Survey on fan-beam computed tomography for radiotherapy: Current implementation and future perspectives of motion management and surface guidance devices.

Background and purpose: This work reports on the results of a survey performed on the use of computed tomography (CT) imaging for motion management, surface guidance devices, and their quality assurance (QA). Additionally, it details the collected user insights regarding professional needs in CT for radiotherapy. The purpose of the survey is to understand current practice, professional needs and future directions in the field of fan-beam CT in radiation therapy (RT). Materials and methods: An online institutional survey was conducted between 1-Sep-2022 and 10-Oct-2022 among medical physics experts at Belgian and Dutch radiotherapy institutions, to assess the current status, challenges, and future directions of motion management and surface image-guided radiotherapy. The survey consisted of a maximum of 143 questions, with the exact number depending on participants' responses. Results: The response rate was 66 % (31/47). Respiratory management was reported as standard practice in all but one institution; surface imaging during CT-simulation was reported in ten institutions. QA procedures are applied with varying frequencies and methodologies, primarily with commercial anatomy-like phantoms. Surface guidance users report employing commercial static and dynamic phantoms. Four main subjects are considered clinically important by the respondents: surface guidance, CT protocol optimisation, implementing gated imaging (4DCT, breath-hold), and a tattoo-less workflow. Conclusions: The survey highlights the scattered pattern of QA procedures for respiratory motion management, indicating the need for well-defined, unambiguous, and practicable guidelines. Surface guidance is considered one of the most important techniques that should be implemented in the clinical radiotherapy simulation workflow.

Survey on fan-beam computed tomography for radiotherapy: Imaging for dose calculation and delineation.

Background and purpose: To obtain an understanding of current practice, professional needs and future directions in the field of fan-beam CT in RT, a survey was conducted. This work presents the collected information regarding the use of CT imaging for dose calculation and structure delineation. Materials and methods: An online institutional survey was distributed to medical physics experts employed at Belgian and Dutch radiotherapy institutions to assess the status, challenges, and future directions of QA practices for fan-beam CT. A maximum of 143 questions covered topics such as CT scanner availability, CT scanner specifications, QA protocols, treatment simulation workflow, and radiotherapy dose calculation. Answer forms were collected between 1-Sep-2022 and 10-Oct-2022. Results: A 66 % response rate was achieved, yielding data on a total of 58 CT scanners. For MV photon therapy, all single-energy CT scans are reconstructed in Hounsfield Units for delineation or dose calculation, and a direct- or stoichiometric method was used to convert CT numbers for dose calculation. Limited use of dual-energy CT is reported for photon (N = 3) and proton dose calculations (N = 1). For brachytherapy, most institutions adopt water-based dose calculation, while approximately 26 % of the institutions take tissue heterogeneity into account. Commissioning and regular QA include eleven tasks, which are performed by two or more professions (29/31) with varying frequencies. Conclusions: Dual usage of a planning CT limits protocol optimization for both tissue characterization and delineation. DECT has been implemented only gradually. A variation of QA testing frequencies and tests are reported.

A novel orthotopic mouse model replicates human lung cancer cachexia.

