Physics in Medicine & Biology

Objective. Given the increased interest in incorporating linear energy transfer (LET) as an optimization parameter in intensity-modulated proton therapy (IMPT), a solution for experimental validation of simulations and patient-specific quality assurance (PSQA) in terms of proton LET is needed. Here, we present the methodology and results of LET spectra measurements for spread-out Bragg peak (SOBP) and IMPT plans using a miniaturized pixel detector Timepix3. Approach. We used a MiniPIX Timepix3 detector that provides single-particle tracking, type-resolving power, and spectral information while allowing measurement in quasi-continuous mode. We performed measurements for SOBP and IMPT plans in homogeneous RW3 and heterogeneous CIRS head phantoms with reduced beam current. An artificial intelligence-based model was applied for proton identification and a GPU-accelerated FRED Monte Carlo (MC) code was applied for corresponding MC simulations. Main results. We compared the deposited energy and LET spectra obtained in mixed radiation fields from measurements and MC simulations. The peak positions of deposited energy and LET spectra for the SOBP and IMPT plans agree within the error bars. Discrepancies exceeding the error bars are only visible in the logarithmic scale in high-energy deposition and high-LET tails of the distributions. The mean relative difference of dose-averaged LET values between measurements and MC simulations for individual energy layers is about 5.1%. Significance. This study presents a methodology for assessing radiation quality in proton therapy through energy deposition and LET spectra measurements in uniform and clinical IMPT fields. Findings show an agreement between experimental data and MC simulations, validating our approach. The presented results demonstrate the feasibility of a commercially available Timepix3 detector to validate LET computations in IMPT fields and perform PSQA in terms of LET. This will support the implementation of LET in treatment planning, which will ultimately increase the effectiveness of the treatment.

Objective. Non-invasive tumor diagnosis and characterization is limited today by the cost and availability of state of the art imaging techniques. Thanks to recent developments, ultrasound (US) imaging can now provide quantitative volumetric maps of different tissue characteristics. This study applied the first fully concurrent 3D ultrasound imaging set-up including B-mode imaging, shear wave elastography (SWE), tissue structure imaging with backscatter tensor imaging (BTI), vascular mapping with ultrasensitive Doppler (uDoppler) and ultrasound localization microscopy (ULM) in-vivo. Subsequent analysis aimed to evaluate its benefits for non-invasive tumor diagnosis. Approach. A total of 26 PyMT-MMTV transgenic mice and 6 control mice were imaged weekly during tumor growth. First-order statistics and radiomic features were extracted from the quantitative maps obtained, and used to build predictive models differentiating healthy from cancerous mammary pads. Imaging features were also compared to histology obtained the last week of imaging. Main results. High quality co-registered quantitative maps were obtained, for which SWE speed, BTI tissue organization, ULM blood vessel count and uDoppler blood vessel density were correlated with histopathology. Significant changes in uDoppler sensitivity and BTI tissue structure were measured during tumor evolution. Predictive models inferring the cancerous state from the multiparametric imaging reached 99% accuracy, and focused mainly on radiomics measures of the BTI maps. Significance. This work indicates the relevance of a multiparametric characterization of lesions, and highlights the strong predictive power of BTI-derived parameters for differentiating tumors from healthy tissue, both before and after the tumor can be detected by palpation.

Objective. To obtain maps of absorbed dose and an estimate of the effective dose to an adult patient for a high resolution peripheral quantitative computed tomography (HR-pQCT) (XtremeCT II) examination of the distal tibia and radius, using a graphical processing unit (GPU)-based Monte Carlo (MC) code. Approach. We adapted the validated code gCTD (GPU-based CT Dose calculator), to replicate the HR-pQCT configuration. MC simulations were performed on digital phantoms of the tibia and radius obtained from bilateral scans of the ankle and wrist of a 25 year-old female volunteer. Scans were segmented using an ad hoc algorithm in Fiji. Simulations run on an NVIDIA GeForce RTX 3090 GPU board. MC dose estimates were validated via computed tomography dose index measurements. Main Results. We obtained the absorbed dose distribution in the skin, bone, bone marrow, fat, and muscle tissues. The effective dose for the HR-pQCT examination were 2.05 μSv and 2.13 μSv for the right and left tibia, and 1.48 μSv and 1.49 μSv for the right and left radius, respectively, with a Type A statistical uncertainty of 0.06% (k = 3) with 4.66 × 10¹¹ photon histories. Corresponding effective dose conversion coefficients (k-factors) were 0.185 μSv mGy⁻¹ · cm⁻¹ (tibia), and 0.133 μSv mGy⁻¹ · cm⁻¹ (radius). Significance. We reported the first independent estimate of the effective dose for standard HR-pQCT clinical scans of the distal tibia and radius with the XtremeCT II scanner. Effective dose estimates (considering a total relative uncertainty of less than 40%) were lower than those indicated by the manufacturer and commonly reported for these scans. With 4.66 × 10⁹ photon histories, the gCTD MC code can produce 3D dose maps from segmented HR-pQCT images in less than 12 s (GPU time), with 0.9% (k = 3) statistical uncertainty, making real-time personalized dose estimate feasible.

