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The linear-quadratic model is one of the key tools in radiation biology and physics. It provides a simple relationship between cell survival and delivered dose: , and has been used extensively to analyse and predict responses to ionising radiation both in vitro and in vivo. Despite its ubiquity, there remain questions about its interpretation and wider applicability—Is it a convenient empirical fit or representative of some deeper mechanistic behaviour? Does a model of single-cell survival in vitro really correspond to clinical tissue responses? Is it applicable at very high and very low doses? Here, we review these issues, discussing current usage of the LQ model, its historical context, what we now know about its mechanistic underpinnings, and the potential challenges and confounding factors that arise when trying to apply it across a range of systems.

In precision radiotherapy, the intrafractional motion can cause a considerable uncertainty of the location of the tumor to be treated. An established approach is the expansion of the target volume to account for the motion. An alternative approach is couch-tracking, in which the patient is continually moved to compensate the intrafractional motion. However, couch-tracking itself might induce uncertainty of the patient's body position, because the body is non-rigid.

One hundred healthy volunteers were positioned supine on a robotic couch. Optical markers were placed on the torso of the volunteers as well as on the couch, and their positions were tracked with an optical surface measurement system. Using these markers, the uncertainty of the body position relative to the couch position was estimated while the couch was static or moving.

Over the included 83 healthy volunteers, the median of the uncertainty increased by 0.8 mm (SI), 0.4 mm (LR) and 0.4 mm (AP) when the couch moved.

Couch motion was found to increase the uncertainty of the body position relative to the couch. However, this uncertainty is one order of magnitude smaller than the intrafractional tumor motion amplitudes to be compensated. Therefore, even with body motion present, the couch-tracking approach is a viable option. The study was registered at ClinicalTrials.gov (NCT02820532) and the Swiss national clinical trials portal (SNCTP000001878).

In proton therapy, the lateral fall-off is often used to spare critical organs. It is therefore crucial to improve the penumbra for proton pencil beam scanning. However, previous work has shown that collimation may not be necessary for depths of  >15 cm in water. As such, in this work we investigate the effectiveness of a thin multi leaf collimator (just thick enough to completely stop protons with ranges of  <15 cm in water) for energy layer specific collimation in patient geometries, when applied in combination with both grid and contour scanned PBS proton therapy. For this, an analytical model of collimated beam shapes, based solely on data available in the treatment planning system, has been included in the optimization, with the resulting optimised plans then being recalculated using Monte Carlo in order to most accurately simulate the full physics effects of the collimator. For grid based scanning, energy specific collimation has been found to reduce the V30 outside the PTV by 19.8% for an example patient when compared to the same pencil beam placement without collimation. V30 could be even reduced by a further 5.6% when combining collimation and contour scanning. In addition, mixed plans, consisting of contour scanning for deep fields (max range  >15 cm WER) and collimated contour scanning for superficial fields (<15 cm), have been created for four patients, by which V30 could be reduced by 0.8% to 8.0% and the mean dose to the brain stem by 1.5% to 3.3%. Target dose homogeneity however is not substantially different when compared to the best un-collimated scenario. In conclusion, we demonstrate the potential advantages of a thin, multi leaf collimator in combination with contour scanning for energy layer specific collimation in PBS proton therapy.

