tomographywith

Tomographywith is a term used to describe a family of imaging approaches that augment tomographic reconstruction by incorporating external information, constraints, or data from other modalities during the reconstruction process. It refers to a class of methods rather than a single technique, with the aim of improving image quality, reducing artefacts, or enriching the tomographic result with additional context.

Key concepts and methods

Tomographywith relies on incorporating priors, cross-modality data, or learned models into the reconstruction algorithm. Common implementations include model-based iterative reconstruction with priors (such as sparsity, smoothness, or anatomical models), joint reconstruction across modalities (for example, using MRI or PET information to guide CT), and data-driven regularization where machine learning models influence the reconstruction. These approaches can be applied to various tomography domains, including X-ray computed tomography, optical tomography, and seismic tomography.

Applications

In medical imaging, tomographywith can enable lower radiation doses by compensating for limited-angle or sparse data, while preserving diagnostic detail. In multimodal imaging, it supports better tissue characterization by fusing structural and functional information. In materials science and geophysics, it aids in recovering more accurate internal structures when direct measurements are incomplete or noisy.

Advantages and challenges

Tomographywith can enhance image fidelity, enable practical scanning under dose or time constraints, and provide richer information content. Challenges include ensuring robust alignment and calibration between modalities, managing computational demands, avoiding bias from incorrect priors, and achieving regulatory acceptance for clinical use. Ongoing work focuses on formalizing the methodology, standardizing priors, and validating performance across applications.