INTRODUCTION: Cancer cachexia, highly prevalent in lung cancer, is a debilitating syndrome characterized by involuntary loss of skeletal muscle mass and is associated with poor clinical outcome, decreased survival and negative impact on tumour therapy. Various lung tumour-bearing animal models have been used to explore underlying mechanisms of cancer cachexia. However, these models do not simulate anatomical and immunological features key to lung cancer and associated muscle wasting. Overcoming these shortcomings is essential to translate experimental findings into the clinic. We therefore evaluated whether a syngeneic, orthotopic lung cancer mouse model replicates systemic and muscle-specific alterations associated with human lung cancer cachexia. METHODS: Immune competent, 11 weeks old male 129S2/Sv mice, were randomly allocated to either (1) sham control group or (2) tumour-bearing group. Syngeneic lung epithelium-derived adenocarcinoma cells (K-rasG12D ; p53R172HΔG ) were inoculated intrapulmonary into the left lung lobe of the mice. Body weight and food intake were measured daily. At baseline and weekly after surgery, grip strength was measured and tumour growth and muscle volume were assessed using micro cone beam CT imaging. After reaching predefined surrogate survival endpoint, animals were euthanized, and skeletal muscles of the lower hind limbs were collected for biochemical analysis. RESULTS: Two-third of the tumour-bearing mice developed cachexia based on predefined criteria. Final body weight (-13.7 ± 5.7%; P < 0.01), muscle mass (-13.8 ± 8.1%; P < 0.01) and muscle strength (-25.5 ± 10.5%; P < 0.001) were reduced in cachectic mice compared with sham controls and median survival time post-surgery was 33.5 days until humane endpoint. Markers for proteolysis, both ubiquitin proteasome system (Fbxo32 and Trim63) and autophagy-lysosomal pathway (Gabarapl1 and Bnip3), were significantly upregulated, whereas markers for protein synthesis (relative phosphorylation of Akt, S6 and 4E-BP1) were significantly decreased in the skeletal muscle of cachectic mice compared with control. The cachectic mice exhibited increased pentraxin-2 (P < 0.001) and CXCL1/KC (P < 0.01) expression levels in blood plasma and increased mRNA expression of IκBα (P < 0.05) in skeletal muscle, indicative for the presence of systemic inflammation. Strikingly, RNA sequencing, pathway enrichment and miRNA expression analyses of mouse skeletal muscle strongly mirrored alterations observed in muscle biopsies of patients with lung cancer cachexia. CONCLUSIONS: We developed an orthotopic model of lung cancer cachexia in immune competent mice. Because this model simulates key aspects specific to cachexia in lung cancer patients, it is highly suitable to further investigate the underlying mechanisms of lung cancer cachexia and to test the efficacy of novel intervention strategies.

11ß-HSD1 determines the extent of muscle atrophy in a model of acute exacerbation of COPD.

Muscle atrophy is an extrapulmonary complication of acute exacerbations (AE) in chronic obstructive pulmonary disease (COPD). The endogenous production and therapeutic application of glucocorticoids (GCs) have been implicated as drivers of muscle loss in AE-COPD. The enzyme 11 ß-hydroxysteroid dehydrogenase 1 (11ß-HSD1) activates GCs and contributes toward GC-induced muscle wasting. To explore the potential of 11ßHSD1 inhibition to prevent muscle wasting here, the objective of this study was to ascertain the contribution of endogenous GC activation and amplification by 11ßHSD1 in skeletal muscle wasting during AE-COPD. Emphysema was induced by intratracheal (IT) instillation of elastase to model COPD in WT and 11ßHSD1/KO mice, followed by vehicle or IT-LPS administration to mimic AE. µCT scans were obtained prior and at study endpoint 48 h following IT-LPS, to assess emphysema development and muscle mass changes, respectively. Plasma cytokine and GC profiles were determined by ELISA. In vitro, myonuclear accretion and cellular response to plasma and GCs were determined in C2C12 and human primary myotubes. Muscle wasting was exacerbated in LPS-11ßHSD1/KO animals compared with WT controls. RT-qPCR and western blot analysis showed elevated catabolic and suppressed anabolic pathways in muscle of LPS-11ßHSD1/KO animals relative to WTs. Plasma corticosterone levels were higher in LPS-11ßHSD1/KO animals, whereas C2C12 myotubes treated with LPS-11ßHSD1/KO plasma or exogenous GCs displayed reduced myonuclear accretion relative to WT counterparts. This study reveals that 11ß-HSD1 inhibition aggravates muscle wasting in a model of AE-COPD, suggesting that therapeutic inhibition of 11ß-HSD1 may not be appropriate to prevent muscle wasting in this setting.

Calibration and time fading characterization of a new optically stimulated luminescence film dosimeter.

