Physics in Medicine & Biology

An open-source library of implementations for deep-learning based image segmentation and outcomes models is presented in this work. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. The library was developed with Computational Environment for Radiological Research (CERR) software platform. CERR is a natural choice to centralize model implementations due to its comprehensiveness, popularity, and ease of use. CERR provides well-validated feature extraction for radiotherapy dosimetry and radiomics with fine control over the calculation settings. This allows users to select the appropriate feature calculation used in the model derivation. Models for automatic image segmentation are distributed via Singularity containers, with seamless i/o to and from CERR. Singularity containers allow for segmentation models to be deployed with a variety of scientific computing architectures. Deployment of models is driven by JSON configuration file, making it convenient to plug-in models. Models from the library can be called programmatically for batch evaluation. The library includes implementations for popular radiotherapy models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from Image Biomarker Standardization features found to be important across multiple sites and image modalities. Deep learning-based image segmentation models include state of the art networks such as Deeplab and other problem-specific architectures. The library is distributed as GNU-copyrighted software at https://www.github.com/cerr/CERR.

This work introduces a user-friendly, cloud-based software framework for conducting Artificial Intelligence (AI) analyses of medical images. The framework allows users to deploy AI-based workflows by customizing software and hardware dependencies. The components of our software framework include the Python-native Computational Environment for Radiological Research (pyCERR) platform for radiological image processing, Cancer Genomics Cloud (CGC) for accessing hardware resources and user management utilities for accessing images from data repositories and installing AI models and their dependencies. GNU-GPL copyright pyCERR was ported to Python from MATLAB-based CERR to enable researchers to organize, access, and transform metadata from high dimensional, multi-modal datasets to build cloud-compatible workflows for AI modeling in radiation therapy and medical image analysis. pyCERR provides an extensible data structure to accommodate metadata from commonly used medical imaging file formats and a viewer to allow for multi-modal visualization. Analysis modules are provided to facilitate cloud-compatible AI-based workflows for image segmentation, radiomics, DCE MRI analysis, radiotherapy dose-volume histogram-based features, and normal tissue complication and tumor control models for radiotherapy. Image processing utilities are provided to help train and infer convolutional neural network-based models for image segmentation, registration and transformation. The framework allows for round-trip analysis of imaging data, enabling users to apply AI models to their images on CGC and retrieve and review results on their local machine without requiring local installation of specialized software or GPU hardware. The deployed AI models can be accessed using APIs provided by CGC, enabling their use in a variety of programming languages. In summary, the presented framework facilitates end-to-end radiological image analysis and reproducible research, including pulling data from sources, training or inferring from an AI model, utilities for data management, visualization, and simplified access to image metadata.

PurposeSingle-energy computed tomography (CT) often suffers from poor contrast, yet it remains critical for effec-tive radiotherapy treatment. Modern therapy systems are often equipped with both megavoltage (MV) and kilovoltage (kV) x-ray sources and thus already possess the hardware needed for dual-energy (DE) CT. There exists an unexplored potential for enhanced image contrast using MV-kV DE-CT in radiotherapy contexts. ApproachA toy model comprising a single-line integral through a two-material object was designed for computing basis material signal-to-noise ratio (SNR) using estimation theory. Five dose-matched spectra (three kV, two MV) and three variables were considered: spectral combination, spectral dose allocation, and object material composition. The single-line model was extended to a simulated fan-beam CT acquisition of an anthropomorphic phantom with and without a metal implant. Basis material sinograms were computed and synthesized into virtual monoenergetic images (VMIs). MV-kV and kV-kV VMIs were compared with single-energy images. ResultsThe 80kV-140kV pair typically yielded the best SNRs, but for bone thicknesses greater than 8 cm, the detunedMV-80kV pair surpassed it. Peak MV-kV SNR was achieved with approximately 90% dose allocated to the MV spectrum. For the CT simulations, MV-kV VMIs yielded a higher contrast-to-noise ratio (CNR) than single-energy CT at specific monoenergies. With the metal implant, MV-kV produced a higher maximum CNR and lower minimum root-mean-square-error than kV-kV. ConclusionsThis work quantitatively analyzes MV-kV DE-CT imaging and assesses its potential advantages. This technique may yield improved contrast and accuracy relative to dose-matched single-energy CT or kV-kV DE-CT, depending on object composition.

