Development of a 4-D digital mouse phantom for molecular imaging research

Dosimetric validation of SmART-RAD Monte Carlo modelling for x-ray cabinet radiobiology irradiators

Objective. Accuracy and reproducibility in the measurement of radiation dose and associated reporting are critically important for the validity of basic and preclinical radiobiological studies performed with kilovolt x-ray radiation cabinets. This is essential to enable results of radiobiological studies to be repeated, as well as enable valid comparisons between laboratories. In addition, the commonly used single point dose value hides the 3D dose heterogeneity across the irradiated sample. This is particularly true for preclinical rodent models, and is generally difficult to measure directly. Radiation transport simulations integrated in an easy to use application could help researchers improve quality of dosimetry and reporting.Approach. This paper describes the use and dosimetric validation of a newly-developed Monte Carlo (MC) tool, SmART-RAD, to simulate the x-ray field in a range of standard commercial x-ray cabinet irradiators used for preclinical irradiations. Comparisons are made between simulated and experimentally determined dose distributions for a range of configurations to assess the potential use of this tool in determining dose distributions through samples, based on more readily available air-kerma calibration point measurements.Main results. Simulations gave very good dosimetric agreement with measured depth dose distributions in phantoms containing both water and bone equivalent materials. Good spatial and dosimetric agreement between simulated and measured dose distributions was obtained when using beam-shaping shielding.Significance. The MC simulations provided by SmART-RAD provide a useful tool to go from a limited number of dosimetry measurements to detailed 3D dose distributions through a non-homogeneous irradiated sample. This is particularly important when trying to determine the dose distribution in more complex geometries. The use of such a tool can improve reproducibility and dosimetry reporting in preclinical radiobiological research.

The feasibility of K XRF bone lead measurements in mice assessed using 3D-printed phantoms

This article describes the development of a system forin vivomeasurements of lead body burden in mice using109Cd K x-ray fluorescence (XRF). This K XRF system could facilitate early-stage studies on interventions that ameliorate or reverse organ tissue damage from lead poisoning by reducing animal numbers through a cross-sectional study approach. A novel mouse phantom was developed based on a mouse atlas and 3D-printed using PLA plastic with plaster of Paris 'bone' inserts. PLA plastic was found to be a good surrogate for soft tissue in XRF measurements and the phantoms were found to be good models of mice. As expected, lead detection limits varied with mouse size, mouse orientation, and mouse position with respect to the source and detector. The work suggests that detection limits of 10 to 20μg Pb per g bone mineral may be possible for a 2 to 3 hour XRF measurement in a single animal, an adequate limit for some pre-clinical studies. The109Cd K XRF mouse measurement system was also modeled using the Monte Carlo code MCNP. The combination of experiment and modeling found that contrary to expectation, accurate measurements of lead levels in mice required calibration using mouse-specific calibration standards due to the coherent scatter peak normalization failing when small animals are measured. MCNP modeling determined that this was because the coherent scatter signal from soft tissue, which until now has been assumed negligible, becomes significant when compared to the coherent scatter signal in bone in small animals. This may have implications for some human measurements. This work suggests that109Cd K x-ray fluorescence measurements of lead body burden are precise enough to make the system feasible for small animals if appropriately calibrated. Further work to validate the technology's measurement accuracy and performancein vivowill be required.

Impact of glioma peritumoral edema, tumor size, and tumor location on alternating electric fields (AEF) therapy in realistic 3D rat glioma models: a computational study

Objective.Alternating electric fields (AEF) therapy is a treatment modality for patients with glioblastoma. Tumor characteristics such as size, location, and extent of peritumoral edema may affect the AEF strength and distribution. We evaluated the sensitivity of the AEFs in a realistic 3D rat glioma model with respect to these properties.Approach.The electric properties of the peritumoral edema were varied based on calculated and literature-reported values. Models with different tumor composition, size, and location were created. The resulting AEFs were evaluated in 3D rat glioma models.Main results.In all cases, a pair of 5 mm diameter electrodes induced an average field strength >1 V cm-1. The simulation results showed that a negative relationship between edema conductivity and field strength was found. As the tumor core size was increased, the average field strength increased while the fraction of the shell achieving >1.5 V cm-1decreased. Increasing peritumoral edema thickness decreased the shell's mean field strength. Compared to rostrally/caudally, shifting the tumor location laterally/medially and ventrally (with respect to the electrodes) caused higher deviation in field strength.Significance.This study identifies tumor properties that are key drivers influencing AEF strength and distribution. The findings might be potential preclinical implications.

Robust, planning-based targeted locoregional tumour heating in small animals

Objective.To improve hyperthermia in clinical practice, pre-clinical hyperthermia research is essential to investigate hyperthermia effects and assess novel treatment strategies. Translating pre-clinical hyperthermia findings into clinically viable protocols requires laboratory animal treatment techniques similar to clinical hyperthermia techniques. The ALBA micro8 electromagnetic heating system (Med-logix SRL, Rome, Italy) has recently been developed to provide the targeted locoregional tumour heating currently lacking for pre-clinical research. This study evaluates the heat focusing properties of this device and its ability to induce robust locoregional tumour heating under realistic physiological conditions using simulations.Approach.Simulations were performed using the Plan2Heat treatment planning package (Amsterdam UMC, the Netherlands). First, the specific absorption rate (SAR) focus was characterised using a homogeneous phantom. Hereafter, a digital mouse model was used for the characterisation of heating robustness in a mouse. Device settings were optimised for treatment of a pancreas tumour and tested for varying circumstances. The impact of uncertainties in tissue property and perfusion values was evaluated using polynomial chaos expansion. Treatment quality and robustness were evaluated based on SAR and temperature distributions.Main results.The SAR distributions within the phantom are well-focused and can be adjusted to target any specific location. The focus size (full-width half-maximum) is a spheroid with diameters 9 mm (radially) and 20 mm (axially). The mouse model simulations show strong robustness against respiratory motion and intestine and stomach filling (∆T90≤0.14°C).Mouse positioning errors in the cranial-caudal direction lead to∆T90≤0.23°C. Uncertainties in tissue property and perfusion values were found to impact the treatment plan up to 0.56 °C (SD), with a variation onT90of 0.32 °C (1 SD).Significance.Our work shows that the pre-clinical phased-array system can provide adequate and robust locoregional heating of deep-seated target regions in mice. Using our software, robust treatment plans can be generated for pre-clinical hyperthermia research.

Methodology for small animals targeted irradiations at conventional and ultra-high dose rates 65 MeV proton beam

As part of translational research projects, mice may be irradiated on radiobiology platforms such as the one at the ARRONAX cyclotron. Generally, these platforms do not feature an integrated imaging system. Moreover, in the context of ultra-high dose-rate radiotherapy (FLASH-RT), treatment planning should consider potential changes in the beam characteristics and internal movements in the animal. A patient-like set-up and methodology has been implemented to ensure target coverage during conformal irradiations of the brain, lungs and intestines. In addition, respiratory cycle amplitudes were quantified by fluoroscopic acquisitions on a mouse, to ensure organ coverage and to assess the impact of respiration during FLASH-RT using the 4D digital phantom MOBY. Furthermore, beam incidence direction was studied from mice µCBCT and Monte Carlo simulations. Finally,in vivodosimetry with dose-rate independent radiochromic films (OC-1) and their LET dependency were investigated. The immobilization system ensures that the animal is held in a safe and suitable position. The geometrical evaluation of organ coverage, after the addition of the margins around the organs, was satisfactory. Moreover, no measured differences were found between CONV and FLASH beams enabling a single model of the beamline for all planning studies. Finally, the LET-dependency of the OC-1 film was determined and experimentally verified with phantoms, as well as the feasibility of using these filmsin vivoto validate the targeting. The methodology developed ensures accurate and reproducible preclinical irradiations in CONV and FLASH-RT without in-room image guidance in terms of positioning, dose calculation andin vivodosimetry.

