Remote dose imaging from Cherenkov light using spatially resolved CT calibration in breast radiotherapy

Abstract

Purpose: Imaging Cherenkov light during radiotherapy allows the visualization and recording of frame-by-frame relative maps of the dose being delivered to the tissue at each control point used throughout treatment, providing one of the most complete real-time means of treatment quality assurance. In non-turbid media, the intensity of Cherenkov light is linear with surface dose deposited, however the emission from patient tissue is well-known to be reduced by absorbing tissue components such as hemoglobin, fat, water, and melanin, and diffused by the scattering components of tissue. Earlier studies have shown that bulk correction could be achieved by using the patient planning computed tomography (CT) scan for attenuation correction.

Methods: In this study, CT maps were used for correction of spatial variations in emissivity. Testing was completed on Cherenkov images from radiotherapy treatments of post-lumpectomy breast cancer patients (n = 13), combined with spatial renderings of the patient radiodensity (CT number) from their planning CT scan.

Results: The correction technique was shown to provide a pixel-by-pixel correction that suppressed many of the inter- and intra-patient differences in the Cherenkov light emitted per unit dose. This correction was established from a calibration curve that correlated Cherenkov light intensity to surface-rendered CT number ( $R_{6\mathrm{MV}}^2=0.70$ and $R_{10\mathrm{MV}}^2=0.72$ ). The corrected Cherenkov intensity per unit dose standard error was reduced by nearly half (from ∼30% to ∼17%).

Conclusions: This approach provides evidence that the planning CT scan can mitigate some of the tissue-specific attenuation in Cherenkov images, allowing them to be translated into near surface dose images.

Keywords: Cherenkov; breast cancer; dose imaging; dosimetry; quantitative imaging.

Figure 1:

In (a) and (b) the axial and coronal views (respectively) reflect a primarily adipose breast composition (low CT number) with interior scattered fibroglandular tissue (higher CT number). In (c) the coronal view is shown with the sampled region outlined in purple. In (d), the CT slices are rendered into an isosurface using the slice and pixel spacing DICOM information provided with the scan and displayed with the surface normal generated at each vertex. In (e) the average superficial tissue CT# from the region sampled in (c) is averaged and displayed over the surface. The isosurface is projected to the same view as the Cherenkov image (f), and surface is co-registered to the background image. The intensity mask from the Cherenkov image is applied to spatial CT image.

Figure 2:

The spatial surface CT is shown from −200 to 100 HU in gray for each patient analyzed in this study (a1, b1, etc.). In the next row, Cherenkov images are shown (dose-normalized) and uncorrected. The final row gives the corrected Cherenkov image, after implementing the correction delineated in Equation 1 using the model in Figure 5. (All HU maps, uncorrected Cherenkov images, and corrected Cherenkov images correspond to the same, respective color bar.)

Figure 3:

Dose-Normalized images are organized into pre-CT correction (a), (c), and post-CT correction (b), (d), images for the 13-patient cohort. All thirteen patients were treated with 6 MV beams. In short, the qualitative disparity is evident from patient-to-patient moreso in the images not corrected by CT. The quantitative result is shown using the COV (σ/μ) is improved, as well as the strength of the linear regression.

Figure 4:

Dose-Normalized images are organized into pre-CT correction and post-CT correction images for the 5-patient cohort receiving 10 MV treatments. Similar to that which was shown for 6 MV beams, the corrected images are much more qualitatively similar, and the statistics are notably improved.

Figure 5:

In (a), the relationship between the CT# and the amount of Cherenkov light emitted per unit dose (for both 6 MV entrance and exit beams combined) is modeled using a summed exponential with an R² of roughly 0.70. Recall that Cherenkov data has been dose normalized to render each image independent of incident beam energy. The darker gray points are representative of areas that were sampled in regions of substantial Cherenkov light attenuation, and the lighter gray points represent ROIs averaged from regions in the surrounding tissue, outside of any localized absorbing features. In (b), the same is shown for 10 MV data with a linear regression fit of R² = 0.72.