Microcomputed tomography (micro-CT) is a frequently used imaging tool for a wide spectrum of in-vivo mouse models in basic and translational research. To allow an accurate interpretation of micro-CT images, high spatial resolution is necessary. However, this may also lead to a high radiation exposure of the animals. Therefore, animal welfare requires exact information about the expected radiation doses for experimental planning. To gain this, a mouse cadaver was herein used for micro-CT analyses under different conditions. For each radiation dose measurement, the cadaver was labeled with thermoluminescent dosimeter chips around the thoracic skin surface. Micro-CT scans of the thorax were performed with spatial resolutions of 35 {micro}m, 18 {micro}m and 9 {micro}m in combination with Al0.5, Al1.0, CuAl and Cu filters. As a surrogate of image quality, the number of identifiable lung vessels was counted on a transversal micro-CT slice. Measured radiation doses varied from 0.09 Gy up to 5.18 Gy dependent on resolution and filter settings. A significant dose reduction of > 75% was achieved by a Cu filter when compared to an Al0.5 filter. However, this resulted in a markedly reduced image quality and interpretability of microstructures due to higher radiation shielding and lower spatial resolution. Thus, the right combination of distinct filters and several scan protocol settings adjusted to the individual requirements can significantly reduce the radiation dose of micro-CT leading to a higher animal welfare standard.

Diagnostic of radiation effects can be obtained within hours from delivery relying on spectroscopic detection of cell metabolite concentrations. Clinical and pre-clinical studies show that radiation delivery with elevated dose-rates can achieve tumor suppression while minimizing toxicity to surrounding areas. Diagnostic biomarkers detected on short timescales are needed to orient high dose-rate radiation delivery. We have designed an 1H magnetic resonance approach to observe metabolite concentrations, in particular Choline, Creatine, and Lactate, in order to detect radiation dose and dose-rate effects within hours from radiation delivery. The results of our metabolic profiling method in glioblastoma cells are consistent with observations from clinical studies guided by magnetic resonance spectroscopy for radiotherapy of head tumors. At 5 Gy/min we have observed increases in lactate concentrations and decreases in [Cho]/[Cr] ratios at increasing radiation doses. An increase of the radiation dose-rate to 35 Gy/min is correlated with an increase of [Cho]/[Cr] consistent with a reduction in radiation-induced oxidative effects at high dose-rates. The observed biomarkers can be translated for radiation pulse sequences optimization. One Sentence SummaryMagnetic resonance biomarkers to monitor biological effectiveness within hours after radiation delivery can be optimized for glioblastoma cells and are of potential use for the design of radiotherapy with high dose-rates.