Electric field distributions in realistic 3D rat head models during alternating electric field (AEF) therapy: a computational study

Objective.Application of alternating electrical fields (AEFs) in the kHz range is an established treatment modality for primary and recurrent glioblastoma. Preclinical studies would enable innovations in treatment monitoring and efficacy, which could then be translated to benefit patients. We present a practical translational process converting image-based data into 3D rat head models for AEF simulations and study its sensitivity to parameter choices.Approach.Five rat head models composed of up to 7 different tissue types were created, and relative permittivity and conductivity of individual tissues obtained from the literature were assigned. Finite element analysis was used to model the AEF strength and distribution in the models with different combinations of head tissues, a virtual tumor, and an electrode pair.Main results.The simulations allowed for a sensitivity analysis of the AEF distribution with respect to different tissue combinations and tissue parameter values.Significance.For a single pair of 5 mm diameter electrodes, an average AEF strength inside the tumor exceeded 1.5 V cm-1, expected to be sufficient for a relevant therapeutic outcome. This study illustrates a robust and flexible approach for simulating AEF in different tissue types, suitable for preclinical studies in rodents and translatable to clinical use.

Study of Alternative Imaging Methods for In Vivo Boron Neutron Capture Therapy

Boron Neutron Capture Therapy (BNCT) is an innovative and highly selective treatment against cancer. Nowadays, in vivo boron dosimetry is an important method to carry out such therapy in clinical environments. In this work, different imaging methods were tested for dosimetry and tumor monitoring in BNCT based on a Compton camera detector. A dedicated dataset was generated through Monte Carlo tools to study the imaging capabilities. We first applied the Maximum Likelihood Expectation Maximization (MLEM) iterative method to study dosimetry tomography. As well, two methods based on morphological filtering and deep learning techniques with Convolutional Neural Networks (CNN), respectively, were studied for tumor monitoring. Furthermore, clinical aspects such as the dependence on the boron concentration ratio in image reconstruction and the stretching effect along the detector position axis were analyzed. A simulated spherical gamma source was studied in several conditions (different detector distances and boron concentration ratios) using MLEM. This approach proved the possibility of monitoring the boron dose. Tumor monitoring using the CNN method shows promising results that could be enhanced by increasing the training dataset.

Preclinical Evaluation of a 64Cu-Based Theranostic Approach in a Murine Model of Multiple Myeloma

Although the concept of theranostics is neither new nor exclusive to nuclear medicine, it is a particularly promising approach for the future of nuclear oncology. This approach is based on the use of molecules targeting specific biomarkers in the tumour or its microenvironment, associated with optimal radionuclides which, depending on their emission properties, allow the combination of diagnosis by molecular imaging and targeted radionuclide therapy (TRT). Copper-64 has suitable decay properties (both β⁺ and β- decays) for PET imaging and potentially for TRT, making it both an imaging and therapy agent. We developed and evaluated a theranostic approach using a copper-64 radiolabelled anti-CD138 antibody, [⁶⁴Cu]Cu-TE1PA-9E7.4 in a MOPC315.BM mouse model of multiple myeloma. PET imaging using [⁶⁴Cu]Cu-TE1PA-9E7.4 allows for high-resolution PET images. Dosimetric estimation from ex vivo biodistribution data revealed acceptable delivered doses to healthy organs and tissues, and a very encouraging tumour absorbed dose for TRT applications. Therapeutic efficacy resulting in delayed tumour growth and increased survival without inducing major or irreversible toxicity has been observed with 2 doses of 35 MBq administered at a 2-week interval. Repeated injections of [⁶⁴Cu]Cu-TE1PA-9E7.4 are safe and can be effective for TRT application in this syngeneic preclinical model of MM.

Spectral CT reconstruction via low-rank representation and structure preserving regularization

Objective:With the development of computed tomography (CT) imaging technology, it is possible to acquire multi-energy data by spectral CT. Being different from conventional CT, the X-ray energy spectrum of spectral CT is cut into several narrow bins which leads to the result that only a part of photon can be collected in each individual energy channel.This can severely degrade the image qualities. To address this problem, we propose a spectral CT reconstruction algorithm based on low-rank representation and structure preserving regularization in this paper.Approach:To make full use of the prior knowledge about both the inter-channel correlation and the sparsity in gradient domain of inner-channel data, this paper combines a low-rank correlation descriptor with a structure extraction operator as priori regularization terms for spectral CT reconstruction. Furthermore, a split-Bregman based iterative algorithm is developed to solve the reconstruction model. Finally, we propose a multi-channel adaptive parameters generation strategy according to CT values of each individual energy channel.Main results: Experimental results on numerical simulations and real mouse data indicate that the proposed algorithm achieves higher accuracy on both reconstruction and material decomposition than the methods based on simultaneous algebraic reconstruction technique (SART), total variation minimization (TVM), total variation with low-rank (LRTV), and spatial-spectral cube matching frame (SSCMF). Compared with SART, our algorithm improves the feature similarity (FSIM) by 40.4% on average for numerical simulation reconstruction, whereas TVM, LRTV, and SSCMF correspond to 26.1%, 28.2%, and 29.5%, respectively.Significance: We outline a multi-channel reconstruction algorithm tailored for spectral CT. The qualitative and quantitative comparisons present a significant improvement of image quality, indicating its promising potential in spectral CT imaging.

Serial intravital 2-photon microscopy and analysis of the kidney using upright microscopes

Serial intravital 2-photon microscopy of the kidney and other abdominal organs is a powerful technique to assess tissue function and structure simultaneously and over time. Thus, serial intravital microscopy can capture dynamic tissue changes during health and disease and holds great potential to characterize (patho-) physiological processes with subcellular resolution. However, successful image acquisition and analysis require significant expertise and impose multiple potential challenges. Abdominal organs are rhythmically displaced by breathing movements which hamper high-resolution imaging. Traditionally, kidney intravital imaging is performed on inverted microscopes where breathing movements are partly compensated by the weight of the animal pressing down. Here, we present a custom and easy-to-implement setup for intravital imaging of the kidney and other abdominal organs on upright microscopes. Furthermore, we provide image processing protocols and a new plugin for the free image analysis software FIJI to process multichannel fluorescence microscopy data. The proposed image processing pipelines cover multiple image denoising algorithms, sample drift correction using 2D registration, and alignment of serial imaging data collected over several weeks using landmark-based 3D registration. The provided tools aim to lower the barrier of entry to intravital microscopy of the kidney and are readily applicable by biomedical practitioners.

