Table of contents

The destructive growth and collapse of cavitation bubbles are used for therapeutic purposes in focused ultrasound procedures and can contribute to tissue damage in traumatic injuries. Histotripsy is a focused ultrasound procedure that relies on controlled cavitation to homogenize soft tissue. Experimental studies of histotripsy cavitation have shown that the extent of ablation in different tissues depends on tissue mechanical properties and waveform parameters. Variable tissue susceptibility to the large stresses, strains, and strain rates developed by cavitation bubbles has been suggested as a basis for localized liver tumor treatments that spare large vessels and bile ducts. However, field quantities developed within microns of cavitation bubbles are too localized and transient to measure in experiments. Previous numerical studies have attempted to circumvent this challenge but made limited use of realistic tissue property data. In this study, numerical simulations are used to calculate stress, strain, and strain rate fields produced by bubble oscillation under histotripsy forcing in a variety of tissues with literature-sourced viscoelastic and acoustic properties. Strain field calculations are then used to predict a theoretical damage radius using tissue ultimate strain data. Simulation results support the hypothesis that differential tissue responses could be used to design tissue-selective treatments. Results agree with studies correlating tissue ultimate fractional strain with resistance to histotripsy ablation and are also consistent with experiments demonstrating smaller lesion size under exposure to higher frequency waveforms. Methods presented in this study provide an approach for modeling tissue-selective cavitation damage in general.

The Vero system can treat intra-fractionally moving tumors with gimbaled dynamic tumor tracking (DTT) by rotating the treatment beam so that it follows the motion of the tumor. However, the changes in the beam geometry and the constant breathing motion of the patient influence the dose applied to the patient. This study aims to perform a full 4D dose reconstruction for thirteen patients treated with DTT at the Vero system at the Universitätsklinikum Erlangen and investigates the temporal resolution required to perform an accurate 4D dose reconstruction. For all patients, a 4DCT was used to train a 4D motion model, which is able to calculate pseudo-CT images for arbitrary breathing phases. A new CT image was calculated for every 100 ms of treatment and a dose calculation was performed according to the current beam geometry (i.e. the rotation of the treatment beam at this moment in time) by rotating according to the momentary beam rotation, which is extracted from log-files. The resulting dose distributions were accumulated on the planning CT and characteristic parameters were extracted and compared. -evaluations of dose accumulations with different spatial-temporal resolutions were performed to determine the minimal required resolution. In total 173 700 dose calculations were performed. The accumulated 4D dose distributions show a reduced mean GTV dose of 0.77% compared to the static treatment plan. For some patients larger deviations were observed, especially in the presence of a poor 4DCT quality. The -evaluation showed that a temporal resolution of 500 ms is sufficient for an accurate dose reconstruction. If the tumor motion is regarded as well, a spatial-temporal sampling of 1400 ms and 2 mm yields accurate results, which reduces the workload by 84%.

Online adaptive treatment procedures in magnetic resonance (MR)-guided radiotherapy (MRgRT) allow compensating for inter-fractional anatomical variations in the patient. Clinical implementation of these procedures, however, requires specific end-to-end tests to validate the treatment chain including imaging, treatment planning, positioning, treatment plan adaption and accurate dose delivery. For this purpose, a new phantom with reproducibly adjustable anthropomorphic structures has been developed. These structures can be filled either with contrast materials providing anthropomorphic image contrast in MR and CT or with polymer dosimetry gel (PG) allowing for 3D dose measurements. To test an adaptive workflow at a 0.35 T MR-Linac, the phantom was employed in two settings simulating inter-fractional anatomical variations within the patient. The settings included two PG-filled structures representing a tumour and an adjacent organ at risk (OAR) as well as five additional structures. After generating a treatment plan, three irradiation experiments were performed: (i) delivering the treatment plan to the phantom in reference setting, (ii) delivering the treatment plan after changing the phantom to a displaced setting without adaption, and (iii) adapting the treatment plan online to the new setting and delivering it to the phantom. PG measurements revealed a homogeneous tumour coverage and OAR sparing for experiment (i) and a significant under-dosage in the PTV (down to 45% of the prescribed dose) and over-dosage in the OAR (up to 180% relative to the planned dose) in experiment (ii). In experiment (iii), a uniform dose in the PTV and a significantly reduced dose in the OAR was obtained, well-comparable to that of experiment (i) where no adaption of the treatment plan was necessary. PG measurements were well comparable with the corresponding treatment plan in all irradiation experiments. The developed phantom can be used to perform end-to-end tests of online adaptive treatment procedures at MR-Linac devices before introducing them to patients.

