Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning

Radiation therapy is powered by modern techniques in precise planning and executionof radiation delivery, which are being rapidly improved to maximize its benefit to cancerpatients. In the last decade, radiotherapy experienced the introduction of advanced methodsfor automatic beam orientation optimization, real-time tumor tracking, daily planadaptation, and many others, which improve the radiation delivery precision, planning easeand reproducibility, and treatment efficacy. However, such advanced paradigms necessitatethe calculation of orders of magnitude more causal dose deposition data, increasing the timerequirement of all pre-planning dose calculation. Principles of high-performance computingand machine learning were applied to address the insufficient speeds of widely-used dosecalculation algorithms to facilitate translation of these advanced treatment paradigms intoclinical practice.To accelerate CT-guided X-ray therapies, Collapsed-Cone Convolution-Superposition(CCCS), a state-of-the-art analytical dose calculation algorithm, was accelerated through itsnovel implementation on highly parallelized GPUs. This context-based GPU-CCCS approachtakes advantage of X-ray dose deposition compactness to parallelize calculation acrosshundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration bytwo to three orders of magnitude compared to existing GPU-based beamlet-by-beamletapproaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPUimplementation of context-based GPU-CCCS.Dose calculation for MR-guided treatment is complicated by electron return effects(EREs), exhibited by ionizing electrons in the strong magnetic field of the MRI scanner. EREsnecessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinicalapplication of advanced treatment paradigms due to time restrictions. An automaticallydistributed framework for very-large-scale MC dose calculation was developed, grantinglinear scaling of dose calculation speed with the number of utilized computational cores. Itwas then harnessed to efficiently generate a large dataset of paired high- and low-noise MCdoses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutionalneural network (CNN), DeepMC, to predict low-noise dose from faster high-noise MC-simulation. DeepMC enables 38-fold acceleration of MR-guided X-ray beamlet dosecalculation, while remaining synergistic with existing MC acceleration techniques to achievemultiplicative speed improvements.This work redefines the expectation of X-ray dose calculation speed, making it possibleto apply new highly-beneficial treatment paradigms to standard clinical practice for the firsttime

Optical imaging and spectroscopy for the study of the human brain: status report.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions. Keywords: DCS; NIRS; diffuse optics; functional neuroscience; optical imaging; optical spectroscop

Deep Tissue Light Delivery and Fluorescence Tomography with Applications in Optogenetic Neurostimulation

