Development of fast patient position verification software using 2D-3D image registration and its clinical experience

Inter-fractional error and intra-fractional motion of prostate and dosimetry comparisons of patient position registrations with versus without fiducial markers during treatment with carbon-ion radiotherapy

A few reports have discussed the influence of inter-fractional position error and intra-fractional motion on dose distribution, particularly regarding a spread-out Bragg peak. We investigated inter-fractional and intra-fractional prostate position error by monitoring fiducial marker positions. In 2020, data from 15 patients with prostate cancer who received carbon-ion beam radiotherapy (CIRT) with gold markers were investigated. We checked marker positions before and during irradiation to calculate the inter-fractional positioning and intra-fractional movement and evaluated the CIRT dose distribution by adjusting the planning beam isocenter and clinical target volume (CTV) position. We compared the CTV dose coverages (CTV receiving 95% [V95%] or 98% [V98%] of the prescribed dose) between skeletal and fiducial matching irradiation on the treatment planning system. For inter-fractional error, the mean distance between the marker position in the planning images and that in a patient starting irradiation with skeletal matching was 1.49 ± 1.11 mm (95th percentile = 1.85 mm). The 95th percentile (maximum) values of the intra-fractional movement were 0.79 mm (2.31 mm), 1.17 mm (2.48 mm), 1.88 mm (4.01 mm), 1.23 mm (3.00 mm), and 2.09 mm (8.46 mm) along the lateral, inferior, superior, dorsal, and ventral axes, respectively. The mean V95% and V98% were 98.2% and 96.2% for the skeletal matching plan and 99.5% and 96.8% for the fiducial matching plan, respectively. Fiducial matching irradiation improved the CTV dose coverage compared with skeletal matching irradiation for CIRT for prostate cancer.

Modeling of the resensitization effect on carbon-ion radiotherapy for stage I non-small cell lung cancer

Objective. To investigate the effect of redistribution and reoxygenation on the 3-year tumor control probability (TCP) of patients with stage I non-small cell lung cancer (NSCLC) treated with carbon-ion radiotherapy.Approach. A meta-analysis of published clinical data of 233 NSCLC patients treated by carbon-ion radiotherapy under 18-, 9-, 4-, and single-fraction schedules was conducted. The linear-quadratic (LQ)-based cell-survival model incorporating the radiobiological 5Rs, radiosensitivity, repopulation, repair, redistribution, and reoxygenation, was developed to reproduce the clinical TCP data. Redistribution and reoxygenation were regarded together as a single phenomenon and termed 'resensitization' in the model. The optimum interval time between fractions was investigated for each fraction schedule using the determined model parameters.Main results.The clinical TCP data for 18-, 9-, and 4-fraction schedules were reasonably reproduced by the model without the resensitization effect, whereas its incorporation was essential to reproduce the TCP data for all fraction schedules including the single fraction. The curative dose for the single-fraction schedule was estimated to be 49.0 Gy (RBE), which corresponds to the clinically adopted dose prescription of 50.0 Gy (RBE). For 18-, 9-, and 4-fraction schedules, a 2-to-3-day interval is required to maximize the resensitization effect during the time interval. In contrast, the single-fraction schedule cannot benefit from the resensitization effect, and the shorter treatment time is preferable to reduce the effect of sub-lethal damage repair during the treatment.Significance.The LQ-based cell-survival model incorporating the radiobiological 5Rs was developed and used to evaluate the effect of the resensitization on clinical results of NSCLC patients treated with hypo-fractionated carbon-ion radiotherapy. The incorporation of the resensitization into the cell-survival model improves the reproducibility to the clinical TCP data. A shorter treatment time is preferable in the single-fraction schedule, while a 2-to-3-day interval between fractions is preferable in the multi-fraction schedules for effective treatments.