In presence of inter-fractional anatomical changes, clinical benefits are anticipated from image-guided adaptive radiotherapy. Nowadays, cone-beam CT (CBCT) imaging is mostly utilized during pre-treatment imaging for position verification. Due to various artifacts, image quality is typically not sufficient for photon or proton dose calculation, thus demanding accurate CBCT correction, as potentially provided by deep learning techniques.

This work aimed at investigating the feasibility of utilizing a cycle-consistent generative adversarial network (cycleGAN) for prostate CBCT correction using unpaired training. Thirty-three patients were included. The network was trained to translate uncorrected, original CBCT images (CBCT_org) into planning CT equivalent images (CBCT_cycleGAN). HU accuracy was determined by comparison to a previously validated CBCT correction technique (CBCT_cor). Dosimetric accuracy was inferred for volumetric-modulated arc photon therapy (VMAT) and opposing single-field uniform dose (OSFUD) proton plans, optimized on CBCT_cor and recalculated on CBCT_cycleGAN. Single-sided SFUD proton plans were utilized to assess proton range accuracy.

The mean HU error of CBCT_cycleGAN with respect to CBCT_cor decreased from 24 HU for CBCT_org to  −6 HU. Dose calculation accuracy was high for VMAT, with average pass-rates of 100%/89% for a 2%/1% dose difference criterion. For proton OSFUD plans, the average pass-rate for a 2% dose difference criterion was 80%. Using a (2%, 2 mm) gamma criterion, the pass-rate was 96%. 93% of all analyzed SFUD profiles had a range agreement better than 3 mm. CBCT correction time was reduced from 6–10 min for CBCT_cor to 10 s for CBCT_cycleGAN.

Our study demonstrated the feasibility of utilizing a cycleGAN for CBCT correction, achieving high dose calculation accuracy for VMAT. For proton therapy, further improvements may be required. Due to unpaired training, the approach does not rely on anatomically consistent training data or potentially inaccurate deformable image registration. The substantial speed-up for CBCT correction renders the method particularly interesting for adaptive radiotherapy.

In proton therapy high energy protons are used to irradiate a tumor. Ideally, the delivered proton dose distribution is measured during treatment to ensure patient safety and treatment effectiveness. Here we investigate if we can use the ionoacoustic wave field to monitor the actual proton dose distribution for the two most commonly used proton accelerators; the isochronous cyclotron and the synchrocyclotron. To this end we model the acoustic field generated by the protons when irradiating a heterogeneous cancerous breast with a 89 MeV proton beam. To differentiate between the systems, idealized temporal micro-structures of the beams have been implemented. Results show that by employing model-based inversion we are able to reconstruct the 3D dose distributions from the simulated noisy pressure fields. Good results are obtained for both systems; the absolute error in the position of the maximum amplitude of the dose distribution is 5.0 mm for the isochronous cyclotron and 5.2 mm for the synchrocyclotron. In conclusion, this numerical study suggests that the ionoacoustic wave field may be used to monitor the proton dose distribution during breast cancer treatment.

To suppress scatter in cone beam computed tomography (CBCT), two-dimensional antiscatter grids (2D grid) have been recently proposed. In this work, we developed several grid prototypes with higher grid ratios and smaller grid pitches than previous designs, and quantified their primary and scatter transmission properties in the context of CBCT for radiation therapy.