Development of a digital zebrafish phantom and its application to dedicated small-fish PET

Objective. We are developing a small-fish positron emission tomography (PET) scanner dedicated to small aquatic animals relevant for biomedical and biological research, e.g. zebrafish. We plan to use Monte Carlo simulations to optimize its configuration and the required water-filled imaging chambers. Our objectives were: (1) to create a digital 3D zebrafish phantom using conventional micro-CT, (2) include the phantom into a simulated PET environment based on the framework GATE, and (3) investigate the effects of the water environment on the reconstructed images. Approach. To create the phantom, we performed ex vivo measurements of zebrafish specimen using a tabletop micro-CT and compared three methods to fixate the specimen. From segmented micro-CT images we created digital emission and transmission phantoms which were incorporated in GATE via tessellated volumes. Two chamber sizes were considered. For reference, a simulation with the zebrafish in air was implemented. The simulated data were reconstructed using CASToR. For attenuation correction, we used the exact attenuation information or a uniform distribution (only water). Several realizations of each scenario were performed; the reconstructed images were quantitatively evaluated. Main results. Fixation in formalin led to the best soft-tissue contrast at the cost of some specimen deformation. After attenuation correction, no significant differences were found between the reconstructed images. The PET images reflected well the higher uptake simulated in the brain and heart, despite their small size and surrounding background activity; the swim bladder (no activity) was clearly identified. The simplified attenuation map, consisting only of water, slightly worsened the images. Significance. A conventional micro-CT can provide sufficient image quality to generate numerical phantoms of small fish without contrast media. Such phantoms are useful to evaluate in-silico small aquatic animal imaging concepts and develop imaging protocols. Our results support the feasibility of zebrafish PET with an aqueous environment.

A novel multichannel deep learning model for fast denoising of Monte Carlo dose calculations: preclinical applications

Objective. In preclinical radiotherapy with kilovolt (kV) x-ray beams, accurate treatment planning is needed to improve the translation potential to clinical trials. Monte Carlo based radiation transport simulations are the gold standard to calculate the absorbed dose distribution in external beam radiotherapy. However, these simulations are notorious for their long computation time, causing a bottleneck in the workflow. Previous studies have used deep learning models to speed up these simulations for clinical megavolt (MV) beams. For kV beams, dose distributions are more affected by tissue type than for MV beams, leading to steep dose gradients. This study aims to speed up preclinical kV dose simulations by proposing a novel deep learning pipeline. Approach. A deep learning model is proposed that denoises low precision (∼106 simulated particles) dose distributions to produce high precision (109 simulated particles) dose distributions. To effectively denoise the steep dose gradients in preclinical kV dose distributions, the model uses the novel approach to use the low precision Monte Carlo dose calculation as well as the Monte Carlo uncertainty (MCU) map and the mass density map as additional input channels. The model was trained on a large synthetic dataset and tested on a real dataset with a different data distribution. To keep model inference time to a minimum, a novel method for inference optimization was developed as well. Main results. The proposed model provides dose distributions which achieve a median gamma pass rate (3%/0.3 mm) of 98% with a lower bound of 95% when compared to the high precision Monte Carlo dose distributions from the test set, which represents a different dataset distribution than the training set. Using the proposed model together with the novel inference optimization method, the total computation time was reduced from approximately 45 min to less than six seconds on average. Significance. This study presents the first model that can denoise preclinical kV instead of clinical MV Monte Carlo dose distributions. This was achieved by using the MCU and mass density maps as additional model inputs. Additionally, this study shows that training such a model on a synthetic dataset is not only a viable option, but even increases the generalization of the model compared to training on real data due to the sheer size and variety of the synthetic dataset. The application of this model will enable speeding up treatment plan optimization in the preclinical workflow.

Dose Rate Assessment Exercises with Stylized Phantom of Neon Flying Squid from Northwest Pacific

Radiation protection for non-human marine organisms still faces many challenges. To establish a more realistic radiation dosimetry model of cephalopods, this study developed a stylized phantom of neon flying squid (Ommastrephesbartramii) containing ten organs and tissues based on magnetic resonance imaging (MRI) technology. The internal and external dose conversion coefficients for eight radionuclides (134Cs, 137Cs, 131I, 110mAg, 60Co, 54Mn, 65Zn, 95Zr) of each organ/tissue were determined with Monte Carlo simulation using the Geant4 toolkit. Furthermore, with the reported coastal seawater radioactivity levels at the coastal area of Fukushima Daiichi Nuclear Power Plant after the accident in 2011 as the source term, the radiological dose rate for O. bartramii was evaluated with the stylized phantom developed in this study and with the conventional whole-organism ellipsoidal model in the ERICA Assessment Tool. Both results showed that the dose rate for O. bartramii derived from the FDNPP accident releases exceeded the generic no-effects screening benchmark level (10 μGy h−1).

Comparison of absorbed dose extrapolation methods for mouse-to-human translation of radiolabelled macromolecules

BACKGROUND

Extrapolation of human absorbed doses (ADs) from biodistribution experiments on laboratory animals is used to predict the efficacy and toxicity profiles of new radiopharmaceuticals. Comparative studies between available animal-to-human dosimetry extrapolation methods are missing. We compared five computational methods for mice-to-human AD extrapolations, using two different radiopharmaceuticals, namely [111In]CHX-DTPA-scFv78-Fc and [68Ga]NODAGA-RGDyK. Human organ-specific time-integrated activity coefficients (TIACs) were derived from biodistribution studies previously conducted in our centre. The five computational methods adopted are based on simple direct application of mice TIACs to human organs (M1), relative mass scaling (M2), metabolic time scaling (M3), combined mass and time scaling (M4), and organ-specific allometric scaling (M5), respectively. For [68Ga]NODAGA-RGDyK, these methods for mice-to-human extrapolations were tested against the ADs obtained on patients, previously published by our group. Lastly, an average [68Ga]NODAGA-RGDyK-specific allometric parameter αnew was calculated from the organ-specific biological half-lives in mouse and humans and retrospectively applied to M3 and M4 to assess differences in human AD predictions with the α = 0.25 recommended by previous studies.

RESULTS

For both radiopharmaceuticals, the five extrapolation methods showed significantly different AD results (p < 0.0001). In general, organ ADs obtained with M3 were higher than those obtained with the other methods. For [68Ga]NODAGA-RGDyK, no significant differences were found between ADs calculated with M3 and those obtained directly on human subjects (H) (p = 0.99; average M3/H AD ratio = 1.03). All other methods for dose extrapolations resulted in ADs significantly different from those calculated directly on humans (all p ≤ 0.0001). Organ-specific allometric parameters calculated using combined experimental [68Ga]NODAGA-RGDyK mice and human biodistribution data varied significantly. ADs calculated with M3 and M4 after the application of αnew = 0.17 were significantly different from those obtained by the application of α = 0.25 (both p < 0.001).

CONCLUSIONS

Available methods for mouse-to-human dosimetry extrapolations provided significantly different results in two different experimental models. For [68Ga]NODAGA-RGDyK, the best approximation of human dosimetry was shown by M3, applying a metabolic scaling to the mouse organ TIACs. The accuracy of more refined extrapolation algorithms adopting model-specific metabolic scaling parameters should be further investigated.

