BACKGROUND:Small animal irradiation is essential to study the radiation response of new interventions before or parallel to human therapy. Image-guided radiotherapy (IGRT) and intensity-modulated radiotherapy (IMRT) are recently adopted in small animal irradiation to more closely mimic human treatments. However, sophisticated techniques require exceedingly high time, resources, and expertize that are often impractical. PURPOSE:We propose a high throughput and high precision platform named Multiple Mouse Automated Treatment Environment (Multi-MATE) to streamline image-guided small animal irradiation. METHODS:Multi-MATE consists of six parallel and hexagonally arranged channels, each equipped with a transfer railing, a 3D-printed immobilization pod, and an electromagnetic control unit, computer-controlled via an Arduino interface. The mouse immobilization pods are transferred along the railings between the home position outside the radiation field and the imaging/irradiation position at the irradiator isocenter. All six immobilization pods are transferred to the isocenter in the proposed workflow for parallel CBCT scans and treatment planning. The immobilization pods are then sequentially transported to the imaging/therapy position for dose delivery. The positioning reproducibility of Multi-MATE are evaluated using CBCT and radiochromic films. RESULTS:While parallelizing and automating the image-guided small animal radiation delivery, Multi-MATE achieved the average pod position reproducibility of 0.17 ± 0.04 mm in the superior-inferior direction, 0.20 ± 0.04 mm in the left-right direction, and 0.12 ± 0.02mm in the anterior-posterior direction in repeated CBCT tests. Additionally, in image-guided dose delivery tasks, Multi-MATE demonstrated the positioning reproducibility of 0.17 ± 0.06 mm in the superior-inferior direction, 0.19 ± 0.06 mm in the left-right direction. CONCLUSIONS:We designed, fabricated, and tested a novel automated irradiation platform, Multi-MATE to accelerate and automate image-guided small animal irradiation. The automated platform minimizes human operation and achieves high setup reproducibility and image-guided dose delivery accuracy. Multi-MATE thus removes a major barrier to implementing high-precision preclinical radiation research.

Measuring the radiation dose reaching a patient’s body is difficult. Here we report a technique for the tomographic reconstruction of the location of photon pairs originating from the annihilation of positron–electron pairs produced by high-energy X-rays travelling through tissue. We used Monte Carlo simulations on pre-recorded data from tissue-mimicking phantoms and from a patient with a brain tumour to show the feasibility of this imaging modality, which we named ‘pair-production tomography’, for the monitoring of radiotherapy dosing. We simulated three image-reconstruction methods, one applicable to a pencil X-ray beam scanning through a region of interest, and two applicable to the excitation of tissue volumes via broad beams (with temporal resolution sufficient to identify coincident photon pairs via filtered back projection, or with higher temporal resolution sufficient for the estimation of a photon’s time-of-flight). In addition to the monitoring of radiotherapy dosing, we show that image contrast resulting from pair-production tomography is highly proportional to the material’s atomic number. The technique may thus also allow for element mapping and for soft-tissue differentiation.

X-ray Computed Tomography (CT) is an exceptionally versatile tool for diagnosis and detection. Despite numerous technological evolutions, the underlying mechanism for CT image formation has not changed. To markedly expand beyond the current capacity of X-ray CT based on a new image formation mechanism, we introduce pair production tomography (P 2 T) imaging. Unlike CT, whose signals arise from attenuation of the incident photons, P 2 T collects coincident annihilation photons originated from the pair production interaction with high energy X-rays for tomographic reconstruction. We studied three P 2 T acquisition methods, including filtered back projection (FBP), time-of-flight (TOF), and scanning pencil beam (SPB). Using Monte Carlo simulation on phantom and patient data, we demonstrate three distinctly new capabilities for P 2 T: high linearity with the material atomic number for element mapping and soft tissue differentiation, the ability to form tomography with as few as a single heavily truncated X-ray beam, and in vivo radiotherapy treatment dose verification and monitoring. Among the three P 2 T acquisition methods, FBP is the least technically demanding, but its utility is limited to high radiation dose procedures such as radiotherapy treatment monitoring. Both TOF and SPB result in high signal-to-noise ratio (SNR) P 2 T images with a typical imaging dose. The quality of TOF relies on the time resolution of detectors. In comparison, SPB suppresses localization errors based on the known excitation path, which significantly mitigates the detector time resolution requirement.