Three focused 2D grid prototypes were developed with grid ratios at 12 and 16, and grid pitches at 2 and 3 mm. Their scatter transmission properties were measured between 80–140 kVp, and benchmarked against a high performance radiographic grid (1D grid) using a Varian TrueBeam CBCT system. The effect of source–grid misalignment on the primary transmission and the improvement in contrast-to-noise ratio (CNR) were also evaluated.

Changing the grid pitch from two to three mm increased the average primary transmission from 84% to 89%. Maximum scatter-to-primary ratio (SPR) with grid ratio of 12 was 0.3, and increasing the grid ratio to 16 reduced SPR by 30%. A 10 mm misalignment in 2D grid position led to a 6%–8% reduction in average primary transmission, and reduction was more pronounced for the higher grid ratio. 2D grids provided up to factor of seven lower SPR and 21% better primary transmission than the 1D grid, and their scatter transmission exhibited lower energy dependence. While 2D grids provided up to factor of 2.3 higher CNR improvement, a significant variation in CNR improvement was not observed among different grid pitch and ratios.

In summary, grid ratio of 16 and grid pitch of 2 mm can keep SPRs below 0.2 even in high scatter conditions, while keeping primary transmission fractions above 80%, key benefits of the investigated 2D grids in improving image quality of CBCT. However, such grids require precise alignment in source-grid geometry during CBCT acquisitions. This study also implies that 2D grids can provide substantially better scatter suppression and primary transmission than high-performance 1D grids currently available.

The goal of this work is to validate the use of the Exradin W1 plastic scintillation detector (PSD) to measure profiles and output factors from Gamma Knife Perfexion collimators in a Lucy phantom.

The Exradin W1 PSD has a small-volume, near-water-equivalent, energy-independent sensitive element. Output measurements were performed for all 3 collimators (4 mm, 8 mm, and 16 mm) of the Gamma Knife Perfexion system, and these measurements were compared to measurements made with an A16 ion chamber and an EBT3 film and to the nominal values. We showed that a configuration in which the focus or 'shot' moves while the detector remains fixed is essentially equivalent to a configuration in which the focus is fixed while the detector moves. A Lucy phantom containing a PSD was moved in small steps to acquire profiles in all three dimensions. EBT3 film was inserted in the Lucy phantom and exposed to a single shot for each collimator.

The relative values for output factors measured with the PSD were 1.000, 0.892, and 0.795, for the 16 mm, 8 mm, and 4 mm collimators, respectively. The values measured with EBT3 film were 1.000, 0.881, and 0.793, and the values measured with the A16 ion chamber were 1.000, 0.883, and 0.727. The nominal output factors for the Gamma Knife Perfexion are 1.000, 0.900, and 0.814, respectively. There was excellent agreement between all profiles measured with the PSD and EBT3 as well as with the treatment planning system data provided by the vendor.

In light of our results, the Exradin W1 PSD is well suited for beam quality assurance of a Gamma Knife Perfexion irradiator.

In TTFields therapy, Optune^® is used to deliver the electric field to the tumor via 4 transducer arrays. This device monitors the temperature of the transducers and reduces the current whenever a transducer reaches 41 °C. Our aim is to quantify Optune's duty cycle and to predict the steady-state temperature distribution in the head during GBM treatment. We used a realistic head model and the finite element method to solve Pennes equation and to simulate how Optune operates considering that current reduces to zero when the thermal limit is reached. The thermal impact was evaluated considering the maximum temperature reached by each tissue and using the CEM 43 °C metric. We observed that Optune switches the current on and off intermittently. In our model, one transducer reached the temperature limit quicker than the others and consequently it was the one that controlled current injection. This led to different duty cycles for the anterior–posterior and left–right array pairs. The thermal analysis indicated that the highest temperature in the model, 41.7 °C, was reached on the scalp under a transducer. However, TTFields may lead to significant changes only at the brain level such as BBB permeability increase, cerebral blood flow variation and changes in the concentration of some neurotransmitters. The duty cycle may be increased, e.g. by controlling the current at the transducer level. These predictions should be validated by comparison with experimental data and reconciled with the lack of evidence of thermal impact in clinical trials.

