Impact of computed tomography (CT) reconstruction kernels on radiotherapy dose calculation

Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy

BACKGROUND AND PURPOSE

Studies have shown large variations in stopping-power ratio (SPR) prediction from computed tomography (CT) across European proton centres. To standardise this process, a step-by-step guide on specifying a Hounsfield look-up table (HLUT) is presented here.

MATERIALS AND METHODS

The HLUT specification process is divided into six steps: Phantom setup, CT acquisition, CT number extraction, SPR determination, HLUT specification, and HLUT validation. Appropriate CT phantoms have a head- and body-sized part, with tissue-equivalent inserts in regard to X-ray and proton interactions. CT numbers are extracted from a region-of-interest covering the inner 70% of each insert in-plane and several axial CT slices in scan direction. For optimal HLUT specification, the SPR of phantom inserts is measured in a proton beam and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. Including both phantom inserts and tabulated human tissues increases HLUT stability. Piecewise linear regressions are performed between CT numbers and SPRs for four tissue groups (lung, adipose, soft tissue, and bone) and then connected with straight lines. Finally, a thorough but simple validation is performed.

RESULTS

The best practices and individual challenges are explained comprehensively for each step. A well-defined strategy for specifying the connection points between the individual line segments of the HLUT is presented. The guide was tested exemplarily on three CT scanners from different vendors, proving its feasibility.

CONCLUSION

The presented step-by-step guide for CT-based HLUT specification with recommendations and examples can contribute to reduce inter-centre variations in SPR prediction.

Effects of convolution filter with beam hardening correction on computed tomography image quality

PURPOSE

To quantify the effects of convolution filters (FC) with beam hardening correction (BHC) compared to FC without BHC on the computed tomography (CT) image quality.

METHODS

This study was conducted on a Canon® Aquilion Lightning scanner. The exposure protocol includes acquisitions at 120 and 100 kVp. Sixteen FCs (8 with and 8 without BHC) were investigated using a Catphan®504 phantom. Uniformity, slice thickness, spatial resolution, Hounsfield unit and noise were analysed using the SPICE-CT ImageJ plugin and the noise power spectrum was analysed using the Imquest software.

RESULTS

It was observed that the BHC did not significantly influence the uniformity, slice thickness, noise and noise power spectrum. Comparisons of 10% MTF between FC01 and FC11 showed relative differences of -29% and -5% at 120 and 100 kVp, respectively, while those between FC09 and FC19 were -55% and -25%. The Hounsfield unit of the Catphan's region of highest electron density was reduced by -7.29% at 120 kVp for FC with BHC. In both cases (FC with and without BHC), the noise values agreed with CT operating manual. At 120 kVp, FC11 and FC09 presented the maximum and minimum noise values, respectively.

CONCLUSION

In CT procedures that quantitatively evaluate the bone or calcium Hounsfield unit, FC with BHC should be avoided due to its effects on Hounsfield units, in special at higher voltage, such as 120 kVp.

Technical Note: Volumetric computed tomography for radiotherapy simulation and treatment planning

Purpose

For lung and liver tumors requiring radiotherapy, motion artifacts are common in 4D‐CT images due to the small axial field‐of‐view (aFOV) of conventional CT scanners. This may negatively impact contouring and dose calculation accuracy and could lead to a geographic miss during treatment. Recent advancements in volumetric CT (vCT) enable an aFOV up to 160 mm in a single rotation, which may reduce motion artifacts. However, the impact of large aFOV on CT number required for dose calculation needs to be evaluated before clinical implementation. The objective of this study was to determine the utility of a 256‐slice vCT scanner for 4D‐CT simulation by evaluating image quality and generating relative electron density (RED) curves.

Methods

Images were acquired on a 256‐slice GE Revolution CT scanner with 40 mm, 80 mm, 120 mm, 140 mm, and 160 mm aFOV. Image quality was assessed by evaluating CT number linearity, uniformity, noise, and low‐contrast resolution. The relationship between each quality metric and aFOV was assessed.

Results

CT number linearity, uniformity, noise, and low‐contrast resolution were within the expected range for each image set, except CT number in Teflon and Delrin, which were underestimated. Spearman correlation coefficient (ρ) showed that the CT number for Teflon (ρ = 1.0, p = 0.02), Delrin (ρ = 1.0, p = 0.02), and air (ρ = 1.0, p = 0.02) was significantly related to aFOV, while all other measurements were not. The measured deviations from expected values were not clinically significant.

Conclusion

These results suggest that vCT can be used for CT simulation for radiation treatment planning.