BACKGROUND: Optically stimulated luminescence (OSL) dosimeters produce a signal linear to the dose, which fades with time due to the spontaneous recombination of energetically unstable electron/hole traps. When used for radiotherapy (RT) applications, fading affects the signal-to-dose conversion and causes an error in the final dose measurement. Moreover, the signal fading depends to some extent on treatment-specific irradiation conditions such as irradiation times. PURPOSE: In this work, a dose calibration function for a novel OSL film dosimeter was derived accounting for signal fading. The proposed calibration allows to perform dosimetry evaluation for different RT treatment regimes. METHODS: A novel BaFBr:Eu2+ -based OSL film (Zeff , 6 MV  = 4.7) was irradiated on a TrueBeam STx using a 6 MV beam with setup: 0° gantry angle, 90 cm SSD, 10 cm depth, 10 × 10 cm2 field. A total of 86 measurements were acquired for dose-rates ( D ̇ $\dot{D}$ ) of 600, 300, and 200 MU/min for irradiation times (tir ) of 0.2, 1, 2, 4.5, 12, and 23 min and various readout times (tscan ) between 4 and 1440 min from the start of the exposure (beam-on time). The OSL signal, S ( D ̇ , t i r , t s c a n ) $S(\dot{D},{t}_{ir},{t}_{scan})$ , was modeled via robust nonlinear regression, and two different power-law fading models were tested, respectively, independent (linear model) and dependent on the specific t i r ${t}_{ir}$ (delivery-dependent model). RESULTS: After 1 day from the exposure, the error on the dose measurement can be as high as 48% if a fading correction is not considered. The fading contribution was characterized by two accurate models with adjusted-R2 of 0.99. The difference between the two models is <4.75% for all t i r ${t}_{ir}$ and t s c a n ${t}_{scan}$ . For different beam-on times, 3, 10.5, and 20 min, the optimum t s c a n ${t}_{scan}$ was calculated in order to achieve a signal-to-dose conversion with a model-related error <1%. In the case of a 3 min irradiation, this condition is already met when the OSL-film is scanned immediately after the end of the irradiation. For an irradiation of 10.5 and 20 min, the minimum scanning time to achieve this model-related error increases, respectively, to 30 and 90 min. Under these conditions, the linear model can be used for the signal-to-dose conversion as an approximation of the delivery-dependent model. The signal-to-dose function, D(Mi , j , t s c a n $\ {t}_{scan}$ ), has a residual mean error of 0.016, which gives a residual dose uncertainty of 0.5 mGy in the region of steep signal fading (i.e., t s c a n ${t}_{scan}\ $ = 4 min). The function of two variables is representable as a dose surface depending on the signal (Mi , j ) measured for each i,j-pixel and the time of scan ( t s c a n ${t}_{scan}$ ). CONCLUSIONS: The calibration of a novel OSL-film usable for dosimetry in different RT treatments was corrected for its signal fading with two different models. A linear calibration model independent from the treatment-specific irradiation condition results in a model-related error <1% if a proper scanning time is used for each irradiation length. This model is more practical than the delivery-dependent model because it does not need a pixel-to-pixel fading correction for different t i r ${t}_{ir}$ .

Characterisation and Quenching Correction for an Al2O3:C Optical Fibre Real Time System in Therapeutic Proton, Helium, and Carbon-Charged Beams.

Real time radioluminescence fibre-based detectors were investigated for application in proton, helium, and carbon therapy dosimetry. The Al2O3:C probes are made of one single crystal (1 mm) and two droplets of micro powder in two sizes (38 µm and 4 µm) mixed with a water-equivalent binder. The fibres were irradiated behind different thicknesses of solid slabs, and the Bragg curves presented a quenching effect attributed to the nonlinear response of the radioluminescence (RL) signal as a function of linear energy transfer (LET). Experimental data and Monte Carlo simulations were utilised to acquire a quenching correction method, adapted from Birks' formulation, to restore the linear dose-response for particle therapy beams. The method for quenching correction was applied and yielded the best results for the '4 µm' optical fibre probe, with an agreement at the Bragg peak of 1.4% (160 MeV), and 1.5% (230 MeV) for proton-charged particles; 2.4% (150 MeV/u) for helium-charged particles and of 4.8% (290 MeV/u) and 2.9% (400 MeV/u) for the carbon-charged particles. The most substantial deviations for the '4 µm' optical fibre probe were found at the falloff regions, with ~3% (protons), ~5% (helium) and 6% (carbon).

Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging.