Following the clinical introduction of the Elekta Unity MR-linac, there is an urgent need for development of dosimetry protocols and tools, not affected by the presence of a magnetic field. This work presents a benchmarking methodology comprising 2D/3D passive dosimetry and involving on-couch adaptive treatment planning, a unique step in MR-linac workflows.

Two identical commercially available 3D-printed head phantoms (featuring realistic bone anatomy and MR/CT contrast) were employed. One phantom incorporated a film dosimetry insert, while the second was filled with polymer gel. Gel dose-response characteristics were evaluated under the Unity irradiation and read-out conditions, using vials and a cubic container filled with gel from the same batch.

Treatment plan for the head phantoms involved a hypothetical large C-shape brain lesion, partly surrounding the brainstem. An IMRT step-and-shoot 7-beam plan was employed. Pre-treatment on-couch MR-images were acquired in order for the treatment planning system to calculate the virtual couch shifts and perform adaptive planning. Absolute 2D and relative 3D measurements were compared against calculations related to both adapted and original plans. Real-time dose accumulation monitoring in the gel-filled phantom was also performed.

Results from the vials and cubic container suggest that gel dose-response is linear in the dose range investigated and signal integrity is mature at the read-out timings considered. Head phantom 2D and 3D measurements agreed well with calculations with 3D gamma index passing rates above 90% in all cases, even with the most stringent criteria used (2 mm/2%). By exploiting the 3D information provided by the gel, comparison also involved DVHs, dose-volume and plan quality metrics, which also reflected the agreement between adapted and delivered plans within  ±4%. No considerable discrepancies were detected between adapted and original plans.

A novel methodology was developed and implemented, suitable for QA procedures in Unity. TPS calculations were validated within the experimental uncertainties involved.

This paper considers the kinematic physical characteristics of ionic beams for maximum relative bio-effectiveness (RBE).

RBE studies, based on heterogenous cell survival studies at different laboratories and linear energy transfer (LET) conditions for proton, helium, carbon, neon and argon ions, have been further analysed to determine the LET_U values where RBE is maximal and the LET-RBE relationship has a turnover point. The SRIM stopping power software and other classical equations are used to determine the particle velocities, kinetic energies and their effective ionic charges at LET_U.

The estimated mean LET_U values increase with atomic number (Z). Each LET_U has a unique relativistic velocity, β  =  v/c, the velocity v expressed as a fraction of the speed of light, (c), and which is non-linearly proportional to Z. For ions helium and heavier ions, these velocities indicate that the effective charge Z^* is around 0.99 of the full Z value at each LET_U, with remarkably stable velocities of 3–4 nm · fs⁻¹ per nucleon, or around 6–8 nm · fs⁻¹ per unit Z. For Z  =  1, (protons and deuterium) some values fall outside these ranges but the result depends on the mix of proton and deuterium used in experiments. An alternative index of βA/Z² (A is the atomic mass number), suggests an average velocity of around 15 nm · fs⁻¹ for each particle at LET_U. These distances, traversed in the time of the radiochemical process initiation, are all within the dimensions of the nucleosome. Curve fitting of the data set provides a predictive equation for LET_U for any ion, as LET_U  =  30.4  +   (1  −  Exp[−0.61  √  (Z  −  1)]) when normalised to protons. These data can be extended to heavier ions such as silicon and iron and give values that are consistent with experimental data.

Each ion probably has a unique LET_U value. Kinematic studies show maximum bio-effectiveness occurs at particle velocities where electron stripping remains at around 99% and where the velocity per nucleon is around 3–4 nm · fs⁻¹.

This study enhances the limited prior knowledge about the physical conditions of particle beams that provide maximum bio-effectiveness, with applications in particle radiotherapy, radiation protection and space travel.