Digital breast tomosynthesis (DBT) is currently used as an adjunct technique to digital mammography (DM) for breast cancer imaging. Being a quasi-3D image, DBT is capable of providing depth information on the internal breast glandular tissue distribution, which may be enough to obtain an accurate patient-specific radiation dose estimate. However, for this, information regarding the location of the glandular tissue, especially in the vertical direction (i.e. x-ray source to detector), is needed. Therefore, a dedicated reconstruction algorithm designed to localize the amount of glandular tissue, rather than for optimal diagnostic value, could be desirable. Such a reconstruction algorithm, or, alternatively, a reconstructed DBT image classification algorithm, could benefit from the use of larger voxels, rather than the small sizes typically used for the diagnostic task. In addition, the Monte Carlo (MC) based dose estimates would be accelerated by the representation of the breast tissue with fewer and larger voxels. Therefore, in this study we investigate the optimal DBT reconstructed voxel size that allows accurate dose evaluations (i.e. within 5%) using a validated Geant4-based MC code. For this, sixty patient-based breast models, previously acquired using dedicated breast computed tomography (BCT) images, were deformed to reproduce the breast during compression under a given DBT scenario. Two re-binning approaches were applied to the compressed phantoms, leading to isotropic and anisotropic voxels of different volumes. MC DBT simulations were performed reproducing the acquisition geometry of a SIEMENS Mammomat Inspiration system. Results show that isotropic cubic voxels of 2.73 mm size provide a dose estimate accurate to within 5% for 51/60 patients, while a comparable accuracy is obtained with anisotropic voxels of dimension 5.46  ×  5.46  ×  2.73 mm³. In addition, the MC simulation time is reduced by more than half in respect to the original voxel dimension of 0.273  ×  0.273  ×  0.273 mm³ when either of the proposed re-binning approaches is used. No significant differences in the effect of binning on the dose estimates are observed (Wilcoxon–Mann–Whitney test, p-value  >  0.4) between the 0° the 23° (i.e. the widest angular range) exposure.

To develop an online plan adaptation algorithm for intensity modulated proton therapy (IMPT) based on fast Monte Carlo dose calculation and cone beam CT (CBCT) imaging.

A cohort of ten head and neck cancer patients with an average of six CBCT scans were studied. To adapt the treatment plan to the new patient geometry, contours were propagated to the CBCTs with a vector field (VF) calculated with deformable image registration between the CT and the CBCTs. Within the adaptive planning algorithm, beamlets were shifted following the VF at their distal falloff and raytraced in the CBCT to adjust their energies, creating a geometrically adapted plan. Four geometric adaptation modes were studied: unconstrained geometric shifts (Free), isocenter shift (Iso), a range shifter (RS), or isocenter shift and range shifter (Iso-RS). After evaluation of the geometrical adaptation, the weights of a selected subset of beamlets were automatically tuned using MC-generated influence matrices to fulfill the original plan requirements. All beamlet calculations were done with a fast Monte Carlo running on a GPU (graphics processing unit).

Geometrical adaptation alone only worked with small anatomy changes. The weight-tuned adaptation worked for every fraction, with the Free and Iso modes performing similarly and being superior than the two range shifters modes. The mean V95 and V107 were 99.4  ±  0.9 and 6.4%  ±  4.7% in the Free mode with weight tuning. The calculation time per fraction was ~5 min, but further task parallelization could reduce it to ~1–2 min for delivery adaptation right after patient setup.

An online adaptation algorithm was developed that significantly improved the treatment quality for inter-fractional geometry changes. Clinical implementation of the algorithm would allow delivery adaptation right before treatment and thus allow planning margin reductions for IMPT.

Peripheral nerve stimulation (PNS) has become an important limitation for fast MR imaging using the latest gradient hardware. We have recently developed a simulation framework to predict PNS thresholds and stimulation locations in the body for arbitrary coil geometries to inform the gradient coil optimization process. Our approach couples electromagnetic field simulations in realistic body models to a neurodynamic model of peripheral nerve fibers. In this work, we systematically analyze the impact of key parameters on the predicted PNS thresholds to assess the robustness of the simulation results. We analyze the sensitivity of the simulated thresholds to variations of the most important simulation parameters, including parameters of the electromagnetic field simulations (dielectric tissue properties, body model size, position, spatial resolution, and coil model discretization) and parameters of the neurodynamic simulation (length of the simulated nerves, position of the nerve model relative to the extracellular potential, temporal resolution of the nerve membrane dynamics). We found that for the investigated setup, the subject-dependent parameters (e.g. tissue properties or body size) can affect PNS prediction by up to ~26% when varied in a natural range. This is in accordance with the standard deviation of ~30% reported in human subject studies. Parameters related to numerical aspects can cause significant simulation errors (>30%), if not chosen cautiously. However, these perturbations can be controlled to yield errors below 5% for all investigated parameters without an excessive increase in computation time. Our sensitivity analysis shows that patient-specific parameter fluctuations yield PNS threshold variations similar to the variations observed in experimental PNS studies. This may become useful to estimate population-average PNS thresholds and understand their standard deviation. Our analysis indicates that the simulated PNS thresholds are numerically robust, which is important for ranking different MRI gradient coil designs or assessing different PNS mitigation strategies.