The fundamental goal in radiation therapy (RT) is to simultaneously maximize tumor cell killing and healthy tissue sparing. Reducing uncertainty margins improves normal tissue sparing, but generally requires advanced techniques. Adaptive RT (ART) is a compelling technique that leverages daily imaging and anatomical information to support reduced margins and to optimize plan quality for each treatment fraction. An especially exciting avenue for ART is proton therapy (PT), which aims to combine daily plan re-optimization with the unique advantages provided by protons, including reduced integral dose and near-zero dose deposition distal to the target along the beam direction. A core component for ART is onboard image guidance, and currently two options are available on proton systems, including cone-beam computed tomography (CBCT) and CT-on-rail (CToR) imaging. While CBCT suffers from poorer image quality compared to CToR imaging, CBCT platforms can be more easily integrated with PT systems and thus may support more streamlined adaptive proton therapy (APT). In this review, we present current status of CBCT application to proton therapy dose evaluation and plan adaptation, including progress, challenges and future directions.

The fundamental goal in radiation therapy (RT) is to simultaneously maximize tumor cell killing and healthy tissue sparing. Reducing uncertainty margins improves normal tissue sparing, but generally requires advanced techniques. Adaptive RT (ART) is a compelling technique that leverages daily imaging and anatomical information to support reduced margins and to optimize plan quality for each treatment fraction. An especially exciting avenue for ART is proton therapy (PT), which aims to combine daily plan re-optimization with the unique advantages provided by protons, including reduced integral dose and near-zero dose deposition distal to the target along the beam direction. A core component for ART is onboard image guidance, and currently two options are available on proton systems, including cone-beam computed tomography (CBCT) and CT-on-rail (CToR) imaging. While CBCT suffers from poorer image quality compared to CToR imaging, CBCT platforms can be more easily integrated with PT systems and thus may support more streamlined adaptive proton therapy (APT). In this review, we present current status of CBCT application to proton therapy dose evaluation and plan adaptation, including progress, challenges and future directions.

The detectability index, originally developed in psychophysics, has been applied in medical imaging to integrate objective metrics with subjective assessments. This index accounts for both image processing properties and the limitations of the human visual system, thus enhancing the clinical efficacy of imaging technologies. By providing a single metric that captures multiple aspects of image quality, the detectability index offers a comprehensive evaluation of clinical images. Numerous applications of this index across various areas of medical imaging are documented in the literature, along with recommendations for its use in periodic performance evaluations of imaging devices. However, since different modalities of images may require different detectability indices, it is crucial to assess the adequacy of the properties of the image being analyzed and those from the adopted index. A thorough understanding of this metric, including its statistical nature and complex relationship with model observers, is essential to ensure its proper application and interpretation, and to prevent misuse. Medical physicists face the challenge of a lack of organized guidance on the detectability index, necessitating a comprehensive review of its merits and drawbacks. This paper aims to trace the origins, concepts, and clinical applications of the detectability index, offering insight into its strengths, limitations, and future potential. To achieve this, an extensive literature review was conducted, covering the evolution of the index from its early use in radar interpretation to its current applications in modern imaging techniques and future trends. The paper includes supplementary materials such as a compendium of fundamental concepts, ancillary information, and mathematical deductions to help readers less experienced in the subject.