An anatomically correct 3D printed mouse phantom for magnetic particle imaging studies

We report anatomically correct 3D‐printed mouse phantoms that can be used to plan experiments and evaluate analysis protocols for magnetic particle imaging (MPI) studies. The 3D‐printed phantoms were based on the Digimouse 3D whole body mouse atlas and incorporate cavities representative of a liver, brain tumor, and orthotopic breast cancer tumor placed in anatomically correct locations, allowing evaluation of the effect of precise doses of MPI tracer. To illustrate their use, a constant tracer iron mass was present in the liver for the breast (200 μg_Fe) and brain tumor (10 μg_Fe) model, respectively, while a series of decreasing tracer iron mass was placed in the tumor region. MPI scans were acquired in 2D and 3D high sensitivity and high sensitivity/high resolution (HSHR) modes using a MOMENTUM imager. A thresholding algorithm was used to define regions of interest (ROIs) in the scans and the tracer mass in the liver and tumors was calculated by comparison of the signal in their respective ROI against that of known mass fiducials that were included in each scan. The results demonstrate that this approach to image analysis provides accurate estimates of tracer mass. Additionally, the results show how the limit of detection in MPI is sensitive to the details of tracer distribution in the subject, as we found that a greater tracer mass in the liver cavity resulted in poorer sensitivity in tumor regions. These experiments illustrate the utility of the reported 3D‐printed anatomically correct mouse phantoms in evaluating methods to analyze MPI scans and plan in vivo experiments.

Deep learning-based segmentation of the thorax in mouse micro-CT scans

For image-guided small animal irradiations, the whole workflow of imaging, organ contouring, irradiation planning, and delivery is typically performed in a single session requiring continuous administration of anaesthetic agents. Automating contouring leads to a faster workflow, which limits exposure to anaesthesia and thereby, reducing its impact on experimental results and on animal wellbeing. Here, we trained the 2D and 3D U-Net architectures of no-new-Net (nnU-Net) for autocontouring of the thorax in mouse micro-CT images. We trained the models only on native CTs and evaluated their performance using an independent testing dataset (i.e., native CTs not included in the training and validation). Unlike previous studies, we also tested the model performance on an external dataset (i.e., contrast-enhanced CTs) to see how well they predict on CTs completely different from what they were trained on. We also assessed the interobserver variability using the generalized conformity index ([Formula: see text]) among three observers, providing a stronger human baseline for evaluating automated contours than previous studies. Lastly, we showed the benefit on the contouring time compared to manual contouring. The results show that 3D models of nnU-Net achieve superior segmentation accuracy and are more robust to unseen data than 2D models. For all target organs, the mean surface distance (MSD) and the Hausdorff distance (95p HD) of the best performing model for this task (nnU-Net 3d_fullres) are within 0.16 mm and 0.60 mm, respectively. These values are below the minimum required contouring accuracy of 1 mm for small animal irradiations, and improve significantly upon state-of-the-art 2D U-Net-based AIMOS method. Moreover, the conformity indices of the 3d_fullres model also compare favourably to the interobserver variability for all target organs, whereas the 2D models perform poorly in this regard. Importantly, the 3d_fullres model offers 98% reduction in contouring time.

Temperature Dynamics in Rat Brains Exposed to Near-Field Waveguide Outputs at 2.8 GHz

Biological effects in the microwave band of the radiofrequency (RF) spectrum are thermally mediated. For acute high-power microwave exposures, these effects will depend on transient time-temperature histories within the tissue. In this article, we summarize the transient temperature response of rats exposed to RF energy emanating from an open-ended rectangular waveguide. These exposures produced specific absorption rates of approximately 36 and 203 W/kg in the whole body and brain, respectively. We then use the experimentally measured thermal data to infer the baseline perfusion rate in the brain and modify a custom thermal modeling tool based upon these findings. Finally, we compare multi-physics simulations of rat brain temperature against empirical measurements in both live and euthanized subjects and find close agreement between model and experimentation. This research revealed that baseline brain perfusion rates in rat subjects could be larger than previously assumed in the RF thermal modeling literature, and plays a significant role in the transient thermal response to high-power microwave exposures. © 2021 Bioelectromagnetics Society.

An in-silico method to predict and quantify the effect of gold nanoparticles in X-ray imaging

PURPOSE

Over the last few years studies are conducted, highlighting the feasibility of Gold Nanoparticles (GNPs) to be used in clinical CT imaging and as an efficient contrast agent for cancer research. After ensuring that GNPs formulations are appropriate for in vivo or clinical use, the next step is to determine the parameters for an X-ray system's optimal contrast for applications and to extract quantitative information. There is currently a gap and need to exploit new X-ray imaging protocols and processing algorithms, through specific models avoiding trial-and-error procedures and provide an imaging prognosis tool. Such a model can be used to confirm the accumulation of GNPs in target organs before radiotherapy treatments with a system easily available in hospitals, as low energy X-rays.

METHODS

In this study a complete, easy-to-use, simulation platform is designed and built, where simple parameters, as the X-ray's specifications and experimentally defined biodistributions of specific GNPs are imported. The induced contrast and images can be exported, and accurate quantification can be performed. This platform is based on the GATE Monte Carlo simulation toolkit, based on the GEANT4 toolkit and the MOBY phantom, a realistic 4D digital mouse.

RESULTS

We have validated this simulation platform to predict the contrast induction and minimum detectable concentration of GNPs on any given X-ray system. The study was applied to preclinical studies but is also expandable to clinical studies.

CONCLUSIONS

According to our knowledge, no other such validated simulation model currently exists, and this model could help radiology imaging with GNPs to be truly deployed.

Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine

The National Institutes of Health’s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.

Low-dose spectral CT reconstruction based on image-gradient L0-norm and adaptive spectral PICCS

The photon-counting detector based spectral computed tomography (CT) is promising for lesion detection, tissue characterization, and material decomposition. However, the lower signal-to-noise ratio within multi-energy projection dataset can result in poorly reconstructed image quality. Recently, as prior information, a high-quality spectral mean image was introduced into the prior image constrained compressed sensing (PICCS) framework to suppress noise, leading to spectral PICCS (SPICCS). In the original SPICCS model, the image gradient L₁-norm is employed, and it can cause blurred edge structures in the reconstructed images. Encouraged by the advantages in edge preservation and finer structure recovering, the image gradient L₀-norm was incorporated into the PICCS model. Furthermore, due to the difference of energy spectrum in different channels, a weighting factor is introduced and adaptively adjusted for different channel-wise images, leading to an L₀-norm based adaptive SPICCS (L₀-ASPICCS) algorithm for low-dose spectral CT reconstruction. The split-Bregman method is employed to minimize the objective function. Extensive numerical simulations and physical phantom experiments are performed to evaluate the proposed method. By comparing with the state-of-the-art algorithms, such as the simultaneous algebraic reconstruction technique, total variation minimization, and SPICCS, the advantages of our proposed method are demonstrated in terms of both qualitative and quantitative evaluation results.

Ultrafast Ultrasound Imaging for Super-Resolution Preclinical Cardiac PET

PURPOSE

Physiological motion and partial volume effect (PVE) significantly degrade the quality of cardiac positron emission tomography (PET) images in the fast-beating hearts of rodents. Several Super-resolution (SR) techniques using a priori anatomical information have been proposed to correct motion and PVE in PET images. Ultrasound is ideally suited to capture real-time high-resolution cine images of rodent hearts. Here, we evaluated an ultrasound-based SR method using simultaneously acquired and co-registered PET-CT-Ultrafast Ultrasound Imaging (UUI) of the beating heart in closed-chest rodents.