Mammograms represent data that can inform future risk of breast cancer. Data from two case-control study populations were analyzed. Population 1 included women (N  =  180 age matched case-control pairs) with mammograms acquired with one indirect x-ray conversion mammography unit. Population 2 included women (N  =  319 age matched case-control pairs) with mammograms acquired from 6 direct x-ray conversion units. The Fourier domain was decomposed into n concentric rings (radial spatial frequency bands). The power in each ring was summarized giving a set of measures. We investigated images in raw, for presentation (processed) and calibrated representations and made comparison with the percentage of breast density (BD) determined with the operator assisted Cumulus method. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs), per standard deviation increase derived from the respective breast density distributions and 95% confidence intervals (CIs) were estimated.

A measure from a lower radial frequency ring, corresponding 0.083–0.166 cycles mm⁻¹ and BD had significant associations with risk in both populations. In Population 1, the Fourier measure produced significant associations in each representation: OR  =  1.76 (1.33, 2.32) for raw; OR  =  1.43 (1.09, 1.87) for processed; and OR  =  1.68 (1.26, 2.25) for calibrated. BD also provided significant associations in Population 1: OR  =  1.72 (1.27, 2.33). In Population 2, the Fourier measure produced significant associations for each representation as well: OR  =  1.47 (1.19, 1.80) for raw; OR  =  1.38 (1.15, 1.67) for processed; and OR  =  1.42 (1.15, 1.75) for calibrated. BD provided significant associations in Population 2: OR  =  1.43 (1.17, 1.76). Other coincident spectral regions were also predictive of case-control status.

In sum, generalized breast density measures were significantly associated with breast cancer in both FFDM technologies.

Respiratory and cardiac motion can strongly impair cardiac PET image quality and tracer uptake quantification. Standard gating techniques can minimize these motion artefacts but suffer from low signal-to-noise ratio because only a small percentage of the total data is utilized. Motion correction approaches have been proposed to overcome this problem but require accurate knowledge of such physiological motion. Here we present a joint PET-MR motion estimation approach which combines complimentary dynamic image information from simultaneously acquired MR and PET to ensure improved cardiac and respiratory motion estimation for motion-corrected image reconstruction (MCIR) of PET images. A 3D triple-echo Dixon MR scan is used both for calculation of MR-based attenuation correction (AC) maps and estimation of physiological motion. PET listmode data is obtained simultaneously to the MR acquisition which is used for a joint motion estimation and reconstruction of the final MCIR PET. In a first step, dynamic cardiac and respiratory motion resolved 4D MR and PET images are reconstructed. These image series are used in a joint image registration to estimate non-rigid cardiac and respiratory motion fields. In a second step, the motion fields are utilized in a MR MCIR to obtain cardiac and respiratory resolved dynamic MR-based AC maps. In the last step, the non-rigid motion fields and the dynamic AC maps are applied in a PET MCIR to obtain the final motion-corrected PET images. PET-MR data has been obtained in six patients without any known heart disease. Motion amplitudes were between 5.6 and 16 mm, with higher values in the basal compared to the mid-ventricular and apical segments. The proposed joint PET-MR motion estimation provided more accurate motion estimation than using either modality separately. The underestimation of PET uptake due to respiratory and cardiac motion artefacts in the AC maps was up to 17%. The average increase in uptake values using MCIR was 23%  ±  10% (p  <  0.0001), with values of 28%  ±  11% (p  <  0.0001) for basal, 21%  ±  8% (p  <  0.0001) for mid-cavity and 17%  ±  7% (p  <  0.0001) for apical segments. With the proposed scheme we could ensure high PET image quality and improve local PET uptake quantification by up to 30%. Attenuation correction and motion information was obtained from the same PET-MR raw data, which was obtained during free-breathing to minimize scan times and to increase patient comfort.