Objective.External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified by quality assurance methods. Recently, online ultrasound imaging of vaporized radiosensitive nanodroplets was proposed as a promising tool forin vivodosimetry in radiotherapy. Previously, the detection of sparse vaporization events was achieved by applying differential ultrasound (US) imaging followed by intensity thresholding using subjective parameter tuning, which is sensitive to image artifacts.Approach. A generalized deep learning solution (i.e. BubbleNet) is proposed to localize vaporized nanodroplets on differential US frames, while overcoming the aforementioned limitation. A 5-fold cross-validation was performed on a diversely composed 5747-frame training/validation dataset by manual segmentation. BubbleNet was then applied on a test dataset of 1536 differential US frames to evaluate dosimetric features. The intra-observer variability was determined by scoring the Dice similarity coefficient (DSC) on 150 frames segmented twice. Additionally, the BubbleNet generalization capability was tested on an external test dataset of 432 frames acquired by a phased array transducer at a much lower ultrasound frequency and reconstructed with unconventional pixel dimensions with respect to the training dataset.Main results.The median DSC in the 5-fold cross validation was equal to ∼0.88, which was in line with the intra-observer variability (=0.86). Next, BubbleNet was employed to detect vaporizations in differential US frames obtained during the irradiation of phantoms with a 154 MeV proton beam or a 6 MV photon beam. BubbleNet improved the bubble-count statistics by ∼30% compared to the earlier established intensity-weighted thresholding. The proton range was verified with a -0.8 mm accuracy.Significance.BubbleNet is a flexible tool to localize individual vaporized nanodroplets on experimentally acquired US images, which improves the sensitivity compared to former thresholding-weighted methods.

Virtual monoenergetic micro-CT imaging in mice with artificial intelligence.

Micro cone-beam computed tomography (µCBCT) imaging is of utmost importance for carrying out extensive preclinical research in rodents. The imaging of animals is an essential step prior to preclinical precision irradiation, but also in the longitudinal assessment of treatment outcomes. However, imaging artifacts such as beam hardening will occur due to the low energetic nature of the X-ray imaging beam (i.e., 60 kVp). Beam hardening artifacts are especially difficult to resolve in a 'pancake' imaging geometry with stationary source and detector, where the animal is rotated around its sagittal axis, and the X-ray imaging beam crosses a wide range of thicknesses. In this study, a seven-layer U-Net based network architecture (vMonoCT) is adopted to predict virtual monoenergetic X-ray projections from polyenergetic X-ray projections. A Monte Carlo simulation model is developed to compose a training dataset of 1890 projection pairs. Here, a series of digital anthropomorphic mouse phantoms was derived from the reference DigiMouse phantom as simulation geometry. vMonoCT was trained on 1512 projection pairs (= 80%) and tested on 378 projection pairs (= 20%). The percentage error calculated for the test dataset was 1.7 ± 0.4%. Additionally, the vMonoCT model was evaluated on a retrospective projection dataset of five mice and one frozen cadaver. It was found that beam hardening artifacts were minimized after image reconstruction of the vMonoCT-corrected projections, and that anatomically incorrect gradient errors were corrected in the cranium up to 15%. Our results disclose the potential of Artificial Intelligence to enhance the µCBCT image quality in biomedical applications. vMonoCT is expected to contribute to the reproducibility of quantitative preclinical applications such as precision irradiations in X-ray cabinets, and to the evaluation of longitudinal imaging data in extensive preclinical studies.

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans.

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

The potential application of dual-energy subtraction radiography for COVID-19 pneumonia imaging.

X-ray imaging plays a crucial role in the confirmation of COVID-19 pneumonia. Chest X-ray radiography and CT are two major imaging techniques that are currently adopted in the diagnosis of COVID-19 pneumonia. However, dual-energy subtraction radiography is hardly discussed as potential COVID-19 imaging application. More advanced X-ray radiography equipment often supports dual-energy subtraction X-ray radiography. Dual-energy subtraction radiography enables the calculation of pseudo-radiographs, in which bones are removed and only soft-tissues are highlighted. In this commentary, the author would like to draw the attention to the potential use of dual-energy subtraction X-ray radiography (i.e. soft-tissue pseudo-radiography) for the assessment and the longitudinal follow-up of COVID-19 pneumonia.

Artificial intelligence supported single detector multi-energy proton radiography system.

Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specific CT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus®One accelerator was built at 200 and 220 MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4% ± 0.4% in our test dataset. For one example patient, detector dose to WET conversions were performed based on the total dose (ITotal), the primary proton dose (IPrimary), and the ProtonDSE corrected detector dose (ICorrected). The determined WET accuracy was compared with respect to the reference WET by idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was determined 4.3% ± 4.1% forWET(ITotal),2.2% ± 1.4% forWET(IPrimary),and 2.5% ± 2.0% forWET(ICorrected).

A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder.