Photoacoustic (PA) imaging, by integrating optical and ultrasound (US) modalities, combines high spatial resolution with deep tissue penetration, making it a transformative tool in biomedical research. This review presents a comprehensive analysis of the current status of dual PA/US imaging technologies, emphasising their applications in preclinical research. It details advancements in light excitation strategies, including tomographic and microscopic modalities, innovations in pulsed laser and alternative light sources, and US instrumentation. The review further explores preclinical methodologies, encompassing dedicated instrumentation, signal processing, and data analysis techniques essential for PA/US systems. Key applications discussed include the visualisation of blood vessels, micro-circulation, and tissue perfusion; diagnosis and monitoring of inflammation; evaluation of infections, atherosclerosis, burn injuries, healing, and scar formation; assessment of liver and renal diseases; monitoring of epilepsy and neurodegenerative conditions; studies on brain disorders and preeclampsia; cell therapy monitoring; and tumour detection, staging, and recurrence monitoring. Challenges related to imaging depth, resolution, cost, and the translation of contrast agents to clinical practice are analysed, alongside advancements in high-speed acquisition, artificial intelligence-driven reconstruction, and innovative light-delivery methods. While clinical translation remains complex, this review underscores the crucial role of preclinical studies in unravelling fundamental biomedical questions and assessing novel imaging strategies. Ultimately, this review delves into the future trends of dual PA/US imaging, highlighting its potential to bridge preclinical discoveries with clinical applications and drive advances in diagnostics, therapeutic monitoring, and personalised medicine.

Acoustic holography can be used to construct an arbitrary wavefront at a desired 2D plane or 3D volume by beam shaping an emitted field and is a relatively new technique in the field of biomedical applications. Acoustic holography was first theorized in 1985 following Gabor's work in creating optical holograms in the 1940s. Recent developments in 3D printing have led to an easier and faster way to manufacture monolithic acoustic holographic lenses that can be attached to single-element transducers. As ultrasound passes through the lens material, a phase shift is applied to the waves, causing an interference pattern at the 2D image plane or 3D volume, which forms the desired pressure field. This technology has many applications already in use and has become of increasing interest for the biomedical community, particularly for treating regions that are notoriously difficult to operate on, such as the brain. Acoustic holograms could provide a non-invasive, precise, and patient specific way to deliver drugs, induce hyperthermia, or create tissue cell patterns. However, there are still limitations in acoustic holography, such as the difficulties in creating 3D holograms and the passivity of monolithic lenses. This review aims to outline the biomedical applications of acoustic holograms reported to date and discuss their current limitations and the future work that is needed for them to reach their full potential in the biomedical community.

There has been an increase in the availability and utilization of commercially available 3D printers in radiotherapy, with applications in phantoms, brachytherapy applicators, bolus, compensators, and immobilization devices. Additive manufacturing in the form of 3D printing has the advantage of rapid production of personalized patient specific prints or customized phantoms within a short timeframe. One of the barriers to uptake has been the lack of guidance. The aim of this topical review is to present the radiotherapy applications and provide guidance on important areas for establishing a 3D printing service in a radiotherapy department including procurement, commissioning, material selection, establishment of relevant quality assurance, multidisciplinary team creation and training.

This study aims to evaluate the performance of five distinct plastic scintillation dosimeters (PSDs) in magnetic fields, and to validate the accuracy of the hyperspectral approach for stem-effect correction. The effect of the magnetic field on different base core materials and components within the PSDs was also investigated, along with the effect of field size and orientation. 

Each PSD was placed in a water tank inside an electromagnet. Magnetic fields, between 0 and 1.5T, were perpendicular to the 6 MeV photon beam and to the PSD axis. The detector axis was either parallel or perpendicular to the beam. The hyperspectral technique was validated and used to determine the scintillation, fluorescence and Cherenkov components at different magnetic fields.

The hyperspectral method accurately removes stem-effects in magnetic fields, even when calibration is performed at 0T. The stem light yield shows good agreement with clear fiber measurements, with relative differences within 2.0%. In the parallel orientation, the corrected PSD response is highly symmetric relative to magnetic field polarity, with a maximum variation of only 0.2% from unity. 

Scintillation light yield increases with magnetic field by 3.6%-6.25% depending on PSD properties. Cherenkov light yield varies up to 230% and down to 0.30% of the 0T value, depending on magnetic field polarity. The magnetic field impact depends primarily on the scintillator properties, with polyvinyltoluene-based probes showing greater sensitivity than polystyrene-based probes. The inclusion of a wavelength shifter has minimal on the magnetic field's effect on scintillation light yield. Normalized scintillation light yield decreases with smaller field sizes. 

PSDs are well-suited for accurate dosimetry in magnetic fields, provided that accurate stem-effect correction techniques are applied. The scintillator properties play a significant role in determining the PSD's sensitivity to magnetic fields. The hyperspectral method is a robust approach for accurate stem-effect removal in such conditions.

This study aims to investigate and validate the response of PSDs in the presence of magnetic fields using Monte Carlo simulations, focusing on the accuracy of electron fluence, dose calculations, and the optical processes of scintillation and Cherenkov radiation.

