Validation of the design of a high-sensitivity and high-resolution PET system for a preclinical PET/EPR hybrid scanner.

We report the development of a high-sensitivity and high-resolution PET subsystem for a next-generation preclinical PET/EPR hybrid scanner for investigating and improving hypoxia imaging with PET. The PET subsystem consists of 14 detector modules (DM) installed within a cylindrical supporting frame whose outer and inner diameters are 115mm and 60mm, respectively. Each DM contains eight detector units (DU) in a row and each DU is made of a 12×12 array of 1×1×10mm3 LYSO crystals (with a 1.05mm pitch) coupled to a 4×4 silicon photomultiplier (SiPM) array that has a 3.2mm pitch (Hamamatsu multi-pixel photon counter (MPPC) array 14161-3050HS-04). The PET subsystem has a 104mm axial field-of-view (AFOV) that is sufficient for full-body mouse imaging, therefore enabling temporal and spatial correlation studies of tumor hypoxia between PET and EPR. It employs 1mm-width crystals to support sub-millimeter image resolution that is desired for mouse imaging. Al-though a DM contains 1,152 LYSO crystals, by use of a newly devised signal readout method only six outputs are produced. Recently a partial prototype of this subsystem consisting of four DMs is built. In this paper, we present performance measurement results obtained for the developed DMs and initial imaging results obtained by the prototype. The developed DMs show uniformly superior performance in identifying the hit crystal and detector unit, in energy resolution, and in coincidence time resolution. The images obtained for a 22Na point source and a 18F-filled U-shaped tube source show an image resolution of about 1.1mm and 1.2mm FWHM in the transverse and axial directions respectively, and demonstrate successful imaging over the entire 104mm AFOV of the prototype. This estimated image resolution however includes the contribution by the source size.

Directional TV algorithm for image reconstruction from sparse-view projections in EPR imaging.

OBJECTIVE: Electron paramagnetic resonance (EPR) imaging is an advanced in vivo oxygen imaging modality. The main drawback of EPR imaging is the long scanning time. Sparse-view projections collection is an effective fast scanning pattern. However, the commonly-used filtered backprojection (FBP) algorithm is not competent to accurately reconstruct images from sparse-view projections because of the severe streak artifacts. The aim of this work is to develop an advanced algorithm for sparse reconstruction of 3D EPR imaging. METHODS: The optimization based algorithms including the total variation (TV) algorithm have proven to be effective in sparse reconstruction in EPR imaging. To further improve the reconstruction accuracy, we propose the directional TV (DTV) model and derive its Chambolle-Pock (CP) solving algorithm. RESULTS: After the algorithm correctness validation on simulation data, we explore the sparse reconstruction capability of the DTV algorithm via a simulated six-sphere phantom and two real bottle phantoms filled with OX063 trityl solution and scanned by an EPR imager with a magnetic field strength of 250G. CONCLUSION: Both the simulated and real data experiments show that the DTV algorithm is superior to the existing FBP and TV-type algorithms and a deep learning based method according to visual inspection and quantitative evaluations in sparse reconstruction of EPR imaging. SIGNIFICANCE: These insights gained in this work may be used in the development of fast EPR imaging workflow of practical significance.

Sensing and Imaging Molecular Oxygen in Mammals with Spin Lattice Relaxation Electron Paramagnetic Resonance.

Molecular oxygen and its thermodynamic transformation drive nearly all life processes. Quantitative measurement and imaging of oxygen in living systems is of fundamental importance for the study of life processes and their aberrations-disease- many of which are affected by hypoxia, or low levels of oxygen. Cancer is among the disease processes profoundly affected by hypoxia. Electron paramagnetic resonance has been shown to provide remarkably accurate images of normal and cancerous tissue. In this review, we emphasize the reactivity of molecular oxygen particularly highlighting the metabolic processes of living systems to store free energy in the reactants. The history of hypoxic resistance of living systems to cytotoxic therapy, particularly radiation therapy is also reviewed. The measurement and imaging of molecular oxygen with pulse spin lattice relaxation (SLR) electron paramagnetic resonance (EPR) is reviewed briefly. This emphasizes the advantages of the spin lattice relaxation based measurement paradigm to reduce the sensitivity of the measurement to the presence of the oxygen sensing probe itself. The involvement of a novel small mammal external beam radiation delivery system is described. This enables an experimental paradigm based on control by radiation of the last resistant clonogen. This is much more specific for tumor cure than growth delay assays which primarily reflects control of tumor cells most sensitive to therapy.

