Basis Vector Model Method for Proton Stopping Power Estimation using Dual-Energy Computed Tomography

Accurate estimation of the proton stopping power ratio (SPR) is important for treatment planning and dose prediction for proton beam therapy. The state-of-the-art clinical practice for estimating patient-specific SPR distributions is the stoichiometric calibration method using single-energy computed tomography (SECT) images, which in principle may introduce large intrinsic uncertainties into estimation results. One major factor that limits the performance of SECT-based methods is the Hounsfield unit (HU) degeneracy in the presence of tissue composition variations. Dual-energy computed tomography (DECT) has shown the potential of reducing uncertainties in proton SPR prediction via scanning the patient with two different source energy spectra. Numerous methods have been studied to estimate the SPR by dual-energy CT DECT techniques using either image-domain or sinogram-domain decomposition approaches. In this work, we implement and evaluate a novel DECT approach for proton SPR mapping, which integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). This method reconstructs two images of material parameters simultaneously from the DECT measurement data and then uses them to predict the electron densities and the mean excitation energies, which are required by the Bethe equation for computing proton SPR. The proposed JSIR-BVM method is first compared with image-domain and sinogram-domain decomposition approaches based on three available SPR models including the BVM in a well controlled simulation framework that is representative of major uncertainty sources existing in practice. The intrinsic SPR modeling accuracy of the three DECT-SPR models is validated via theoretical computed radiological quantities for various reference human tissues. The achievable performances of the investigated methods in the presence of image formation uncertainties are evaluated using synthetic DECT transmission sinograms of virtual cylindrical phantoms and virtual patients, which consist of reference human tissues with known densities and compositions. The JSIR-BVM method is then experimentally commissioned using the DECT measurement data acquired on a Philips Brilliance Big Bore CT scanner at 90 kVp and 140 kVp for two phantoms of different sizes, each of which contains 12 different soft and bony tissue surrogates. An image-domain decomposition method that utilizes the two HU images reconstructed via the scanner\u27s software is implemented for comparison The JSIR-BVM method outperforms the other investigated methods in both the simulation and experimental settings. Although all investigated DECT-SPR models support low intrinsic modeling errors (i.e., less than 0.2% RMS errors for reference human tissues), the achievable accuracy of the image- and sinogram-domain methods is limited by the image formation uncertainties introduced by the reconstruction and decomposition processes. In contrast, by taking advantage of an accurate polychromatic CT data model and a joint DECT statistical reconstruction algorithm, the JSIR-BVM method accounts for both systematic bias and random noise in the acquired DECT measurement data. Therefore, the JSIR-BVM method achieves much better accuracy and precision on proton SPR estimation compared to the image- and sinogram-domain methods for various materials and object sizes, with an overall RMS-of-mean error of 0.4% and a maximum absolute-mean error of 0.7% for test samples in the experimental setting. The JSIR-BVM method also reduces the pixel-wise random variation by 4-fold to 6-fold within homogeneous regions compared to the image- and sinogram-domain methods while exhibiting relatively higher spatial resolution. The results suggest that the JSIR-BVM method has the potential for better SPR prediction in clinical settings

Tumor vasculature and microenvironment during progression and treatment : insights from optical microscopy