PurposeExternal beam radiotherapy (XRT) is a widely used cancer treatment, yet responses vary dramatically between patients. These differences are not accounted for in clinical practice, in part due to a lack of sensitive biomarkers of early response. In this work, we test the hypothesis that quantification of intratumor heterogeneity is a sensitive and robust biomarker of early response to XRT. A novel Magnetic Resonance Imaging (MRI) approach is proposed, utilizing histogram analysis of clinically-used T2 relaxation measurements to assess early changes in the tumor heterogeneity following irradiation in murine models of pancreatic cancer, indicative of radiotherapy response. Methods and MaterialsDynamic Magnetic Resonance T2 relaxation imaging was performed every 72h following 10 Gy dose XRT in two murine models of pancreatic cancer. Proposed biomarker of radiotherapy response was compared with tumor growth kinetics, and biological validation was performed through quantitative histology analysis. ResultsQuantification of tumor T2 interquartile range (IQR) as a measure of histogram width showed excellent sensitivity for detection of XRT-induced tumor changes as early as 72h after treatment, outperforming whole tumor T2 and Diffusion weighted MRI metrics. This response was observed both in quantitative T2 maps and in T2-weighted images that are routine in clinical practice. Histological comparison revealed the T2 IQR provides a measure of spatial heterogeneity in tumor cell density, related to radiation-induced necrosis. The early IQR changes were found to presage subsequent tumor volume changes in two distinct pancreatic models, suggesting promise for treatment response prediction. The metric showed excellent test-retest robustness. ConclusionsOur preclinical findings indicate that spatial heterogeneity analysis of T2 MRI can provide a sensitive and readily translatable method for early radiotherapy response assessment in pancreatic cancer. We propose that this will be useful in adaptive radiotherapy, specifically in MRI-guided treatment paradigms.

Radiopharmaceutical therapies are poised to enhance patient care for several currently untreatable metastatic cancers. Radium-223 dichloride citrate is indicated for treatment of bone metastatic castrate resistant prostate cancer, serving as the primary use of alpha particle emitting radium to irradiate bone lesions. Improvements and refinement of such therapies relies on patient centric and micro scale quantification to assess and compare efficacy. Computational modeling and Monte Carlo simulations provide a valuable tool for understanding micro scale phenomena of radiopharmaceutical therapies. Via simulation, we undertake and illuminate dose profiles of radium-223 dichloride treatment, evaluating energy and dose distributions based on primary, patient-derived specimens. A set of four activity distributions were simulated on three patient bone lesion biopsy samples. These simulations validate the novel tool for micron-scale modeling with patient-derived specimens. Ablative dose profiles are shown to be driven by uptake distributions as well as the target tissues microstructure.

PurposeThis study investigates the robustness of quantitative radiomic features derived from computed tomography (CT) images of a novel patient informed 3-D printed phantom, which captures the morphological heterogeneity of tumors and normal tissue observed on CT scans.\n\nMethodsUsing a novel voxel-based multi-material three-dimensional (3D) printer, an anthropomorphic phantom that was modeled after diseased tissue seen on 6 patient CT scans was manufactured. Four patients presented with pancreatic adenocarcinoma tumors (PDAC), 1 with non-small cell lung carcinoma (NSCLC) and 1 with advanced stage hepatic cirrhosis. The 5 tumors were segmented, extracted and then imbedded into CT images of the heterogenous portion of the cirrhotic liver. The composite scan of the implanted tumor within the background cirrhotic liver was then 3D printed. The resultant phantom was scanned sequentially, 30 times with a clinical CT scanner using a reference CT protocol. One hundred and four quantitative radiomic features were then extracted from images of each lesion to determine their repeatability. Repeatability of each radiomic feature was evaluated using the within subject coefficient of variation (wCV, %). A feature with a wCV (%) > 10% was considered as being unrepeatable. A subset of the repeatable features that were also found to be prognostic for lung and pancreatic cancers were then assessed for their percent deviation (pDV, %) from reference values. The reference values were those derived from the repeatability portion of this study. The assessment was conducted by re-scanning the phantom with 11 different clinically relevant sets of scanning parameters. Deviation of radiomic features derived from images of each tumor across all sets of scanning parameters was assessed using the percent deviation relative to the reference values.\n\nResultsTwenty nine of the 104 features presented with wCV (%) > 10%. The lack of repeatability was found to depend on tumor type. The only class of radiomic features with a wCV (%) < 10% were those calculated using the neighboring grey level dependence-based matrices (NGLDM). Notably, skewness, first information correlation, cluster shade, Haralick correlation, autocorrelation, busyness, complexity, high gray level zone emphasis, small area high gray level emphasis, large area low gray level emphasis, large area high gray level emphasis, short run high grey level emphasis, and valley radiomic features had wCV (%) values > 10% for select tumors within the phantom. Two radiomic features prognostic for NSCLC, energy and grey level non-uniformity, had pDVs (%) that exceeded 30% across all scanning techniques. The pDV (%) for the 4 radiomic features prognostic for PDAC tumors depended on tumor type and selected scanning parameter. Application of the lung kernel caused the largest pDVs (%). Scans acquired with the reduced tube current of 100 mA and reconstructed with the bone kernel yielded pDVs (%) within {+/-} 10%.\n\nConclusionWe demonstrated the feasibility with which patient informed 3D printed phantoms can be manufactured directly from lesions seen on CT scans, and demonstrate their potential use for the assessment of robust quantitative radiomic features.

