Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 172

This bibliography lists 132 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1977

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

A hybrid multi-particle approach to range assessment-based treatment verification in particle therapy

Particle therapy (PT) used for cancer treatment can spare healthy tissue and reduce treatment toxicity. However, full exploitation of the dosimetric advantages of PT is not yet possible due to range uncertainties, warranting development of range-monitoring techniques. This study proposes a novel range-monitoring technique introducing the yet unexplored concept of simultaneous detection and imaging of fast neutrons and prompt-gamma rays produced in beam-tissue interactions. A quasimonolithic organic detector array is proposed, and its feasibility for detecting range shifts in the context of proton therapy is explored through Monte Carlo simulations of realistic patient models and detector resolution efects. The results indicate that range shifts of 1 mm can be detected at relatively low proton intensities (22.30(13) × 107 protons/spot) when spatial information obtained through imaging of both particle species are used simultaneously. This study lays the foundation for multiparticle detection and imaging systems in the context of range verifcation in PTpublishedVersio

Accurate location of tumor in head and neck cancer radiotherapy treatment with respect to machine isocentre

Indiana University-Purdue University Indianapolis (IUPUI)Radiation Therapy has been one of the most common techniques to treat various types of cancers, in particular is Head and Neck Cancer (HNC) which accounts for three percent of all cancers in the United States. During the treatment procedure, the patient is immobilized using immobilization devices such as the full head face mask, bite blocks, stereotactic frame, etc. to get accurate location of tumor. The disadvantage of these devices is that they are very uncomfortable to the patient especially people suffering from Post-Traumatic Stress Disorder (PTSD) and claustrophobia who cannot wear any confined masked system such as the full head mask or bite block during the treatment procedure. To mitigate this problem, there has been a lot of research in modifying such immobilizing devices without neglecting the accurate location of tumor. To this end, the research presented in this thesis focuses on developing a mask less system with accurately locating the position of tumor using the technique of coordinate transformation at the same time fulfilling the three important characteristics: • Comfort • Accuracy • Low price Such a system is comfortable to the patient because no confining mask system is used and we choose minimal contact points on the patient for fixing the patient. Traditionally, such type of cancer treatment is carried out in two stages: Diagnosis stage, which identifies the location of the tumor and the external markers and the Treatment stage where the tumor is treated with immobilization device being common in both the stages. In the new system, the immobilization devices vary at the two stages. The head position is monitored by using pressure sensor assembly where spring and pressure sensor setup detects the amount and direction of head deviation. We also prepare a customized 3D printed nose bridge part for extra referencing in the treatment room. Also, it is important that we use material for our immobilization devices which does not contain any metal and MRI compatible. Once the patient lies down on the treatment couch and is immobilized using the immobilization devices, then tumor location is calculated using the theory of coordinate transformation and transformation matrix in the Diagnosis and Treatment Stage. To validate the system, simulation of immobilization devices used in the new design was carried out using ANSYS Workbench 15.0 and LS-Dyna software’s Explicit Dynamics method. The simulation for the head-fixing device showed a deflection of ±0.1974 mm with respect to machine isocenter with a load of 60 N, which is lower than the customer requirement of ±3 mm with respect to machine isocenter of head deviation. The material used for the external markers for patient positioning was selected to be polyetheretherketone (PEEK) which is a radiolucent and widely used MRI compatible material. The system also takes into consideration the effect of weight loss, which is one of the drawbacks of the current systems. Although still in the development stage, this mask less system holds to be the next new variety of immobilization devices that are comfortable to the patient and less expensive to be implemented in future cancer treatment practices

Sedation and Anesthesia Options for Pediatric Patients in the Radiation Oncology Suite

External beam radiation therapy (XRT) has become one of the cornerstones in the management of pediatric oncology cases. While the procedure itself is painless, the anxiety it causes may necessitate the provision of sedation or anesthesia for the patient. This review paper will briefly review the XRT procedure itself so that the anesthesia provider has an understanding of what is occurring during the simulation and treatment phases. We will then examine several currently used regimens for the provision of pediatric sedation in the XRT suite as well as a discussion of when and how general anesthesia should be performed if deemed necessary. Standards of care with respect to patient monitoring will be addressed. We will conclude with a survey of the developing field of radiation-based therapy administered outside of the XRT suite