Accurate reconstruction of 4D spectral-spatial images from sparse-view data in continuous-wave EPRI.

In continuous-wave electron paramagnetic resonance imaging (CW EPRI), data are collected generally at densely sampled views sufficient for achieving accurate reconstruction of a four dimensional spectral-spatial (4DSS) image by use of the conventional filtered-backprojection (FBP) algorithm. It is desirable to minimize the scan time by collection of data only at sparsely sampled views, referred to as sparse-view data. Interest thus remains in investigation of algorithms for accurate reconstruction of 4DSS images from sparse-view data collected for potentially enabling fast data acquisition in CW EPRI. In this study, we investigate and demonstrate optimization-based algorithms for accurate reconstruction of 4DSS images from sparse-view data. Numerical studies using simulated and real sparse-view data acquired in CW EPRI are conducted that reveal, in terms of image visualization and physical-parameter estimation, the potential of the algorithms developed for yielding accurate 4DSS images from sparse-view data in CW EPRI. The algorithms developed may be exploited for enabling sparse-view scans with minimized scan time in CW EPRI for yielding 4DSS images of quality comparable to, or better than, that of the FBP reconstruction from data collected at densely sampled views.

Directional TV algorithm for fast EPR imaging.

Precise radiation guided by oxygen images has demonstrated superiority over the traditional radiation methods. Electron paramagnetic resonance (EPR) imaging has proven to be the most advanced oxygen imaging modality. However, the main drawback of EPR imaging is the long scan time. For each projection, we usually need to collect the projection many times and then average them to achieve high signal-to-noise ratio (SNR). One approach to fast scan is to reduce the repeating time for each projection. While the projections would be noisy and thus the traditional commonly-use filtered backprojection (FBP) algorithm would not be capable of accurately reconstructing images. Optimization-based iterative algorithms may accurately reconstruct images from noisy projections for they may incorporate prior information into optimization models. Based on the total variation (TV) algorithms for EPR imaging, in this work, we propose a directional TV (DTV) algorithm to further improve the reconstruction accuracy. We construct the DTV constrained, data divergence minimization (DTVcDM) model, derive its Chambolle-Pock (CP) solving algorithm, validate the correctness of the whole algorithm, and perform evaluations via simulated and real data. The experimental results show that the DTV algorithm outperforms the existing TV and FBP algorithms in fast EPR imaging. Compared to the standard FBP algorithm, the proposed algorithm may achieve 10 times of acceleration.

Corrigendum: Absolute oxygen-guided radiation therapy improves tumor control in three preclinical tumor models.

[This corrects the article DOI: 10.3389/fmed.2023.1269689.].

A Preclinical Positron Emission Tomography (PET) and Electron-Paramagnetic-Resonance-Imaging (EPRI) Hybrid System: PET Detector Module.

We report the design and experimental validation of a compact positron emission tomography (PET) detector module (DM) intended for building a preclinical PET and electron-paramagnetic-resonance-imaging hybrid system that supports sub-millimeter image resolution and high-sensitivity, whole-body animal imaging. The DM is eight detector units (DU) in a row. Each DU contains 12×12 lutetium-yttrium oxyorthosilicate (LYSO) crystals having a 1.05 mm pitch read by 4×4 silicon photomultipliers (SiPM) having a 3.2 mm pitch. A small-footprint, highly-multiplexing readout employing only passive electronics is devised to produce six outputs for the DM, including two outputs derived from SiPM cathodes for determining event time and active DU and four outputs derived from SiPM anodes for determining energy and active crystal. Presently, we have developed two DMs that are 1.28×10.24 cm2 in extent and approximately 1.8 cm in thickness, with their outputs sampled at 0.7 GS/s and analyzed offline. For both DMs, our results show successfully discriminated DUs and crystals. With no correction for SiPM nonlinearity, the average energy resolution for crystals in a DU ranges from 14% to 16%. While not needed for preclinical imaging, the DM may support 300-400 ps time-of-flight resolution.

Absolute oxygen-guided radiation therapy improves tumor control in three preclinical tumor models.