Shortening image registration time using a deep neural network for patient positional verification in radiotherapy

We sought to accelerate 2D/3D image registration computation time using image synthesis with a deep neural network (DNN) to generate digitally reconstructed radiographic (DRR) images from X-ray flat panel detector (FPD) images. And we explored the feasibility of using our DNN in the patient setup verification application. Images of the prostate and of the head and neck (H&N) regions were acquired by two oblique X-ray fluoroscopic units and the treatment planning CT. DNN was designed to generate DRR images from the FPD image data. We evaluated the quality of the synthesized DRR images to compare the ground-truth DRR images using the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Image registration accuracy and computation time were evaluated by comparing the 2D-3D image registration algorithm using DRR and FPD image data with DRR and synthesized DRR images. Mean PSNR values were 23.4 ± 3.7 dB and 24.1 ± 3.9 dB for the pelvic and H&N regions, respectively. Mean SSIM values for both cases were also similar (= 0.90). Image registration accuracy was degraded by a mean of 0.43 mm and 0.30°, it was clinically acceptable. Computation time was accelerated by a factor of 0.69. Our DNN successfully generated DRR images from FPD image data, and improved 2D-3D image registration computation time up to 37% in average.

Planning strategies for robust carbon-ion scanning radiotherapy for stage I esophageal cancer: a retrospective study

This study aimed to establish a treatment planning strategy with carbon-ion scanning radiotherapy (CIRTs) for stage I esophageal cancer. The clinical data of seven patients treated with CIRTs were used. The setup error and interfractional and intrafractional motion error were analyzed using in-room computed tomography (CT) images for each treatment day. Finally, the planning target volume (PTV) margin was identified according to the accuracy of the treatment system. To ensure robustness against the positional displacements of the target and organs at risk (OAR), the replacement areas were placed as a contour adjacent to the tumor or OAR on the CT-image. The CT values of these areas were replaced by those of the target or OAR. Further, the dose distributions were optimized. Moreover, the variations in the target coverage from the initial plan for each treatment day (ΔV95%) were evaluated. By contrast, the risk of OAR was not evaluated in this study. The setup error was within 1.0 mm. The interfractional and intrafractional target motion errors were 2.8 and 5.0 mm, respectively. The PTV margins were 6.5 and 6.8 mm in the axial and depth directions, respectively. The robustness to target and OAR displacement was evaluated. The results showed that the target coverage with replacement could suppress decreased target coverage more than that without replacement. The PTV determination and replacement methods used in this study improved the target coverage in CIRTs for stage I esophageal cancer. Despite the need for a clinical follow-up, this method may help to improve clinical outcomes.

Deep neural network-based synthetic image digital fluoroscopy using digitally reconstructed tomography

We developed a deep neural network (DNN) to generate X-ray flat panel detector (FPD) images from digitally reconstructed radiographic (DRR) images. FPD and treatment planning CT images were acquired from patients with prostate and head and neck (H&N) malignancies. The DNN parameters were optimized for FPD image synthesis. The synthetic FPD images' features were evaluated to compare to the corresponding ground-truth FPD images using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). The image quality of the synthetic FPD image was also compared with that of the DRR image to understand the performance of our DNN. For the prostate cases, the MAE of the synthetic FPD image was improved (= 0.12 ± 0.02) from that of the input DRR image (= 0.35 ± 0.08). The synthetic FPD image showed higher PSNRs (= 16.81 ± 1.54 dB) than those of the DRR image (= 8.74 ± 1.56 dB), while SSIMs for both images (= 0.69) were almost the same. All metrics for the synthetic FPD images of the H&N cases were improved (MAE 0.08 ± 0.03, PSNR 19.40 ± 2.83 dB, and SSIM 0.80 ± 0.04) compared to those for the DRR image (MAE 0.48 ± 0.11, PSNR 5.74 ± 1.63 dB, and SSIM 0.52 ± 0.09). Our DNN successfully generated FPD images from DRR images. This technique would be useful to increase throughput when images from two different modalities are compared by visual inspection.