Study of the brain microcircuits using optogenetics is an active area of research. This method has a few advantages over the conventional electrical stimulation including the bi-directional control of neural activity, and more importantly, specificity in neuromodulation. The first step in all optogenetic experiments is to express certain light sensitive ion channels/pumps in the target cell population and then confirm the proper expression of these proteins before running any experiment. Fluorescent bio-markers, such as green fluorescent protein (GFP), have been used for this purpose and co-expressed in the same cell population. The fluorescent signal from such proteins provides a monitory mechanism to evaluate the expression of optogenetic opsins over time. The conventional method to confirm the success in gene delivery is to sacrifice the animal, retract and slice the brain tissue, and image the corresponding slices using a fluorescent microscope. Obviously, determining the level of expression over time without sacrificing the animal is highly desirable. Also, optogenetics can be combined with cell-type specific optical recording of neural activity for example by imaging the fluorescent signal of genetically encoded calcium indicators. One challenging step in any optogenetic experiment is delivering adequate amount of light to target areas for proper stimulation of light sensitive proteins. Delivering sufficient light density to a target area while minimizing the off-target stimulation requires a precise estimation of the light distribution in the tissue. Having a good estimation of the tissue optical properties is necessary for predicting the distribution of light in any turbid medium. The first objective of this project was the design and development of a high resolution optoelectronic device to extract optical properties of rats\u27 brain tissue (including the absorption coefficient, scattering coefficient, and anisotropy factor) for three different wavelengths: 405nm, 532nm and 635nm and three different cuts: transverse, sagittal, and coronal. The database of the extracted optical properties was linked to a 3D Monte Carlo simulation software to predict the light distribution for different light source configurations. This database was then used in the next phase of the project and in the development of a fluorescent tomography scanner. Based on the importance of the fluorescent imaging in optogenetics, another objective of this project was to design a fluorescence tomography system to confirm the expression of the light sensitive proteins and optically recording neural activity using calcium indicators none or minimally invasively. The method of fluorescence laminar optical tomography (FLOT) has been used successfully in imaging superficial areas up to 2mm deep inside a scattering medium with the spatial resolution of ~200µm. In this project, we developed a FLOT system which was specifically customized for in-vivo brain imaging experiments. While FLOT offers a relatively simple and non-expensive design for imaging superficial areas in the brain, still it has imaging depth limited to 2mm and its resolution drops as the imaging depth increases. To address this shortcoming, we worked on a complementary system based on the digital optical phase conjugation (DOPC) method which was shown previously that is capable of performing fluorescent tomography up to 4mm deep inside a biological tissue with lateral resolution of ~50 µm. This system also provides a non-invasive method to deliver light deep inside the brain tissue for neurostimulation applications which are not feasible using conventional techniques because of the high level of scattering in most tissue samples. In the developed DOPC system, the performance of the system in focusing light through and inside scattering mediums was quantified. We also showed how misalignments and imperfections of the optical components can immensely reduce the capability of a DOPC setup. Then, a systematic calibration algorithm was proposed and experimentally applied to our DOPC system to compensate main aberrations such as reference beam aberrations and also the backplane curvature of the spatial light modulator. In a highly scattering sample, the calibration algorithm achieved up to 8 fold increase in the PBR

De animais a máquinas : humanos tecnicamente melhores nos imaginários de futuro da convergência tecnológica

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Sociais, Departamento de Sociologia, 2020.O tema desta investigação é discutir os imaginários sociais de ciência e tecnologia que emergem a partir da área da neuroengenharia, em sua relação com a Convergência Tecnológica de quatro disciplinas: Nanotecnologia, Biotecnologia, tecnologias da Informação e tecnologias Cognitivas - neurociências- (CT-NBIC). Estas áreas desenvolvem-se e são articuladas por meio de discursos que ressaltam o aprimoramento das capacidades físicas e cognitivas dos seres humanos, com o intuito de construir uma sociedade melhor por meio do progresso científico e tecnológico, nos limites das agendas de pesquisa e desenvolvimento (P&D). Objetivos: Os objetivos nesse cenário, são discutir as implicações éticas, econômicas, políticas e sociais deste modelo de sistema sociotécnico. Nos referimos, tanto as aplicações tecnológicas, quanto as consequências das mesmas na formação dos imaginários sociais, que tipo de relações se estabelecem e como são criadas dentro desse contexto. Conclusão: Concluímos na busca por refletir criticamente sobre as propostas de aprimoramento humano mediado pela tecnologia, que surgem enquanto parte da agenda da Convergência Tecnológica NBIC. No entanto, as propostas de melhoramento humano vão muito além de uma agenda de investigação. Há todo um quadro de referências filosóficas e políticas que defendem o aprimoramento da espécie, vertentes estas que se aliam a movimentos trans-humanistas e pós- humanistas, posições que são ao mesmo tempo éticas, políticas e econômicas. A partir de nossa análise, entendemos que ciência, tecnologia e política estão articuladas, em coprodução, em relação às expectativas de futuros que são esperados ou desejados. Ainda assim, acreditamos que há um espaço de diálogo possível, a partir do qual buscamos abrir propostas para o debate público sobre questões de ciência e tecnologia relacionadas ao aprimoramento da espécie humana.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The subject of this research is to discuss the social imaginaries of science and technology that emerge from the area of neuroengineering in relation with the Technological Convergence of four disciplines: Nanotechnology, Biotechnology, Information technologies and Cognitive technologies -neurosciences- (CT-NBIC). These areas are developed and articulated through discourses that emphasize the enhancement of human physical and cognitive capacities, the intuition it is to build a better society, through the scientific and technological progress, at the limits of the research and development (R&D) agendas. Objectives: The objective in this scenery, is to discuss the ethic, economic, politic and social implications of this model of sociotechnical system. We refer about the technological applications and the consequences of them in the formation of social imaginaries as well as the kind of social relations that are created and established in this context. Conclusion: We conclude looking for critical reflections about the proposals of human enhancement mediated by the technology. That appear as a part of the NBIC technologies agenda. Even so, the proposals of human enhancement go beyond boundaries that an investigation agenda. There is a frame of philosophical and political references that defend the enhancement of the human beings. These currents that ally to the transhumanism and posthumanism movements, positions that are ethic, politic and economic at the same time. From our analysis, we understand that science, technology and politics are articulated, are in co-production, regarding the expected and desired futures. Even so, we believe that there is a space of possible dialog, from which we look to open proposals for the public discussion on questions of science and technology related to enhancement of human beings