PROCEDURES

The method was tested with numerical and animal data (n = 2) acquired with the non-invasive hybrid imaging system PETRUS that acquires simultaneously PET, CT, and UUI.

RESULTS

We showed that ultrasound-based SR drastically enhances the quality of PET images of the beating rodent heart. For the simulations, the deviations between expected and mean reconstructed values were 2 % after applying SR. For the experimental data, when using Ultrasound-based SR correction, contrast was improved by a factor of two, signal-to-noise ratio by 11 %, and spatial resolution by 56 % (~ 0.88 mm) with respect to static PET. As a consequence, the metabolic defect following an acute cardiac ischemia was delineated with much higher anatomical precision.

CONCLUSIONS

Our results provided a proof-of-concept that image quality of cardiac PET in fast-beating rodent hearts can be significantly improved by ultrasound-based SR, a portable low-cost technique. Improved PET imaging of the rodent heart may allow new explorations of physiological and pathological situations related with cardiac metabolism.

Wave optics simulation of grating-based X-ray phase-contrast imaging using 4D Mouse Whole Body (MOBY) phantom

PURPOSE

Demonstrate realistic simulation of grating-based x-ray phase-contrast imaging (GB-XPCI) using wave optics and the four-dimensional Mouse Whole Body (MOBY) phantom defined with non-uniform rational B-splines (NURBS).

METHODS

We use a full-wave approach, which uses wave optics for x-ray wave propagation from the source to the detector. This forward imaging model can be directly applied to NURBS-defined numerical phantoms such as MOBY. We assign the material properties (attenuation coefficient and electron density) of each model part using the data for adult human tissues. The Poisson noise is added to the simulated images based on the calculated photon flux at each pixel.

RESULTS

We simulate the intensity images of the MOBY phantom for eight different grating positions. From the simulated images, we calculate the absorption, differential phase, and normalized visibility contrast images. We also predict how the image quality is affected by different exposure times.

CONCLUSIONS

GB-XPCI can be simulated with the full-wave approach and a realistic numerical phantom defined with NURBS.

Ultrafast Ultrasound Imaging for Super-Resolution Preclinical Cardiac PET

Purpose

Physiological motion and partial volume effect (PVE) significantly degrade the quality of cardiac positron emission tomography (PET) images in the fast-beating hearts of rodents. Several Super-resolution (SR) techniques using a priori anatomical information have been proposed to correct motion and PVE in PET images. Ultrasound is ideally suited to capture real-time high-resolution cine images of rodent hearts. Here, we evaluated an ultrasound-based SR method using simultaneously acquired and co-registered PET-CT-Ultrafast Ultrasound Imaging (UUI) of the beating heart in closed-chest rodents.

Procedures

The method was tested with numerical and animal data (n = 2) acquired with the non-invasive hybrid imaging system PETRUS that acquires simultaneously PET, CT, and UUI.

Results

We showed that ultrasound-based SR drastically enhances the quality of PET images of the beating rodent heart. For the simulations, the deviations between expected and mean reconstructed values were 2 % after applying SR. For the experimental data, when using Ultrasound-based SR correction, contrast was improved by a factor of two, signal-to-noise ratio by 11 %, and spatial resolution by 56 % (~ 0.88 mm) with respect to static PET. As a consequence, the metabolic defect following an acute cardiac ischemia was delineated with much higher anatomical precision.

Conclusions

Our results provided a proof-of-concept that image quality of cardiac PET in fast-beating rodent hearts can be significantly improved by ultrasound-based SR, a portable low-cost technique. Improved PET imaging of the rodent heart may allow new explorations of physiological and pathological situations related with cardiac metabolism.

Preclinical Voxel-Based Dosimetry in Theranostics: a Review

Due to the increasing use of preclinical targeted radionuclide therapy (TRT) studies for the development of novel theranostic agents, several studies have been performed to accurately estimate absorbed doses to mice at the voxel level using reference mouse phantoms and Monte Carlo (MC) simulations. Accurate dosimetry is important in preclinical theranostics to interpret radiobiological dose-response relationships and to translate results for clinical use. Direct MC (DMC) simulation is believed to produce more realistic voxel-level dose distribution with high precision because tissue heterogeneities and nonuniform source distributions in patients or animals are considered. Although MC simulation is considered to be an accurate method for voxel-based absorbed dose calculations, it is time-consuming, computationally demanding, and often impractical in daily practice. In this review, we focus on the current status of voxel-based dosimetry methods applied in preclinical theranostics and discuss the need for accurate and fast voxel-based dosimetry methods for pretherapy absorbed dose calculations to optimize the dose computation time in preclinical TRT.

Quantitative γ-H2AX immunofluorescence method for DNA double-strand break analysis in testis and liver after intravenous administration of 111InCl3

Background

It is well known that a severe cell injury after exposure to ionizing radiation is the induction of DNA double-strand breaks (DSBs). After exposure, an early response to DSBs is the phosphorylation of the histone H2AX molecule regions adjacent to the DSBs, referred to as γ-H2AX foci. The γ-H2AX assay after external exposure is a good tool for investigating the link between the absorbed dose and biological effect. However, less is known about DNA DSBs and γ-H2AX foci within the tissue microarchitecture after internal irradiation from radiopharmaceuticals. Therefore, in this study, we aimed to develop and validate a quantitative ex vivo model using γ-H2AX immunofluorescence staining and confocal laser scanning microscopy (CLSM) to investigate its applicability in nuclear medicine dosimetry research. Liver and testis were selected as the organs to study after intravenous administration of ¹¹¹InCl₃.

Results

In this study, we developed and validated a method that combines ex vivo γ-H2AX foci labeling of tissue sections with in vivo systemically irradiated mouse testis and liver tissues. The method includes CLSM imaging for intracellular cell-specific γ-H2AX foci detection and quantification and absorbed dose calculations. After exposure to ionizing radiation from ¹¹¹InCl₃, both hepatocytes and non-hepatocytes within the liver showed an absorbed dose-dependent elevation of γ-H2AX foci, whereas no such correlation was seen for the testis tissue.

Conclusion

It is possible to detect and quantify the radiation-induced γ-H2AX foci within the tissues of organs at risk after internal irradiation. We conclude that our method developed is an appropriate tool to study dose–response relationships in animal organs and human tissue biopsies after internal exposure to radiation.

Statistical image-based material decomposition for triple-energy computed tomography using total variation regularization

BACKGROUND

Triple-energy computed tomography (TECT) can obtain x-ray attenuation measurements at three energy spectra, thereby allowing identification of different material compositions with same or very similar attenuation coefficients. This ability is known as material decomposition, which can decompose TECT images into different basis material image. However, the basis material image would be severely degraded when material decomposition is directly performed on the noisy TECT measurements using a matrix inversion method.

OBJECTIVE

To achieve high quality basis material image, we present a statistical image-based material decomposition method for TECT, which uses the penalized weighted least-squares (PWLS) criteria with total variation (TV) regularization (PWLS-TV).

METHODS

The weighted least-squares term involves the noise statistical properties of the material decomposition process, and the TV regularization penalizes differences between local neighboring pixels in a decomposed image, thereby contributing to improving the quality of the basis material image. Subsequently, an alternating optimization method is used to minimize the objective function.