Respiratory motion management techniques in radiotherapy (RT) planning are primarily focused on maintaining tumor target coverage. An inadequately addressed need is accounting for motion in dosimetric estimations in smaller serial structures. Accurate dose estimations in such structures are more sensitive to motion because respiration can cause them to move completely in or out of a high dose-gradient field. In this work, we study three motion management strategies (m1–m3) to find an accurate method to estimate the dosimetry in airways. To validate these methods, we generated a 'ground truth' digital breathing model based on a 4DCT scan from a lung stereotactic ablative radiotherapy (SAbR) patient. We simulated 225 breathing cycles with  ±10% perturbations in amplitude, respiratory period, and time per respiratory phase. A high-resolution breath-hold CT (BHCT) was also acquired and used with a research virtual bronchoscopy software to autosegment 239 airways. Contours for planning target volume (PTV) and organs at risk (OARs) were defined on the maximum intensity projection of the 4DCT (CT_MIP) and transferred to the average of the 10 4DCT phases (CT_AVG). To design the motion management methods, the RT plan was recreated using different images and structure definitions. Methods m1 and m2 recreated the plan using the CT_AVG image. In method m1, airways were deformed to the CT_AVG. In m2, airways were deformed to each of the 4DCT phases, and union structures were transferred onto the CT_AVG. In m3, the RT plan was recreated on each of the 10 phases, and the dose distribution from each phase was deformed to the BHCT and summed. Dose errors (mean [min, max]) in airways were: m1: 21% (0.001%, 93%); m2: 45% (0.1%, 179%); and m3: 4% (0.006%, 14%). Our work suggests that accurate dose estimation in moving small serial structures requires customized motion management techniques (like m3 in this work) rather than current clinical and investigational approaches.

High dose-rate brachytherapy is a modality of radiation therapy used for cancer treatment, in which the radiation source is placed within the body. The treatment goal is to give a high enough dose to the tumour while sparing nearby healthy tissue and organs (organs-at-risk). The most common criteria for evaluating dose distributions are dosimetric indices. For the tumour, such an index is the portion of the volume that receives at least a specified dose level (e.g. the prescription dose), while for organs-at-risk it is instead the portion of the volume that receives at most a specified dose level. Dosimetric indices are aggregate criteria and do not consider spatial properties of the dose distribution. Further, there are neither any established evaluation criteria for characterizing spatial properties, nor have such properties been studied in the context of mathematical optimization of brachytherapy. Spatial properties are however of clinical relevance and therefore dose plans are sometimes adjusted manually to improve them. We propose an optimization model for reducing the prevalence of contiguous volumes with a too high dose (hot spots) or a too low dose (cold spots) in a tentative dose plan. This model is independent of the process of constructing the tentative plan. We conduct computational experiments with tentative plans obtained both from optimization models and from clinical practice. The objective function considers pairs of dose points and each pair is given a distance-based penalty if the dose is either too high or too low at both dose points. Constraints are included to retain dosimetric indices at acceptable levels. Our model is designed to automate the manual adjustment step in the planning process. In the automatic adjustment step large-scale optimization models are solved. We show reductions of the volumes of the largest hot and cold spots, and the computing times are feasible in clinical practice.

Complex intra-fractional motion and deformation of the liver significantly impacts the accuracy of delivered dose in radiotherapy. It limits margin reduction, dose escalation and normal tissue sparing. A critical component of motion management is to accurately reconstruct tumor motion. In this study, we developed a six degrees of freedom projection marker matching method (6-DoF PM³) to reconstruct translational and rotational liver tumor motion in a rotational treatment delivery, such as volumetric modulated arc therapy (VMAT). Specifically, we modeled the use of two gold markers implanted in a linear form. The four endpoints of the two gold linear markers were used as tracking surrogates. During delivery, kV x-ray projection images were acquired. A method was developed to automatically identify the 2D marker-endpoints on the projection images. 3D marker positions were determined by solving an optimization problem with the objective function penalizing the distance from the reconstructed 3D position of each fiducial marker endpoint to the corresponding straight line defined by the kV x-ray projection of the endpoints. We performed a series of tests to evaluate different components of the method. For 2D marker endpoints identification, 99.9% of the marker endpoints were identified with an error (1 pixel) along both u and v directions. For 3D reconstruction of motion in simulation studies, error of rotational angle was ° without considering the 2D marker identification error. The rotational angle error was relatively sensitive to the accuracy of 2D marker identification. When the 2D error raised from 0.22 mm to 0.776 mm, the error of 3D rotational angle increased from 0.5° to 2.5°. In the experimental end-to-end tests, the mean root-mean square error of the 3D reconstructed marker positions was 0.75 mm and the mean error of rotational angle was within 1.7°. Our method can accurately determine intra-fractional liver tumor motion in rotational radiotherapy using kV projections of only two linear fiducial markers.