Ultra-high dose rate in radiotherapy (FLASH) has been shown to increase the therapeutic index with markedly reduced normal tissue toxicity and the same or better tumor cell killing. The challenge to achieve FLASH using x-rays, besides developing a high output linac, is to intensity-modulate the high-dose-rate x-rays so that the biological gain is not offset by the lack of physical dose conformity. In this study, we develop the ROtational direct Aperture optimization with a Decoupled ring-collimator (ROAD) to achieve simultaneous ultrafast delivery and complex dose modulation. The ROAD design includes a fast-rotating slip-ring linac and a decoupled collimator-ring with 75 pre-shaped multi-leaf-collimator (MLC) modules. The ring-source rotates at 1 rotation per second (rps) clockwise while the ring-collimator is either static or rotating at 1 rps counterclockwise, achieving 75 (ROAD-75) or 150 (ROAD-150) equal-angular beams for one full arc. The Direct Aperture Optimization (DAO) for ROAD was formulated to include a least-square dose fidelity, an anisotropic total variation term, and a single segment term. The FLASH dose (FD) and FLASH biological equivalent dose (FBED) were computed voxelwise, with the latter using a spatiotemporal model accounting for radiolytic oxygen depletion. ROAD was compared with clinical volumetric modulated arc therapy (VMAT) on a brain, a lung, a prostate, and a head and neck cancer patient. The mean dose rate of ROAD-75 and ROAD-150 are 76.2 Gy s−1 and 112 Gy s−1 respectively to deliver 25 Gy single-fraction dose in 1 s. With improved PTV homogeneity, ROAD-150 reduced (max, mean) OAR physical dose by (4.8 Gy, 6.3 Gy). The average R50 and integral dose of (VMAT, ROAD-75, ROAD-150) are (4.8, 3.2, 3.2) and (89, 57, 56) Gy×Liter, respectively. The FD and FBED showed model dependent FLASH effects. The novel ROAD design achieves ultrafast dose delivery and improves physical dosimetry compared with clinical VMAT, providing a potentially viable engineering solution for x-ray FLASH radiotherapy.

Author(s): Neph, Ryan | Advisor(s): Sheng, Ke | Abstract: Radiation therapy is powered by modern techniques in precise planning and executionof radiation delivery, which are being rapidly improved to maximize its benefit to cancerpatients. In the last decade, radiotherapy experienced the introduction of advanced methodsfor automatic beam orientation optimization, real-time tumor tracking, daily planadaptation, and many others, which improve the radiation delivery precision, planning easeand reproducibility, and treatment efficacy. However, such advanced paradigms necessitatethe calculation of orders of magnitude more causal dose deposition data, increasing the timerequirement of all pre-planning dose calculation. Principles of high-performance computingand machine learning were applied to address the insufficient speeds of widely-used dosecalculation algorithms to facilitate translation of these advanced treatment paradigms intoclinical practice.To accelerate CT-guided X-ray therapies, Collapsed-Cone Convolution-Superposition(CCCS), a state-of-the-art analytical dose calculation algorithm, was accelerated through itsnovel implementation on highly parallelized GPUs. This context-based GPU-CCCS approachtakes advantage of X-ray dose deposition compactness to parallelize calculation acrosshundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration bytwo to three orders of magnitude compared to existing GPU-based beamlet-by-beamletapproaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPUimplementation of context-based GPU-CCCS.Dose calculation for MR-guided treatment is complicated by electron return effects(EREs), exhibited by ionizing electrons in the strong magnetic field of the MRI scanner. EREsnecessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinicalapplication of advanced treatment paradigms due to time restrictions. An automaticallydistributed framework for very-large-scale MC dose calculation was developed, grantinglinear scaling of dose calculation speed with the number of utilized computational cores. Itwas then harnessed to efficiently generate a large dataset of paired high- and low-noise MCdoses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutionalneural network (CNN), DeepMC, to predict low-noise dose from faster high-noise MC-simulation. DeepMC enables 38-fold acceleration of MR-guided X-ray beamlet dosecalculation, while remaining synergistic with existing MC acceleration techniques to achievemultiplicative speed improvements.This work redefines the expectation of X-ray dose calculation speed, making it possibleto apply new highly-beneficial treatment paradigms to standard clinical practice for the firsttime.