Optimizing 3DCT image registration for interfractional changes in carbon-ion prostate radiotherapy

To perform setup procedures including both positional and dosimetric information, we developed a CT-CT rigid image registration algorithm utilizing water equivalent pathlength (WEPL)-based image registration and compared the resulting dose distribution with those of two other algorithms, intensity-based image registration and target-based image registration, in prostate cancer radiotherapy using the carbon-ion pencil beam scanning technique. We used the data of the carbon ion therapy planning CT and the four-weekly treatment CTs of 19 prostate cancer cases. Three CT-CT registration algorithms were used to register the treatment CTs to the planning CT. Intensity-based image registration uses CT voxel intensity information. Target-based image registration uses target position on the treatment CTs to register it to that on the planning CT. WEPL-based image registration registers the treatment CTs to the planning CT using WEPL values. Initial dose distributions were calculated using the planning CT with the lateral beam angles. The treatment plan parameters were optimized to administer the prescribed dose to the PTV on the planning CT. Weekly dose distributions using the three different algorithms were calculated by applying the treatment plan parameters to the weekly CT data. Dosimetry, including the dose received by 95% of the clinical target volume (CTV-D95), rectal volumes receiving > 20 Gy (RBE) (V20), > 30 Gy (RBE) (V30), and > 40 Gy (RBE) (V40), were calculated. Statistical significance was assessed using the Wilcoxon signed-rank test. Interfractional CTV displacement over all patients was 6.0 ± 2.7 mm (19.3 mm maximum standard amount). WEPL differences between the planning CT and the treatment CT were 1.2 ± 0.6 mm-H₂O (< 3.9 mm-H₂O), 1.7 ± 0.9 mm-H₂O (< 5.7 mm-H₂O) and 1.5 ± 0.7 mm-H₂O (< 3.6 mm-H₂O maxima) with the intensity-based image registration, target-based image registration, and WEPL-based image registration, respectively. For CTV coverage, the D95 values on the planning CT were > 95% of the prescribed dose in all cases. The mean CTV-D95 values were 95.8 ± 11.5% and 98.8 ± 1.7% with the intensity-based image registration and target-based image registration, respectively. The WEPL-based image registration was CTV-D95 to 99.0 ± 0.4% and rectal Dmax to 51.9 ± 1.9 Gy (RBE) compared to 49.4 ± 9.1 Gy (RBE) with intensity-based image registration and 52.2 ± 1.8 Gy (RBE) with target-based image registration. The WEPL-based image registration algorithm improved the target coverage from the other algorithms and reduced rectal dose from the target-based image registration, even though the magnitude of the interfractional variation was increased.

Creation, evolution, and future challenges of ion beam therapy from a medical physicist's viewpoint (Part 2). Chapter 2. Biophysical model, treatment planning system and image guided radiotherapy

When an ion beam penetrates deeply into the body, its kinetic energy decreases, and its biological effect increases due to the change of the beam quality. To give a uniform biological effect to the target, it is necessary to reduce the absorbed dose with the depth. A bio-physical model estimating the relationship between ion beam quality and biological effect is necessary to determine the relative biological effectiveness (RBE) of the ion beam that changes with depth. For this reason, Lawrence Berkeley Laboratory, National Institute of Radiological Sciences (NIRS) and GSI have each developed their own model at the starting of the ion beam therapy. Also, NIRS developed a new model at the starting of the scanning irradiation. Although the Local Effect Model (LEM) at the GSI and the modified Microdosimetric Kinetic Model (MKM) at the NIRS, the both are currently used, can similarly predict radiation quality-induced changes in surviving fraction of cultured cell, the clinical RBE-weighted doses for the same absorbed dose are different. This is because the LEM uses X-rays as a reference for clinical RBE, whereas the modified MKM uses carbon ion beam as a reference and multiplies it by a clinical factor of 2.41. Therefore, both are converted through the absorbed dose. In PART 2, I will describe the development of such a bio-physical model, as well as the birth and evolution of a treatment planning system and image guided radiotherapy.