Background: Clinical attempts to find benefit from specifically targeting and boosting resistant hypoxic tumor subvolumes have been promising but inconclusive. While a first preclinical murine tumor type showed significant improved control with hypoxic tumor boosts, a more thorough investigation of efficacy from boosting hypoxic subvolumes defined by electron paramagnetic resonance oxygen imaging (EPROI) is necessary. The present study confirms improved hypoxic tumor control results in three different tumor types using a clonogenic assay and explores potential confounding experimental conditions. Materials and methods: Three murine tumor models were used for multi-modal imaging and radiotherapy: MCa-4 mammary adenocarcinomas, SCC7 squamous cell carcinomas, and FSa fibrosarcomas. Registered T2-weighted MRI tumor boundaries, hypoxia defined by EPROI as pO2 ≤ 10 mmHg, and X-RAD 225Cx CT boost boundaries were obtained for all animals. 13 Gy boosts were directed to hypoxic or equal-integral-volume oxygenated tumor regions and monitored for regrowth. Kaplan-Meier survival analysis was used to assess local tumor control probability (LTCP). The Cox proportional hazards model was used to assess the hazard ratio of tumor progression of Hypoxic Boost vs. Oxygenated Boost for each tumor type controlling for experimental confounding variables such as EPROI radiofrequency, tumor volume, hypoxic fraction, and delay between imaging and radiation treatment. Results: An overall significant increase in LTCP from Hypoxia Boost vs. Oxygenated Boost treatments was observed in the full group of three tumor types (p < 0.0001). The effects of tumor volume and hypoxic fraction on LTCP were dependent on tumor type. The delay between imaging and boost treatments did not have a significant effect on LTCP for all tumor types. Conclusion: This study confirms that EPROI locates resistant tumor hypoxic regions for radiation boost, increasing clonogenic LTCP, with potential enhanced therapeutic index in three tumor types. Preclinical absolute EPROI may provide correction for clinical hypoxia images using additional clinical physiologic MRI.

A Simple but Universal Fully Linearized ADMM Algorithm for Optimization Based Image Reconstruction.

Background and Objective: Optimization based image reconstruction algorithm is an advanced algorithm in medical imaging. However, the corresponding solving algorithm is challenging because the optimization model is usually large-scale and non-smooth. This work aims to devise a simple but universal solver for optimization models. Methods: The alternating direction method of multipliers (ADMM) algorithm is a simple and effective solver of the optimization models. However, there always exists a sub-problem that has not closed-form solution. One may use gradient descent algorithm to solve this sub-problem, but the step-size selection via line search is time-consuming. Or, one may use fast Fourier transform (FFT) to get a closed-form solution if the system matrix and the sparse transform matrix are both of special structure. In this work, we propose a simple but universal fully linearized ADMM (FL-ADMM) algorithm that avoids line search to determine step-size and applies to system matrix and sparse transform of any structures. Results: We derive the FL-ADMM algorithm instances for three total variation (TV) models in 2D computed tomography (CT). Further, we validate and evaluate one FL-ADMM algorithm and explore how the two important factors impact convergence rate. Also, we compare this algorithm with the Chambolle-Pock algorithm via real CT phantom reconstructions. These studies show that the FL-ADMM algorithm may accurately solve optimization models in image reconstruction. Conclusion: The FL-ADMM algorithm is a simple, effective, convergent and universal solver of optimization models in image reconstruction. Compared to the existing ADMM algorithms, the new algorithm does not need time-consuming step-size line-search or special demand to system matrix and sparse transform. It is a rapid prototyping tool for optimization based image reconstruction.

4D-image reconstruction directly from limited-angular-range data in continuous-wave electron paramagnetic resonance imaging.

OBJECTIVE: We investigate and develop optimization-based algorithms for accurate reconstruction of four-dimensional (4D)-spectral-spatial (SS) images directly from data collected over limited angular ranges (LARs) in continuous-wave (CW) electron paramagnetic resonance imaging (EPRI). METHODS: Basing on a discrete-to-discrete data model devised in CW EPRI employing the Zeeman-modulation (ZM) scheme for data acquisition, we first formulate the image reconstruction problem as a convex, constrained optimization program that includes a data fidelity term and also constraints on the individual directional total variations (DTVs) of the 4D-SS image. Subsequently, we develop a primal-dual-based DTV algorithm, simply referred to as the DTV algorithm, to solve the constrained optimization program for achieving image reconstruction from data collected in LAR scans in CW-ZM EPRI. RESULTS: We evaluate the DTV algorithm in simulated- and real-data studies for a variety of LAR scans of interest in CW-ZM EPRI, and visual and quantitative results of the studies reveal that 4D-SS images can be reconstructed directly from LAR data, which are visually and quantitatively comparable to those obtained from data acquired in the standard, full-angular-range (FAR) scan in CW-ZM EPRI. CONCLUSION: An optimization-based DTV algorithm is developed for accurately reconstructing 4D-SS images directly from LAR data in CW-ZM EPRI. Future work includes the development and application of the optimization-based DTV algorithm for reconstructions of 4D-SS images from FAR and LAR data acquired in CW EPRI employing schemes other than the ZM scheme. SIGNIFICANCE: The DTV algorithm developed may be exploited potentially for enabling and optimizing CW EPRI with minimized imaging time and artifacts by acquiring data in LAR scans.