In charged particle therapy, the objective is to exploit both the physical and radiobiological advantages of charged particles to improve the therapeutic index. Use of the beam scanning technique provides the flexibility to implement biological dose optimized intensity-modulated ion therapy (IMIT). An easy-to-implement algorithm was developed in the current study to rapidly generate a uniform biological dose distribution, namely the product of physical dose and the relative biological effectiveness (RBE), within the target volume using scanned ion beams for charged particle radiobiological studies. Protons, helium ions and carbon ions were selected to demonstrate the feasibility and flexibility of our method. The general-purpose Monte Carlo simulation toolkit Geant4 was used for particle tracking and generation of physical and radiobiological data needed for later dose optimizations. The dose optimization algorithm was developed using the Python (version 3) programming language. A constant RBE-weighted dose (RWD) spread-out Bragg peak (SOBP) in a water phantom was selected as the desired target dose distribution to demonstrate the applicability of the optimization algorithm. The mechanistic repair-misrepair-fixation (RMF) model was incorporated into the Monte Carlo particle tracking to generate radiobiological parameters and was used to predict the RBE of cell survival in the iterative process of the biological dose optimization for the three selected ions. The post-optimization generated beam delivery strategy can be used in radiation biology experiments to obtain radiobiological data to further validate and improve the accuracy of the RBE model. This biological dose optimization algorithm developed for radiobiology studies could potentially be extended to implement biologically optimized IMIT plans for patients.

The accuracy in the dosimetry of therapeutically used carbon ion beams is predominantly affected by the large uncertainty of the so-called k_Q factor of the ionization chamber used for the measurements. Due to a lack of experimental data, the k_Q factor of ionization chambers in carbon ion beams is still derived by calculation, and, for instance, a standard uncertainty of about 3% is given for k_Q factors tabulated in the TRS-398 dosimetric protocol. Recently, k_Q factors for two Farmer-type ionization chambers have been determined experimentally in the entrance channel of 429 MeV/u carbon ions, achieving about a threefold reduction of the uncertainty. To further improve the data basis on experimental k_Q factors with low uncertainties, k_Q factors for the same irradiation condition have now been determined for eight different cylindrical ionization chambers (NE2571, FC65-P, FC23-C, CC25, CC13, TM30010, TM30011, TM30012) and three different plane-parallel ionization chambers (PPC-40, PPC-05, TM34001) by means of a cross-calibration procedure. Generally, standard measurement uncertainties of 1.1% could be achieved. Deviations of less than 1.2% were found between the experimental and the tabulated k_Q values. Moreover, the consideration of the experimental values with their smaller uncertainties in updated versions of the dosimetric protocols might enable a substantial reduction of the uncertainties in the dosimetry of carbon ion beams.

The 3D-dose module (3DVH) of the ArcCHECK-phantom reconstructs the dose distribution in the phantom volume and transfers it to the patient geometry. Our aim was to evaluate the 3DVH-reconstructed dose systematically building up from simple to complex cases. Therefore, the influence of different field sizes without and with blocking the isocenter was tested. The dose distributions of different radiation techniques, error-free and error-induced VMAT-plans were verified by measuring with films and other detectors in the phantom. It was checked how the inclusion of the dose measured separately in the ArcCHECK-isocenter affects the reconstruction. Thus it was also investigated which detector should be used for the dosimetry in the isocenter.