Real-time deep neural network-based automatic bowel gas segmentation on X-ray images for particle beam treatment

Since particle beam distribution is vulnerable to change in bowel gas because of its low density, we developed a deep neural network (DNN) for bowel gas segmentation on X-ray images. We used 6688 image datasets from 209 cases as training data, 736 image datasets from 23 cases as validation data and 102 image datasets from 51 cases as test data (total 283 cases). For the training data, we prepared three types of digitally reconstructed radiographic (DRR) images (all-density, bone and gas) by projecting the treatment planning CT image data. However, the real X-ray images acquired in the treatment room showed low contrast that interfered with manual delineation of bowel gas. Therefore, we used synthetic X-ray images converted from DRR images in addition to real X-ray images.We evaluated DNN segmentation accuracy for the synthetic X-ray images using Intersection over Union, recall, precision, and the Dice coefficient, which measured 0.708 ± 0.208, 0.832 ± 0.170, 0.799 ± 0.191, and 0.807 ± 0.178, respectively. The evaluation metrics for the real X-images were less accurate than those for the synthetic X-ray images (0.408 ± 0237, 0.685 ± 0.326, 0.490 ± 0272, and 0.534 ± 0.271, respectively). Computation time was 29.7 ± 1.3 ms/image. Our DNN appears useful in increasing treatment accuracy in particle beam therapy.

Robust treatment planning in scanned carbon-ion radiotherapy for pancreatic cancer: Clinical verification using in-room computed tomography images

Purpose

Carbon-ion beam (C-beam) has a sharp dose distribution called the Bragg peak. Carbon-ion radiation therapy, such as stereotactic body radiotherapy in photon radiotherapy, can be completed in a short period by concentrating the radiation dose on the tumor while minimizing the dose to organs at-risk. However, the stopping position of C-beam is sensitive to density variations along the beam path and such variations can lower the tumor dose as well as cause the delivery of an unexpectedly high dose to the organs at risk. We evaluated the clinical efficacy of a robust planning technique considering gastrointestinal gas (G-gas) to deliver accurate radiation doses in carbon-ion radiotherapy for pancreatic cancer.

Materials and methods

We focused on the computed tomography (CT) value replacement method. Replacement signifies the overwriting of CT values in the CT images. The most effective replacement method for robust treatment planning was determined by verifying the effects of the three replacement patterns. We selected 10 consecutive patients. Pattern 1 replaces the CT value of the G-gas contours with the value of the region without G-gas (P1). This condition indicates a no-gas state. Pattern 2 replaces each gastrointestinal contour using the mean CT value of each contour (P2). The effect of G-gas was included in the replacement value. Pattern 3 indicates no replacement (P3). We analyzed variations in the target coverage (TC) and homogeneity index (HI) from the initial plan using in-room CT images. We then performed correlation analysis on the variations in G-gas, TC, and HI to evaluate the robustness against G-gas.

Results

Analysis of variations in TC and HI revealed a significant difference between P1 and P3 and between P2 and P3. Although no statistically significant difference was observed between P1 and P2, variations, including the median, tended to be fewer in P2. The correlation analyses for G-gas, TC, and HI showed that P2 was less likely to be affected by G-gas.

Conclusion

For a treatment plan that is robust to G-gas, P2 mean replacement method should be used. This method does not necessitate any particular software or equipment, and is convenient to implement in clinical practice.

Perturbation analysis of 4D dose distribution for scanned carbon-ion beam radiotherapy

PURPOSE

To evaluate the patients' set-up error-induced perturbation effects on 4D dose distributions (4DDD) of range-adapted internal target volume-based (raITV) treatment plan using lung and liver 4DCT data sets.

METHODS

We enrolled 20 patients with lung and liver cancer treated with respiratory-gated carbon-ion beam scanning therapy. PTVs were generated by adding a 2 mm range-adapted set-up margin on the raITVs. Set-up errors were simulated by shifting the beam isocenter in three translational directions of ±2 mm, ±4 mm, and ±6 mm. 4DDDs were calculated for both nominal and isocenter-shifted situations. Dose metrics of CTV dose coverage (D95) and normal tissue sparing were evaluated. Statistical significance with p < 0.01 was considered by Wilcoxon signed rank test.

RESULTS

The CTV dose coverage was more sensitive to set-up errors for lung cases than for liver cases, and more serious in superior-inferior direction. The sufficient CTV-D95 > 98% could be achieved with set-up errors less than ±2 mm in all shift directions both for lung and liver cases. With the increase of set-up error, the CTV dose coverage decreased gradually. The clinical criterial of CTV-D95 > 95% could not be fulfilled with set-up error reached to ±4 mm for lung cases, and ±6 mm for liver cases. OAR doses did not have a significant difference with each set-up error for both lung and liver cases.