Signal Processing and Machine Learning Techniques Towards Various Real-World Applications

abstract: Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Imaging Techniques for Proton Range Determination in Proton Therapy

Proton therapy can achieve better sparing of normal tissues than the conventional photon radiation therapy due to proton’s Bragg Peak property. However, to unlock the full potential of protons, accurate prediction of in vivo proton stopping power ratio (SPR) is required for proton therapy treatment planning. The current standard practice is to map SPR from Hounsfield Unit (HU) values of a single-energy computed tomography (SECT) scan through a stoichiometric calibration technique. This technique is subjected to a variety of factors that congregate on the uncertainties in SPR estimation, including the calibration uncertainty (up to 0.5% to 1.8% of the total proton beam range), SECT uncertainty (beam hardening, reconstruction artifacts, etc.), and patient positioning uncertainty (misalignment, motions, and anatomical changes). Two emerging techniques have been proposed to improve proton SRP estimation accuracy in proton therapy: dual-energy computed tomography (DECT) and proton computed tomography (pCT). The former attempts to achieve better material differentiation than SECT by scanning the patient at two different photon energies. The latter aims to avoid sources of uncertainties in HU-to-SPR conversion by using protons directly as the imaging particle. A previously proved highly accurate DECT-based SPR mapping technique using a joint statistical image reconstruction method with a linear basis vector model (JSIR-BVM) was integrated with a clinical Monte Carlo-based treatment planning system (TPS) for dose prediction comparison with the standard stoichiometric SECT method. Percentage deviation from the ground-truth volume receiving 80% of the prescription dose within a 5 mm distal-ring region around the planning target volume was 2.6% for JSIR-BVM and 6.8% for SECT in the simulated case, showing a nontrivial risk of underdosing to the tumor region if planned with SECT. For the clinical head-and-neck cancer patient case, the percentage difference between JSIR-BVM and SECT in the mean dose and the volume receiving 80% of the prescription dose in a similarly defined ROI was 2.35% and 13.86%, respectively. The results demonstrate that our JSIR-BVM method provides more accurate and less variable mass-density maps than SECT for a simulation case with known ground truth, resulting in noticeable improvements in dose-calculation accuracy. Hence, this work constitutes an important transitional step towards realizing the clinical benefits of more accurate imaging of radiological quantities by JSIR-BVM. The clinical impact of the DECT-based JSIR-BVM SPR mapping technique was evaluated based on dose-volume histograms (DVHs), the mean dose in clinical target volume (CTV), and maximum dose within serial organs at risk (OARs). No recalculated DVH metric differed by more than 0.37% in 2 of the 3 cases. However, in the third case with the brainstem overlapping the CTV, when recalculated on the DECT SPR map, the mean dose to the CTV and the maximum dose in the brainstem increased from 54 Gy to 56 Gy and 55.1 Gy to 57.7 Gy, respectively, indicating a nontrivial risk in treatment toxicity associated with inaccurate prediction of proton beam range. The results validate that a methodology for evaluating the clinical impact of highly accurate DECT SPR maps has been developed. The differences between SECT and DECT dose distributions were clinically meaningful in one of the three evaluated patient cases. On the other hand, a novel pCT system has been proposed and developed as discussed in this dissertation. We first demonstrated the clinical feasibility through Monte Carlo simulation, then expanded the generality and compatibility of this technology for various beam characteristics with a model-based reconstruction explicitly developed for the system. The prototype of the pCT detector is composed of two strip ionization chambers measuring locations