BackgroundProton and ion therapy have gained significant importance in radiation therapy cancer treatment due to their favorable dose distribution and tissue-sparing properties. In conventional gamma radiation therapy some methods of in vivo dose verification are possible with current medical devices. Proton and ion therapy dose verification is limited, mainly using PET for particle range. Prompt gamma methods offer low spatial resolution. This study presents initial results for in-vivo dose verification with PET imaging of F-18 during proton therapy. Although the activity concentration of F-18 generated by typical clinical doses (several Gy) is low, PET imaging performed approximately one hour post-irradiation yields sufficient image quality to derive dose-volume histograms (DVH), enabling spatial dose verification. PurposeTo verify the applied dose in proton therapy in vivo using Positron Emission Tomography with millimetric precision. Materials and MethodsWe simulated proton treatment in a brain phantom using Gate and RayStation platforms to assess the production of several positron emitting isotopes. We focused on the production of fluorine-18 (F-18), given its low positron energy, which enables accurate reproduction of the dose distribution. To evaluate the detectability of the anticipated low activity concentrations (on the order of a few Bq/mL) following a 3 Gy proton irradiation, we tested three PET systems: two preclinical scanners based on LYSO detectors and one clinical scanner based on BGO crystals. Finally, we have analyzed the dose-volume histograms for simulated and measured dose and activity distributions and compared them with the planned ones. ResultsF-18 PET imaging in proton therapy correlates with delivered dose within 5% error and matches the planned dose fall-off edge within 1 mm, enabling accurate and precise in vivo dose verification. ConclusionThe dose verification in proton therapy using F-18 Positron Emission Tomography allows higher precision of dose than other positron emitters like C-11, N-12 or O-15. 1 Key ResultsKey Results: In proton therapy, in-vivo F-18 production correlates with the deposited dose in the patient with a 5% margin of error. The leading-edge position of the F-18 activity distribution agrees with the planned dose fall off within 1 mm. Furthermore, the feasibility of detecting low activity concentrations typical of proton therapy (Bq/ml range) has been demonstrated using both preclinical and clinical PET scanners. 2 Required Summary StatementIn proton therapy the delivered dose to the patient can be measured in vivo using Positron Emission Tomography by imaging the production of F-18 isotope, achieving millimetric spatial accuracy.