RESULTS

The performance of PWLS-TV is quantitatively evaluated using digital and mouse thorax phantoms. The experimental results show that PWLS-TV material decomposition method can greatly improve the quality of decomposed basis material image compared to the quality of images obtained using the competing methods in terms of suppressing noise and preserving edge and fine structure details.

CONCLUSIONS

The PWLS-TV method can simultaneously perform noise reduction and material decomposition in one iterative step, and it results in a considerable improvement of basis material image quality.

Dosimetric modeling of Tc-99, Cs-137, Np-237, and U-238 in the grass species Andropogon Virginicus: Development and comparison of stylized, voxel, and hybrid phantom geometry

This paper discusses the development, comparison, and application of three anatomically representative computational phantoms for the grass species Andropogon virginicus, an indigenous grass species in the Southeastern United States. Specifically, the phantoms developed in this work are: (1) a stylized phantom where plant organs (roots or shoots) are represented by simple geometric shapes, (2) a voxel phantom developed from micro-CT imagery of a plant specimen, and (3) a hybrid phantom resulting from the refinement of (2) by use of non-uniform rational basis spline (NURBS) surfaces. For each computational phantom, Monte Carlo dosimetric modeling was utilized to determine whole-organism and organ specific dose coefficients (DC) associated with external and internal exposure to ⁹⁹Tc, ¹³⁷Cs, ²³⁷Np, and ²³⁸U for A. virginicus. Model DCs were compared to each other and to current values for the ICRP reference wild grass in order to determine if noteworthy differences resulted from the utilization of more anatomically realistic phantom geometry. Modeled internal DCs were comparable with ICRP values. However, modeled external DCs were more variable with respect to ICRP values; this is proposed to be primarily due to differences in organism and source geometry definitions. Overall, the three anatomical phantoms were reasonably consistent. Some noticeable differences in internal DCs were observed between the stylized model and the voxel or hybrid models for external DCs for shoots and for cases of crossfire between plant organs. Additionally, uptake data from previous hydroponic (HP) experiments was applied in conjunction with hybrid model DCs to determine dose rates to the plant from individual radionuclides as an example of practical application. Although the models within are applied to a small-scale, hypothetical scenario as proof-of-principle, the potential, real-world utility of such complex dosimetric models for non-human biota is discussed, and a fit-for purpose approach for application of these models is proposed.

Prediction of major torso organs in low-contrast micro-CT images of mice using a two-stage deeply supervised fully convolutional network

Delineation of major torso organs is a key step of mouse micro-CT image analysis. This task is challenging due to low soft tissue contrast and high image noise, therefore anatomical prior knowledge is needed for accurate prediction of organ regions. In this work, we develop a deeply supervised fully convolutional network which uses the organ anatomy prior learned from independently acquired contrast-enhanced micro-CT images to assist the segmentation of non-enhanced images. The network is designed with a two-stage workflow which firstly predicts the rough regions of multiple organs and then refines the accuracy of each organ in local regions. The network is trained and evaluated with 40 mouse micro-CT images. The volumetric prediction accuracy (Dice score) varies from 0.57 for the spleen to 0.95 for the heart. Compared to a conventional atlas registration method, our method dramatically improves the Dice of the abdominal organs by 18%-26%. Moreover, the incorporation of anatomical prior leads to more accurate results for small-sized low-contrast organs (e.g. the spleen and kidneys). We also find that the localized stage of the network has better accuracy than the global stage, indicating that localized single organ prediction is more accurate than global multiple organ prediction. With this work, the accuracy and efficiency of mouse micro-CT image analysis are greatly improved and the need for using contrast agent and high x-ray dose is potentially reduced.

Internal radiation dosimetry of a 152Tb-labeled antibody in tumor-bearing mice

Background

Biodistribution studies based on organ harvesting represent the gold standard pre-clinical technique for dose extrapolations. However, sequential imaging is becoming increasingly popular as it allows the extraction of longitudinal data from single animals, and a direct correlation with deterministic radiation effects. We assessed the feasibility of mouse-specific, microPET-based dosimetry of an antibody fragment labeled with the positron emitter ¹⁵²Tb [(T_1/2 = 17.5 h, Eβ⁺mean = 1140 keV (20.3%)]. Image-based absorbed dose estimates were compared with those obtained from the extrapolation to ¹⁵²Tb of a classical biodistribution experiment using the same antibody fragment labeled with ¹¹¹In.

¹⁵²Tb was produced by proton-induced spallation in a tantalum target, followed by mass separation and cation exchange chromatography. The endosialin-targeting scFv78-Fc fusion protein was conjugated with the chelator p-SCN-Bn-CHX-A”-DTPA, followed by labeling with either ¹⁵²Tb or ¹¹¹In. Micro-PET images of four immunodeficient female mice bearing RD-ES tumor xenografts were acquired 4, 24, and 48 h after the i.v. injection of ¹⁵²Tb-CHX-DTPA-scFv78-Fc. After count/activity camera calibration, time-integrated activity coefficients (TIACs) were obtained for the following compartments: heart, lungs, liver, kidneys, intestines, tumor, and whole body, manually segmented on CT. For comparison, radiation dose estimates of ¹⁵²Tb-CHX-DTPA-scFv78-Fc were extrapolated from mice dissected 4, 24, 48, and 96 h after the injection of ¹¹¹In-CHX-DTPA-scFv78-Fc (3–5 mice per group). Imaging-derived and biodistribution-derived organ TIACs were used as input in the 25 g mouse model of OLINDA/EXM® 2.0, after appropriate mass rescaling. Tumor absorbed doses were obtained using the OLINDA2 sphere model. Finally, the relative percent difference (RD%) between absorbed doses obtained from imaging and biodistribution were calculated.

Results

RD% between microPET-based dosimetry and biodistribution-based dose extrapolations were + 12, − 14, and + 17 for the liver, the kidneys, and the tumors, respectively. Compared to biodistribution, the imaging method significantly overestimates the absorbed doses to the heart and the lungs (+ 89 and + 117% dose difference, respectively).

Conclusions

MicroPET-based dosimetry of ¹⁵²Tb is feasible, and the comparison with organ harvesting resulted in acceptable dose discrepancies for body districts that can be segmented on CT. These encouraging results warrant additional validation using radiolabeled biomolecules with a different biodistribution pattern.

Preclinical voxel-based dosimetry through GATE Monte Carlo simulation using PET/CT imaging of mice

Internal dosimetry is of critical importance to obtain an accurate absorbed dose-response relationship during preclinical molecular imaging and targeted radionuclide therapy (TRT). Conventionally, absorbed dose calculations have been performed using organ-level dosimetry based on the Medical Internal Radiation Dose (MIRD) schema. However, recent research has focused on developing more accurate voxel-level calculation methods. Geant4 application for emission tomography (GATE) Monte Carlo (MC) is a simulation toolkit gaining attention in voxel-based dosimetry. In this study, we used PET/CT images of real mice to estimate the absorbed doses in sensitive organs at voxel-level to evaluate the suitability of GATE MC simulation for preclinical dosimetry. Thirteen normal C57BL/6 mice (male, body weight: 27.71  ±  4.25 g) were used to acquire dynamic positron emission tomography/computed tomography (PET/CT) images after IV injection of ¹⁸F-FDG. GATE MC toolkit was applied to estimate the absorbed doses in various organs of mice at voxel-level using CT and PET images as voxelized phantom and voxelized source, respectively. In addition, mean absorbed dose at organ-level was calculated using MIRD schema for comparison purposes. The differences in the respective absorbed doses (mGy MBq^-1) between GATE MC and MIRD schema for brain, heart wall, liver, lungs, stomach wall, spleen, kidneys, and bladder wall were 1.36, 12.3, -22.4, -11.2, -16.9, -2.87, -4.29, and 3.71%, respectively. Considering that the PET/CT data of real mice were used for GATE simulation, the absorbed doses estimated in this study are mouse-specific. Therefore, the GATE-based Monte Carlo is likely to allow for more accurate internal dosimetry calculations. This method can be used in TRT for personalized dosimetry because it considers patient-specific heterogeneous tissue compositions and activity distributions.