PET scanners with partial-ring geometry have been proposed for various imaging purposes. The incomplete projection data obtained from this design cause undesirable artifacts in the reconstructed images. In this study, we investigated the performance of a deep learning (DL) based method for the recovery of partial-ring PET images. Twenty digital brain phantoms were used in the Monte Carlo simulation toolkit, SimSET, to simulate 15 min full-ring PET scans. Partial-ring PET data were generated from full-ring PET data by removing coincidence events that hit these specific detector blocks. A convolutional neural network based on the residual U-Net architecture was trained to predict full-ring data from partial-ring data in either the projection or image domain. The performance of the proposed DL-based method was evaluated by comparing with the PET images reconstructed using the full-ring projection data in terms of the mean squared error (MSE), structural similarity (SSIM) index and recovery coefficient (RC). The MSE results showed the superiority of the image-domain approach in reduction of 91.7% in contrast to 14.3% for the projection-domain approach. Therefore, the image-domain approach was used to study the influence of the number of detector block removal. The SSIM results were 0.998, 0.996 and 0.993 for 3, 5 and 7 detector block removals, respectively. The activity of gray and white matters could be fully recovered even with 7 detector block removal, while the RCs of two artificially inserted small lesions (3 pixels in diameter) in the testing data were 94%, 89% and 79% for 3, 5, and 7 detector block removals, respectively. Our simulation results suggest that DL has the potential to recover partial-ring PET images.

Dual-panel PET system configuration can lead to spatially variable point-spread functions (PSF) of considerable deformations due to depth-of-interaction effects and limited angular coverage. If not modelled properly, these effects result in decreased and inconsistent recovery of lesion activity across the field-of-view (FOV), as well as mispositioning of lesions in the reconstructed image caused by strong PSF asymmetries. We implemented and evaluated models of such PSF deformations with spatially-variant image-based resolution modeling (IRM) within reconstruction (varRM) using the Direct Image REConstruction for Time-of-flight (DIRECT) method and within post-reconstruction deconvolution methods. In addition, DIRECT reconstruction was performed with a spatially-invariant IRM (invRM) and without resolution modeling (noRM) for comparison. The methods were evaluated using simulated data for a realistic breast model with a set of 5 mm lesions located throughout the FOV of a dual-panel Breast-PET scanner. We simulated high-count data to focus on the ability of each method to correctly recover the PSF deformations, and a clinically realistic count level to assess the impact of low count data on the quantitative performance of the evaluated techniques. Performance of the methods evaluated herein was assessed by comparing lesion activity recovery (%BIAS), consistency (%SD) across the FOV, overall error (%RMSE), and recovery of each lesion location. As expected, all techniques using IRM provide considerable improvement over the noRM reconstruction. For the high-count cases, the overall quantitative performance of all IRM techniques, whether within reconstruction or within post-reconstruction, is similar if the lesion location misplacements are ignored. However, invRM provides less consistent performance on activity across lesions and is not able to recover accurate lesion locations. For a clinically realistic count level, varRM reconstruction consistently outperforms all compared approaches, while the post-reconstruction IRM approaches exhibit higher %SD and %RMSE values due to being more affected by the data noise than the within-reconstruction IRM approaches.

