Observer uncertainties of soft tissue-based patient positioning in IGRT

Dual-encoder architecture for metal artifact reduction for kV-cone-beam CT images in head and neck cancer radiotherapy

During a radiotherapy (RT) course, geometrical variations of target volumes, organs at risk, weight changes (loss/gain), tumor regression and/or progression can significantly affect the treatment outcome. Adaptive RT has become the effective methods along with technical advancements in imaging modalities including cone-beam computed tomography (CBCT). Planning CT (pCT) can be modified via deformable image registration (DIR), which is applied to the pair of pCT and CBCT. However, the artifact existed in both pCT and CBCT is a vulnerable factor in DIR. The dose calculation on CBCT is also suggested. Missing information due to the artifacts hinders the accurate dose calculation on CBCT. In this study, we aim to develop a deep learning-based metal artifact reduction (MAR) model to reduce the metal artifacts in CBCT for head and neck cancer RT. To train the proposed MAR model, we synthesized the kV-CBCT images including metallic implants, with and without metal artifacts (simulated image data pairs) through sinogram image handling process. We propose the deep learning architecture which focuses on both artifact removal and reconstruction of anatomic structure using a dual-encoder architecture. We designed four single-encoder models and three dual-encoder models based on UNet (for an artifact removal) and FusionNet (for a tissue restoration). Each single-encoder model contains either UNet or FusionNet, while the dual-encoder models have both UNet and FusionNet architectures. In the dual-encoder models, we implemented different feature fusion methods, including simple addition, spatial attention, and spatial/channel wise attention. Among the models, a dual-encoder model with spatial/channel wise attention showed the highest scores in terms of peak signal-to-noise ratio, mean squared error, structural similarity index, and Pearson correlation coefficient. CBCT images from 34 head and neck cancer patients were used to test the developed models. The dual-encoder model with spatial/channel wise attention showed the best results in terms of artifact index. By using the proposed model to CBCT, one can achieve more accurate synthetic pCT for head and neck patients as well as better tissue recognition and structure delineation for CBCT image itself.

Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy

Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.

Organ-contour-driven auto-matching algorithm in image-guided radiotherapy

PURPOSE

This study aimed to demonstrate the potential clinical applicability of an organ-contour-driven auto-matching algorithm in image-guided radiotherapy.

METHODS

This study included eleven consecutive patients with cervical cancer who underwent radiotherapy in 23 or 25 fractions. Daily and reference magnetic resonance images were converted into mesh models. A weight-based algorithm was implemented to optimize the distance between the mesh model vertices and surface of the reference model during the positioning process. Within the cost function, weight parameters were employed to prioritize specific organs for positioning. In this study, three scenarios with different weight parameters were prepared. The optimal translation and rotation values for the cervix and uterus were determined based on the calculated translations alone or in combination with rotations, with a rotation limit of ±3°. Subsequently, the coverage probabilities of the following two planning target volumes (PTV), an isotropic 5 mm and anisotropic margins derived from a previous study, were evaluated.

RESULTS

The percentage of translations exceeding 10 mm varied from 9% to 18% depending on the scenario. For small PTV sizes, more than 80% of all fractions had a coverage of 80% or higher. In contrast, for large PTV sizes, more than 90% of all fractions had a coverage of 95% or higher. The difference between the median coverage with translational positioning alone and that with both translational and rotational positioning was 1% or less.

CONCLUSION

This algorithm facilitates quantitative positioning by utilizing a cost function that prioritizes organs for positioning. Consequently, consistent displacement values were algorithmically generated. This study also revealed that the impact of rotational corrections, limited to ±3°, on PTV coverage was minimal.

Evaluating the impact of possible interobserver variability in CBCT-based soft-tissue matching using TCP/NTCP models for prostate cancer radiotherapy

Background

Prostate alignment is subject to interobserver variability in cone-beam CT (CBCT)-based soft-tissue matching. This study aims to analyze the impact of possible interobserver variability in CBCT-based soft-tissue matching for prostate cancer radiotherapy.

Methods

Retrospective data, consisting of 156 CBCT images from twelve prostate cancer patients with elective nodal irradiation were analyzed in this study. To simulate possible interobserver variability, couch shifts of 2 mm relative to the resulting patient position of prostate alignment were assumed as potential patient positions (27 possibilities). For each CBCT, the doses of the potential patient positions were re-calculated using deformable image registration-based synthetic CT. The impact of the simulated interobserver variability was evaluated using tumor control probabilities (TCPs) and normal tissue complication probabilities (NTCPs).

Results

No significant differences in TCPs were found between prostate alignment and potential patient positions (0.944 ± 0.003 vs 0.945 ± 0.003, P = 0.117). The average NTCPs of the rectum ranged from 5.16 to 7.29 (%) among the potential patient positions and were highly influenced by the couch shift in the anterior–posterior direction. In contrast, the average NTCPs of the bladder ranged from 0.75 to 1.12 (%) among the potential patient positions and were relatively negligible.