Monte Carlo simulations, using EGSnrc and TOPAS, of the PSD response under magnetic fields were performed. First, electron fluence simulations were conducted with three different physics lists g4em-penelope, g4em-standard_opt3 and g4em-standard_opt4, with the goal of benchmarking their performance in magnetic fields. Secondly, a Fano test for dose calculations was performed using only the g4em-penelope physics list. Thirdly, the Cherenkov process under magnetic fields was validated against theoretical prediction. Finally, a PSD probe was modeled and simulated, with results compared against measurements. 

The g4em-penelope physics list demonstrated a most balanced performance, showing the closest agreement with EGSnrc simulations and lower variability in magnetic fields than g4em-standard_opt4. Fano test results showed an accuracy of at least 0.36% for dose calculations. Simulations of Cherenkov radiation in ideal conditions were in agreement with theoretical predictions at both 0 T and 1.5 T.

Monte Carlo simulations successfully reproduced experimental trends for Cherenkov radiation under magnetic fields. However, discrepancies were found, with deviations of up to 7.7% when electrons were deflected toward the tip and up to 21.0% in the opposite direction, likely due to modeling limitations. A key result is that Monte Carlo simulations of the scintillation process in magnetic fields failed to reproduce experimental observations. While experimental results showed a significant effect of magnetic fields on scintillation yield, the simulations did not reflect this behavior.


This study establishes that TOPAS, specifically using the g4em-penelope physics list, is a reliable tool for simulating dose, electron fluence, and Cherenkov radiation in the presence of magnetic fields. However, significant discrepancies were observed in the scintillation processes, where Monte Carlo simulations failed to reproduce the effect of magnetic fields seen in experimental measurements. These findings underscore the need for further refinement of simulation models, particularly in accurately representing scintillation under magnetic fields.

Objective:
Radiotherapy (RT) in nasopharyngeal cancer (NPC) patients presents challenges due to proximity of many anatomical structures to the target volume. Furthermore, inter-fractional changes must be considered to assure target coverage. Proton arc therapy (PAT) potentially reduces healthy tissue dose compared to IMPT and VMAT. The impact of PAT on dose to organs-at-risk (OARs), predicted acute- and late radiation toxicities and robust target coverage to inter-fraction changes in NPC patients were investigated.
Approach:
Robustly optimized PAT plans were compared to clinical VMAT and IMPT plans for 10 NPC patients treated with 70.00 Gy to the primary target (CTV 7000) and 54.25 Gy to the prophylactic lymph nodal area (CTV 5425). Integral body dose and mean and max in 0.03cc dose (Dmean and max D0.03cc) in OARs were compared. Normal tissue complication probability (NTCP) values for 22 acute and late radiation-induced toxicities were evaluated. A PAT "base approach" and nine PAT planning approaches to improve PAT inter-fraction robust target coverage were investigated. Target coverage was evaluated on in total 54 weekly repeated CT images (rCTs).
Main Results:
PAT integral dose reduced by on average 55% and 15% compared with clinical VMAT and IMPT, respectively. Compared to IMPT, average Dmean and max D0.03cc were significantly reduced in all evaluated neurological structures. Compared to IMPT, in the PAT plans Dmean was reduced most in the arytenoids, PCM medius and brainstem by on average 8.0 Gy, 6.4 Gy and 6.1 Gy, respectively and all evaluated NTCP's for both acute and late timepoint were significantly reduced. Compared with IMPT, PAT base approach target coverage on rCTs was worse. Approaches to improve PAT inter-fraction target coverage were successful, while maintaining the reduction in NTCP compared to IMPT.
Significance:
Compared to IMPT and VMAT, PAT reduces healthy tissue dose and subsequent estimated toxicity risks in NPC patients. PAT planning approaches to improve inter-fraction robustness were employed successfully, while NTCP benefits of PAT were maintained.