Brachytherapy seeks to treat cancer through insertion of radioactive sources aligned by needles within standardized blocking templates. However, many patients have anatomy that does not conform to standard tools. We created a 3D printed device to provide customized needle alignment for a patient. Device was validated through CT scan and treatment plan creation on a custom anatomical phantom based on patient data. CT scan of a cervix tumor from anonymized patient data was used to develop a 3D printed brachytherapy alignment tool and phantom anatomical mold. Multiple materials were evaluated to match patient anatomy in density and Hounsfield Units present on CT scan, with additional considerations for toxicity, compliance, and practicality. Alignment device and molds were developed in PLA. Silicone of T20 hardness was used to create relevant anatomical organs (Uterus, Rectum, Bladder). Tumor tissue was mimicked by addition of 1CC of Iodine contrast agent to silicone. Device and needles were arranged, inserted into anatomical phantom, and scanned by CT to mimic brachytherapy procedure. 3D printed Silicone uterus of 1.08 g/cm^3 and 40 HU mimicked human uterus on CT scan. Constructed uterus dimensions of 6.5 cm x 5.5 cm x 3.3 cm were verified on imaging to be within + 1 mm of original patient scan. The 1 CC of contrast agent provided sufficient differentiation of "tumor" ring from "tissue." CT scan and treatment plan creation verified that the alignment device provided correction insertion of needles into the phantom tumor tissue and uterus. This pilot study provides a potential methodology to develop future anatomical phantoms and alignment devices from CT scans of patient data. Additional modifications could make this a viable training tool for future residents and medical students to learn brachytherapy. Key PointsO_LI3D printing can be leveraged to create a Brachytherapy alignment device from patients anatomy. C_LIO_LIAnatomical phantom can be generated from 3D printing for use testing device or for training. C_LIO_LIRelevant materials to create phantom that mimic patients anatomy on CT scan. C_LIO_LIGeneration of a treatment plan based on CT scan was able to validate device and phantom C_LI

PurposeThis work presents a framework for deployment of deep learning image segmentation models for medical images across different operating systems and programming languages. MethodsComputational Environment for Radiological Research (CERR) platform was extended for deploying deep learning-based segmentation models to leverage CERRs existing functionality for radiological data import, transformation, management, and visualization. The framework is compatible with MATLAB as well as GNU Octave and Python for license-free use. Pre and post processing configurations including parameters for pre-processing images, population of channels, and post-processing segmentations was standardized using JSON format. CPU and GPU implementations of pre-trained deep learning segmentation models were packaged using Singularity containers for use in Linux and Conda environment archives for Windows, macOS and Linux operating systems. The framework accepts images in various formats including DICOM and CERRs planC and outputs segmentation in various formats including DICOM RTSTRUCT and planC objects. The ability to access the results readily in planC format enables visualization as well as radiomics and dosimetric analysis. The framework can be readily deployed in clinical software such as MIM via their extensions. ResultsThe open-source, GPL copyrighted framework developed in this work has been successfully used to deploy Deep Learning based segmentation models for five in-house developed and published models. These models span various treatment sites (H&N, Lung and Prostate) and modalities (CT, MR). Documentation for their usage and demo workflow is provided at https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. The framework has also been used in clinical workflow for segmenting images for treatment planning and for segmenting publicly available large datasets for outcomes studies. ConclusionsThis work presented a comprehensive, open-source framework for deploying deep learning-based medical image segmentation models. The framework was used to translate the developed models to clinic as well as reproducible and consistent image segmentation across institutions, facilitating multi-institutional outcomes modeling studies.

Spectral CT has been increasingly implemented clinically for its better characterization and quantification of materials through its multi-energy results. It also facilitates calculation of physical density utilizing the Alvarez-Macovski model without approximations. These spectral physical density quantifications allow for non-invasive mass measurements and temperature evaluations by manipulating the definition of physical density and thermal volumetric expansion, respectively. To develop the model, original and parametrized versions of the Alvarez-Macovski model and electron density-physical density model were validated with a phantom. The best physical density model was then implemented on clinical spectral CT scans of ex vivo bovine muscle to determine the accuracy and effect of acquisition parameters on mass measurements. In addition, the relationship between physical density and changes in temperature was evaluated by scanning and subjecting the tissue to a range of temperatures. A linear fit utilizing the thermal volumetric expansion was performed to assess the correlation. The parametrized Alvarez-Macovski model performed best in both model development and validation with errors within {+/-}0.02 g/mL. As observed with muscle, physical density was not significantly affected by dose and acquisition mode but was slightly affected by collimation. These effects were also reflected in mass measurements, which demonstrated accuracy with a maximum percent error of 0.34%, further validating the physical density model. Furthermore, physical density was strongly correlated (R of 0.9781) to temperature changes through thermal volumetric expansion. Accurate and precise spectral physical density quantifications enable non-invasive mass measurements for pathological detection and temperature evaluation for thermal therapy monitoring in interventional oncology.