Direct Measurement and Imaging of Redox Status with Electron Paramagnetic Resonance.

Significance: Fundamental to the application of tissue redox status to human health is the quantification and localization of tissue redox abnormalities and oxidative stress and their correlation with the severity and local extent of disease to inform therapy. The centrality of the low-molecular-weight thiol, glutathione, in physiological redox balance has long been appreciated, but direct measurement of tissue thiol status in vivo has not been possible hitherto. Recent advances in instrumentation and molecular probes suggest the feasibility of real-time redox assessment in humans. Recent Advances: Recent studies have demonstrated the feasibility of using low-frequency electron paramagnetic resonance (EPR) techniques for quantitative imaging of redox status in mammalian tissues in vivo. Rapid-scan (RS) EPR spectroscopy and imaging, new disulfide-dinitroxide spin probes, and novel analytic techniques have led to significant advances in direct, quantitative imaging of thiol redox status. Critical Issues: While novel RS EPR imaging coupled with first-generation molecular probes has demonstrated the feasibility of imaging thiol redox status in vivo, further technical advancements are desirable and ongoing. These include developing spin probes that are tailored for specific tissues with response kinetics tuned to the physiological environment. Equally critical are RS instrumentation with higher signal-to-noise ratio and minimal signal distortion, as well as optimized imaging protocols for image acquisition with sparsity adapted to image information content. Future Directions: Quantitative images of tissue glutathione promise to enable acquisition of a general image of mammalian and potentially human tissue health.

An iterative reconstruction algorithm without system matrix for EPR imaging.

Electron paramagnetic resonance (EPR) imaging is an advanced oxygen imaging modality for oxygen-image guided radiation. The iterative reconstruction algorithm is the research hot-point in image reconstruction for EPR imaging (EPRI) for this type of algorithm may incorporate image-prior information to construct advanced optimization model to achieve accurate reconstruction from sparse-view projections and/or noisy projections. However, the system matrix in the iterative algorithm needs complicated calculation and needs huge memory-space if it is stored in memory. In this work, we propose an iterative reconstruction algorithm without system matrix for EPRI to simplify the whole iterative reconstruction process. The function of the system matrix is to calculate the projections, whereas the function of the transpose of the system matrix is to perform backprojection. The existing projection and backprojection methods are all based on the configuration that the imaged-object remains stationary and the scanning device rotates. Here, we implement the projection and backprojection operations by fixing the scanning device and rotating the object. Thus, the core algorithm is only the commonly-used image-rotation algorithm, while the calculation and store of the system matrix are avoided. Based on the idea of image rotation, we design a specific iterative reconstruction algorithm for EPRI, total variation constrained data divergence minimization (TVcDM) algorithm without system matrix, and named it as image-rotation based TVcDM (R-TVcDM). Through a series of comparisons with the original TVcDM via real projection data, we find that the proposed algorithm may achieve similar reconstruction accuracy with the original one. But it avoids the complicated calculation and store of the system matrix. The insights gained in this work may be also applied to other imaging modalities, for example computed tomography and positron emission tomography.

The optimal 18F-fluoromisonidazole PET threshold to define tumor hypoxia in preclinical squamous cell carcinomas using pO2 electron paramagnetic resonance imaging as reference truth.