The primary cone-beam computed tomography (CBCT) imaging beam scatters inside the patient and produces a contaminating photon fluence that is registered by the detector. Scattered photons cause artifacts in the image reconstruction, and are partially responsible for the inferior image quality compared to diagnostic fan-beam CT. In this work, a deep convolutional autoencoder (DCAE) and projection-based scatter removal algorithm were constructed for the ImagingRingTM system on rails (IRr), which allows for non-isocentric acquisitions around virtual rotation centers with its independently rotatable source and detector arms. A Monte Carlo model was developed to simulate (i) a non-isocentric training dataset of ≈1200 projection pairs (primary + scatter) from 27 digital head-and-neck cancer patients around five different virtual rotation centers (DCAENONISO), and (ii) an isocentric dataset existing of ≈1200 projection pairs around the physical rotation center (DCAEISO). The scatter removal performance of both DCAE networks was investigated in two digital anthropomorphic phantom simulations and due to superior performance only the DCAENONISO was applied on eight real patient acquisitions. Measures for the quantitative error, the signal-to-noise ratio, and the similarity were evaluated for two simulated digital head-and-neck patients, and the contrast-to-noise ratio (CNR) was investigated between muscle and adipose tissue in the real patient image reconstructions. Image quality metrics were compared between the uncorrected data, the currently implemented heuristic scatter correction data, and the DCAE corrected image reconstruction. The DCAENONISO corrected image reconstructions of two digital patient simulations showed superior image quality metrics compared to the uncorrected and corrected image reconstructions using a heuristic scatter removal. The proposed DCAENONISO scatter correction in this study was successfully demonstrated in real non-isocentric patient CBCT acquisitions and achieved statistically significant higher CNRs compared to the uncorrected or the heuristic corrected image data. This paper presents for the first time a projection-based scatter removal algorithm for isocentric and non-isocentric CBCT imaging using a deep convolutional autoencoder trained on Monte Carlo composed datasets. The algorithm was successfully applied to real patient data.

Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy.

BACKGROUND AND PURPOSE: In radiotherapy, automatic organ-at-risk segmentation algorithms allow faster delineation times, but clinically relevant contour evaluation remains challenging. Commonly used measures to assess automatic contours, such as volumetric Dice Similarity Coefficient (DSC) or Hausdorff distance, have shown to be good measures for geometric similarity, but do not always correlate with clinical applicability of the contours, or time needed to adjust them. This study aimed to evaluate the correlation of new and commonly used evaluation measures with time-saving during contouring. MATERIALS AND METHODS: Twenty lung cancer patients were used to compare user-adjustments after atlas-based and deep-learning contouring with manual contouring. The absolute time needed (s) of adjusting the auto-contour compared to manual contouring was recorded, from this relative time-saving (%) was calculated. New evaluation measures (surface DSC and added path length, APL) and conventional evaluation measures (volumetric DSC and Hausdorff distance) were correlated with time-recordings and time-savings, quantified with the Pearson correlation coefficient, R. RESULTS: The highest correlation (R = 0.87) was found between APL and absolute adaption time. Lower correlations were found for APL with relative time-saving (R = -0.38), for surface DSC with absolute adaption time (R = -0.69) and relative time-saving (R = 0.57). Volumetric DSC and Hausdorff distance also showed lower correlation coefficients for absolute adaptation time (R = -0.32 and 0.64, respectively) and relative time-saving (R = 0.44 and -0.64, respectively). CONCLUSION: Surface DSC and APL are better indicators for contour adaptation time and time-saving when using auto-segmentation and provide more clinically relevant and better quantitative measures for automatically-generated contour quality, compared to commonly-used geometry-based measures.

Modelling of the focal spot intensity distribution and the off-focal spot radiation in kilovoltage x-ray tubes for imaging.

X-ray tubes for medical applications typically generate x-rays by accelerating electrons, emitted from a cathode, with an interelectrode electric field, towards an anode target. X-rays are not emitted from one point, but from an irregularly shaped area on the anode, the focal spot. Focal spot intensity distributions and off-focal radiation negatively affect the imaging spatial resolution and broadens the beam penumbra. In this study, a Monte Carlo simulation model of an x-ray tube was developed to evaluate the spectral and spatial characteristics of off-focal radiation for multiple photon energies. Slit camera measurements were used to determine the horizontal and vertical intensity profiles of the small and the large focal spot of a diagnostic x-ray tube. First, electron beamlet weighting factors were obtained via an iterative optimization method to represent both focal spot sizes. These weighting factors were then used to extract off-focal spot radiation characteristics for the small and large focal spot sizes at 80, 100, and 120 kV. Finally, 120 kV simulations of a steel sphere (d = 4 mm) were performed to investigate image blurring with a point source, the small focal spot, and the large focal spot. The magnitude of off-focal radiation strongly depends on the anode size and the electric field coverage, and only minimally on the tube potential and the primary focal spot size. In conclusion, an x-ray tube Monte Carlo simulation model was developed to simulate focal spot intensity distributions and to evaluate off-focal radiation characteristics at several energies. This model can be further employed to investigate focal spot correction methods and to improve cone-beam CT image quality.

Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network.

The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decreased quality of life. Relevant and reliable animal models in which muscle wasting can be monitored noninvasively over time are instrumental to investigate and develop new therapies. In this work, we developed a fully automatic deep learning algorithm for segmentation of micro cone beam computed tomography images of the lower limb muscle complex in mice and subsequent muscle mass calculation. A deep learning algorithm was trained on manually segmented data from 32 mice. Muscle wet mass measurements were obtained from 47 mice and served as a data set for model validation and reverse model validation. The automatic algorithm performance was ~150 times faster than manual segmentation. Reverse validation of the algorithm showed high quantitative metrics (i.e., a Dice similarity coefficient of 0.93, a Hausdorff distance of 0.4 mm, and a center of mass displacement of 0.1 mm), substantiating the robustness and accuracy of the model. A high correlation (R2 = 0.92) was obtained between the computed tomography-derived muscle mass measurements and the muscle wet masses. Longitudinal follow-up revealed time-dependent changes in muscle mass that separated control from lung tumor-bearing mice, which was confirmed as cachexia. In conclusion, this deep learning model for automated assessment of the lower limb muscle complex provides highly accurate noninvasive longitudinal evaluation of skeletal muscle mass. Furthermore, it facilitates the workflow and increases the amount of data derived from mouse studies while reducing the animal numbers.NEW & NOTEWORTHY This deep learning application enables highly accurate noninvasive longitudinal evaluation of skeletal muscle mass changes in mice with minimal requirement for operator involvement in the data analysis. It provides a unique opportunity to increase and analyze the amount of data derived from animal studies automatically while reducing animal numbers and analytical workload.

Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach.

In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while optimally sparing healthy tissue surrounding the tumor, referred to as organs-at-risk (OARs). Based on CT scan and/or magnetic resonance images, OARs have to be manually delineated by clinicians, which is one of the most time-consuming tasks in the clinical workflow. Recent multi-atlas (MA) or deep-learning (DL) based methods aim to improve the clinical routine by an automatic segmentation of OARs on a CT dataset. However, so far no studies investigated the performance of these MA or DL methods on dual-energy CT (DECT) datasets, which have been shown to improve the image quality compared to conventional 120 kVp single-energy CT. In this study, the performance of an in-house developed MA and a DL method (two-step three-dimensional U-net) was quantitatively and qualitatively evaluated on various DECT-derived pseudo-monoenergetic CT datasets ranging from 40 keV to 170 keV. At lower energies, the MA method resulted in more accurate OAR segmentations. Both the qualitative and quantitative metric analysis showed that the DL approach often performed better than the MA method.

On the determination of planning target margins due to motion for mice lung tumours using a four-dimensional MOBY phantom.

OBJECTIVE:: This work aims to analyse the effect of respiratory motion on optimal irradiation margins for murine lung tumour models. METHODS:: Four-dimensional mathematical phantoms with different lung tumour locations affected by respiratory motion were created. Two extreme breathing curves were adopted and divided into time-points. Each time-point was loaded in a treatment planning system and Monte Carlo (MC) dose calculations were performed for a 360° arc plan. A time-resolved dose was derived, considering the gantry rotation and the breathing motion. Radiotherapy metrics were derived to assess the final treatment plans. An interpolation function was investigated to reduce calculation cost. RESULTS:: The effect of respiratory motion on the treatment plan quality is strongly dependent on the breathing pattern and the tumour position. Tumours located closer to the diaphragm required a compromise between tumour conformity and healthy tissue damage. A recipe, which considers collimator size, was proposed to derive tumour margins and spare the organs at risk (OARs) by respecting constraints on user-defined metrics. CONCLUSION:: It is recommended to add a target margin, especially on sites where movement is substantial. A simple recipe to derive tumour margins was developed. ADVANCES IN KNOWLEDGE:: This work is a first step towards a standard planning target volume concept in pre-clinical radiotherapy.

Murine vs human tissue compositions: implications of using human tissue compositions for photon energy absorption in mice.