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2010.Vita. Cataloged from PDF version of thesis.Includes bibliographical references.In addition to cancer cells, solid tumors consist of a variety of cell types and tissues defining a complex microenvironment that influences disease progression and response to therapy. To fully characterize and probe the tumor microenvironment, new tools are needed to quantitatively assess microanatomical and physiological changes during tumor growth and treatment. Particularly important, is the metabolic microenvironment defined in tumors by hypoxia (low p02) and acidity (low pH). These parameters have been shown to influence response to radiation therapy and chemotherapy. However, very little is known about spatio-temporal changes in p02 and pH during tumor progression and therapy. By modifying the technique of intravital multiphoton microscopy (MPM) to perform phosphorescence quenching microscopy, I developed a non-invasive method to quantify oxygen tension (p02) in living tissue at high three-dimensional resolution. To probe functional changes in the metabolic microenvironment, I measured in vivo P02 during tumor growth and antiangiogenic (vascular targeted) treatment in preclinical tumor models. Nanotechnology is rapidly emerging as an important source of biocompatible tools that may shape the future of medical practice. Fluorescent semiconductor nanocrystals (NCs), also known as quantum dots, are a powerful tool for biological imaging, cellular targeting and molecular sensing.(cont.) I adapted novel fluorescence resonance energy transfer (FRET) -based nanocrystal (NC) biosensors for use with MPM to qualitatively measure in vivo extracellular pH in tumors at high-resolution. While intravital multiphoton microscopy demonstrates utility and adaptability in the study of cancer and response to therapy, the requisite high numerical aperture and exogenous contrast agents result in a limited capacity to investigate substantial tissue volumes or probe dynamic changes repeatedly over prolonged periods. By applying optical frequency domain imaging (OFDI) as an intravital microscopic tool, the technical limitations of multiphoton microscopy can be circumvented providing unprecedented access to previously unexplored, critically important aspects of tumor biology. Using entirely intrinsic mechanisms of contrast within murine tumor models, OFDI is able to simultaneously, rapidly, and repeatedly probe the microvasculature, lymphatic vessels, and tissue microstructure and composition over large volumes. Using OFDI-based techniques, measurements of tumor angiogenesis, lymphangiogenesis, tissue viability and both vascular and cellular responses to therapy were demonstrated, thereby highlighting the potential of OFDI to facilitate the exploration of pathophysiological processes and the evaluation of treatment strategies.by Ryan M. Lanning.Ph.D

[¹⁸F]fluorination of biorelevant arylboronic acid pinacol ester scaffolds synthesized by convergence techniques

Aim: The development of small molecules through convergent multicomponent reactions (MCR) has been boosted during the last decade due to the ability to synthesize, virtually without any side-products, numerous small drug-like molecules with several degrees of structural diversity.(1) The association of positron emission tomography (PET) labeling techniques in line with the “one-pot” development of biologically active compounds has the potential to become relevant not only for the evaluation and characterization of those MCR products through molecular imaging, but also to increase the library of radiotracers available. Therefore, since the [18F]fluorination of arylboronic acid pinacol ester derivatives tolerates electron-poor and electro-rich arenes and various functional groups,(2) the main goal of this research work was to achieve the 18F-radiolabeling of several different molecules synthesized through MCR. Materials and Methods: [18F]Fluorination of boronic acid pinacol esters was first extensively optimized using a benzaldehyde derivative in relation to the ideal amount of Cu(II) catalyst and precursor to be used, as well as the reaction solvent. Radiochemical conversion (RCC) yields were assessed by TLC-SG. The optimized radiolabeling conditions were subsequently applied to several structurally different MCR scaffolds comprising biologically relevant pharmacophores (e.g. β-lactam, morpholine, tetrazole, oxazole) that were synthesized to specifically contain a boronic acid pinacol ester group. Results: Radiolabeling with fluorine-18 was achieved with volumes (800 μl) and activities (≤ 2 GBq) compatible with most radiochemistry techniques and modules. In summary, an increase in the quantities of precursor or Cu(II) catalyst lead to higher conversion yields. An optimal amount of precursor (0.06 mmol) and Cu(OTf)2(py)4 (0.04 mmol) was defined for further reactions, with DMA being a preferential solvent over DMF. RCC yields from 15% to 76%, depending on the scaffold, were reproducibly achieved. Interestingly, it was noticed that the structure of the scaffolds, beyond the arylboronic acid, exerts some influence in the final RCC, with electron-withdrawing groups in the para position apparently enhancing the radiolabeling yield. Conclusion: The developed method with high RCC and reproducibility has the potential to be applied in line with MCR and also has a possibility to be incorporated in a later stage of this convergent “one-pot” synthesis strategy. Further studies are currently ongoing to apply this radiolabeling concept to fluorine-containing approved drugs whose boronic acid pinacol ester precursors can be synthesized through MCR (e.g. atorvastatin)