Influence of the type of imaging on the delineation process during the treatment planning

MR-CT image fusion method of intracranial tumors based on Res2Net

BACKGROUND

Information complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation.

PURPOSE

The purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method.

METHODS

This paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image.

RESULTS

Qualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics.

CONCLUSION

The proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

Feasibility of artificial intelligence-driven interfractional monitoring of organ changes by mega-voltage computed tomography in intensity-modulated radiotherapy of prostate cancer

PURPOSE

High-dose radiotherapy (RT) for localized prostate cancer requires careful consideration of target position changes and adjacent organs-at-risk (OARs), such as the rectum and bladder. Therefore, daily monitoring of target position and OAR changes is crucial in minimizing interfractional dosimetric uncertainties. For efficient monitoring of the internal condition of patients, we assessed the feasibility of an auto-segmentation of OARs on the daily acquired images, such as megavoltage computed tomography (MVCT), via a commercial artificial intelligence (AI)-based solution in this study.

MATERIALS AND METHODS

We collected MVCT images weekly during the entire course of RT for 100 prostate cancer patients treated with the helical TomoTherapy system. Based on the manually contoured body outline, the bladder including prostate area, and rectal balloon regions for the 100 MVCT images, we trained the commercially available fully convolutional (FC)-DenseNet model and tested its auto-contouring performance.

RESULTS

Based on the optimally determined hyperparameters, the FC-DenseNet model successfully auto-contoured all regions of interest showing high dice similarity coefficient (DSC) over 0.8 and a small mean surface distance (MSD) within 1.43 mm in reference to the manually contoured data. With this well-trained AI model, we have efficiently monitored the patient's internal condition through six MVCT scans, analyzing DSC, MSD, centroid, and volume differences.

CONCLUSION

We have verified the feasibility of utilizing a commercial AI-based model for auto-segmentation with low-quality daily MVCT images. In the future, we will establish a fast and accurate auto-segmentation and internal organ monitoring system for efficiently determining the time for adaptive replanning.

Determination of the absorbed dose to water for medium-energy x-ray beams using Fricke dosimetry

PURPOSE

For x-ray beams in the low and medium energy range, reference dosimetry is established in terms of air kerma. Fricke dosimetry has shown great potential in the absolute measurements of the absorbed dose to water for high-energy ranges. Therefore, the main purpose of this work was to compare the absorbed dose to water for medium-energy x-ray beams obtained through Fricke dosimetry with that obtained from the air kerma rate.

METHODS

To determine the absorbed dose to water using Fricke dosimetry, the polyethylene bags methodology was chosen. Fricke solution was irradiated at four different beam qualities. The absorbed dose to water values obtained using Fricke dosimetry were compared to those obtained using the standard protocol, using the Z-score.

RESULTS

Values of the Z-score were <2 for all measurements of absorbed dose to water, which means that the values obtained using Fricke dosimetry are equivalent to those obtained using the reference protocol. The combined standard uncertainty for the absorbed dose to water obtained by Fricke dosimetry was lower than that obtained with the ionization chamber.

CONCLUSIONS

Chemical dosimetry using a standard FeSO₄ solution has been demonstrated to be a potential option as a standard for the quantity absorbed dose to water for medium kV x-ray qualities.

Autosegmentation of the rectum on megavoltage image guidance scans

Autosegmentation of image guidance (IG) scans is crucial for streamlining and optimising delivered dose calculation in radiotherapy. By accounting for interfraction motion, daily delivered dose can be accumulated and incorporated into automated systems for adaptive radiotherapy. Autosegmentation of IG scans is challenging due to poorer image quality than typical planning kilovoltage computed tomography (kVCT) systems, and the resulting reduction of soft tissue contrast in regions such as the pelvis makes organ boundaries less distinguishable. Current autosegmentation solutions generally involve propagation of planning contours to the IG scan by deformable image registration (DIR). Here, we present a novel approach for primary autosegmentation of the rectum on megavoltage IG scans acquired during prostate radiotherapy, based on the Chan-Vese algorithm. Pre-processing steps such as Hounsfield unit/intensity scaling, identifying search regions, dealing with air, and handling the prostate, are detailed. Post-processing features include identification of implausible contours (nominally those affected by muscle or air), 3D self-checking, smoothing, and interpolation. In cases where the algorithm struggles, the best estimate on a given slice may revert to the propagated kVCT rectal contour. Algorithm parameters were optimised systematically for a training cohort of 26 scans, and tested on a validation cohort of 30 scans, from 10 patients. Manual intervention was not required. Comparing Chan-Vese autocontours with contours manually segmented by an experienced clinical oncologist achieved a mean Dice Similarity Coefficient of 0.78 (SE < 0.011). This was comparable with DIR methods for kVCT and CBCT published in the literature. The autosegmentation system was developed within the VoxTox Research Programme for accumulation of delivered dose to the rectum in prostate radiotherapy, but may have applicability to further anatomical sites and imaging modalities.

Influence of the contrast agents on treatment planning dose calculations of prostate and rectal cancers

AIM

The aim of the present study is to quantify differences in dose calculations caused by using CA and determine if the resulting differences are clinically significant.

BACKGROUND

The influence of contrast agents (CA) on radiation dose calculations must be taken into account in treatment planning.

MATERIALS AND METHODS

Eleven patients with pelvic cancers were included in this study and two sets of CTs were taken for each patient (without and with CA) in the same position and coordinates. Both sets of images were transferred to the DosiSoft ISOgray treatment planning system for contouring and calculating the dose distribution and monitor units (MUs) with Collapsed Cone and Superposition algorithms, respectively. All plans were generated on pre-contrast CT and subsequently copied to the post-contrast CT. Radiation dose calculations from the two sets of CTs were compared using a paired sample t-test.

RESULTS

The results showed a statistically insignificant difference between pre- and post-contrast CT treatment plans for target volume and OARs (p > 0.05), except bladder organ in the prostate region (p < 0.05) but the relative mean dose and MU differences were less than 2% in any patient for 18 MV photon beam.

CONCLUSIONS

Treatment planning on contrasted images generally showed a lower radiation dose to both target volume and OARs than plans on non-contrasted images. The results of this research showed that the small radiation dose differences between the plans for the CT scans with and without CA seem to be clinically insignificant; therefore, contrast-enhanced CT can be used for both target delineation and treatment planning of prostate and rectal cancers.