arXiv subjects

Foucauld Chamming's

Publications and source records attributed to Foucauld Chamming's.

4 recordsLinked to original sources

arXiv2026

An angular distortion matrix approach for joint wave-speed tomography and aberration correction in scattering media

Despite being one of the most fundamental properties governing wave propagation, the wave-speed distribution is rarely known accurately in reflection imaging. Its estimation relies on analyzing wave distortions undergone by the incident and reflected waves; however, their contribution is difficult to disentangle from the medium reflectivity, particularly in complex media dominated by speckle. Mismatches between assumed and actual wave speeds result in aberrations that degrade the quality of reflectivity images. Yet these aberrations carry information about the underlying wave-speed heterogeneities. Here, we show that an angular distortion matrix, built upon matrix imaging, can unscramble this information to map the wave speed and correct aberrations across the entire field of view. At each point, this matrix isolates the distortions accumulated by the incident and reflected waves along well-defined propagation directions. It reveals strong angular correlations that can be exploited through time-reversal analysis to estimate local phase aberrations, providing observables for wave-speed tomography. The resulting map improves the propagation model used for matrix imaging, after which the process is iterated to refine the wave speed until residual aberrations become negligible. Using ultrasound as proof of concept, we validate the approach in a tissue-mimicking phantom and illustrate its clinical potential in vivo for breast and liver imaging. Sound-speed maps show contrast consistent with a malignant breast lesion and expected values in a healthy liver, while aberration correction sharpens reflectivity images routinely interpreted by clinicians. Beyond ultrasound, the approach extends naturally to any wave modality in which reflection-matrix imaging can be implemented.

physics.app-ph↗

arXiv2026

Physics-Based Learning of the Wave Speed Landscape in Complex Media

Wave velocity is a key parameter for imaging complex media, but in vivo measurements are typically limited to reflection geometries, where only backscattered waves from short-scale heterogeneities are accessible. As a result, conventional reflection imaging fails to recover large-scale variations of the wave velocity landscape. Here we show that matrix imaging overcomes this limitation by exploiting the quality of wave focusing as an intrinsic guide star. We model wave propagation as a trainable multi-layer network that leverages optimization and deep learning tools to infer the wave velocity distribution. We validate this approach through ultrasound experiments on tissue-mimicking phantoms and human breast tissues, demonstrating its potential for tumour detection and characterization. Our method is broadly applicable to any kind of waves and media for which a reflection matrix can be measured.

physics.app-ph↗

arXiv2025

The LongiMam model for improved breast cancer risk prediction using longitudinal mammograms

Risk-adapted breast cancer screening requires robust models that leverage longitudinal imaging data. Most current deep learning models use single or limited prior mammograms and lack adaptation for real-world settings marked by imbalanced outcome distribution and heterogeneous follow-up. We developed LongiMam, an end-to-end deep learning model that integrates both current and up to four prior mammograms. LongiMam combines a convolutional and a recurrent neural network to capture spatial and temporal patterns predictive of breast cancer. The model was trained and evaluated using a large, population-based screening dataset with disproportionate case-to-control ratio typical of clinical screening. Across several scenarios that varied in the number and composition of prior exams, LongiMam consistently improved prediction when prior mammograms were included. The addition of prior and current visits outperformed single-visit models, while priors alone performed less well, highlighting the importance of combining historical and recent information. Subgroup analyses confirmed the model's efficacy across key risk groups, including women with dense breasts and those aged 55 years or older. Moreover, the model performed best in women with observed changes in mammographic density over time. These findings demonstrate that longitudinal modeling enhances breast cancer prediction and support the use of repeated mammograms to refine risk stratification in screening programs. LongiMam is publicly available as open-source software.

cs.CV↗

arXiv2024

The DeepJoint algorithm: An innovative approach for studying the longitudinal evolution of quantitative mammographic density and its association with screen-detected breast cancer risk

Mammographic density is a dynamic risk factor for breast cancer and affects the sensitivity of mammography-based screening. While automated machine and deep learning-based methods provide more consistent and precise measurements compared to subjective BI-RADS assessments, they often fail to account for the longitudinal evolution of density. Many of these methods assess mammographic density in a cross-sectional manner, overlooking correlations in repeated measures, irregular visit intervals, missing data, and informative dropouts. Joint models, however, are well-suited for capturing the longitudinal relationship between biomarkers and survival outcomes. We present the DeepJoint algorithm, an open-source solution that integrates deep learning for quantitative mammographic density estimation with joint modeling to assess the longitudinal relationship between mammographic density and breast cancer risk. Our method efficiently analyzes processed mammograms from various manufacturers, estimating both dense area and percent density--established risk factors for breast cancer. We utilize a joint model to explore their association with breast cancer risk and provide individualized risk predictions. Bayesian inference and the Monte Carlo consensus algorithm make the approach reliable for large screening datasets. Our method allows for accurate analysis of processed mammograms from multiple manufacturers, offering a comprehensive view of breast cancer risk based on individual longitudinal density profiles. The complete pipeline is publicly available, promoting broader application and comparison with other methods.

stat.AP↗