In vivo verification of light ion therapy based on positron-emission tomography (PET) imaging of irradiation induced patient activation relies on activity predictions from Monte-Carlo (MC) or analytical computational engines for comparison to the measurements. In order to achieve the necessary accuracy, experimental data are indispensable for the validation of the calculation models. For this we irradiated thick reference targets with mono-energetic helium, carbon and oxygen ion beams and measured the resulting material activation offline with a commercial full-ring PET/CT scanner located nearby the treatment room.

Acquired PET data were analysed over time to separate the activity contribution of different radionuclides. Determined production yields were compared to published findings obtained from in-beam activation measurements with a limited-angle double-head PET camera. In addition, we investigated the time-dependence of the measured radionuclide-specific contributions and of the distal activity range, as well as the lateral spread of the activity signal as a function of beam penetration depth.

We present radionuclide-specific depth-resolved activity distributions and production yields for the radionuclides , and , dominating irradiation-induced patient activation. We observe systematically lower production yields with a ratio between the dual-head and our full-ring PET measurements of, on average, 1.7 and 1.3 for the oxygen and carbon beam irradiations, and 1.7 (2.1) for the high (low) energy helium beam irradiations. Findings on the temporal development of the activity range confirm the expectation, with the oxygen beam induced signal being the most sensitive scenario.

The experimental data reported in this work, acquired with a state-of-the-art full ring PET scanner, provide a comprehensive and consistent basis for the benchmarking of PET signal calculation engines. In particular, they can support a fine-tuning of the underlying physics models used by the respective implementation and therefore improve the accuracy of PET-based therapy verifications at current and future treatment facilities.

RECA (Radiotherapy enhanced with Cherenkov photo-activation) is a proposed treatment where the anti-cancer drug psoralen is photo-activated in situ by UVA (Ultraviolet A, 320–400 nm) Cherenkov light (CL) produced directly by the treatment beam itself. In this study, we develop a UVA-imaging technique to quantify relative UVA CL produced by bulk tissues and other phantoms upon clinical x-ray megavoltage irradiation. UVA CL emission (320–400 nm) was quantified in tissue samples of porcine and poultry and in two kinds of solid waters (SW): brown (Virtual Waters, Standard Imaging, WI) and white (Diagnostic Therapy, CIRS, VA), and in 1% agarose gels variously doped with absorbing dye. Quantification was achieved through cumulative imaging of the samples placed in a dark, light-blocking chamber during irradiation on a Varian 21 EX accelerator. UVA imaging required a specialized high-sensitivity cooled camera equipped with UVA lenses and a filter. At 15 MV, white SW emitted , and less UVA than chicken breast, pork loin and pork belly, respectively. Similar under-response was observed at 6 MV. Brown SW had less UVA emission than white SW at 15 MV, and negligible emission at 6 MV. Agarose samples (1% by weight) doped with 250 ppm India ink exhibited equivalent UVA CL emission to chicken breast (within 8%). The results confirm that for the same absorbed dose, SW emits less UVA light than the tissue samples, indicating that prior in vitro studies utilizing SW as the CL-generating source may have underestimated the RECA therapeutic effect. Agarose doped with 250 ppm India ink is a convenient tissue-equivalent phantom for further work.