Voxel-Based Dosimetry of Iron Oxide Nanoparticle-Conjugated 177Lu-Labeled Folic Acid Using SPECT/CT Imaging of Mice

Several radiolabeled folic acid conjugates have been developed for targeted imaging and therapy. However, the therapeutic concept with radiolabeled folate conjugates has not yet been applied to clinical applications owing to the high renal absorbed dose. The effectiveness of targeted radionuclide therapy (TRT) depends primarily on the absorbed dose rate and on the total absorbed dose delivered to the tumor and to normal tissue. Owing to various limitations associated with organ level dosimetry, voxel-based dosimetry has become essential for the assessment of a more  accurate absorbed dose during TRT. In this study, we synthesized iron oxide nanoparticle (IONP)-conjugated radiolabeled folate (¹⁷⁷Lu-IONP-Folate) and performed voxel-based dosimetry using SPECT/CT images of normal mice through direct Geant4 application for emission tomography (GATE) Monte Carlo (MC) simulation. We also prepared ¹⁷⁷Lu-Folate and ¹⁷⁷Lu-IONPs for the comparison of absorbed doses with that of ¹⁷⁷Lu-IONP-Folate. In addition, we calculated the mean absorbed dose at the organ-level using the medical internal radiation dose (MIRD) schema. The radioactivities of all three radiotracers were mainly accumulated in the liver and kidneys immediately after injection. For the kidneys, the voxel-based absorbed doses obtained with ¹⁷⁷Lu-IONP-Folate, ¹⁷⁷Lu-Folate, and ¹⁷⁷Lu-IONPs were 1.01 ± 0.17, 2.46 ± 0.50, and 0.52 ± 0.08 Gy/MBq, respectively. The renal absorbed dose decreased significantly (∼half) when ¹⁷⁷Lu-IONP-Folate was used compared with when the ¹⁷⁷Lu-Folate only was used. The mean absorbed dose values obtained at organ-level using the MIRD schema were comparable to voxel-based absorbed doses estimated with GATE MC. The voxel-based absorbed dose values obtained in this study of individualized activity show that the renal absorbed dose could be reduced to almost half with ¹⁷⁷Lu-IONP-Folate. Therefore, ¹⁷⁷Lu-IONP-Folate could be clinically applicable in the TRT of folate receptor-positive cancers in a personalized manner when using the voxel-based dosimetry 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.

Innovations in Computer Technologies Have Impacted Radiation Dosimetry Through Anatomically Realistic Phantoms and Fast Monte Carlo Simulations

Radiological physics principles have not changed in the past 60 y when computer technologies advanced exponentially. The research field of anatomical modeling for the purpose of radiation dose calculations has experienced an explosion in activity in the past two decades. Such an exciting advancement is due to the feasibility of creating three-dimensional geometric details of the human anatomy from tomographic imaging and of performing Monte Carlo radiation transport simulations on increasingly fast and cheap personal computers. The advent of a new type of high-performance computing hardware in recent years-graphics processing units-has made it feasible to carry out time-consuming Monte Carlo calculations at near real-time speeds. This paper introduces the history of three generations of computational human phantoms (the stylized medical internal radiation dosimetry-type phantoms, the voxelized tomographic phantoms, and the boundary representation deformable phantoms) and new development of the graphics processing unit-based Monte Carlo radiation dose calculations. Examples are given for research projects performed by my students in applying computational phantoms and a new Monte Carlo code, ARCHER, to problems in radiation protection, imaging, and radiotherapy. Finally, the paper discusses challenges and future opportunities for research.

Differences in the S value between male and female murine model for diagnostic, therapeutic and theragnostic radionuclides

The aim of this work was to calculate S values for ⁹⁹mTc, ⁶⁷Ga, ⁶⁸Ga, ¹⁸F, ²²³Ra, ¹⁶⁶Ho, ⁹⁰Y, ¹⁶¹Tb ¹³¹I and ¹⁷⁷Lu, using a mouse phantom (MOBY) standard and considering the anatomic sizes from males and females, the simulation of radiation transport was performed with GATE/Geant4 platform. This indicates that in the internal dosimetry the use of a customized geometry is relevant for each gender and a standard model is not a good choice.

Tensor decomposition and non-local means based spectral CT image denoising

BACKGROUNDAs one type of the state-of-the-art detectors, photon counting detectors are used in spectral computed tomography (CT) to classify the received photons into several energy channels and generate multichannel projections simultaneously. However, FBP reconstructed images contain severe noise due to the low photon counts in each energy channel.OBJECTIVEA spectral CT image denoising method based on tensor-decomposition and non-local means (TDNLM) is proposed.METHODSIn a CT image, it is widely accepted that there exists self-similarity over the spatial domain. In addition, because a multichannel CT image is obtained from the same object at different energies, images among different channels are highly correlated. Motivated by these two characteristics of the spectral CT images, tensor decomposition and non-local means are employed to recover fine structures in spectral CT images. Moreover, images in all energy channels are added together to form a high signal-to-noise ratio image, which is applied to encourage the signal preservation of the TDNLM. The combination of TD, NLM and the guidance of a high-quality image enhances the low-dose spectral CT, and a parameter selection strategy is designed to achieve the optimal image quality.RESULTSThe effectiveness of the developed algorithm is validated on both numerical simulations and realistic preclinical applications. The root mean square error (RMSE) and the structural similarity (SSIM) are used to quantitatively assess the image quality. The proposed method successfully restored high-quality images (average RMSE=0.0217 cm-1 and SSIM=0.987) from noisy spectral CT images (average RMSE=0.225 cm-1 and SSIM=0.633). In addition, RMSE of each decomposed material component is also remarkably reduced. Compared to the state-of-the-art iterative spectral CT reconstruction algorithms, the proposed method achieves comparable performance with dramatically reduced computational cost, resulting in a speedup of >50.CONCLUSIONSThe outstanding denoising performance, the high computational efficiency and the adaptive parameter selection strategy make the proposed method practical for spectral CT applications.

Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature

Positron emission tomography (PET) provides images of metabolic activity in the body, and it is used in the research, monitoring, and diagnosis of several diseases. However, the raw data produced by the scanner are severely corrupted by noise, causing a degraded quality in the reconstructed images. In this paper, we proposed a reconstruction algorithm to improve the image reconstruction process, addressing the problem from a variational geometric perspective. We proposed using the weighted Gaussian curvature (WGC) as a regularization term to better deal with noise and preserve the original geometry of the image, such as the lesion structure. In other contexts, the WGC term has been found to have excellent capabilities for preserving borders and structures of low gradient magnitude, such as ramp-like structures; at the same time, it effectively removes noise in the image. We presented several experiments aimed at evaluating contrast and lesion detectability in the reconstructed images. The results for simulated images and real data showed that our proposed algorithm effectively preserves lesions and removes noise.