Applications of a Biomechanical Patient Model for Adaptive Radiation Therapy

Biomechanical patient modeling incorporates physical knowledge of the human anatomy into the image processing that is required for tracking anatomical deformations during adaptive radiation therapy, especially particle therapy. In contrast to standard image registration, this enforces bio-fidelic image transformation. In this thesis, the potential of a kinematic skeleton model and soft tissue motion propagation are investigated for crucial image analysis steps in adaptive radiation therapy. The first application is the integration of the kinematic model in a deformable image registration process (KinematicDIR). For monomodal CT scan pairs, the median target registration error based on skeleton landmarks, is smaller than (1.6 ± 0.2) mm. In addition, the successful transferability of this concept to otherwise challenging multimodal registration between CT and CBCT as well as CT and MRI scan pairs is shown to result in median target registration error in the order of 2 mm. This meets the accuracy requirement for adaptive radiation therapy and is especially interesting for MR-guided approaches. Another aspect, emerging in radiotherapy, is the utilization of deep-learning-based organ segmentation. As radiotherapy-specific labeled data is scarce, the training of such methods relies heavily on augmentation techniques. In this work, the generation of synthetically but realistically deformed scans used as Bionic Augmentation in the training phase improved the predicted segmentations by up to 15% in the Dice similarity coefficient, depending on the training strategy. Finally, it is shown that the biomechanical model can be built-up from automatic segmentations without deterioration of the KinematicDIR application. This is essential for use in a clinical workflow

Microscope Embedded Neurosurgical Training and Intraoperative System

In the recent years, neurosurgery has been strongly influenced by new technologies. Computer Aided Surgery (CAS) offers several benefits for patients\u27 safety but fine techniques targeted to obtain minimally invasive and traumatic treatments are required, since intra-operative false movements can be devastating, resulting in patients deaths. The precision of the surgical gesture is related both to accuracy of the available technological instruments and surgeon\u27s experience. In this frame, medical training is particularly important. From a technological point of view, the use of Virtual Reality (VR) for surgeon training and Augmented Reality (AR) for intra-operative treatments offer the best results. In addition, traditional techniques for training in surgery include the use of animals, phantoms and cadavers. The main limitation of these approaches is that live tissue has different properties from dead tissue and that animal anatomy is significantly different from the human. From the medical point of view, Low-Grade Gliomas (LGGs) are intrinsic brain tumours that typically occur in younger adults. The objective of related treatment is to remove as much of the tumour as possible while minimizing damage to the healthy brain. Pathological tissue may closely resemble normal brain parenchyma when looked at through the neurosurgical microscope. The tactile appreciation of the different consistency of the tumour compared to normal brain requires considerable experience on the part of the neurosurgeon and it is a vital point. The first part of this PhD thesis presents a system for realistic simulation (visual and haptic) of the spatula palpation of the LGG. This is the first prototype of a training system using VR, haptics and a real microscope for neurosurgery. This architecture can be also adapted for intra-operative purposes. In this instance, a surgeon needs the basic setup for the Image Guided Therapy (IGT) interventions: microscope, monitors and navigated surgical instruments. The same virtual environment can be AR rendered onto the microscope optics. The objective is to enhance the surgeon\u27s ability for a better intra-operative orientation by giving him a three-dimensional view and other information necessary for a safe navigation inside the patient. The last considerations have served as motivation for the second part of this work which has been devoted to improving a prototype of an AR stereoscopic microscope for neurosurgical interventions, developed in our institute in a previous work. A completely new software has been developed in order to reuse the microscope hardware, enhancing both rendering performances and usability. Since both AR and VR share the same platform, the system can be referred to as Mixed Reality System for neurosurgery. All the components are open source or at least based on a GPL license