Objective: Carbon ion radiotherapy (CIRT) can provide higher biological effectiveness and cause more damage to cancer cells compared to photon or proton radiotherapy, especially for radio-resistant tumors. The optimization of biological dose is essential for CIRT, to achieve the desirable tumoricidal dose while mitigating biological damage to normal tissues and organs at risk (OAR). However, the biological optimization for CIRT is mathematically challenging, due to the nonlinear nature of biological dose model, which can lead to computational inaccuracy and inefficiency. This work will develop an accurate and efficient biological optimization method for CIRT.
Approach: The proposed method is called iterative Jacobian-based linearization (IJL). In IJL, the biological dose is modeled as the product of the physical dose and relative biological effect (RBE), which is based on the linear-quadratic model via the local effect model in this work, and the optimization objective consists of dose-volume histogram (DVH) based biological dose objectives within clinical target volume and OAR. The optimization algorithm for IJL is through iterative convex relaxation, in which the nonlinear biological dose is iteratively linearized using Jacobian-based approximations and the linear subproblems are solved using alternating direction method of multipliers (ADMM). To compare with IJL, the limited-memory quasi-Newton (QN) method (limited-memory version) is developed that directly solves the same nonlinear biological optimization problem.
Main results: Compared to the QN, IJL demonstrated superior plan accuracy, e.g., better OAR sparing with the reduction of biological dose in the CTV-surrounding volume (PTV1cm) to 89.7%, 95.0%, 88.3% for brain, lung, and abdomen, respectively; IJL also had higher computational efficiency, with approximately 1/10 the computational time per iteration and continuously decreasing objectives (while being stagnated for QN after certain number of iterations).
Significance: A novel optimization algorithm, IJL, incorporating iterative linearization of biological dose, is proposed to accurately and efficiently solve the biological optimization problem for CIRT. It demonstrates superior plan accuracy and computational efficiency compared to the direct nonlinear QN optimization method.

Objective. Personalized transcranial magnetic stimulation (TMS) requires individualized head models that incorporate non-uniform conductivity to enable target-specific stimulation. Accurately estimating non-uniform conductivity in individualized head models remains a challenge due to the difficulty of obtaining precise ground truth data. To address this issue, we have developed a novel transfer learning-based approach for automatically estimating non-uniform conductivity in a human head model with limited data. 
Approach. The proposed method complements the limitations of the previous conductivity network (CondNet) and improves the conductivity estimation accuracy. This method generates a segmentation model from T1- and T2-weighted magnetic resonance images, which is then used for conductivity estimation via transfer learning. To enhance the model's representation capability, a Transformer was incorporated into the segmentation model, while the conductivity estimation model was designed using a combination of Attention Gates and Residual Connections, enabling efficient learning even with a small amount of data. 
Main results. The proposed method was evaluated using 1494 images, demonstrating a 2.4% improvement in segmentation accuracy and a 29.1% increase in conductivity estimation accuracy compared with CondNet. Furthermore, the proposed method achieved superior conductivity estimation accuracy even with only three training cases, outperforming CondNet, which was trained on an adequate number of cases. The conductivity maps generated by the proposed method yielded better results in brain electrical field simulations than CondNet. 
Significance. These findings demonstrate the high utility of the proposed method in brain electrical field simulations and suggest its potential applicability to other medical image analysis tasks and simulations.

Objective. Given the increased interest in incorporating linear energy transfer (LET) as an optimization parameter in intensity-modulated proton therapy (IMPT), a solution for experimental validation of simulations and patient-specific quality assurance (PSQA) in terms of proton LET is needed. Here, we present the methodology and results of LET spectra measurements for spread-out Bragg peak (SOBP) and IMPT plans using a miniaturized pixel detector Timepix3. Approach. We used a MiniPIX Timepix3 detector that provides single-particle tracking, type-resolving power, and spectral information while allowing measurement in quasi-continuous mode. We performed measurements for SOBP and IMPT plans in homogeneous RW3 and heterogeneous CIRS head phantoms with reduced beam current. An artificial intelligence-based model was applied for proton identification and a GPU-accelerated FRED Monte Carlo (MC) code was applied for corresponding MC simulations. Main results. We compared the deposited energy and LET spectra obtained in mixed radiation fields from measurements and MC simulations. The peak positions of deposited energy and LET spectra for the SOBP and IMPT plans agree within the error bars. Discrepancies exceeding the error bars are only visible in the logarithmic scale in high-energy deposition and high-LET tails of the distributions. The mean relative difference of dose-averaged LET values between measurements and MC simulations for individual energy layers is about 5.1%. Significance. This study presents a methodology for assessing radiation quality in proton therapy through energy deposition and LET spectra measurements in uniform and clinical IMPT fields. Findings show an agreement between experimental data and MC simulations, validating our approach. The presented results demonstrate the feasibility of a commercially available Timepix3 detector to validate LET computations in IMPT fields and perform PSQA in terms of LET. This will support the implementation of LET in treatment planning, which will ultimately increase the effectiveness of the treatment.

