PURPOSE: Dual source CT (DSCT) systems utilize two measurement systems (A) and (B) offset by about 90 degrees. A special challenge in DSCT is cross-scattered radiation, i.e., scattered radiation from x-ray tube (B) detected in detector (A) and vice versa. Cross-scattered radiation can produce artifacts and degrade the contrast-to-noise ratio (CNR) of the images. Correction algorithms are mandatory to mitigate the negative effects of cross-scattered radiation. The purpose of this work is to describe and evaluate different methods for cross-scatter correction in DSCT. METHODS: The authors present two techniques for cross-scatter correction in DSCT. The first technique (1) is model-based. Assuming that cross-scatter is predominantly surface scatter, adequate knowledge about the surface of the scattering object is sufficient to describe the magnitude and distribution of cross-scatter. The relevant surface information is derived from an analysis of the raw-data sinogram during the CT-scan. The correction is performed by a table look-up into previously measured and stored cross-scatter distributions for a variety of objects with different surface characteristics. The second technique (2) is measurement-based. Dedicated sensors outside the penumbra of the fan beam in the z direction on both detectors (A) and (B) are used for an online measurement of both cross-scattered and forward scattered radiation during the CT-scan. In addition to the two scatter-correction techniques, the authors describe a low-pass filter method for the scatter-correction term with the goal to improve the CNR of the corrected images. This filter can be applied to both model-based (1) and measurement-based (2) scatter correction. Both scatter-correction techniques (1) and (2) are quantitatively assessed and the performance of the low-pass filter method is evaluated using DSCT data of phantoms (water cylinders and anthropomorphic phantoms) and DSCT patient scan data. RESULTS: Both scatter-correction techniques restore image contrasts and reduce cross-scatter induced artifacts in DSCT images. The measurement-based technique results in higher CNR than the model-based technique if the proposed low-pass filtering of the scatter-correction term is applied. Low-pass filtering improves the CNR of cross-scatter-correction approaches beyond the limits published in the literature [Engel et al., "X-ray scattering in single- and dual-source CT," Med. Phys. 35(1), 318-332 (2008)]. CONCLUSIONS: Both model-based and measurement-based scatter correction can mitigate the negative effects of cross-scatter in DSCT. The application of low-pass filtering to the scatter-correction term improves the CNR whenever the ratio of scattered radiation to total signal is high, as in larger patients.
PURPOSE: Dual source CT (DSCT) systems utilize two measurement systems (A) and (B) offset by about 90 degrees. A special challenge in DSCT is cross-scattered radiation, i.e., scattered radiation from x-ray tube (B) detected in detector (A) and vice versa. Cross-scattered radiation can produce artifacts and degrade the contrast-to-noise ratio (CNR) of the images. Correction algorithms are mandatory to mitigate the negative effects of cross-scattered radiation. The purpose of this work is to describe and evaluate different methods for cross-scatter correction in DSCT. METHODS: The authors present two techniques for cross-scatter correction in DSCT. The first technique (1) is model-based. Assuming that cross-scatter is predominantly surface scatter, adequate knowledge about the surface of the scattering object is sufficient to describe the magnitude and distribution of cross-scatter. The relevant surface information is derived from an analysis of the raw-data sinogram during the CT-scan. The correction is performed by a table look-up into previously measured and stored cross-scatter distributions for a variety of objects with different surface characteristics. The second technique (2) is measurement-based. Dedicated sensors outside the penumbra of the fan beam in the z direction on both detectors (A) and (B) are used for an online measurement of both cross-scattered and forward scattered radiation during the CT-scan. In addition to the two scatter-correction techniques, the authors describe a low-pass filter method for the scatter-correction term with the goal to improve the CNR of the corrected images. This filter can be applied to both model-based (1) and measurement-based (2) scatter correction. Both scatter-correction techniques (1) and (2) are quantitatively assessed and the performance of the low-pass filter method is evaluated using DSCT data of phantoms (water cylinders and anthropomorphic phantoms) and DSCT patient scan data. RESULTS: Both scatter-correction techniques restore image contrasts and reduce cross-scatter induced artifacts in DSCT images. The measurement-based technique results in higher CNR than the model-based technique if the proposed low-pass filtering of the scatter-correction term is applied. Low-pass filtering improves the CNR of cross-scatter-correction approaches beyond the limits published in the literature [Engel et al., "X-ray scattering in single- and dual-source CT," Med. Phys. 35(1), 318-332 (2008)]. CONCLUSIONS: Both model-based and measurement-based scatter correction can mitigate the negative effects of cross-scatter in DSCT. The application of low-pass filtering to the scatter-correction term improves the CNR whenever the ratio of scattered radiation to total signal is high, as in larger patients.
Authors: Derek S Tsang; Thomas E Merchant; Sophie E Merchant; Hanna Smith; Yoad Yagil; Chia-Ho Hua Journal: Br J Radiol Date: 2017-07-27 Impact factor: 3.039
Authors: Ehsan Abadi; Brian Harrawood; Jayasai R Rajagopal; Shobhit Sharma; Anuj Kapadia; William Paul Segars; Karl Stierstorfer; Martin Sedlmair; Elizabeth Jones; Ehsan Samei Journal: Biomed Phys Eng Express Date: 2019-08-09
Authors: Serdar Charyyev; Tonghe Wang; Yang Lei; Beth Ghavidel; Jonathan J Beitler; Mark McDonald; Walter J Curran; Tian Liu; Jun Zhou; Xiaofeng Yang Journal: Br J Radiol Date: 2021-10-28 Impact factor: 3.039