated against analytical and numerical models from the literature. Excellent agreement was obtained for the free electron fraction (FEF), CCE, induced current, and electric field evolution under UHDR conditions. The simulations show that space charge can locally increase the electric field by more than a factor of four or reduce it to nearly zero in some regions of the chamber. The results further suggest that the reduction of the CCE under UHDR conditions is mainly driven by the decrease of the FEF caused by electric-field-dependent electron attachment. This finding indicates that the complex problem of recombination under UHDR conditions may be largely governed by the evolution of the FEF, opening promising perspectives for the development of improved analytical models and real-time correction methods for ionisation chamber dosimetry under UHDR irradiation conditions. This work provides a flexible and self-consistent Monte Carlo framework for investigating recombination phenomena and improving dosimetry models for FLASH radiotherapy.

physics.med-ph↗