Helical & Multislice CT

Helical (spiral) CT moves the patient through the gantry while the source rotates, so the source traces a helix around the body. Almost every clinical CT scanner acquires data this way: it covers long regions quickly, and with today's multislice detectors each turn of the helix is a cone-beam measurement.

Reconstruction has followed the hardware. Early helical scanners interpolated between turns and used fan-beam FBP; as detectors gained rows, approximate cone-beam methods and then theoretically exact inversion formulas appeared (Katsevich's formula, reconstruction on PI-lines). Statistical iterative reconstruction modelled the full 3D helical geometry and the photon statistics, and became the basis of commercial model-based reconstruction.

Current work addresses what makes helical data hard for learned methods: the volume is large and three-dimensional, the geometry differs between scanners, and fast high-pitch or sparse helical protocols undersample each slice. The papers here include memory-efficient unrolled networks for full 3D helical data, dual-domain methods for sparse and high-pitch scans, and volume-of-interest imaging.

Browse all 8 Helical & Multislice CT papers in the searchable directory

Start here

If you are new to this topic, these are good first reads, chosen by the maintainers.

Theoretically Exact Filtered Backprojection-Type Inversion Algorithm for Spiral CT (SIAM J. Appl. Math., 2002)
Katsevich's theoretically exact FBP-type inversion for helical cone-beam CT, with shift-invariant filtering.
Exact image reconstruction on PI-lines from minimum data in helical cone-beam CT (PMB, 2004)
Exact reconstruction on PI-lines from minimal data, the basis of backprojection-filtration methods.
A three-dimensional statistical approach to improved image quality for multislice helical CT (Med Phys, 2007)
Statistical iterative reconstruction for multislice helical scanners, a forerunner of clinical model-based reconstruction.
3D Helical CT Reconstruction With a Memory Efficient Learned Primal-Dual Architecture (IEEE Trans. Comput. Imaging, 2024)
A memory-efficient learned primal-dual network for full 3D helical CT, unrolled reconstruction at clinical scale.
High Pitch Helical CT Reconstruction (TMI, 2021)
Reconstruction from high-pitch helical scans, which cover the body faster but sample each slice less.

Papers

All 8 papers, newest first.

Paper	Venue	Year	Links
Delta-Net: Deep Dual-Domain Alternating Optimization Network for High Pitch Helical CT Reconstruction	TMI	2026	paper
Deep learning-based volume of interest imaging in helical CT for image quality improvement and radiation dose reduction	Br. J. Radiol.	2025	paper
3D Helical CT Reconstruction With a Memory Efficient Learned Primal-Dual Architecture	IEEE Trans. Comput. Imaging	2024	paper
High Pitch Helical CT Reconstruction	TMI	2021	paper
A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT	PMB	2020	paper
A three-dimensional statistical approach to improved image quality for multislice helical CT	Med Phys	2007	paper
Exact image reconstruction on PI-lines from minimum data in helical cone-beam CT	PMB	2004	paper
Theoretically Exact Filtered Backprojection-Type Inversion Algorithm for Spiral CT	SIAM J. Appl. Math.	2002	paper

Paper

Venue

Year

Links

Delta-Net: Deep Dual-Domain Alternating Optimization Network for High Pitch Helical CT Reconstruction

TMI

2026

paper

Deep learning-based volume of interest imaging in helical CT for image quality improvement and radiation dose reduction

Br. J. Radiol.

2025

paper