1Background and PurposeFLASH radiotherapy, a technique based on delivering large doses in a single fraction at the micro/millisecond timescale, spares normal tissues from late radiation-induced toxicity, in an oxygen-dependent process, whilst keeping full anti-tumor efficiency. The original model of physical-chemical mechanisms [5] underlying the FLASH effect was modified to include a two-compartment (aqueous/lipid) system to take into account key interfacial reactions, and the pulsed nature of the beam. Materials and MethodsThe model predictions were tested by showing a linear correlation between experimentally measured biological outcomes reported in the literature and the final hydroperoxyl lipid [LOOH]f predicted by the model for the different irradiation timing patterns and oxygen concentrations. ResultsThe primary, carbon-centered lipid radical [L*] fades away in less than 5 ms, reproducing the experimental observation. The model predicts a linear correlation of [LOOH]f with the inverse of the square root of the dose rate, as experimentally observed. The predicted [LOOH]f correlates with the recognition ratio of mice irradiated at different dose rates and oxygen concentrations; with zebrafish embryos mean body length for different beam timing structures; with mouse skin toxicity even with dose splitting; and with the survival of mice for different doses per pulse and average dose rates. ConclusionsThe proposed radio-kinetic model attempts to synthesize the experimental results for different beam timing patterns. It successfully shows a correlation between the predicted [LOOH]f and the experimentally observed biological outcomes following irradiation with different dose rates, beam timing structures and oxygen concentrations.

SummaryUnderstanding the molecular impact of ionizing radiation exposure is essential for both biomedical research and public health. Among the possible approaches to study this phenomenon, gene expression profiling via transcriptomics assays has been a valuable approach over the last decades to unravel the mechanisms of cellular responses to radiation. To our knowledge, there is no data package gathering well-curated radiation transcriptomic datasets covering microarrays and, more recently, RNA sequencing. Therefore, we present DoReMiTra, an R/Bioconductor data package that represents the first unified radiation transcriptomics dataset collection integrated with Bioconductors ExperimentHub for efficient distribution. DoReMiTra standardizes and harmonizes sample-level metadata and provides pre-processed SummarizedExperiment (SE) objects to facilitate comparative analyses. Additionally, we introduce a lightweight Shiny app interface for interactive visualization and preliminary exploration. DoReMiTra serves as a valuable resource and tool in radiation research for benchmarking, integrative analyses, and biomarker discovery. Availability and ImplementationDoReMiTra is available under the MIT license at https://bioconductor.org/packages/DoReMiTra.

PurposeRadiological cancer imaging features, or radiomics features, can be derived to diagnose disease or predict treatment response. However, variability between vendors, scanners, protocols, and even reconstruction software versions is an obstacle to the clinical use of radiomics features. This study aimed to characterize the impact of kernel reconstruction differences and integral tube current settings on radiomic features extracted from computed tomography (CT) scans. MethodsRadiomic features were extracted from CT scans of a 3D-printed phantom with five imprinted tumors using the CERR software system, resulting in 282 features. Batch effects were assessed via principal component analysis (PCA) and correlation measures. Robustness was measured using the concordance correlation coefficient (CCC) and Pearson correlation coefficient. Statistical analysis was performed using R software. ResultsPCA identified two clusters comprised of Standard, ASIRs, ASIRV, and soft kernels in one, and Lung and Bone Kernels in the other. Features displayed a gradient from ASIR10 to ASIR50 and ASIRV1 to ASIRV5 in terms of nearness to Standard Kernel feature values. Feature correlation matrices revealed little change in ASIRs, ASIRVs, and the Standard Kernel, but showed significant changes in Bone and Lung Kernel results. Combat-algorithm correction improved robustness, particularly in first-order statistic features, and mitigated batch effects due to the ASIRs and the standard kernel. Forty (40) out of 282 features were identified as robust. However, Combat-based correction performed poorly in harmonizing Bone and Lung reconstruction kernels. ConclusionsThe robustness of means and median radiomic features across kernel reconstruction choices, in contrast to the lack of robustness in many other radiomic features, suggests that kernel reconstruction effects are not well-addressed by current harmonization methods.