Objective. Non-invasive tumor diagnosis and characterization is limited today by the cost and availability of state of the art imaging techniques. Thanks to recent developments, ultrasound (US) imaging can now provide quantitative volumetric maps of different tissue characteristics. This study applied the first fully concurrent 3D ultrasound imaging set-up including B-mode imaging, shear wave elastography (SWE), tissue structure imaging with backscatter tensor imaging (BTI), vascular mapping with ultrasensitive Doppler (uDoppler) and ultrasound localization microscopy (ULM) in-vivo. Subsequent analysis aimed to evaluate its benefits for non-invasive tumor diagnosis. Approach. A total of 26 PyMT-MMTV transgenic mice and 6 control mice were imaged weekly during tumor growth. First-order statistics and radiomic features were extracted from the quantitative maps obtained, and used to build predictive models differentiating healthy from cancerous mammary pads. Imaging features were also compared to histology obtained the last week of imaging. Main results. High quality co-registered quantitative maps were obtained, for which SWE speed, BTI tissue organization, ULM blood vessel count and uDoppler blood vessel density were correlated with histopathology. Significant changes in uDoppler sensitivity and BTI tissue structure were measured during tumor evolution. Predictive models inferring the cancerous state from the multiparametric imaging reached 99% accuracy, and focused mainly on radiomics measures of the BTI maps. Significance. This work indicates the relevance of a multiparametric characterization of lesions, and highlights the strong predictive power of BTI-derived parameters for differentiating tumors from healthy tissue, both before and after the tumor can be detected by palpation.

Objective. To obtain maps of absorbed dose and an estimate of the effective dose to an adult patient for a high resolution peripheral quantitative computed tomography (HR-pQCT) (XtremeCT II) examination of the distal tibia and radius, using a graphical processing unit (GPU)-based Monte Carlo (MC) code. Approach. We adapted the validated code gCTD (GPU-based CT Dose calculator), to replicate the HR-pQCT configuration. MC simulations were performed on digital phantoms of the tibia and radius obtained from bilateral scans of the ankle and wrist of a 25 year-old female volunteer. Scans were segmented using an ad hoc algorithm in Fiji. Simulations run on an NVIDIA GeForce RTX 3090 GPU board. MC dose estimates were validated via computed tomography dose index measurements. Main Results. We obtained the absorbed dose distribution in the skin, bone, bone marrow, fat, and muscle tissues. The effective dose for the HR-pQCT examination were 2.05 μSv and 2.13 μSv for the right and left tibia, and 1.48 μSv and 1.49 μSv for the right and left radius, respectively, with a Type A statistical uncertainty of 0.06% (k = 3) with 4.66 × 10¹¹ photon histories. Corresponding effective dose conversion coefficients (k-factors) were 0.185 μSv mGy⁻¹ · cm⁻¹ (tibia), and 0.133 μSv mGy⁻¹ · cm⁻¹ (radius). Significance. We reported the first independent estimate of the effective dose for standard HR-pQCT clinical scans of the distal tibia and radius with the XtremeCT II scanner. Effective dose estimates (considering a total relative uncertainty of less than 40%) were lower than those indicated by the manufacturer and commonly reported for these scans. With 4.66 × 10⁹ photon histories, the gCTD MC code can produce 3D dose maps from segmented HR-pQCT images in less than 12 s (GPU time), with 0.9% (k = 3) statistical uncertainty, making real-time personalized dose estimate feasible.

This study aims to evaluate the performance of five distinct plastic scintillation dosimeters (PSDs) in magnetic fields, and to validate the accuracy of the hyperspectral approach for stem-effect correction. The effect of the magnetic field on different base core materials and components within the PSDs was also investigated, along with the effect of field size and orientation. 

Each PSD was placed in a water tank inside an electromagnet. Magnetic fields, between 0 and 1.5T, were perpendicular to the 6 MeV photon beam and to the PSD axis. The detector axis was either parallel or perpendicular to the beam. The hyperspectral technique was validated and used to determine the scintillation, fluorescence and Cherenkov components at different magnetic fields.

The hyperspectral method accurately removes stem-effects in magnetic fields, even when calibration is performed at 0T. The stem light yield shows good agreement with clear fiber measurements, with relative differences within 2.0%. In the parallel orientation, the corrected PSD response is highly symmetric relative to magnetic field polarity, with a maximum variation of only 0.2% from unity. 

Scintillation light yield increases with magnetic field by 3.6%-6.25% depending on PSD properties. Cherenkov light yield varies up to 230% and down to 0.30% of the 0T value, depending on magnetic field polarity. The magnetic field impact depends primarily on the scintillator properties, with polyvinyltoluene-based probes showing greater sensitivity than polystyrene-based probes. The inclusion of a wavelength shifter has minimal on the magnetic field's effect on scintillation light yield. Normalized scintillation light yield decreases with smaller field sizes. 