Ultra-high dose rate in radiotherapy (FLASH) has been shown to increase the therapeutic index with markedly reduced normal tissue toxicity and the same or better tumor cell killing. In previous study, we developed the Rotational direct Aperture optimization with a Decoupled ring-collimator (ROAD) to achieve simultaneous ultrafast delivery and complex dose modulation. This study investigates the biological gain of the ROAD model based on a radiolytic oxygen depletion model proposed by Pratx et al 2019. The ROAD design includes a fast-rotating slip ring Linac and a decoupled ring-collimator with 75 pre-shaped multi-leaf collimator (MLC) modules, with the ring-source rotates at 1 rps clockwise while the ring-collimator rotating at 1 rps counterclockwise. The X-ray source is triggered when the source is aligned with individual MLCs, achieving 150 equal-angular beams for one full arc. For each ROAD plan, the oxygen tension was computed voxelwise using a spatiotemporal model that took into account of the cell respiration, tissue diffusion, and radiolytic oxygen depletion. The oxygen enhancement ratio and the biological equivalent dose (BED) were computed based on the transient oxygen tension at each control point. The ROAD plans were compared with Volumetric Modulated Arc Therapy (VMAT) plans on both the physical dose and the BED, of a brain, a lung, a prostate, and a head and neck cancer patient. The ROAD plans can be delivered within 1s, achieving 112 Gy/s average per-beam dose rate. Table 1 reports the statistics of the physical dose and the BED of ROAD and VMAT plans. The BED of VMAT plans is equivalent to its physical dose. For physical dose, the average PTV homogeneity is 0.956 and 0.955 for VMAT and ROAD respectively, and differences in the average OAR mean and max physical dose are within 2% of the prescription dose. For the BED, ROAD plans reduced the R50 and integral dose by 48% and 21% respectively, compared with VMAT. The average reduction of OAR mean and max BED of ROAD from VMAT were 2.78 Gy and 8.29 Gy, respectively. The ROAD method achieved comparable physical dosimetry and superior BED compared with state-of-the-art VMAT.Abstract 3223; Table 1Statistics of the physical dose and the BED in VMAT and ROAD plans. The BED of VMAT plans is equivalent to its physical dose.CaseMethodR50Integral Dose (Gy·cm3)OAR Dmean (Gy)OAR Dmax (Gy)Dose ConformityGBMphysical doseVMAT4.612.351.693.961.00ROAD4.842.541.593.491.01BEDROAD2.172.091.543.060.95LNGphysical doseVMAT4.1573.894.5915.720.98ROAD3.2070.764.6515.120.98BEDROAD1.6254.223.6610.170.95PRTphysical doseVMAT2.8339.284.8721.130.96ROAD2.8539.624.5620.400.96BEDROAD1.4730.343.3113.290.95H&Nphysical doseVMAT2.23113.7121.7748.331.15ROAD2.14111.7020.7147.031.14BEDROAD1.5488.6413.2929.450.97 Open table in a new tab