CONCLUSIONS

The range-adapted set-up margin successfully prevented dose degradation of 4DDDs in the presence of the same magnitude of set-up error for raITV-based carbon-ion beam scanning therapy.

Clinical experience of craniospinal intensity-modulated spot-scanning proton therapy using large fields for central nervous system medulloblastomas and germ cell tumors in children, adolescents, and young adults

The outcomes of intensity-modulated proton craniospinal irradiation (ipCSI) are unclear. We evaluated the clinical benefit of our newly developed ipCSI system that incorporates two gantry-mounted orthogonal online X-ray imagers with a robotic six-degrees-of-freedom patient table. Nine patients (7-19 years old) were treated with ipCSI. The prescribed dose for CSI ranged from 23.4 to 36.0 Gy (relative biological effectiveness) in 13-20 fractions. Four adolescent and young adult (AYA) patients (15 years or older) were treated with vertebral-body-sparing ipCSI (VBSipCSI). Myelosuppression following VBSipCSI was compared with that of eight AYA patients treated with photon CSI at the same institution previously. The mean homogeneity index (HI) in the nine patients was 0.056 (95% confidence interval: 0.044-0.068). The mean time from the start to the end of all beam delivery was 37 min 39 s ± 2 min 24 s (minimum to maximum: 22 min 49 s - 42 min 51 s). The nadir white blood cell, hemoglobin, and platelet levels during the 4 weeks following the end of the CSI were significantly higher in the VBSipCSI group than in the photon CSI group (P = 0.0071, 0.0453, 0.0024, respectively). The levels at 4 weeks after the end of CSI were significantly higher in the VBSipCSI group than in the photon CSI group (P = 0.0023, 0.0414, 0.0061). Image-guided ipCSI was deliverable in a reasonable time with sufficient HI. Using VBSipCSI, AYA patients experienced a lower incidence of serious acute hematological toxicity than AYA patients treated with photon CSI.

Commissioning of a fluoroscopic-based real-time markerless tumor tracking system in a superconducting rotating gantry for carbon-ion pencil beam scanning treatment

PURPOSE

To perform the final quality assurance of our fluoroscopic-based markerless tumor tracking for gated carbon-ion pencil beam scanning (C-PBS) radiotherapy using a rotating gantry system, we evaluated the geometrical accuracy and tumor tracking accuracy using a moving chest phantom with simulated respiration.

METHODS

The positions of the dynamic flat panel detector (DFPD) and x-ray tube are subject to changes due to gantry sag. To compensate for this, we generated a geometrical calibration table (gantry flex map) in 15° gantry angle steps by the bundle adjustment method. We evaluated five metrics: (a) Geometrical calibration was evaluated by calculating chest phantom positional error using 2D/3D registration software for each 5° step of the gantry angle. (b) Moving phantom displacement accuracy was measured (±10 mm in 1-mm steps) with a laser sensor. (c) Tracking accuracy was evaluated with machine learning (ML) and multi-template matching (MTM) algorithms, which used fluoroscopic images and digitally reconstructed radiographic (DRR) images as training data. The chest phantom was continuously moved ±10 mm in a sinusoidal path with a moving cycle of 4 s and respiration was simulated with ±5 mm expansion/contraction with a cycle of 2 s. This was performed with the gantry angle set at 0°, 45°, 120°, and 240°. (d) Four types of interlock function were evaluated: tumor velocity, DFPD image brightness variation, tracking anomaly detection, and tracking positional inconsistency in between the two corresponding rays. (e) Gate on/off latency, gating control system latency, and beam irradiation latency were measured using a laser sensor and an oscilloscope.