PURPOSE: To identify the optimal threshold in 18F-fluoromisonidazole (FMISO) PET images to accurately locate tumor hypoxia by using electron paramagnetic resonance imaging (pO2 EPRI) as ground truth for hypoxia, defined by pO2 [Formula: see text] 10 mmHg. METHODS: Tumor hypoxia images in mouse models of SCCVII squamous cell carcinoma (n = 16) were acquired in a hybrid PET/EPRI imaging system 2 h post-injection of FMISO. T2-weighted MRI was used to delineate tumor and muscle tissue. Dynamic contrast enhanced (DCE) MRI parametric images of Ktrans and ve were generated to model tumor vascular properties. Images from PET/EPR/MRI were co-registered and resampled to isotropic 0.5 mm voxel resolution for analysis. PET images were converted to standardized uptake value (SUV) and tumor-to-muscle ratio (TMR) units. FMISO uptake thresholds were evaluated using receiver operating characteristic (ROC) curve analysis to find the optimal FMISO threshold and unit with maximum overall hypoxia similarity (OHS) with pO2 EPRI, where OHS = 1 shows perfect overlap and OHS = 0 shows no overlap. The means of dice similarity coefficient, normalized Hausdorff distance, and accuracy were used to define the OHS. Monotonic relationships between EPRI/PET/DCE-MRI were evaluated with the Spearman correlation coefficient ([Formula: see text]) to quantify association of vasculature on hypoxia imaged with both FMISO PET and pO2 EPRI. RESULTS: FMISO PET thresholds to define hypoxia with maximum OHS (both OHS = 0.728 [Formula: see text] 0.2) were SUV [Formula: see text] 1.4 [Formula: see text] SUVmean and SUV [Formula: see text] 0.6 [Formula: see text] SUVmax. Weak-to-moderate correlations (|[Formula: see text]|< 0.70) were observed between PET/EPRI hypoxia images with vascular permeability (Ktrans) or fractional extracellular-extravascular space (ve) from DCE-MRI. CONCLUSION: This is the first in vivo comparison of FMISO uptake with pO2 EPRI to identify the optimal FMISO threshold to define tumor hypoxia, which may successfully direct hypoxic tumor boosts in patients, thereby enhancing tumor control.

Estimating the mechanical energy of histotripsy bubble clouds with high frame rate imaging.

Mechanical ablation with the focused ultrasound therapy histotripsy relies on the generation and action of bubble clouds. Despite its critical role for ablation, quantitative metrics of bubble activity to gauge treatment outcomes are still lacking. Here, plane wave imaging was used to track the dissolution of bubble clouds following initiation with the histotripsy pulse. Information about the rate of change in pixel intensity was coupled with an analytic diffusion model to estimate bubble size. Accuracy of the hybrid measurement/model was assessed by comparing the predicted and measured dissolution time of the bubble cloud. Good agreement was found between predictions and measurements of bubble cloud dissolution times in agarose phantoms and murine subcutaneous SCC VII tumors. The analytic diffusion model was extended to compute the maximum bubble size as well as energy imparted to the tissue due to bubble expansion. Regions within tumors predicted to have undergone strong bubble expansion were collocated with ablation. Further, the dissolution time was found to correlate with acoustic emissions generated by the bubble cloud during histotripsy insonation. Overall, these results indicate a combination of modeling and high frame rate imaging may provide means to quantify mechanical energy imparted to the tissue due to bubble expansion for histotripsy.

A balanced total-variation-Chambolle-Pock algorithm for EPR imaging.

Total variation (TV) minimization algorithm is an effective algorithm capable of accurately reconstructing images from sparse projection data in a variety of imaging modalities including computed tomography (CT) and electron paramagnetic resonance imaging (EPRI). The data divergence constrained, TV minimization (DDcTV) model and its Chambolle-Pock (CP) solving algorithm have been proposed for CT. However, when the DDcTV-CP algorithm is applied to 3D EPRI, it suffers from slow convergence rate or divergence. We hypothesize that this is due to the magnitude imbalance between the data fidelity term and the TV regularization term. In this work, we propose a balanced TV (bTV) model incorporating a balance parameter and demonstrate its capability to avoid convergence issues for the 3D EPRI application. Simulation and real experiments show that the DDcTV-CP algorithm cannot guarantee convergence but the bTV-CP algorithm may guarantee convergence and achieve fast convergence by use of an appropriate balance parameter. Experiments also show that underweighting the balance parameter leads to slow convergence, whereas overweighting the balance parameter leads to divergence. The iteration-behavior change-law with the variation of the balance parameter is explained by use of the data tolerance ellipse and gradient descent principle. The findings and insights gained in this work may be applied to other imaging modalities and other constrained optimization problems.