Without including the isocentrically measured dose, the reconstruction for the smallest field (2  ×  2 cm²) was 5% (6 MV) and 3.7% (10 MV) higher than measured with an ionization chamber. With increasing field size, the deviation decreased. For fields with blocked isocenters, the reconstructed dose was between  −10.6% and  −24% lower than determined with a microDiamond. Measurements with the Semiflex of the spinal plan resulted in higher doses than calculated by the treatment planning system (TPS) and measured with the film and the other detectors. Through the inclusion of the isocentric dose in the reconstruction its accordance with the film increased mostly. With exception of an error-induced head and neck plan, the induced errors in the reconstructed dose volume histogram became visible, but were underestimated.

With the 3DVH-algorithm not every induced-error was detected. The 3DVH underestimated the dose in blocked areas. To protect organs at risk (OAR), these are often blocked. Consequently, there is a risk that a clinical decision is based on a 3DVH that underestimated the dose for the OAR. We recommend including the isocentric dose in the reconstruction. The detector used for the isocentric measurements should be carefully chosen.

Automatic tumor segmentation from medical images is an important step for computer-aided cancer diagnosis and treatment. Recently, deep learning has been successfully applied to this task, leading to state-of-the-art performance. However, most of existing deep learning segmentation methods only work for a single imaging modality. PET/CT scanner is nowadays widely used in the clinic, and is able to provide both metabolic information and anatomical information through integrating PET and CT into the same utility. In this study, we proposed a novel multi-modality segmentation method based on a 3D fully convolutional neural network (FCN), which is capable of taking account of both PET and CT information simultaneously for tumor segmentation. The network started with a multi-task training module, in which two parallel sub-segmentation architectures constructed using deep convolutional neural networks (CNNs) were designed to automatically extract feature maps from PET and CT respectively. A feature fusion module was subsequently designed based on cascaded convolutional blocks, which re-extracted features from PET/CT feature maps using a weighted cross entropy minimization strategy. The tumor mask was obtained as the output at the end of the network using a softmax function. The effectiveness of the proposed method was validated on a clinic PET/CT dataset of 84 patients with lung cancer. The results demonstrated that the proposed network was effective, fast and robust and achieved significantly performance gain over CNN-based methods and traditional methods using PET or CT only, two V-net based co-segmentation methods, two variational co-segmentation methods based on fuzzy set theory and a deep learning co-segmentation method using W-net.

In orthodontic diagnosis and oral treatment planning, 3D tooth model constructed by dental computed tomography (CT) images is an essential and useful assisted tool. In virtue of the higher spatial resolution and lower radiation of x-ray, cone beam computed tomography (CBCT) has been widely used in dental application. However, due to lower signal to noise ratio, vague and weak edge between tooth root and sockets as well as intensity inhomogeneity, the tooth root is easy to be under-segmented and appears false boundary. This paper presents a new hybrid active contour model in a variational level set formulation to segment the tooth root accurately. Initial shape and intensity information from the upper layer is used for next layer's enhancement and shape constraint. The hybrid level set model is constituted by multi-scale local likelihood image fitting (LLIF) energy term, prior shape constraint energy term with adaptive weight and reaction–diffusion (RD) regularization energy term. For detailed interpretation of this hybrid energy model, the intensity information in a narrowband region outside the contour was used to enhance the contrast between tooth dentine and sockets. The LLIF energy term was incorporated into the level set function to overcome the edge fuzziness and intensity inhomogeneity. The shape prior energy term with adaptive weight was used to differentiate the constraint of the contour evolution inside and outside the level set function to improve the capability of curve topology changes. The RD energy term was introduced to effectively regularize the level set evolution. A new measurement for tooth segmentation evaluation was proposed for quantitative validation. The experimental result of the proposed method was compared with two other typical approaches, and was demonstrated to achieve a higher segmentation accuracy.