RESULTS

By applying the gantry flex map, phantom positional accuracy was improved from 1.03 mm/0.33° to <0.45 mm/0.27° for all gantry angles. The moving phantom displacement error was 0.1 mm. Due to long computation time, the tracking accuracy achieved with ML was <0.49 mm (=95% confidence interval [CI]) for imaging rates of 15 and 7.5 fps; those at 30 fps were decreased to 1.84 mm (95% CI: 1.79 mm-1.92 mm). The tracking positional accuracy with MTM was <0.52 mm (=95% CI) for all gantry angles and imaging frame rates. The tumor velocity interlock signal delay time was 44.7 ms (=1.3 frame). DFPD image brightness interlock latency was 34 ms (=1.0 frame). The tracking positional error was improved from 2.27 ± 2.67 mm to 0.25 ± 0.24 mm by the tracking anomaly detection interlock function. Tracking positional inconsistency interlock signal was output within 5.0 ms. The gate on/off latency was <82.7 ± 7.6 ms. The gating control system latency was <3.1 ± 1.0 ms. The beam irradiation latency was <8.7 ± 1.2 ms.

CONCLUSIONS

Our markerless tracking system is now ready for clinical use. We hope to shorten the computation time needed by the ML algorithm at 30 fps in the future.

Scanned carbon-ion beam therapy throughput over the first 7 years at National Institute of Radiological Sciences

INTRODUCTION

In the 7 years since our facility opened, we have treated >2000 patients with pencil-beam scanned carbon-ion beam therapy.

METHODS

To summarize treatment workflow, we evaluated the following five metrics: i) total number of treated patients; ii) treatment planning time, not including contouring procedure; iii) quality assurance (QA) time (daily and patient-specific); iv) treatment room occupancy time, including patient setup, preparation time, and beam irradiation time; and v) daily treatment hours. These were derived from the oncology information system and patient handling system log files.

RESULTS

The annual number of treated patients reached 594, 7 years from the facility startup, using two treatment rooms. Mean treatment planning time was 6.0 h (minimum: 3.4 h for prostate, maximum: 9.3 h for esophagus). Mean time devoted to daily QA and patient-specific QA were 22 min and 13.5 min per port, respectively, for the irradiation beam system. Room occupancy time was 14.5 min without gating for the first year, improving to 9.2 min (8.2 min without gating and 12.8 min with gating) in the second. At full capacity, the system ran for 7.5 h per day.

CONCLUSIONS

We are now capable of treating approximately 600 patients per year in two treatment rooms. Accounting for the staff working time, this performance appears reasonable compared to the other facilities.

Evaluation of the accuracy and clinical practicality of a calculation system for patient positional displacement in carbon ion radiotherapy at five sites

PURPOSE

We developed a system for calculating patient positional displacement between digital radiography images (DRs) and digitally reconstructed radiography images (DRRs) to reduce patient radiation exposure, minimize individual differences between radiological technologists in patient positioning, and decrease positioning time. The accuracy of this system at five sites was evaluated with clinical data from cancer patients. The dependence of calculation accuracy on the size of the region of interest (ROI) and initial position was evaluated for clinical use.

METHODS

For a preliminary verification, treatment planning and positioning data from eight setup patterns using a head and neck phantom were evaluated. Following this, data from 50 patients with prostate, lung, head and neck, liver, or pancreatic cancer (n = 10 each) were evaluated. Root mean square errors (RMSEs) between the results calculated by our system and the reference positions were assessed. The reference positions were manually determined by two radiological technologists to best-matching positions with orthogonal DRs and DRRs in six axial directions. The ROI size dependence was evaluated by comparing RMSEs for three different ROI sizes. Additionally, dependence on initial position parameters was evaluated by comparing RMSEs for four position patterns.

RESULTS

For the phantom study, the average (± standard deviation) translation error was 0.17 ± 0.05, rotation error was 0.17 ± 0.07, and ΔD was 0.14 ± 0.05. Using the optimal ROI size for each patient site, all cases of prostate, lung, and head and neck cancer with initial position parameters of 10 mm or under were acceptable in our tolerance. However, only four liver cancer cases and three pancreatic cancer cases were acceptable, because of low-reproducibility regions in the ROIs.

CONCLUSION

Our system has clinical practicality for prostate, lung, and head and neck cancer cases. Additionally, our findings suggest ROI size dependence in some cases.