METHODS:: Dual energy CT (DECT) images of 9 female mice were used to extract the effective atomic number Zeff and the relative electron density ρe for each voxel in the images. To investigate the influence of the tissue compositions on the absorbed radiation dose for a typical kilovoltage photon beam, mass energy-absorption coefficients µen/ρ were calculated for 10 different tissues in each mouse. RESULTS: Differences between human and murine tissue compositions can lead to errors around 7.5 % for soft tissues and 20.1 % for bone tissues in µen/ρ values for kilovoltage photon beams. When considering the spread within tissues, these errors can increase up to 17.5 % for soft tissues and 53.9 % for bone tissues within only a single standard deviation away from the mean tissue value. CONCLUSION:: This study illustrates the need for murine reference tissue data. However, assigning only a single mean reference value to an entire tissue can still lead to large errors in dose calculations given the large spread within tissues of µen/ρ values found in this study. Therefore, new methods such as DECT and spectral CT imaging need to be explored, which can be important next steps in improving tissue assignment for dose calculations in small animal radiotherapy. ADVANCES IN KNOWLEDGE:: This is the first study that investigates the implications of using human tissue compositions for dose calculations in mice for kilovoltage photon beams.

The effect of different image reconstruction techniques on pre-clinical quantitative imaging and dual-energy CT.

OBJECTIVE:: To analyse the effect of different image reconstruction techniques on image quality and dual energy CT (DECT) imaging metrics. METHODS:: A software platform for pre-clinical cone beam CT X-ray image reconstruction was built using the open-source reconstruction toolkit. Pre-processed projections were reconstructed with filtered back-projection and iterative algorithms, namely Feldkamp, Davis, and Kress (FDK), Iterative FDK, simultaneous algebraic reconstruction technique (SART), simultaneous iterative reconstruction technique and conjugate gradient. Imaging metrics were quantitatively assessed, using a quality assurance phantom, and DECT analysis was performed to determine the influence of each reconstruction technique on the relative electron density (ρe) and effective atomic number (Zeff) values. RESULTS:: Iterative reconstruction had favourable results for the DECT analysis: a significantly smaller spread for each material in the ρe-Zeff space and lower Zeff and ρe residuals (on average 24 and 25% lower, respectively). In terms of image quality assurance, the techniques FDK, Iterative FDK and SART provided acceptable results. The three reconstruction methods showed similar geometric accuracy, uniformity and CT number results. The technique SART had a contrast-to-noise ratio up to 76% higher for solid water and twice as high for Teflon, but resolution was up to 28% lower when compared to the other two techniques. CONCLUSIONS:: Advanced image reconstruction can be beneficial, but the benefit is small, and calculation times may be unacceptable with current technology. The use of targeted and downscaled reconstruction grids, larger, yet practicable, pixel sizes and GPU are recommended. ADVANCES IN KNOWLEDGE:: An iterative CBCT reconstruction platform was build using RTK.

Exploring the feasibility of a clinical proton beam with an adaptive aperture for pre-clinical research.

OBJECTIVE:: To investigate whether the Mevion S250i with HYPERSCAN clinical proton system could be used for pre-clinical research with millimetric beams. METHODS:: The nozzle of the proton beam line, consisting of an energy modulation system (EMS) and an adaptive aperture (AA), was modelled with the TOPAS Monte Carlo Simulation Toolkit. With the EMS, the 230 MeV beam nominal range can be decreased in multiples of 2.1 mm. Monte Carlo dose calculations were performed in a mouse lung tumour CT image. The AA allows fields as small as 5 × 1 mm2 to be used for irradiation. The best plans to give 2 Gy to the tumour were derived from a set of discrete energies allowed by the EMS, different field sizes and beam directions. The final proton plans were compared to a precision photon irradiation plan. Treatment times were also assessed. RESULTS:: Seven different proton beam plans were investigated, with a good coverage to the tumour (D95 > 1.95 Gy, D5 < 2.3 Gy) and with potentially less damage to the organs at risk than the photon plan. For very small fields and low energies, the number of protons arriving to the target drops to 1-3%, nevertheless the treatment times would be below 5 s. CONCLUSION:: The proton plans made in this study, collimated by an AA, could be used for animal irradiation. ADVANCES IN KNOWLEDGE:: This is one of the first study to demonstrate the feasibility of pre-clinical research with a clinical proton beam with an adaptive aperture used to create small fields.