A multi-layer strip ionization chamber (MLSIC) device for proton pencil beam scan quality assurance

Zhou, Shuang; Rao, Wei; Chen, Qinghao; Tan, Yuewen; Smith, W. L.; Sun, Baozhou; Zhou, Jun; Chang, Chung‐Cheng; Lin, Liyong; Darafsheh, Arash; Zhao, Tianyu; Zhang, Tiezhi

doi:10.1088/1361-6560/ac8593

Cited by 9 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this approach requires a considerable amount of numerical experiment time. Future investigation will likely focus on developing advanced validation experiments using human-mimicking phantoms and state-ofthe-art instrumentation 44 to quantify proton range uncertainty. Then the experiment data can be used to identify which DL models can work compatibly, effectively, and robustly with the proposed image-guided framework for proton FLASH radiotherapy.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based Fast Volumetric Image Generation for Image-guided Proton FLASH Radiotherapy

Chang

Lei

Wang

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Objective: FLASH radiotherapy leverages ultra-high dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. This may allow dose escalation, toxicity mitigation, or both. To prepare for the ultra-high dose-rate delivery, we aim to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for proton FLASH beam delivery. Approach: The proposed framework comprises four modules, including orthogonal kV x-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a four-dimensional computed tomography (CT) dataset with ten respiratory phases. Leave-phase-out cross-validation was performed to investigate the DL model’s robustness for each patient. Main results: The proposed framework reconstructed patients’ volumetric anatomy, including tumors and organs at risk from orthogonal x-ray projections. Considering all evaluation metrics, the kV projections with source angles of 135° and 225° yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were 75±22 HU, 19±3.7 dB, 0.938±0.044, and -1.3%±4.1%. Significance: The proposed framework has been demonstrated to reconstruct volumetric images with a high degree of accuracy using two orthogonal x-ray projections. The embedded WET module can be used to detect potential proton beam-specific patient anatomy variations. This framework can rapidly deliver volumetric images to potentially guide proton FLASH therapy treatment delivery systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep learning-based Fast Volumetric Image Generation for Image-guided Proton FLASH Radiotherapy

Chang

Lei

Wang

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…MatriXX PT (IBA dosimetry, Germany) was used to measured dose imaging with a 7x7 mm 2 pixel resolution. The state-of-the-art multilayer strip ionization chamber (MLSIC) detector [18] was used to provide four-dimensional (4D) measurement in a spatiotemporal domain. The time and spatial resolutions of MLSIC were 320 µs and 2 mm with the active area of 256x256 mm 2 .…”

Section: Ct Scan Phantom and Detectormentioning

confidence: 99%

In vivo proton range validation using pseudo proton radiography

Chang

Zhou

Gao

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

View full text Add to dashboard Cite

The current clinical practice for Monte Carlo (MC) treatment planning reserves a 3.5% margin to compensate for proton range uncertainty. Additionally, patient positional uncertainty is typically 3-5 mm for proton craniospinal irradiation (CSI) treatment planning. These two uncertainties compromise the sparing of spine vertebrae in proton CSI patients. Computer tomography (CT) material characterization contributes approximately 2.5% proton range uncertainty. Multiple CT-tomaterial conversion methods have been investigated using dual-energy CT or magnetic resonance imaging to improve the range uncertainty. However, there is a lack of experimental data to validate the credibility of those material characterization models. We aim to develop an in vivo proton range method using pseudo proton radiography to validate imaging-based material characterization models consistently. Proton radiography techniques, such as proton water equivalent thickness (WET) and dose maps, were used to evaluate the in vivo proton range accuracy. Anteroposterior proton beams were penetrated through an anthropomorphic phantom. Then the exit doses were measured from proton radiography imaging. The validation experiment applied a newly designed multi-layer strip ionization chamber (MLSIC) for the first time to perform four-dimensional (4D) measurement for depth doses from 625 proton spots in two minutes. The depth doses of each spot were post-processed into WET imaging. A MatriXX PT was applied for 2D measurement from 19x19 cm2 proton fields. We compared the performance of the empirical DECT model and physics-informed machine learning (PIML) models for material conversion. The results indicated that the PIML-based material characteristic method generated more accurate WET and dose imaging using DECT compared to conventional machine learning and empirical material inference methods. The proposed in vivo proton range validation method can be used to quantify the credibility of DECT-based material conversion models for proton range enhancement. The method can potentially provide in-room patient anatomy changes to accomplish online adaption for modification. This technique will significantly benefit proton flash therapy, which demands high accuracy.

