arXiv subjects

Clémentine Phung-Ngoc

Publications and source records attributed to Clémentine Phung-Ngoc.

4 recordsLinked to original sources

arXiv2026

Joint Reconstruction of Activity and Attenuation in PET by Diffusion Posterior Sampling in Wavelet Coefficient Space

Attenuation correction (AC) is necessary for accurate activity quantification in positron emission tomography (PET). Conventional reconstruction methods typically rely on attenuation maps derived from a co-registered computed tomography (CT) or magnetic resonance (MR) scan. However, this additional scan may complicate the imaging workflow, introduce misalignment artifacts and increase radiation exposure. In this paper, we propose a joint reconstruction of activity and attenuation (JRAA) approach that eliminates the need for auxiliary anatomical imaging by relying solely on emission data. This framework combines wavelet diffusion model (WDM) and diffusion posterior sampling (DPS) to reconstruct fully three-dimensional (3-D) data. Experimental results on simulated data show our method outperforms maximum likelihood activity and attenuation (MLAA) and MLAA-UNet with U-Net-based postprocessing, and yields high-quality noise-free reconstructions across various count settings with time-of-flight (TOF). It is also able to reconstruct non-TOF data, although the reconstruction quality significantly degrades in low-count (LC) conditions, limiting its practical effectiveness in such settings. Nonetheless, a non-TOF Biograph mMR real data reconstruction with joint scatter estimation highlights the potential of the method for clinical applications. This approach represents a step towards stand-alone PET imaging by reducing the dependence on anatomical modalities while maintaining quantification accuracy, even in LC scenarios when TOF information is available. Our code is available on GitHub at https://github.com/clemphg/jraa-dps.

physics.med-ph↗

arXiv2025

Adaptive Diffusion Models for Sparse-View Motion-Corrected Head Cone-beam CT

Cone-beam computed tomography (CBCT) is an imaging modality widely used in head and neck diagnostics due to its accessibility and lower radiation dose. However, its relatively long acquisition times make it susceptible to patient motion, especially under sparse-view settings used to reduce dose, which can result in severe image artifacts. In this work, we propose a novel framework combining joint reconstruction and motion estimation (JRM) with an adaptive diffusion model (ADM) that simultaneously addresses motion compensation and sparse-view reconstruction in head CBCT. Leveraging recent advances in diffusion-based generative models, our method integrates a wavelet-domain diffusion prior into an iterative reconstruction pipeline to guide the solution toward anatomically plausible volumes while estimating rigid motion parameters in a blind fashion. We evaluate our method on simulated motion-affected CBCT data derived from real clinical computed tomography (CT) volumes. Experimental results demonstrate that JRM- ADM achieves consistent quantitative improvements over both traditional and learning-based baselines. In highly undersampled cases, JRM-ADM improves peak signal-to-noise ratio (PSNR) by more than 4 dB and structural similarity index measure (SSIM) by 0.10 compared to the baseline motion-corrected (MC) reconstruction method. These results highlight the potential of our approach to enable motion-robust, low-dose CBCT imaging, paving the way for improved clinical viability. The project page is available at https://antoinedepaepe.github.io/jrm-adm-io/.

physics.med-ph↗

arXiv2025

Solving Blind Inverse Problems: Adaptive Diffusion Models for Motion-corrected Sparse-view 4DCT

Four-dimensional computed tomography (4DCT) is essential for medical imaging applications like radiotherapy, which demand precise respiratory motion representation. Traditional methods for reconstructing 4DCT data suffer from artifacts and noise, especially in sparse-view, low-dose contexts. Motion-corrected (MC) reconstruction is a blind inverse problem that we propose to solve with a novel diffusion model (DM) framework that calibrates an adaptive unknown forward model for motion correction. Furthermore, we used a wavelet diffusion model (WDM) to address computational cost and memory usage. By leveraging the prior probability distribution function (PDF) from the DMs, we enhance the joint reconstruction and motion estimation (JRM) process, improving image quality and preserving resolution. Experiments on extended cardiac-torso (XCAT) phantom data demonstrate that our method outperforms existing techniques, yielding artifact-free, high-resolution reconstructions even under irregular breathing conditions. These results showcase the potential of combining DMs with motion correction to advance sparse-view 4DCT imaging.

physics.med-ph↗

arXiv2025

Joint Reconstruction of the Activity and the Attenuation in PET by Diffusion Posterior Sampling: a Feasibility Study

This study introduces a novel framework for joint reconstruction of the activity and the attenuation (JRAA) in positron emission tomography (PET) using diffusion posterior sampling (DPS). By leveraging diffusion models (DMs), this approach directly addresses activity-attenuation dependencies, mitigating crosstalk issues prevalent in non-time-of-flight (TOF) settings. Experimental evaluations, conducted using 2-dimensional (2-D) XCAT phantom data, demonstrate that DPS significantly outperforms traditional maximum likelihood activity and attenuation (MLAA) methods, producing consistent and high-quality reconstructions even in the absence of TOF information. Ongoing work aims to extend our method to real 3-dimensional (3-D) data with encouraging preliminary findings.

physics.med-ph↗