arXiv subjects

Shirin A. Enger

Publications and source records attributed to Shirin A. Enger.

6 recordsLinked to original sources

arXiv2026

Real-time tissue-equivalent measurement of individual clinical radiotherapy pulses

We apply the precision tools of cavity-enhanced absorption sensing to clinical oncology, demonstrating a dosimeter paradigm in which a centimeter-scale volume of water serves as a tissue-equivalent sensing medium. Our proof-of-concept, all-optical scheme achieves real-time readout of clinical radiation pulses with a nominal single-pulse resolution of 90 $\mu$Gy. This demonstration paves the way toward fiber-integrated, micron-scale devices for $\textit{in situ}$ universal absolute dosimetry during treatment.

physics.med-ph↗

arXiv2025

Automatic segmentation of Organs at Risk in Head and Neck cancer patients from CT and MRI scans

Purpose: To present a high-performing, robust, and flexible deep learning pipeline for automatic segmentation of 30 organs-at-risk (OARs) in head and neck (H&N) cancer patients, using MRI, CT, or both. Method: We trained a segmentation pipeline on paired CT and MRI-T1 scans from 296 patients. We combined data from the H&N OARs CT and MR segmentation (HaN-Seg) challenge and the Burdenko and GLIS-RT datasets from the Cancer Imaging Archive (TCIA). MRI was rigidly registered to CT, and both were stacked as input to an nnU-Net pipeline. Left and right OARs were merged into single classes during training and separated at inference time based on anatomical position. Modality Dropout was applied during the training, ensuring the model would learn from both modalities and robustly handle missing modalities during inference. The trained model was evaluated on the HaN-Seg test set and three TCIA datasets. Predictions were also compared with Limbus AI software. Dice Score (DS) and Hausdorff Distance (HD) were used as evaluation metrics. Results: The pipeline achieved state-of-the-art performance on the HaN-Seg challenge with a mean DS of 78.12% and HD of 3.42 mm. On TCIA datasets, the model maintained strong agreement with Limbus AI software (DS: 77.43% , HD: 3.27 mm), while also flagging low-quality contours. The pipeline can segment seamlessly from the CT, the MRI scan, or both. Conclusion: The proposed pipeline achieved the best DS and HD scores among all HaN-Seg challenge participants and establishes a new state-of-the-art for fully automated, multi-modal segmentation of H&N OARs.

eess.IV↗

arXiv2024

Radiation hardness of open Fabry-Perot microcavities

High-finesse microcavities offer a platform for compact, high-precision sensing by employing high-reflectivity, low-loss mirrors to create effective optical path lengths that are orders of magnitude larger than the device geometry. Here, we investigate the radiation hardness of Fabry-Perot microcavities formed from dielectric mirrors deposited on the tips of optical fibers. The microcavities are irradiated under both conventional (~0.1 Gy/s) and ultrahigh (FLASH, ~20 Gy/s) radiotherapy dose rates. Within our measurement sensitivity of ~40 ppm loss, we observe no degradation in the mirror absorption after irradiation with over 300 Gy accumulated dose. This result highlights the excellent radiation hardness of the dielectric mirrors forming the cavities, enabling new optics-based, real-time, in-vivo, tissue-equivalent radiation dosimeters with ~10 micron spatial resolution (our motivation), as well as other applications in high-radiation environments.

physics.med-ph↗

arXiv2021

Cavity-enhanced absorption sensing with robust sideband locking

We present a simple, continuous, cavity-enhanced optical absorption measurement technique based on high-bandwidth Pound-Drever-Hall (PDH) sideband locking. The technique provides a resonant amplitude quadrature readout that can be mapped onto the cavity's internal loss rate, and is naturally compatible with weak probe beams. With a proof-of-concept 5-cm-long Fabry-Perot cavity, we measure an absorption sensitivity of $6.6 \times 10^{-11} ~\text{cm}^{-1}/\sqrt{\text{Hz}}$ at 100 kHz (roughly the cavity bandwidth) and $\sim 10^{-10}~\text{cm}^{-1}/\sqrt{\text{Hz}}$ from 30 kHz to 1 MHz, with $38~μ$W collected from the cavity's circulating power.

physics.optics↗

arXiv2021

Deep learning-based tumor segmentation on digital images of histopathology slides for microdosimetry applications

$\bf{Purpose:}$ The goal of this study was (i) to use artificial intelligence to automate the traditionally labor-intensive process of manual segmentation of tumor regions in pathology slides performed by a pathologist and (ii) to validate the use of a well-known and readily available deep learning architecture. Automation will reduce the human error involved in manual delineation, increase efficiency, and result in accurate and reproducible segmentation. This advancement will alleviate the bottleneck in the workflow in clinical and research applications due to a lack of pathologist time. Our application is patient-specific microdosimetry and radiobiological modeling, which builds on the contoured pathology slides. $\bf{Methods:}$ A U-Net architecture was used to segment tumor regions in pathology core biopsies of lung tissue with adenocarcinoma stained using hematoxylin and eosin. A pathologist manually contoured the tumor regions in 56 images with binary masks for training. Overlapping patch extraction with various patch sizes and image downsampling were investigated individually. Data augmentation and 8-fold cross-validation were used. $\bf{Results:}$ The U-Net achieved accuracy of 0.91$\pm$0.06, specificity of 0.90$\pm$0.08, sensitivity of 0.92$\pm$0.07, and precision of 0.8$\pm$0.1. The F1/DICE score was 0.85$\pm$0.07, with a segmentation time of 3.24$\pm$0.03 seconds per image, achieving a 370$\pm$3 times increased efficiency over manual segmentation. In some cases, the U-Net correctly delineated the tumor's stroma from its epithelial component in regions that were classified as tumor by the pathologist. $\bf{Conclusion:}$ The U-Net architecture can segment images with a level of efficiency and accuracy that makes it suitable for tumor segmentation of histopathological images in fields such as radiotherapy dosimetry, specifically in the subfields of microdosimetry.

physics.med-ph↗

arXiv2020

RapidBrachyMCTPS 2.0: A Comprehensive and Flexible Monte Carlo-Based Treatment Planning System for Brachytherapy Applications

We have previously described RapidBrachyMCTPS, a brachytherapy treatment planning toolkit consisting of a graphical user interface (GUI) and a Geant4-based Monte Carlo (MC) dose calculation engine. This work describes the tools that have recently been added to RapidBrachyMCTPS, such that it now serves as the first stand-alone application for MC-based brachytherapy treatment planning. Notable changes include updated applicator import and positioning, three-plane contouring tools, and updated dose optimization algorithms that, in addition to optimizing dwell position and dwell time, also optimize the rotating shield angles in intensity modulated brachytherapy. The main modules of RapidBrachyMCTPS were validated including DICOM import, applicator import and positioning, contouring, material assignment, source specification, catheter reconstruction, EGSphant generation, interface with the MC code, and dose optimization and analysis tools. Two patient cases were simulated to demonstrate these principles, illustrating the control and flexibility offered by RapidBrachyMCTPS for all steps of the treatment planning pathway. RapidBrachyMCTPS is now a stand-alone application for brachytherapy treatment planning, and offers a user-friendly interface to access powerful MC calculations. It can be used to validate dose distributions from clinical treatment planning systems or model-based dose calculation algorithms, and is also well suited to testing novel combinations of radiation sources and applicators, especially those shielded with high-Z materials.

physics.med-ph↗