PSDs are well-suited for accurate dosimetry in magnetic fields, provided that accurate stem-effect correction techniques are applied. The scintillator properties play a significant role in determining the PSD's sensitivity to magnetic fields. The hyperspectral method is a robust approach for accurate stem-effect removal in such conditions.

This study aims to investigate and validate the response of PSDs in the presence of magnetic fields using Monte Carlo simulations, focusing on the accuracy of electron fluence, dose calculations, and the optical processes of scintillation and Cherenkov radiation.

Monte Carlo simulations, using EGSnrc and TOPAS, of the PSD response under magnetic fields were performed. First, electron fluence simulations were conducted with three different physics lists g4em-penelope, g4em-standard_opt3 and g4em-standard_opt4, with the goal of benchmarking their performance in magnetic fields. Secondly, a Fano test for dose calculations was performed using only the g4em-penelope physics list. Thirdly, the Cherenkov process under magnetic fields was validated against theoretical prediction. Finally, a PSD probe was modeled and simulated, with results compared against measurements. 

The g4em-penelope physics list demonstrated a most balanced performance, showing the closest agreement with EGSnrc simulations and lower variability in magnetic fields than g4em-standard_opt4. Fano test results showed an accuracy of at least 0.36% for dose calculations. Simulations of Cherenkov radiation in ideal conditions were in agreement with theoretical predictions at both 0 T and 1.5 T.

Monte Carlo simulations successfully reproduced experimental trends for Cherenkov radiation under magnetic fields. However, discrepancies were found, with deviations of up to 7.7% when electrons were deflected toward the tip and up to 21.0% in the opposite direction, likely due to modeling limitations. A key result is that Monte Carlo simulations of the scintillation process in magnetic fields failed to reproduce experimental observations. While experimental results showed a significant effect of magnetic fields on scintillation yield, the simulations did not reflect this behavior.


This study establishes that TOPAS, specifically using the g4em-penelope physics list, is a reliable tool for simulating dose, electron fluence, and Cherenkov radiation in the presence of magnetic fields. However, significant discrepancies were observed in the scintillation processes, where Monte Carlo simulations failed to reproduce the effect of magnetic fields seen in experimental measurements. These findings underscore the need for further refinement of simulation models, particularly in accurately representing scintillation under magnetic fields.

Objective:
Radiotherapy (RT) in nasopharyngeal cancer (NPC) patients presents challenges due to proximity of many anatomical structures to the target volume. Furthermore, inter-fractional changes must be considered to assure target coverage. Proton arc therapy (PAT) potentially reduces healthy tissue dose compared to IMPT and VMAT. The impact of PAT on dose to organs-at-risk (OARs), predicted acute- and late radiation toxicities and robust target coverage to inter-fraction changes in NPC patients were investigated.
Approach:
Robustly optimized PAT plans were compared to clinical VMAT and IMPT plans for 10 NPC patients treated with 70.00 Gy to the primary target (CTV 7000) and 54.25 Gy to the prophylactic lymph nodal area (CTV 5425). Integral body dose and mean and max in 0.03cc dose (Dmean and max D0.03cc) in OARs were compared. Normal tissue complication probability (NTCP) values for 22 acute and late radiation-induced toxicities were evaluated. A PAT "base approach" and nine PAT planning approaches to improve PAT inter-fraction robust target coverage were investigated. Target coverage was evaluated on in total 54 weekly repeated CT images (rCTs).
Main Results:
PAT integral dose reduced by on average 55% and 15% compared with clinical VMAT and IMPT, respectively. Compared to IMPT, average Dmean and max D0.03cc were significantly reduced in all evaluated neurological structures. Compared to IMPT, in the PAT plans Dmean was reduced most in the arytenoids, PCM medius and brainstem by on average 8.0 Gy, 6.4 Gy and 6.1 Gy, respectively and all evaluated NTCP's for both acute and late timepoint were significantly reduced. Compared with IMPT, PAT base approach target coverage on rCTs was worse. Approaches to improve PAT inter-fraction target coverage were successful, while maintaining the reduction in NTCP compared to IMPT.
Significance:
Compared to IMPT and VMAT, PAT reduces healthy tissue dose and subsequent estimated toxicity risks in NPC patients. PAT planning approaches to improve inter-fraction robustness were employed successfully, while NTCP benefits of PAT were maintained.

