arXiv subjects

Alejandro Lopez-Montes

Publications and source records attributed to Alejandro Lopez-Montes.

6 recordsLinked to original sources

arXiv2026

A Positron Range Correction with Texture Preservation Framework in PET Imaging

Positron range (PR) blurring is a fundamental resolution limitation in PET imaging with high-energy positron emitters such as 82Rb, causing contrast loss and spill-out effects across heterogeneous tissue interfaces. We propose PRC-TP, a positron range correction (PRC) framework with explicit texture preservation that decouples deterministic resolution recovery from stochastic texture restoration. A nnFormer-based neural network (NN) was trained on patient-derived Monte Carlo simulations to map PR-degraded 82Rb reconstructions to PR-free references using attenuation maps as anatomical context. However, this NN also significantly removed the noise in the images, which could impact some texture analysis methods or make the images look unrealistic. An auxiliary Noise2Noise model estimates that smoothing effect, enabling texture extraction and transfer to the PR-corrected prediction through Model-consistent Texture Re-Injection (MTRI). In simulated patients, PRC-TP preserved contrast recovery close to ground truth (GT) (98.96-99.04%) while restoring noise and CNR closer to the reference. The function-based MTRI formulation achieved near unity global texture amplitude agreement with GT (0.997 +/- 0.011), reducing the input texture amplitude bias (0.951 +/- 0.011). Radiomics analysis showed improved agreement with GT across texture-sensitive feature families. A clinical 82Rb evaluation showed trends consistent with simulations, including comparable contrast-ratio increase (10.18% vs. 10.99%) and restoration of texture suppressed by PRC. These results support PRC-TP as a practical framework for resolution recovery with acquisition-consistent texture preservation in PET imaging. Submitted to IEEE TRPMS.

physics.med-ph↗

arXiv2025

Patient-specific AI for generation of 3D dosimetry imaging from two 2D-planar measurements

In this work we explored the use of patient specific reinforced learning to generate 3D activity maps from two 2D planar images (anterior and posterior). The solution of this problem remains unachievable using conventional methodologies and is of particular interest for dosimetry in nuclear medicine where approaches for post-therapy distribution of radiopharmaceuticals such as 177Lu-PSMA are typically done via either expensive and long 3D SPECT acquisitions or fast, yet only 2D, planar scintigraphy. Being able to generate 3D activity maps from planar scintigraphy opens the gate for new dosimetry applications removing the need for SPECT and facilitating multi-time point dosimetry studies. Our solution comprises the generation of a patient specific dataset with possible 3D uptake maps of the radiopharmaceuticals withing the anatomy of the individual followed by an AI approach (we explored both the use of 3DUnet and diffusion models) able to generate 3D activity maps from 2D planar images. We have validated our method both in simulation and real planar acquisitions. We observed enhanced results using patient specific reinforcement learning (~20% reduction on MAE and ~5% increase in SSIM) and better organ delineation and patient anatomy especially when combining diffusion models with patient specific training yielding a SSIM=0.89 compared to the ground truth for simulations and 0.73 when compared to a SPECT acquisition performed half an hour after the planar. We believe that our methodology can set a change of paradigm for nuclear medicine dosimetry allowing for 3D quantification using only planar scintigraphy without the need of expensive and time-consuming SPECT leveraging the pre-therapy information of the patients.

physics.med-ph↗

arXiv2025

Artificial intelligence for simplified patient-centered dosimetry in radiopharmaceutical therapies

KEY WORDS: Artificial Intelligence (AI), Theranostics, Dosimetry, Radiopharmaceutical Therapy (RPT), Patient-friendly dosimetry KEY POINTS - The rapid evolution of radiopharmaceutical therapy (RPT) highlights the growing need for personalized and patient-centered dosimetry. - Artificial Intelligence (AI) offers solutions to the key limitations in current dosimetry calculations. - The main advances on AI for simplified dosimetry toward patient-friendly RPT are reviewed. - Future directions on the role of AI in RPT dosimetry are discussed.

physics.med-ph↗

arXiv2025

A tissue-informed deep learning-based method for positron range correction in preclinical 68Ga PET imaging