and lateral profiles of the exiting beam and a multi-layer ionization chamber (MLIC) measuring the integral depth doses (IDDs), which can be translated to residual energies of the exiting proton beams. A collimator with a round slit of 1 mm in diameter was placed in the central beam axis upstream from steering magnets to collimate the spot size down to 1 mm. The maximum deviation in reconstructed proton SPR from the ground truths was reported to be 1.02% in one of the 13 inserts when the number of protons per beamlet passing through the slit dropped to 103. The imaging dose was correlated linearly to incident protons and was determined to be 0.94 cGy if 103 protons per beamlet were used. Imaging quality was acceptable for planning purposes and held consistently through all levels of imaging dose. Spatial resolution was measured as 5 lp/cm in all simulations, varying imaging dose. The results prove the clinical feasibility of the pCT system with an imaging dose lower than kV cone-beam computed tomography (CBCT), making it potentially an excellent tool for localization and plan adaption in proton therapy. A reconstruction approach was developed to eliminate the use of a collimator by modeling the IDD of an uncollimated proton beam as a weighted sum of percentage depth doses (PDDs) of constituent narrow beamlets separated by 1mm. The beamlets\u27 water equivalent path lengths (WEPLs) were determined by iteratively minimizing the squared L2-norm of the forward projected and simulated IDDs. The final WEPL values were reconstructed into pCT images, i.e., proton SPR maps, through simultaneous algebraic reconstruction technique with total variation regularization (SART-TV). When the proposed reconstruction approach was applied, the percentage deviations from reference SPR were within ±1% in all selected ROIs. The mean absolute error of the reconstructed SPR was 0.33%, 0.19%, and 0.27% for the cylindrical phantom, and the adult phantom at the head and lung region, respectively. The frequency at 10% of the modulation transfer function (MTF) was 6.38 cm-1. The mean signal-to-noise ratio (SNR) of all the inserts was 2.45. The mean imaging dose was 0.29 cGy and 0.25 cGy at the head and lung region of the adult phantom, respectively. The results suggest that with the proposed reconstruction approach, the pCT system can achieve similar SPR accuracy and spatial resolution as the pCT system with an additional collimator while avoiding the potential side effects caused by extra neutron dose generated by collimating proton beams. Finally, the possibility of using the pCT system to extract proton scattering information was explored. Two forward models of predicting integrated transverse dose distribution of the exiting proton beam were implemented and compared. Moreover, the differential Molière model was utilized to reconstruct the scattering length of the imaging object. The scattering length map achieved 0.83% mean absolute deviation from the reference values when reconstructed through a modified simultaneous algebraic reconstruction technique (SART) algorithm and can be used as a correction for SPR estimation or to provide additional information in proton treatment planning. In summary, an evaluation study of dose prediction and clinical impact of the DECT-based JSIR-BVM SPR mapping technique was conducted. The transition of this highly accurate technique toward clinical application was established. Furthermore, a novel pCT system incorporated with a PBS facility and detected with an MLIC detector was proposed and developed. The feasibility of the system was proved through Monte Carlo simulation. Moreover, a reconstruction approach modeling the IDDs of the exiting proton beam was developed to further improve the system design by eliminating the additional hardware that may cause extra neutron dose and unnecessary quality assurance. Finally, proton scattering information was reconstructed using simulated data based on the pCT design, which can further improve SPR accuracy or provide additional patient anatomic information for proton treatment planning