Low-dose spectral CT reconstruction using image gradient ℓ 0-norm and tensor dictionary

Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient ℓ ₀-norm, which is named as ℓ ₀TDL. The ℓ ₀TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images. On the other hand, by introducing the ℓ ₀-norm constraint in gradient image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The split-bregman method is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed ℓ ₀TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.

Automated quantification of bioluminescence images

We developed a computer-aided analysis tool for quantitatively determining bioluminescent reporter distributions inside small animals. The core innovations are a body-fitting animal shuttle and a statistical mouse atlas, both of which are spatially aligned and scaled according to the animal’s weight, and hence provide data congruency across animals of varying size and pose. In conjunction with a multispectral bioluminescence tomography technique capitalizing on the spatial framework of the shuttle, the in vivo biodistribution of luminescent reporters can rapidly be calculated and, thus, enables operator-independent and computer-driven data analysis. We demonstrate its functionality by quantitatively monitoring a bacterial infection, where the bacterial organ burden was determined and validated with the established serial-plating method. In addition, the statistical mouse atlas was validated and compared to existing techniques providing an anatomical reference. The proposed data analysis tool promises to increase data throughput and data reproducibility and accelerate human disease modeling in mice.

Analysis of bioluminescence images of bacterial distributions in living animals is mostly manual and semiquantitative. Here, the authors present an analysis platform featuring an animal mold, a probabilistic organ atlas, and a mirror gantry to perform automatic in vivo bioluminescence quantification.

Overcoming detector limitations of x-ray photon counting for preclinical microcomputed tomography

Spectral computed tomography (CT) using photon counting detectors (PCDs) can provide accurate tissue composition measurements by utilizing the energy dependence of x-ray attenuation in different materials. PCDs are especially suited for K-edge imaging, revealing the spatial distribution of select imaging probes through quantitative material decomposition. We report on a prototype spectral micro-CT system with a CZT-based PCD (DxRay, Inc.) that has 16 × 16    pixels of 0.5 × 0.5    mm 2 , a thickness of 3 mm, and four energy thresholds. Due to the PCD's limited size ( 8 × 8    mm 2 ), our system uses a translate-rotate projection acquisition strategy to cover a field of view relevant for preclinical imaging ( ∼ 4.5    cm ). Projection corrections were implemented to minimize artifacts associated with dead pixels and projection stitching. A sophisticated iterative algorithm was used to reconstruct both phantom and ex vivo mouse data. To achieve preclinically relevant spatial resolution, we trained a convolutional neural network to perform pan-sharpening between low-resolution PCD data ( 247 - μ m voxels) and high-resolution energy-integrating detector data ( 82 - μ m voxels), recovering a high-resolution estimate of the spectral contrast suitable for material decomposition. Long-term, preclinical spectral CT systems such as ours could serve in the developing field of theranostics (therapy and diagnostics) for cancer research.

Bioluminescence tomography with structural information estimated via statistical mouse atlas registration

Due to an ill-posed and underestimated characteristic of bioluminescence tomography (BLT) reconstruction, a priori anatomical information obtained from computed tomography (CT) or magnetic resonance imaging (MRI), is usually incorporated to improve the reconstruction accuracy. The organs need to be segmented, which is time-consuming and challenging, especially for the low-contrast CT images. In this paper, we present a BLT reconstruction method based on a statistical mouse atlas to improve the efficiency of heterogeneous model generation and the accuracy of target localization. The low-contrast CT image of the mouse was first registered to the statistical mouse atlas model with the constraints of mouse surface and high-contrast organs (bone and lung). Then the other organs, such as the liver and kidney, were determined automatically through the statistical mouse atlas model. The estimated organs were then discretized into tetrahedral meshes for BLT reconstruction. The linearized Bregman method was used to solve the sparse inverse problem of BLT by minimizing the regularization function (L1 norm plus L2 norm with smooth factor). Both numerical simulations and in vivo experiments were conducted, and the results demonstrate that even though the localization of the estimated organs may not be exactly accurate, the proposed method is feasible to reconstruct the bioluminescent source effectively and accurately with the estimated organs. This method would greatly benefit the bioluminescent light source localization for hybrid BLT/CT systems.

Automatic multiatlas based organ at risk segmentation in mice

OBJECTIVE:

During the treatment planning of a preclinical small animal irradiation, which has time limitations for reasons of animal wellbeing and workflow efficiency, the time consuming organ at risk (OAR) delineation is performed manually. This work aimed to develop, demonstrate, and quantitatively evaluate an automated contouring method for six OARs in a preclinical irritation treatment workflow.

METHODS:

Microcone beam CT images of nine healthy mice were contoured with an in-house developed multiatlas-based image segmentation (MABIS) algorithm for six OARs: kidneys, eyes, heart, and brain. The automatic contouring was compared with the manual delineation using three quantitative metrics: the Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance, and the centre of mass displacement.

RESULTS:

A good agreement between manual and automatic contouring was found for OARs with sharp organ boundaries. For the brain and the heart, the median DSC was larger than 0.94, the median 95th Hausdorff Distance smaller than 0.44 mm, and the median centre of mass displacement smaller than 0.20 mm. Lower DSC values were obtained for the other OARs, but the median DSC was still larger than 0.74 for the left eye, 0.69 for the right eye, 0.89 for the left kidney and 0.80 for the right kidney.

CONCLUSION:

The MABIS algorithm was able to delineate six OARs with a relatively high accuracy. Segmenting OARs with sharp organ boundaries performed better than low contrast OARs.

ADVANCES IN KNOWLEDGE:

A MABIS algorithm is developed, evaluated, and demonstrated in a preclinical small animal irradiation research workflow.

Monte Carlo GEANT4-based application for in vivo RBE study using small animals at LNS-INFN preclinical hadrontherapy facility

Preclinical studies represent an important step towards a deep understanding of the biological response to ionizing radiations. The effectiveness of proton therapy is higher than photons and, for clinical purposes, a fixed value of 1.1 is used for the relative biological effectiveness (RBE) of protons considered 1.1. Recent in vitro studies have reported that the RBE along the spread-out Bragg peak (SOBP) is not constant and, in particular, the RBE value increases on the distal part of SOBP. The present work has been carried-out in the perspective of a preclinical hadrontherapy facility at LNS-INFN and was focused on the experimental preparation of an in vivo study concerning the RBE variation along the SOBP. The main purpose of this work was to determine, using GEANT4-based Monte Carlo simulations, the best configuration for small animal treatments. The developed GEANT4 application simulates the proton-therapy beam line of LNS-INFN (CATANA facility) and allows to import the DICOM-CT images as targets. The RBE will be evaluated using a deterministic radiation damage like myelopathy as end-point. In fact, the dose at which the 50% of animals will show the myelopathy is supposed to be LET-dependent. In this work, we studied different treatment configurations in order to choose the best two that maximize the LET difference reducing as much as possible the dose released to healthy tissue. The results will be useful to plan hadrontherapy treatments for preclinical in vivo studies and, in particular, for the future in vivo RBE studies.