Improving Tumor Hypoxia Location in 18F-Misonidazole PET with Dynamic Contrast-enhanced MRI Using Quantitative Electron Paramagnetic Resonance Partial Oxygen Pressure Images.

Purpose: To enhance the spatial accuracy of fluorine 18 (18F) misonidazole (MISO) PET imaging of hypoxia by using dynamic contrast-enhanced (DCE) MR images as a basis for modifying PET images and by using electron paramagnetic resonance (EPR) partial oxygen pressure (pO2) as the reference standard. Materials and Methods: Mice (n = 10) with leg-borne MCa4 mammary carcinomas underwent EPR imaging, T2-weighted and DCE MRI, and 18F-MISO PET/CT. Images were registered to the same space for analysis. The thresholds of hypoxia for PET and EPR images were tumor-to-muscle ratios greater than or equal to 2.2 mm Hg and less than or equal to 14 mm Hg, respectively. The Dice similarity coefficient (DSC) and Hausdorff distance (d H ) were used to quantify the three-dimensional overlap of hypoxia between pO2 EPR and 18F-MISO PET images. A training subset (n = 6) was used to calculate optimal DCE MRI weighting coefficients to relate EPR to the PET signal; the group average weights were then applied to all tumors (from six training mice and four test mice). The DSC and d H were calculated before and after DCE MRI-corrected PET images were obtained to quantify the improvement in overlap with EPR pO2 images for measuring tumor hypoxia. Results: The means and standard deviations of the DSC and d H between hypoxic regions in original PET and EPR images were 0.35 mm ± 0.23 and 5.70 mm ± 1.7, respectively, for images of all 10 mice. After implementing a preliminary DCE MRI correction to PET data, the DSC increased to 0.86 mm ± 0.18 and the d H decreased to 2.29 mm ± 0.70, showing significant improvement (P < .001) for images of all 10 mice. Specifically, for images of the four independent test mice, the DSC improved with correction from 0.19 ± 0.28 to 0.80 ± 0.29 (P = .02), and the d H improved from 6.40 mm ± 2.5 to 1.95 mm ± 0.63 (P = .01). Conclusion: Using EPR information as a reference standard, DCE MRI information can be used to correct 18F-MISO PET information to more accurately reflect areas of hypoxia.Keywords: Animal Studies, Molecular Imaging, Molecular Imaging-Cancer, PET/CT, MR-Dynamic Contrast Enhanced, MR-Imaging, PET/MR, Breast, Oncology, Tumor Mircoenvironment, Electron Paramagnetic ResonanceSupplemental material is available for this article.© RSNA, 2021.

Biological validation of electron paramagnetic resonance (EPR) image oxygen thresholds in tissue.

Measuring molecular oxygen levels in vivo has been the cornerstone of understanding the effects of hypoxia in normal tissues and malignant tumors. Here we discuss the advances in a variety of partial pressure of oxygen ( PO2 ) measurements and imaging techniques and relevant oxygen thresholds. A focus on electron paramagnetic resonance (EPR) imaging shows the validation of treating hypoxic tumours with a threshold of PO2  ≤ 10 Torr, and demonstrates utility for in vivo oxygen imaging, as well as its current and future role in cancer studies.

Small Animal IMRT Using 3D-Printed Compensators.