MotivationIn modern treatment by radiotherapy, different irradiation modalities can be used, potentially producing different amounts of adverse effects. The differences between these modalities are often studied via two-sample time course in vitro experiments. The resulting data may be of high complexity, in which case simple methods are unadapted for extracting all the relevant information. MethodsIn this article we introduce network-based tools for the visualization of the key statistical features, extracted from the data. For the key features extraction we utilize a statistical framework performing estimation, clustering with alignment of temporal omic fold changes originating from two-sample time course data. ResultsThe approach was applied to real transcriptomic data obtained with two different types of irradiation. The results were analyzed using biological literature and enrichment analysis, thus validating the robustness of the proposed tools as well as achieving better understanding of the differences in the impact of the treatments in question. Availability and implementationPython package freely available here: https://github.com/parsenteva/scanofc. Contactpolina.arsenteva@u-bourgogne.fr

Phantom calibration is currently the gold standard for calibrating CT scans and for calculating material properties of dense tissues for computational models. However, in Oncology departments and low-resource settings, it is not routine to include a calibration phantom within the scanning protocol. Therefore, retrospective scan datasets are challenging to calibrate for biomechanical investigations, precluding detailed measurements of material and mechanical properties. In this study, we compared the results from a phantomless calibration technique, where the density within each scan was independently calibrated based on known tissue densities captured within each scan (e.g. air), with those from a traditional inline phantom calibration. To do so we used scans from a cohort of healthy volunteers from the control arm of a clinical trial dataset (ANTELOPE) in which inline calibration phantoms were included. We found that, when selecting air and the aorta as regions for calibration within individual CT scans, a strong individual-specific correlation existed between bone mineral density measured in the phantomless and phantom calibrations. This indicates that the phantomless calibration method can be a useful and reliable tool for quantifying the densitometric material properties of healthy human vertebrae, and provides the opportunity for further analysis of spinal CT scans in either retrospective datasets or in low-resource clinical settings.

ObjectivesThe study aims to assess the capability of Quantitative Ul-trasound (QUS) based on the backscatter coefficient (BSC) for classifying disease states, such as breast cancer response to neoadjuvant chemotherapy and quantifying fatty liver disease. We evaluate the effectiveness of an in situ titanium (Ti) bead as a reference target in calibrating the system and mitigating attenuation and transmission loss effects on BSC estimation. MethodsTraditional BSC estimation methods require external references for calibration, which do not account for ultrasound attenuation or transmis-sion losses through tissues. To address this issue, we use an in situ titanium (Ti) bead as a reference target, because it can be used to calibrate the system and mitigate the attenuation and transmission loss effects on estimation of the BSC. The capabilities of the in situ calibration approach were assessed by quantifying consistency of BSC estimates from rabbit mammary tumors (N = 21). Specifically, mammary tumors were grown in rabbits and when a tumor reached 1 cm or greater in size, a 2-mm Ti bead was implanted into the tumor as a radiological marker and a calibration source for ultrasound. Three days later, the tumors were scanned with a L-14/5 38 array transducer connected to a SonixOne scanner with and without a slab of pork belly placed on top of the tumors. The pork belly acted as an additional source of attenu-ation and transmission loss. QUS parameters, specifically effective scatterer diameter (ESD) and effective acoustic concentration (EAC), were calculated using calibration spectra from both an external reference phantom and the Ti bead. ResultsFor ESD estimation, the 95% confidence interval between measure-ments with and without the pork belly layer was (6.0,27.4) using the in situ bead and (114, 135.1) with the external reference phantom. For EAC esti-mation, the 95% confidence interval were (-8.1, 0.5) for the bead and (-41.5, -32.2) for the phantom. These results indicate that the in situ bead method shows reduced bias in QUS estimates due to intervening tissue losses. ConclusionsThe use of an in situ Ti bead as a radiological marker not only serves its traditional role but also effectively acts as a calibration target for QUS methods. This approach accounts for attenuation and transmission losses in tissue, resulting in more accurate QUS estimates and offering a promising method for enhanced disease state classification in clinical settings.