Clinical trials have shown that hyperthermia is a potent adjuvant to conventional cancer treatments, but the temperatures currently achieved in the clinic are still suboptimal. Hyperthermia treatment planning simulations have potential to improve the heating profile of phased-array applicators. An important open challenge is the development of an effective optimization procedure that enables uniform heating of the target region while keeping temperature below a threshold in healthy tissues. In this work, we analyzed the effectiveness and efficiency of a recently proposed optimization approach, i.e. focusing via constrained power optimization (FOCO), using 3D simulations of twelve clinical patient specific models. FOCO performance was compared against a clinically used particle swarm based optimization approach. Evaluation metrics were target coverage at the 25% iso-SAR level, target hotspot quotient, median target temperature (T50) and computational requirements. Our results show that, on average, constrained power focusing performs slightly better than the clinical benchmark (T50 °C), but outperforms this clinical benchmark for large target volumes (40 cm, T50 °C). In addition, the results are achieved in a shorter time (%) and are repeatable because the approach is formulated as a convex optimization problem.

Currently only flat dose distributions can be generated by electron beams of a linear accelerator for intraoperative radiotherapy (IORT). However, spherical dose distributions are more desirable for certain types of cancers such as breast cancer and brain cancers. In this study, we propose the design of a spherical applicator for delivery of spherical dose distributions. The spherical applicator consists of an upper cylindrical collimator to collimate the electron beam, a middle scattering foil to scatter the beam and a lower hollow sphere with a modulator to shape the beam and a spherical shell used to contain the modulator. Monte Carlo (MC) codes EGSnrc/BEAMnrc and EGS4/DOSXYZ were employed to model the head of the Mobetron, the spherical applicator, and to calculate the dose distributions. Apart from the scattering foil made of tungsten, the region between the scattering foil and the inner surface of the modulator is empty whereas the remainder of the spherical applicator made of soft tissue-equivalent materials such as PMMA. As a measure of how close an object approaches a perfect circle, roundness was introduced to evaluate the dose distributions. In addition, the dose rate after modulation was investigated. A spherical applicator with a 20 mm-diametre cylindrical collimator and a 50 mm-diametre hollow sphere was designed. For electron beams of energies 4, 6, 9 and 12 MeV, the foil thickness was set to 0.3, 0.5, 0.7 and 1.2 mm, and the dose rate was about 30, 40, 50 and 60 cGy min⁻¹, respectively. The roundness of the isodose curves in the coronal plane through the centre of the spherical applicator ranged from 0.01 to 0.12 cm whereas that in the axial plane ranged from 0.05 to 1.38 cm. Experiments are planned to further evaluate the feasibility of this spherical applicator design.

Highlights

• A spherical applicator with a 20 mm-diametre cylindrical collimator and a 50 mm-diametre hollow sphere was designed for delivery of spherical dose distributions for IORT with a linear accelerator.

• For electron beams of energies 4, 6, 9 and 12 MeV, the dose rate was about 30, 40, 50 and 60 cGy min⁻¹, respectively.

• As a measure of how close an object approaches a perfect circle, roundness was introduced to evaluate the spherical dose distributions.

• Experiments are planned to further evaluate the feasibility of this spherical applicator design.

Inverse treatment planning in intensity modulated particle therapy (IMPT) with scanned carbon–ion beams is currently based on the optimization of RBE-weighted dose to satisfy requirements of target coverage and limited toxicity to organs-at-risk (OARs) and healthy tissues. There are many feasible IMPT plans that meet these requirements, which allows the introduction of further criteria to narrow the selection of a biologically optimal treatment plan. We propose a novel treatment planning strategy based on the simultaneous optimization of RBE-weighted dose and nanometric ionization details (ID) as a new physical characteristic of the delivered plan beyond LET. In particular, we focus on the distribution of large ionization clusters (more than 3 ionizations) to enhance the biological effect across the target volume while minimizing biological effect in normal tissues. Carbon–ion treatment plans for different patient geometries and beam configurations generated with the simultaneous optimization strategy were compared against reference plans obtained with RBE-weighted dose optimization alone. Quality indicators, inhomogeneity index and planning volume histograms of RBE-weighted dose and large ionization clusters were used to evaluate the treatment plans. We show that with simultaneous optimization, ID distributions can be optimized in carbon–ion radiotherapy without compromising the RBE-weighted dose distributions. This strategy can potentially be used to account for optimization of endpoints closely related to radiation quality to achieve better tumor control and reduce risks of complications.