Conclusions

The NTCPs of the rectum, rather than the TCPs of the target, were highly influenced by the interobserver variability in CBCT-based soft-tissue matching. This study provides a theoretical explanation for daily CBCT-based image guidance and the prostate-rectum interface matching procedure.

Trial registration: Not applicable.

Low dose cone beam CT for paediatric image-guided radiotherapy: Image quality and practical recommendations

PURPOSE

Cone beam CT (CBCT) is used in paediatric image-guided radiotherapy (IGRT) for patient setup and internal anatomy assessment. Adult CBCT protocols lead to excessive doses in children, increasing the risk of radiation-induced malignancies. Reducing imaging dose increases quantum noise, degrading image quality. Patient CBCTs also include 'anatomical noise' (e.g. motion artefacts), further degrading quality. We determine noise contributions in paediatric CBCT, recommending practical imaging protocols and thresholds above which increasing dose yields no improvement in image quality.

METHODS AND MATERIALS

Sixty CBCTs including the thorax or abdomen/pelvis from 7 paediatric patients (aged 6-13 years) were acquired at a range of doses and used to simulate lower dose scans, totalling 192 scans (0.5-12.8 mGy). Noise measured in corresponding regions of each patient and a 10-year-old phantom were compared, modelling total (including anatomical) noise, and quantum noise contributions as a function of dose. Contrast-to-noise ratio (CNR) was measured between fat/muscle. Soft tissue registration was performed on the kidneys, comparing accuracy to the highest dose scans.

RESULTS

Quantum noise contributed <20% to total noise in all cases, suggesting anatomical noise is the largest determinant of image quality in the abdominal/pelvic region. CNR exceeded 3 in over 90% of cases ≥ 1 mGy, and 57% of cases at 0.5 mGy. Soft tissue registration was accurate for doses > 1 mGy.

CONCLUSION

Anatomical noise dominates quantum noise in paediatric CBCT. Appropriate soft tissue contrast and registration accuracy can be achieved for doses as low as 1 mGy. Increasing dose above 1 mGy holds no benefit in improving image quality or registration accuracy due to the presence of anatomical noise.

Evaluation of the XVI dual registration tool for image-guided radiotherapy in prostate cancer

Purpose

To compare the reliability and the required time for two cone-beam CT (CBCT) registration methods for prostate irradiation (PI) and prostate bed irradiation (PBI).

Material and methods

Two-hundred treatment fractions (in 10 PI and 10 PBI patients) were reanalyzed, using two CBCT registration methods: (1) a combination of an automated chamfer matching (CM) with manual matching (MM), and (2) the automated XVI dual registration tool (DRT). Bland-Altman 95% Limits of Agreement (LoA) were used to assess agreement with manual registration by Radiation Oncologists.

Results

All 95% LoA for CM + MM were ≤ 0.33 cm. For DRT, several 95% LoA were notably larger than the predefined clinical threshold of 0.3 cm: -0.47 to +0.25 cm (PI) and -0.36 to +0.23 cm (PBI) for the superior-inferior direction and -0.52 to +0.24 cm (PI) and -0.38 to +0.31 cm (PBI) for the anterior-posterior direction.For PI, the average time required was 33 s with CM + MM versus only 18 s with DRT (p = 0.002). For PBI, this was 13 versus 19 s, respectively (p = 0.16).

Conclusion

For PI, DRT was significantly faster than CM + MM, but the accuracy is insufficient to use without manual verification. Therefore, manual verification is still warranted, but could offset the time benefit. For PBI, the CM + MM method was faster and more accurate.

Factors affecting accuracy and precision in ultrasound guided radiotherapy

BACKGROUND AND PURPOSE

Transperineal ultrasound (TPUS) is used clinically for directly assessing prostate motion. Factors affecting accuracy and precision in TPUS motion estimation must be assessed to realise its full potential.

METHODS AND MATERIALS

Patients were imaged using volumetric TPUS during the Clarity-Pro trial (NCT02388308). Prostate motion was measured online at patient set-up and offline by experienced observers. Cone beam CT with markers was used as a comparator and observer performance was also quantified. The influence of different clinical factors was examined to establish specific recommendations towards efficacious ultrasound guided radiotherapy.

RESULTS

From 330 fractions in 22 patients, offline observer random errors were 1.5 mm, 1.3 mm, 1.9 mm (left-right, superior-inferior, anteroposterior respectively). Errors increased in fractions exhibiting poor image quality to 3.3 mm, 3.3 mm and 6.8 mm. Poor image quality was associated with inconsistent probe placement, large anatomical changes and unfavourable imaging conditions within the patient. Online matching exhibited increased observer errors of: 3.2 mm, 2.9 mm and 4.7 mm. Four patients exhibited large systematic residual errors, of which three had poor quality images. Patient habitus showed no correlation with observer error, residual error, or image quality.

CONCLUSIONS

TPUS offers the unique potential to directly assess inter- and intra-fraction motion on conventional linacs. Inconsistent image quality, inexperienced operators and the pressures of the clinical environment may degrade precision and accuracy. Experienced operators are essential and cross-centre standards for training and QA should be established that build upon current guidance. Greater use of automation technologies may further minimise uncertainties.