Background and PurposeQuantitative relaxometry on the integrated MRI / linear accelerator (MR-Linac) at high isotropic resolution is currently limited due to prohibitively long scan times and limited field-of-views. Therefore, the purpose of this study was to assess the technical feasibility of the 3D-QALAS technique on the 1.5T MR-Linac which has the ability to acquire whole-brain 1 mm isotropic quantitative T1, T2, and PD maps along with multiple synthetic images in a 7 minute acquisition time. Materials and MethodsA 1 mm isotropic 3D-QALAS acquisition was scanned in both phantoms and a healthy volunteer on the 1.5T Elekta Unity MR-Linac device with scan times around seven minutes. A test-retest protocol across five independent sessions for the phantom was conducted. The correlation, repeatability, and reproducibility between measured and reference quantitative T1, T2, and PD values were determined in the phantom. Distortion was also studied. Vendor-provided reconstruction through SyMRI was performed to extract synthetic images and brain volume metric assessments on a healthy volunteer. ResultsThe slope and concordance between the measured and phantom reference values was 1.02 (1.00), 1.09 (0.90), and 0.99 (1.00) for T1, T2, and PD, respectively. Median distortion across the phantom remained below 2 mm. The repeatability and reproducibility coefficient-of-variation (CoV) was under 8% for all measured values. The measured brain volumes in the healthy volunteer was within expected age-adjusted reference values. DiscussionThe technical feasibility of using 3D-QALAS on the integrated 1.5T MR-Linac was confirmed. Applying this technique to the head and neck adaptive radiation therapy workflow will provide new opportunities to integrate quantitative imaging relaxometry biomarkers at 1 mm isotropic resolution. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/26347967v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1f43093org.highwire.dtl.DTLVardef@a1320eorg.highwire.dtl.DTLVardef@dd750eorg.highwire.dtl.DTLVardef@1300853_HPS_FORMAT_FIGEXP M_FIG C_FIG

ObjectiveThe positron range is a fundamental, detector-independent physical limitation to special resolution in positron emission tomography (PET) as it causes a significant blurring of the reconstructed PET images. A major challenge for positron range correction methods is to provide accurate range kernels that inherently incorporate the generally inhomogeneous stopping power, especially at tissue boundaries. In this work, we propose a novel approach to generate accurate three-dimensional (3-D) blurring kernels both in homogenous and heterogeneous media to improve PET spatial resolution. ApproachIn the proposed approach, positron energy deposition was approximately tracked along straight paths, depending on the positron stopping power of the underlying material. The positron stopping power was derived from the attenuation coefficient of 511keV gamma photons according to the available PET attenuation maps. Thus, the history of energy deposition is taken into account within the range of kernels. Special emphasis was placed on facilitating the very fast computation of the positron annihilation probability in each voxel. ResultsPositron path distributions of 18F in low-density polyurethane were in high agreement with Geant4 simulation at an annihilation probability larger than 10-2[~]10-3 of the maximum annihilation probability. The Geant4 simulation was further validated with measured 18F depth profiles in these polyurethane phantoms. The tissue boundary of water with cortical bone and lung was correctly modeled. Residual artifacts from the numerical computations were in the range of 1%. The calculated annihilation probability in voxels shows an overall difference of less than 20% compared to the Geant4 simulation. SignificanceThe proposed method significantly improves spatial resolution for non-standard isotopes by providing accurate range kernels, even in the case of significant tissue inhomogeneities.