Despite significant dosimetric gains, clinical implementation of the 4π non-coplanar radiotherapy on the widely available C-arm gantry system is hindered by limited clearance, and the need to perform complex coordinated gantry and couch motion. A robotic radiotherapy platform would be conducive to such treatment but a new conflict between field size and MLC modulation resolution needs to be managed for versatile applications. This study investigates the dosimetry and delivery efficiency of purposefully creating many isocenters to achieve simultaneously high MLC modulation resolution and large tumor coverage. An integrated optimization framework was proposed for simultaneous beam orientation optimization (BOO), isocenter selection, and fluence map optimization (FMO). The framework includes a least-square dose fidelity objective, a total variation term for regularizing the fluence smoothness, and a group sparsity term for beam selection. A minimal number of isocenters were identified for efficient target coverage. Colliding beams excluded, high-resolution small-field 4π intensity-modulated radiotherapy (IMRT) treatment plans with 50 cm source-to-isocenter distance (SID-50) on 10 Head and Neck (H&N) cancer patients were compared with low-resolution large-field plans with 100 cm SID (SID-100). With the same or better target coverage, the average reduction of [Dmean, Dmax] of 20-beam SID-50 plans from 20-beam SID-100 plans were [2.09 Gy, 1.19 Gy] for organs at risk (OARs) overall, [3.05 Gy, 0.04 Gy] for parotid gland, [3.62 Gy, 5.19 Gy] for larynx, and [3.27 Gy, 1.10 Gy] for mandible. R50 and integral dose were reduced by 5.3% and 9.6%, respectively. Wilcoxon signed-rank test showed significant difference (p   <  0.05) in planning target volume (PTV) homogeneity, PTV Dmax, R50, Integral dose, and OAR Dmean and Dmax. The estimated delivery time of 20-beam [SID-50, SID-100] plans were [19, 18] min and [14, 9] min, assuming 5 fractions and 30 fractions, respectively. With clinically acceptable delivery efficiency, many-isocenter optimization is dosimetrically desirable for treating large targets with high modulation resolution on the robotic platform.

PurposeA dose‐modulation device for small animal radiotherapy is required to use clinically analogous treatment techniques, which will likely increase the translatability of preclinical research results. Because the clinically used multileaf collimator (MLC) is impractical for miniaturization, we have developed a simpler, better‐suited sparse orthogonal collimator (SOC) for delivering small animal intensity‐modulated radiation therapy (IMRT) using a rectangular aperture optimization (RAO) treatment planning system.MethodsThe SOC system was modeled in computer‐aided design software and fabricated with machined tungsten leaves and three‐dimensional (3D) printed leaf housing. A graphical user interface was developed for controlling and calibrating the SOC leaves, which are driven by Arduino‐controlled stepper motors. A Winston‐Lutz test was performed to assess mechanical alignment, and abutting field and grid dose patterns were created to analyze intra‐ and intercalibration leaf positioning error. Leaf transmission and penumbra were measured over the full range of gantry angles and leaf positions, respectively. Three SOC test plans were delivered, and film measurements were compared to the intended dose distributions. The differences in maximum, mean, and minimum, as well as pixelwise absolute dose differences, were compared for each structure, and a gamma analysis was performed for the target structures using criteria of 4% dose difference and 0.3 mm distance to agreement.ResultsThe Winston‐Lutz test revealed maximum directional offsets between the SOC and primary collimator axes of 0.53 mm at 0° and 0.68 mm over the full 360°. Upper and lower abutting field patterns had maximum dose deviations of 18.8 ± 3.1% and 15.5 ± 2.9%, respectively, and grid patterns showed intra‐ and intercalibration repeatability of 93% and 91%, respectively. Extremely low midleaf (0.15 ± 0.05%) and interleaf (0.27 ± 0.22%) transmission was measured, with no significant rotational variation. The average penumbra was ~0.8 mm for all leaves at field center, with a range of 0.17 mm for all leaf positions. A highly concave test plan was delivered with a ~ 95% gamma analysis pass rate, and a realistic mouse phantom liver irradiation plan achieved a pass rate of ~98%. A highly complex dose distribution was also created with 551 SOC apertures averaging 2.4 mm in size.ConclusionsA sparse orthogonal collimator was developed and commissioned, with promising preliminary dosimetry results. The SOC design, with its limited moving components and high dose‐modulation resolution, is ideal for delivering high‐quality small animal IMRT with our RAO‐based treatment planning system.

