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Real-time CBCT reconstructions using Krylov solvers in repeated scanning procedures

Fred Hastings, S M Ragib Shahriar Islam, Malena Sabaté Landman, Sepideh Hatamikia, Carola-Bibiane Schönlieb, Ander Biguri

TL;DR

This work tackles real-time CBCT reconstruction during repeated scans by exploiting a high-quality prior image through PICCS/PIPLE regularization. It introduces a Krylov-subspace solver within an Iteratively Reweighted Norm (IRN) framework to efficiently solve the regularized inverse problems, transforming TV-based terms into weighted quadratic surrogates and solving inner problems with CGLS. The authors present two algorithms, IRN-PIPLE and IRN-PICCS, and demonstrate their superiority over non-Krylov and non-prior-regularized methods on both a synthetic head phantom and real thorax phantom data, achieving high-quality images with reconstruction times on the order of tens of seconds to a couple of minutes. The results suggest that these Krylov-based methods enable dose-efficient, near real-time CBCT in image-guided interventions, offering a practical path toward clinical translation.

Abstract

This work introduces a new efficient iterative solver for the reconstruction of real-time cone-beam computed tomography (CBCT), which is based on the Prior Image Constrained Compressed Sensing (PICCS) regularization and leverages the efficiency of Krylov subspace methods. In particular, we focus on the setting where a sequence of under-sampled CT scans are taken on the same object with only local changes (e.g. changes in a tumour size or the introduction of a surgical tool). This is very common, for example, in image-guided surgery, where the amount of measurements is limited to ensure the safety of the patient. In this case, we can also typically assume that a (good) initial reconstruction for the solution exists, coming from a previously over-sampled scan, so we can use this information to aid the subsequent reconstructions. The effectiveness of this method is demonstrated in both a synthetic scan and using real CT data, where it can be observed that the PICCS framework is very effective for the reduction of artifacts, and that the new method is faster than other common alternatives used in the same setting.

Real-time CBCT reconstructions using Krylov solvers in repeated scanning procedures

TL;DR

This work tackles real-time CBCT reconstruction during repeated scans by exploiting a high-quality prior image through PICCS/PIPLE regularization. It introduces a Krylov-subspace solver within an Iteratively Reweighted Norm (IRN) framework to efficiently solve the regularized inverse problems, transforming TV-based terms into weighted quadratic surrogates and solving inner problems with CGLS. The authors present two algorithms, IRN-PIPLE and IRN-PICCS, and demonstrate their superiority over non-Krylov and non-prior-regularized methods on both a synthetic head phantom and real thorax phantom data, achieving high-quality images with reconstruction times on the order of tens of seconds to a couple of minutes. The results suggest that these Krylov-based methods enable dose-efficient, near real-time CBCT in image-guided interventions, offering a practical path toward clinical translation.

Abstract

This work introduces a new efficient iterative solver for the reconstruction of real-time cone-beam computed tomography (CBCT), which is based on the Prior Image Constrained Compressed Sensing (PICCS) regularization and leverages the efficiency of Krylov subspace methods. In particular, we focus on the setting where a sequence of under-sampled CT scans are taken on the same object with only local changes (e.g. changes in a tumour size or the introduction of a surgical tool). This is very common, for example, in image-guided surgery, where the amount of measurements is limited to ensure the safety of the patient. In this case, we can also typically assume that a (good) initial reconstruction for the solution exists, coming from a previously over-sampled scan, so we can use this information to aid the subsequent reconstructions. The effectiveness of this method is demonstrated in both a synthetic scan and using real CT data, where it can be observed that the PICCS framework is very effective for the reduction of artifacts, and that the new method is faster than other common alternatives used in the same setting.

Paper Structure

This paper contains 11 sections, 16 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Prior Image Constrained Compressed Sensing in Krylov methods
  4. The Method
  5. Background on IRN for TV and extension to PIPLE
  6. Using IRN for PICCS
  7. Results
  8. Digital Head Phantom
  9. Lung Needle Surgery Scan:
  10. Discussion
  11. Conclusions

Figures (5)

  • Figure 1: Reconstructed images using the digital head phantom data, with heavily undersampled projections (20 angles per set). The reconstructed images are shown in [0, 1]; difference images in [–0.5, 0.5].
  • Figure 2: Error norm histories of the digital head phantom reconstructions using 20 projections.
  • Figure 3: Reconstructed images using real measurements of a thorax phantom with 180 projection. The reconstructed images are shown in [0, 0.03]; difference images in [–0.04, 0.04].
  • Figure 4: Reconstructed images using real measurements of a thorax phantom with 50 projection. The reconstructed images are shown in [0, 0.03]; difference images in [–0.04, 0.04].
  • Figure 5: Reconstructed images using real measurements of a thorax phantom with 20 projection. The reconstructed images are shown in [0, 0.03]; difference images in [–0.04, 0.04].