Positron range (PR) limits spatial resolution and quantitative accuracy in PET imaging, particularly for high-energy positron-emitting radionuclides like 68Ga. We propose a deep learning method using 3D residual encoder-decoder convolutional neural networks (3D RED-CNNs), incorporating tissue-dependent anatomical information through a u-map-dependent loss function. Models were trained with realistic simulations and, using initial PET and CT data, generated positron range corrected images. We validated the models in simulations and real acquisitions. Three 3D RED-CNN architectures, Single-channel, Two-channel, and DualEncoder, were trained on simulated PET datasets and evaluated on synthetic and real PET acquisitions from 68Ga-FH and 68Ga-PSMA-617 mouse studies. Performance was compared to a standard Richardson-Lucy-based positron range correction (RL-PRC) method using metrics such as mean absolute error (MAE), structural similarity index (SSIM), contrast recovery (CR), and contrast-to-noise ratio (CNR). CNN-based methods achieved up to 19 percent SSIM improvement and 13 percent MAE reduction compared to RL-PRC. The Two-Channel model achieved the highest CR and CNR, recovering lung activity with 97 percent agreement to ground truth versus 77 percent for RL-PRC. Noise levels remained stable for CNN models (approximately 5.9 percent), while RL-PRC increased noise by 5.8 percent. In preclinical acquisitions, the Two-Channel model achieved the highest CNR across tissues while maintaining the lowest noise level (9.6 percent). Although no ground truth was available for real data, tumor delineation and spillover artifacts improved with the Two-Channel model. These findings highlight the potential of CNN-based PRC to enhance quantitative PET imaging, particularly for 68Ga. Future work will improve model generalization through domain adaptation and hybrid training strategies.

physics.med-ph↗

arXiv2024

Stationary CT Imaging of Intracranial Hemorrhage with Diffusion Posterior Sampling Reconstruction

Diffusion Posterior Sampling (DPS) can be used in Computed Tomography (CT) reconstruction by leveraging diffusion-based generative models for unconditional image synthesis while matching the observations (data) of a CT scan. Of particular interest is its application in scenarios involving sparse or limited angular sampling, where conventional reconstruction algorithms are often insufficient. We developed a DPS algorithm for 3D reconstruction from a stationary CT (sCT) portable brain stroke imaging unit based on a multi-x-ray source array (MXA) of 31 x-ray tubes and a curved area detector. In this configuration, conventional reconstruction e.g., Penalized Weighted Least Squares (PWLS) with a Huber edge-preserving penalty, suffers from severe directional undersampling artifacts. The proposed DPS integrates a two-dimensional diffusion model, acting on image slices, coupled to sCT data consistency and volumetric regularization terms to enable 3D reconstruction robust to noise and incomplete sampling. To reduce the computational burden of DPS, stochastic contraction with PWLS initialization was used to decrease the number of diffusion steps. The validation studies involved simulations of anthropomorphic brain phantoms with synthetic bleeds and experimental data from an sCT bench. In simulations, DPS achieved ~130% reduction of directional artifacts compared to PWLS and 30% better recovery of lesion shape (DICE coefficient). Benchtop studies demonstrated enhanced visualization of brain features in a Kyoto Kagaku head phantom. The proposed DPS achieved improved visualization of intracranial hemorrhage and brain morphology compared to conventional model-based reconstruction for the highly undersampled sCT system.

physics.med-ph↗

arXiv2023

Data-driven Improved Sampling in PET

Positron Emission Tomography (PET) scanners are usually designed with the goal to obtain the best compromise between sensitivity, resolution, field-of-view size, and cost. Therefore, it is difficult to improve the resolution of a PET scanner with hardware modifications, without affecting some of the other important parameters. Iterative image reconstruction methods such as the ordered subsets expectation maximization (OSEM) algorithm are able to obtain some resolution recovery by using a realistic system response matrix that includes all the relevant physical effects. Nevertheless, this resolution recovery is often limited by reduced sampling in the projection space, determined by the geometry of the detector. The goal of this work is to improve the resolution beyond the detector size limit by increasing the sampling with data-driven interpolated data. A maximum-likelihood estimation of the counts in each virtual sub-line-of-response (subLOR) is obtained after a complete image reconstruction, conserving the statistics of the initial data set. The new estimation is used for the next complete reconstruction. The method typically requires two or three of these full reconstructions (superiterations). We have evaluated it with simulations and real acquisitions for the Argus and Super Argus preclinical PET scanners manufactured by SMI, considering different types of increased sampling. Quantitative measurements of recovery and resolution evolution against noise per iteration for the standard OSEM and successive superiterations show promising results. The procedure is able to reduce significantly the impact of depth-of-interaction in large crystals, and to improve the spatial resolution. The proposed method is quite general and it can be applied to other scanners and configurations.

physics.med-ph↗