Many brachytherapy (BT) errors could be detected with real-time in vivo dosimetry technology. Inorganic scintillation detectors (ISDs) have demonstrated promising capabilities for BT, because some ISD materials can generate scintillation signals large enough that (a) the background signal emitted in the fiber-optic cable (stem signal) is insignificant, and (b) small detector volumes can be used to avoid volume averaging effects in steep dose gradients near BT sources. We investigated the characteristics of five ISD materials to identify one that is appropriate for BT. ISDs consisting of a 0.26 to 1.0 mm³ volume of ruby (Al₂O₃:Cr), a mixture of Y₂O₃:Eu and YVO⁴:Eu, ZnSe:O, or CsI:Tl coupled to a fiber-optic cable were irradiated in a water-equivalent phantom using a high-dose-rate ¹⁹²Ir BT source. Detectors based on plastic scintillators BCF-12 and BCF-60 (0.8 mm³ volume) were used as a reference. Measurements demonstrated that the ruby, Y₂O₃:Eu+YVO₄:Eu, ZnSe:O, and CsI:Tl ISDs emitted scintillation signals that were up to 19, 19, 250, and 880 times greater, respectively, than that of the BCF-12 detector. While the total signals of the plastic scintillation detectors were dominated by the stem signal for source positions 0.5 cm from the fiber-optic cable and  >3.5 cm from the scintillator volume, the stem signal for the ruby and Y₂O₃:Eu+YVO₄:Eu ISDs were  <1% of the total signal for source positions  <3.4 and  <4.4 cm from the scintillator, respectively, and  <0.7% and  <0.5% for the ZnSe:O and CsI:Tl ISDs, respectively, for positions  ⩽8.0 cm. In contrast to the other ISDs, the Y₂O₃:Eu+YVO₄:Eu ISD exhibited unstable scintillation and significant afterglow. All ISDs exhibited significant energy dependence, i.e. their dose response to distance-dependent ¹⁹²Ir energy spectra differed significantly from the absorbed dose in water. Provided that energy dependence is accounted for, ZnSe:O ISDs are promising for use in error detection and patient safety monitoring during BT.

Uncompressed clinical data from modern positron emission tomography (PET) scanners are very large, exceeding 350 million data points (projection bins). The last decades have seen tremendous advancements in mathematical imaging tools many of which lead to non-smooth (i.e. non-differentiable) optimization problems which are much harder to solve than smooth optimization problems. Most of these tools have not been translated to clinical PET data, as the state-of-the-art algorithms for non-smooth problems do not scale well to large data. In this work, inspired by big data machine learning applications, we use advanced randomized optimization algorithms to solve the PET reconstruction problem for a very large class of non-smooth priors which includes for example total variation, total generalized variation, directional total variation and various different physical constraints. The proposed algorithm randomly uses subsets of the data and only updates the variables associated with these. While this idea often leads to divergent algorithms, we show that the proposed algorithm does indeed converge for any proper subset selection. Numerically, we show on real PET data (FDG and florbetapir) from a Siemens Biograph mMR that about ten projections and backprojections are sufficient to solve the MAP optimisation problem related to many popular non-smooth priors; thus showing that the proposed algorithm is fast enough to bring these models into routine clinical practice.

The clinical implementation of a variable relative biological effectiveness (RBE) in proton therapy is currently controversially discussed. Initial clinical evidence indicates a variable proton RBE, which needs to be verified. In this study, a radiation response modelling framework for assessing clinical RBE variability is established. It was applied to four selected glioma patients (grade III) treated with adjuvant radio(chemo)therapy and who developed late morphological image changes on T1-weighted contrast-enhanced (T1w-CE) magnetic resonance (MR) images within approximately two years of recurrence-free follow-up. The image changes were correlated voxelwise with dose and linear energy transfer (LET) values using univariable and multivariable logistic regression analysis. The regression models were evaluated by the area-under-the-curve (AUC) method performing a leave-one-out cross validation. The tolerance dose TD₅₀ at which 50% of patient voxels experienced toxicity was interpolated from the models. A Monte Carlo (MC) model was developed to simulate dose and LET distributions, which includes variance reduction (VR) techniques to decrease computation time. Its reliability and accuracy were evaluated based on dose calculations of the clinical treatment planning system (TPS) as well as absolute dose measurements performed in the patient specific quality assurance.

Morphological image changes were related to a combination of dose and LET. The multivariable models revealed cross-validated AUC values of up to 0.88. The interpolated TD₅₀ curves decreased with increasing LET indicating an increase in biological effectiveness. The MC model reliably predicted average TPS dose within the clinical target volume as well as absolute water phantom dose measurements within 2% accuracy using dedicated VR settings.

The observed correlation of dose and LET with late brain tissue damage suggests considering RBE variability for predicting chronic radiation-induced brain toxicities. The MC model simulates radiation fields in patients precisely and time-efficiently. Hence, this study encourages and enables in-depth patient evaluation to assess the variability of clinical proton RBE.