Objective: Carbon ion radiotherapy (CIRT) can provide higher biological effectiveness and cause more damage to cancer cells compared to photon or proton radiotherapy, especially for radio-resistant tumors. The optimization of biological dose is essential for CIRT, to achieve the desirable tumoricidal dose while mitigating biological damage to normal tissues and organs at risk (OAR). However, the biological optimization for CIRT is mathematically challenging, due to the nonlinear nature of biological dose model, which can lead to computational inaccuracy and inefficiency. This work will develop an accurate and efficient biological optimization method for CIRT.
Approach: The proposed method is called iterative Jacobian-based linearization (IJL). In IJL, the biological dose is modeled as the product of the physical dose and relative biological effect (RBE), which is based on the linear-quadratic model via the local effect model in this work, and the optimization objective consists of dose-volume histogram (DVH) based biological dose objectives within clinical target volume and OAR. The optimization algorithm for IJL is through iterative convex relaxation, in which the nonlinear biological dose is iteratively linearized using Jacobian-based approximations and the linear subproblems are solved using alternating direction method of multipliers (ADMM). To compare with IJL, the limited-memory quasi-Newton (QN) method (limited-memory version) is developed that directly solves the same nonlinear biological optimization problem.
Main results: Compared to the QN, IJL demonstrated superior plan accuracy, e.g., better OAR sparing with the reduction of biological dose in the CTV-surrounding volume (PTV1cm) to 89.7%, 95.0%, 88.3% for brain, lung, and abdomen, respectively; IJL also had higher computational efficiency, with approximately 1/10 the computational time per iteration and continuously decreasing objectives (while being stagnated for QN after certain number of iterations).
Significance: A novel optimization algorithm, IJL, incorporating iterative linearization of biological dose, is proposed to accurately and efficiently solve the biological optimization problem for CIRT. It demonstrates superior plan accuracy and computational efficiency compared to the direct nonlinear QN optimization method.

The fundamental goal in radiation therapy (RT) is to simultaneously maximize tumor cell killing and healthy tissue sparing. Reducing uncertainty margins improves normal tissue sparing, but generally requires advanced techniques. Adaptive RT (ART) is a compelling technique that leverages daily imaging and anatomical information to support reduced margins and to optimize plan quality for each treatment fraction. An especially exciting avenue for ART is proton therapy (PT), which aims to combine daily plan re-optimization with the unique advantages provided by protons, including reduced integral dose and near-zero dose deposition distal to the target along the beam direction. A core component for ART is onboard image guidance, and currently two options are available on proton systems, including cone-beam computed tomography (CBCT) and CT-on-rail (CToR) imaging. While CBCT suffers from poorer image quality compared to CToR imaging, CBCT platforms can be more easily integrated with PT systems and thus may support more streamlined adaptive proton therapy (APT). In this review, we present current status of CBCT application to proton therapy dose evaluation and plan adaptation, including progress, challenges and future directions.

Objective. To quantify interfraction shape and positional variations of primary tumor volumes for rectal cancer patients receiving long course radiotherapy by comparing two quantification strategies: a center-of-mass (COM) method and a surface-based metric that captures local deformations. Approach. This study utilized repeat MRI scans before and during radiotherapy (RT) for rectal cancer to investigate the positional variation of the primary gross tumor volume (GTVp). Sixteen patients underwent six MRI exams, with the initial three before the RT course and the subsequent three at one, two, and four weeks into the RT course. GTVp's were delineated on 3D T2-weighted MRIs, and positional variation analyzed using both COM and point-based surface displacements against the initial scan. Surface displacements were quantified using a bidirectional local distance measure, analyzing 3D displacement vectors. Additionally, the study examined local right–left (RL) and anterior–posterior (AP) surface variations relative to tumor height in the rectum by mapping baseline GTVp volumes onto a reference rectum structure. Main results. Systematic error for COM measurements were 1.7, 1.3 and 2.0 mm for AP, RL, and cranial–caudal (CC) direction, respectively. Random errors were 2.1, 1.2 and 2.2 mm, while the GM errors were −0.3, 0.5 and −0.3 mm for AP, RL, and CC directions, respectively. An increase in systematic and random errors were observed when comparing 95th percentile surface displacements to the COM measurements, indicating local displacements which the COM did not detect. Additionally, a general tendency for higher-located tumors to experience larger left–right and AP surface variations were seen when evaluating the 95th percentile. Significance. COM-based analysis might underestimate local deformations. Consequently, surface-based methods might provide more robust estimations of systematic, random and group mean errors for planning target volume-margin calculation. The surface variations tend to increase for tumors located in the upper part of the rectum.