show abstract

“…For real-time monitoring with higher dose rates, NaI is susceptible to PG count saturation (Haertter et al 2021). More recently, strip ionization chambers are becoming popular for real-time monitoring (Zhou et al 2022, Yang et al 2022. Yang et al successfully demonstrated the feasibility of FLASH beam monitoring up to a 20 kHz sampling rate using a 2D strip ionization chamber.…”

Section: Introductionmentioning

confidence: 99%

Measurement of the time structure of FLASH beams using prompt gamma rays and secondary neutrons as surrogates

Charyyev,

Liu,

Yang

et al. 2023

Phys. Med. Biol.

Self Cite

View full text Add to dashboard Cite

Objective: The aim of this study was to investigate the feasibility of online monitoring of irradiation time (IRT) and scan time for FLASH proton radiotherapy using a pixelated semiconductor detector.Approach: Measurements of the time structure of FLASH irradiations were performed using fast, pixelated spectral detectors based on the Timepix3 (TPX3) chips with two architectures: AdvaPIX-TPX3 and Minipix-TPX3. The latter has a fraction of its sensor coated with a material to increase sensitivity to neutrons. With little or no dead time and an ability to resolve events that are closely spaced in time (tens of nanoseconds), both detectors can accurately determine IRTs as long as pulse pile-up is avoided. To avoid pulse pile-up, the detectors were placed well beyond the Bragg peak or at a large scattering angle. Prompt gamma rays and secondary neutrons were registered in the detectors’ sensors and IRTs were calculated based on timestamps of the first charge carriers (beam-on) and the last charge carriers (beam-off). In addition, scan times in x, y, and diagonal directions were measured. The experiment was carried out for various setups: i) a single spot, ii) a small animal field, iii) a patient field, and iv) an experiment using an anthropomorphic phantom to demonstrate in vivo online monitoring of IRT. All measurements were compared to vendor log files. Main results: Differences between measurements and log files for a single spot, a small animal field, and a patient field were within 1%, 0.3% and 1%, respectively. In vivo monitoring of IRTs (95-270 ms) was accurate within 0.1% for AdvaPIX-TPX3 and within 6.1% for Minipix-TPX3. The scan times in x, y, and diagonal directions were 4.0, 3.4, and 4.0 ms, respectively. Significance: Overall, the AdvaPIX-TPX3 can measure FLASH IRTs within 1% accuracy, indicating that prompt gamma rays are a good surrogate for primary protons. The Minipix-TPX3 showed a somewhat higher discrepancy, likely due to the late arrival of thermal neutrons to the detector sensor and lower readout speed. The scan times (3.4 ± 0.05 ms) in the 60-mm distance of y-direction were slightly less than (4.0 ± 0.06 ms) in the 24-mm distance of x-direction, confirming the much faster scanning speed of the Y magnets than that of X. Diagonal scan speed was limited by the slower X magnets.

show abstract

A multi-layer strip ionization chamber (MLSIC) device for proton pencil beam scan quality assurance

Cited by 9 publications

References 17 publications

Deep learning-based Fast Volumetric Image Generation for Image-guided Proton FLASH Radiotherapy

Deep learning-based Fast Volumetric Image Generation for Image-guided Proton FLASH Radiotherapy

In vivo proton range validation using pseudo proton radiography

Measurement of the time structure of FLASH beams using prompt gamma rays and secondary neutrons as surrogates

Contact Info

Product

Resources

About