arXiv subjects

Jiasen Ma

Publications and source records attributed to Jiasen Ma.

4 recordsLinked to original sources

arXiv2026

A Two-Stage Framework for Fast Proton Spot Map Generation in Pencil Beam Scanning Prostate SBRT Planning

Background: In pencil beam scanning (PBS) proton therapy, plans are delivered as proton spot maps (PSMs). Although deep learning can rapidly predict 3D dose, direct conversion of dose into deliverable spot patterns remains limited. Purpose: We developed GenSpot, a two stage framework that infers deliverable PSMs from CT and dose, and evaluated it in prostate SBRT by comparing Monte Carlo (MC) doses from GenSpot and clinical PSMs. Methods: GenSpot uses a physics informed projected proton spot map (PrPSM) representation, projecting spots through CT with water equivalent thickness and PDD information to align spots with the CT/dose grid while preserving linearity with spot weights. The dataset included 1,036 fields from 259 prostate SBRT plans, split 80%/10%/10% for training, validation, and testing. A 3D SwinUNETR predicted PrPSMs from CT and dose. Field specific PSMs were reconstructed using column wise nonnegative Lasso regression with precomputed PDD curves. GenSpot and clinical MC doses were compared using MAE, 3D gamma analysis, and composite plan DVH metrics. Results: On the test set, SwinUNETR achieved PrPSM MAE of 0.06 +/- 0.02 with high similarity to clinical PrPSMs. GenSpot MC doses showed low MAE of 0.07 +/- 0.03 Gy in the nonzero dose region and gamma passing rates of 0.90 at the field level and 0.97 at plan level. Composite DVH differences were within 1 Gy for targets and organs at risk, though the CTV showed a modest high dose increase. Spot complexity was similar to clinical plans, with slightly more spots. Prediction and reconstruction averaged 0.02 s and 2.1 s/field. Conclusions: GenSpot generated machine deliverable PSMs from CT and dose whose MC doses closely matched clinical PSM doses in a single institution prostate SBRT cohort. This physics informed dose to spots framework may support automated PBS planning and adaptive replanning, pending broader validation.

physics.med-ph↗

arXiv2026

Dose-LET Interactions Predict Capsular Contracture After Proton Postmastectomy Radiation Therapy

Pencil beam scanning (PBS) proton therapy provides highly conformal dose distributions that are increasingly leveraged for postmastectomy radiation therapy (PMRT) to reduce cardiopulmonary exposure. However, implant-based reconstruction in the setting of PMRT remains vulnerable to capsular contracture, and biological mechanisms of possible high linear energy transfer (LET) in PBS have not been well characterized. A retrospective case-control study was conducted on consecutive breast cancer patients who underwent mastectomy followed by implant-based reconstruction and proton PMRT (50 Gy in 25 fractions) between 2015 and 2021. Dose-LET volume histograms (DLVHs) were calculated for peri-implant tissue (5-mm shell around the implant). Generalized linear mixed-effects regression (GLMER) was employed to identify DLVH indices significantly associated with capsular contracture. Spearman correlation analysis was used to eliminate redundance. DLVCs were derived from receiver operating characteristic (ROC) analysis and validated using support vector machine (SVM)-based normal tissue complication probability (NTCP) model. Eight capsular contracture and 16 matched controls patients were analyzed. Three independent and significant DLVH indices were identified(p<0.01). The corresponding DLVCs were: V(55.8 Gy[RBE=1.1], 2.2 keV/μm) < 0.0033%, V(50.3 Gy[RBE=1.1], 5.4 keV/μm) < 0.0017%, and V(32.8 Gy[RBE=1.1], 0.9 keV/μm) > 96.98%. The SVM-based NTCP model achieved an area under the ROC curve (AUROC) of 0.867, with 91.7% accuracy, 87.5% sensitivity, and 93.8% specificity. Capsular contracture following proton PMRT is significantly associated with the synergistic interplay between dose and LETd in peri-implant tissue. The derived DLVCs provide actionable dosimetric constraints that can be integrated into treatment planning to minimize capsular contracture risk in proton PMRT.

physics.med-ph↗

arXiv2019

Monte-Carlo-based 4D robust optimization using physical and temporal uncertainties for intensity-modulated proton therapy

Purpose: Respiratory motion and the interplay effect cause the dose delivered to a patient with spot-scanned proton therapy to differ from the dose planned for during optimization. A new 4D robust optimization methodology was developed which incorporates patient breathing and the interplay effect, as well as their uncertainties, into the optimization process... Methods: The 4D robust optimizer used a 4DCT image set to obtain information regarding patient breathing and included a beam-delivery simulation to incorporate the interplay effect. The doses from potential spots were calculated on all breathing phases using Monte Carlo simulation, deformed to a reference phase using deformable image registration, and then added in a weighted sum. The weights for the different phases were based on the beam-delivery simulation. Robustness was added to the optimization by considering range and setup uncertainties as physical-uncertainty scenarios and uncertainties in the patient breathing and treatment delivery as temporal uncertainty scenarios... Results: The 4D robustly optimized plans exhibited improved target coverage with an average increase in the dose to 98% of the target volume of 4.9%, improved homogeneity with an average decrease in the homogeneity index of 6.0%, and improved robustness with decreased ranges in coverage and homogeneity amongst all uncertainty scenarios by averages of 35.0% and 52.5%, respectively... Conclusions: A 4D robust optimizer was developed which included the interplay effect and did not depend on the synchronization of breathing and delivery...

physics.med-ph↗

arXiv2014

The Data Acquisition System for the KOTO Experiment

We developed and built a new system of readout and trigger electronics, based on the waveform digitization and pipeline readout, for the KOTO experiment at J-PARC, Japan. KOTO aims at observing the rare kaon decay $K_{L}\rightarrowπ^{0}ν\barν$. A total of 4000 readout channels from various detector subsystems are digitized by 14-bit 125-MHz ADC modules equipped with a 10-pole Bessel filter in order to reduce the pile-up effects. The trigger decision is made every 8-ns using the digitized waveform information. To avoid dead time, the ADC and trigger modules have pipelines in their FPGA chips to store data while waiting for the trigger decision. The KOTO experiment performed the first physics run in May 2013. The data acquisition system worked stably during the run.

physics.ins-det↗