Spectral deep learning-based patient and bowtie scatter correction for clinical photon-counting CT

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: MAE reduction from 8 HU to <1 HU for all energy thresholds

• Primary Outcome: Mean absolute error reduction in scatter-corrected images

• Nnt Or Sensitivity: MAE10 reduction from 23.8 HU to 1.6 HU in 25% of image volume most affected by artifacts

• Confidence Interval: Not reported

Clinical Application

Requires photon-counting CT technology and computational infrastructure for deep learning implementation Relevant for Australian centres adopting photon-counting CT technology; aligns with TGA requirements for medical imaging quality assurance Patients undergoing photon-counting CT imaging across all clinical indications

Abstract

BACKGROUND: The presence of scatter in computed tomography degrades image quality, and can be caused by the patient and by other components in the beam path, such as the bowtie filter. While conventional energy-integrating detectors do not provide spectral distinction, photon-counting (PC) detectors are energy-selective and provide spectral information about the incoming X-ray photons. Since each energy threshold is affected differently by scatter, this spectral information implicitly encodes the scatter content of a projection. PURPOSE: The purpose of this work is to investigate how the spectral information can be exploited to improve deep learning (DL)-based scatter correction. Furthermore, the performance of joint and separate patient and bowtie scatter correction will be investigated, addressing that bowtie scatter has not been considered in current DL-based approaches. METHODS: We present a DL-based approach that can estimate bowtie and patient scatter jointly and compare it against a separate correction. We also introduce neural network-based methods that incorporate the spectral information inherent in PCCT for scatter correction. We present networks that estimate scatter for up to four energy thresholds simultaneously. Training and validation was performed with Monte Carlo data as well as with real data measured by a clinical PCCT system. RESULTS: When comparing joint and separate patient and bowtie scatter estimation, both methods reduce the mean absolute error (MAE) from 8 HU to 1 HU. All proposed DSE methods effectively reduce scatter artifacts and perform better than the convolution-based reference approach. Incorporating the spectral information further improves the performance, with the DSE variant with four energy thresholds achieving the best overall results for all thresholds. For all energy thresholds tested, the spectral DSE methods reduced scatter errors originating from the patient and the bowtie in PCCT from up to 8 HU to below 1 HU. In addition to the global MAE, we report a critical MAE (MAE10) restricted to voxels with uncorrected errors > $>$ 10 HU, as such deviations are visually perceptible in soft tissue and exceed the noise level of modern CT systems. In all test cases, the proposed spectral methods reduced the MAE10 from 23.8 HU in the uncorrected images to 1.6 HU after spectral correction. The affected voxels comprised on average 25 % of the image volume, indicating a significant reduction in artifact intensity in the most affected areas. In virtual monoenergetic images (VMI), the application of spectral neural networks resulted in a significant reduction in MAE from ∼ $\sim$ 16 HU to ∼ $\sim$ 2 HU at 45 keV, from ∼ $\sim$ 8 HU to ∼ $\sim$ 1 HU at 70 keV, and from 5 HU to under 1 HU at 100 keV. CONCLUSIONS: This paper presents a combined method for correcting patient and bowtie scatter that delivers results equivalent to separate corrections and thus eliminates the need for multiple networks. Further, we demonstrate that deep scatter estimation can effectively exploit the spectral information available to improve scatter correction, especially for spectral applications, like VMIs. Spectral DSE networks slightly outperformed non-spectral variants, with multiple energy thresholds lead to more accurate estimations. This enables the use of one network for scatter correction and eliminates the need for multiple ones, thereby saving computational cost and complexity.

References

1. 1.Hennemann, L., Erath, J., Heinkele, A., Fournié, E., Petersilka, M., Stierstorfer, K., & Kachelrieß, M. (2026). Spectral deep learning-based patient and bowtie scatter correction for clinical photon-counting CT. Medical Physics. https://doi.org/10.1117/12.2646580