PurposeDose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model‐based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units (GPUs), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under‐utilizing the potential of modern GPUs. In this paper, the authors propose a framework enabling parallel computation of many beamlet doses using a novel beamlet context transformation and further embed this approach in a scalable network of multi‐GPU computational nodes.MethodsThe proposed context‐based transformation separates beamlet‐local density and TERMA into distinct beamlet contexts that independently provide sufficient data for beamlet dose calculation. Beamlet contexts are arranged in a composite context array with dosimetric isolation, and the context array is subjected to a GPU collapsed‐cone convolution superposition procedure, producing the set of beamlet‐specific dose distributions in a single pass. Dose from each context is converted to a sparse representation for efficient storage and retrieval during treatment plan optimization. The context radius is a new parameter permitting flexibility between the speed and fidelity of the dose calculation process. A distributed manager‐worker architecture is constructed around the context‐based GPU dose calculation approach supporting an arbitrary number of worker nodes and resident GPUs. Phantom experiments were executed to verify the accuracy of the context‐based approach compared to Monte Carlo and a reference CPU‐CCCS implementation for single beamlets and broad beams composed by addition of beamlets. Dose for representative 4π beam sets was calculated in lung and prostate cases to compare its efficiency with that of an existing beamlet‐sequential GPU‐CCCS implementation. Code profiling was also performed to evaluate the scalability of the framework across many networked GPUs.ResultsThe dosimetric accuracy of the context‐based method displays <1.35% and 2.35% average error from the existing serialized CPU‐CCCS algorithm and Monte Carlo simulation for beamlet‐specific PDDs in water and slab phantoms, respectively. The context‐based method demonstrates substantial speedup of up to two orders of magnitude over the beamlet‐sequential GPU‐CCCS method in the tested configurations. The context‐based framework demonstrates near linear scaling in the number of distributed compute nodes and GPUs employed, indicating that it is flexible enough to meet the performance requirements of most users by simply increasing the hardware utilization.ConclusionsThe context‐based approach demonstrates a new expectation of performance for beamlet‐based dose calculation methods. This approach has been successful in accelerating the dose calculation process for very large‐scale treatment planning problems ‐ such as automatic 4π IMRT beam orientation and VMAT arc trajectory selection, with hundreds of thousands of beamlets ‐ in clinically feasible timeframes. The flexibility of this framework makes it as a strong candidate for use in a variety of other very large‐scale treatment planning tasks and clinical workflows.

Concerns over the risks of radiation dose from diagnostic CT motivated the utilization of low dose CT (LdCT). However, due to the extremely low X-ray photon statistics in LdCT, the reconstruction problem is ill-posed and noisecontaminated. Conventional Compressed Sensing (CS) methods have been investigated to enhance the signal-to-noise ratio of LdCT at the cost of image resolution and low contrast object visibility. In this work, we adapted a flexible, iterative reconstruction framework, termed Plug-and-Play (PnP) alternating direction method of multipliers (ADMM), that incorporated state-of-the-art denoising algorithms into model-based image reconstruction. The PnP ADMM framework is achieved by combining a least square data fidelity term with a regularization term for image smoothness and was solved through the ADMM. An off-the-shelf image denoiser, the Block-Matching 3D-transform shrinkage (BM3D) filter, is plugged in to substitute an ADMM module. The PnP ADMM was evaluated on low dose scans of ACR 464 phantom and two lung screening data sets and is compared with the Filtered Back Projection (FBP), the Total Variation (TV), the BM3D post-processing method, and the BM3D regularization method. The proposed framework distinguished the line pairs at 9 lp/cm resolution on the ACR phantom and the fissure line in the left lung, resolving the same or better image details than FBP reconstruction of higher dose scans with up to 18 times less dose. Compared with conventional iterative reconstruction methods resulting in comparable image noise, the proposed method is significantly better at recovering image details and improving low contrast conspicuity.

Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model's ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

PURPOSE Dose conformality and robustness are equally important in intensity modulated proton therapy (IMPT). Despite the obvious implication of beam orientation on both dosimetry and robustness, an automated, robust beam orientation optimization algorithm has not been incorporated due to the problem complexity and paramount computational challenge. In this study, we developed a novel IMPT framework that integrates robust beam orientation optimization (BOO) and robust fluence map optimization (FMO) in a unified framework. METHODS The unified framework is formulated to include a dose fidelity term, a heterogeneity-weighted group sparsity term, and a sensitivity regularization term. The L2, 1/2-norm group sparsity is used to reduce the number of active beams from the initial 1162 evenly distributed noncoplanar candidate beams, to between two and four. A heterogeneity index, which evaluates the lateral tissue heterogeneity of a beam, is used to weigh the group sparsity term. With this index, beams more resilient to setup uncertainties are encouraged. There is a symbiotic relationship between the heterogeneity index and the sensitivity regularization; the integrated optimization framework further improves beam robustness against both range and setup uncertainties. This Sensitivity regularization and Heterogeneity weighting based BOO and FMO framework (SHBOO-FMO) was tested on two skull-base tumor (SBT) patients and two bilateral head-and-neck (H&N) patients. The conventional CTV-based optimized plans (Conv) with SHBOO-FMO beams (SHBOO-Conv) and manual beams (MAN-Conv) were compared to investigate the beam robustness of the proposed method. The dosimetry and robustness of SHBOO-FMO plan were compared against the manual beam plan with CTV-based voxel-wise worst-case scenario approach (MAN-WC). RESULTS With SHBOO-FMO method, the beams with superior range robustness over manual beams were selected while the setup robustness was maintained or improved. On average, the lowest [D95%, V95%, V100%] of CTV were increased from [93.85%, 91.06%, 70.64%] in MAN-Conv plans, to [98.62%, 98.61%, 96.17%] in SHBOO-Conv plans with range uncertainties. With setup uncertainties, the average lowest [D98%, D95%, V95%, V100%] of CTV were increased from [92.06%, 94.83%, 94.31%, 78.93%] in MAN-Conv plans, to [93.54%, 96.61%, 97.01%, 91.98%] in SHBOO-Conv plans. Compared with the MAN-WC plans, the final SHBOO-FMO plans achieved comparable plan robustness and better OAR sparing, with an average reduction of [Dmean, Dmax] of [6.31, 6.55] GyRBE for the SBT cases and [1.89, 5.08] GyRBE for the H&N cases from the MAN-WC plans. CONCLUSION We developed a novel method to integrate robust BOO and robust FMO into IMPT optimization for a unified solution of both BOO and FMO, generating plans with superior dosimetry and good robustness.

PURPOSE The thermoluminescence dosimeter (TLD) has desirable features including low cost, reusability, small size, and relatively low energy dependence. However, the commonly available poly-crystal TLDs (e.g., TLD-100) exhibit high inter-detector variability that requires individual calibration for high detection accuracy. To improve individual TLD tracking robustness, we developed an optical fingerprinting method to identify the TLD-100 chips. METHODS 752 images were initially captured using a digital microscope camera to build a feature library for both facets of 376 TLD-100 chips. A median intensity thresholding method was used to segment images into foreground and background. The affine transformation was used to register the segmented images to the same position. The fingerprint of each image was calculated from its registered image. All fingerprints were then recorded in an Elasticsearch® search database. The TLD fingerprint match was tested three times when the library was established and repeated once 20 months later. All chips were irradiated at 0 Gy, 1 Gy, 4 Gy, and 8 Gy on a calibrated clinical MV linac to establish the individual calibration curve. RESULTS The true positive rate of identifying TLDs based on their optical fingerprints was 100% at initialization of the inventory. After 20 months and multiple deployments for characterization, calibration, and dose measurement, the true positive match rate dropped to 99% with zero false positive matches. The TLDs exhibited high self-consistency in the dose-response test with R2 between 0.988 and 1 with linear regression. CONCLUSION The TLD-100 chips surface textures are unique and sufficient to support accurate identification based on the optical fingerprinting. This method provides inexpensive and robust management of the TLDs for individual calibration and dosimetry.