PURPOSE: Preclinical radiation replicating clinical intensity modulated radiation therapy (IMRT) techniques can provide data translatable to clinical practice. For this work, treatment plans were created for oxygen-guided dose-painting in small animals using inverse-planned IMRT. Spatially varying beam intensities were achieved using 3-dimensional (3D)-printed compensators. METHODS AND MATERIALS: Optimized beam fluence from arbitrary gantry angles was determined using a verified model of the XRAD225Cx treatment beam. Compensators were 3D-printed with varied thickness to provide desired attenuation using copper/polylactic-acid. Spatial resolution capabilities were investigated using printed test-patterns. Following American Association of Physicists in Medicine TG119, a 5-beam IMRT plan was created for a miniaturized (∼1/8th scale) C-shape target. Electron paramagnetic resonance imaging of murine tumor oxygenation guided simultaneous integrated boost (SIB) plans conformally treating tumor to a base dose (Rx1) with boost (Rx2) based on tumor oxygenation. The 3D-printed compensator intensity modulation accuracy and precision was evaluated by individually delivering each field to a phantom containing radiochromic film and subsequent per-field gamma analysis. The methodology was validated end-to-end with composite delivery (incorporating 3D-printed tungsten/polylactic-acid beam trimmers to reduce out-of-field leakage) of the oxygen-guided SIB plan to a phantom containing film and subsequent gamma analysis. RESULTS: Resolution test-patterns demonstrate practical printer resolution of ∼0.7 mm, corresponding to 1.0 mm bixels at the isocenter. The miniaturized C-shape plan provides planning target volume coverage (V95% = 95%) with organ sparing (organs at risk Dmax < 50%). The SIB plan to hypoxic tumor demonstrates the utility of this approach (hypoxic tumor V95%,Rx2 = 91.6%, normoxic tumor V95%,Rx1 = 95.7%, normal tissue V100%,Rx1 = 7.1%). The more challenging SIB plan to boost the normoxic tumor rim achieved normoxic tumor V95%,Rx2 = 90.9%, hypoxic tumor V95%,Rx1 = 62.7%, and normal tissue V100%,Rx2 = 5.3%. Average per-field gamma passing rates using 3%/1.0 mm, 3%/0.7 mm, and 3%/0.5 mm criteria were 98.8% ± 2.8%, 96.6% ± 4.1%, and 90.6% ± 5.9%, respectively. Composite delivery of the hypoxia boost plan and gamma analysis (3%/1 mm) gave passing results of 95.3% and 98.1% for the 2 measured orthogonal dose planes. CONCLUSIONS: This simple and cost-effective approach using 3D-printed compensators for small-animal IMRT provides a methodology enabling preclinical studies that can be readily translated into the clinic. The presented oxygen-guided dose-painting demonstrates that this methodology will facilitate studies driving much needed biologic personalization of radiation therapy for improvements in patient outcomes.

How best to interpret measures of levels of oxygen in tissues to make them effective clinical tools for care of patients with cancer and other oxygen-dependent pathologies.

It is well understood that the level of molecular oxygen (O2 ) in tissue is a very important factor impacting both physiology and pathological processes as well as responsiveness to some treatments. Data on O2 in tissue could be effectively utilized to enhance precision medicine. However, the nature of the data that can be obtained using existing clinically applicable techniques is often misunderstood, and this can confound the effective use of the information. Attempts to make clinical measurements of O2 in tissues will inevitably provide data that are aggregated over time and space and therefore will not fully represent the inherent heterogeneity of O2 in tissues. Additionally, the nature of existing techniques to measure O2 may result in uneven sampling of the volume of interest and therefore may not provide accurate information on the "average" O2 in the measured volume. By recognizing the potential limitations of the O2 measurements, one can focus on the important and useful information that can be obtained from these techniques. The most valuable clinical characterizations of oxygen are likely to be derived from a series of measurements that provide data about factors that can change levels of O2 , which then can be exploited both diagnostically and therapeutically. The clinical utility of such data ultimately needs to be verified by careful studies of outcomes related to the measured changes in levels of O2 .

The clinical utility of imaging methods used to measure hypoxia in cervical cancer.

While it is well-established that hypoxia is a major factor that affects clinical outcomes in cervical cancer, widespread usage of clinically available methods to detect and evaluate hypoxia during the course of treatment have not been established. This review compares these methods, summarizes their strengths and weaknesses, and assesses the pathways for their useful employment to alter clinical practice. We conducted a search on PubMed for literature pertaining to imaging hypoxic cervical cancer, and implemented keywords related to oxygen measurement tools to improve the relevance of the search results.Oxygenation level-dependent applications of MRI have demonstrated hypoxia-induced radioresistance, and changes in cervix tumor oxygenation from hyperoxic therapy.The hypoxic areas within tumors can be indirectly identified in dynamic contrast-enhanced images, where they generally display low signal enhancement, and diffusion-weighted images, which demonstrates areas of restricted diffusion (which correlates with hypoxia). Positron emmision tomography, used independently and with other imaging modalities, has demonstrated utility in imaging hypoxia through tracers specific for low oxygen levels, like Cu-ATSM tracers and nitroimidazoles. Detecting hypoxia in the tumors of patients diagnosed with cervical cancer via medical imaging and non-imaging tools like electron paramagnetic resonance oximetry can be utilized clinically, such as for guiding radiation and post-treatment surveillance, for a more personalized approach to treatment. The merits of these methods warrant further investigation via comparative effectiveness research and large clinical trials into their clinical applications.