What are the current and future requirements for magnetic resonance imaging interpretation skills in radiotherapy? A critical review

AbstractPurposeIncreasing usage of magnetic resonance imaging (MRI) in radiotherapy (RT) and the advent of MRI-based image-guided radiotherapy (IGRT) suggests a need for additional training within the RT profession. This critical review aimed to identify potential gaps in knowledge by evaluating the current skill base in MRI among therapeutic radiographers as evidenced by published research.MethodsPapers related to MRI usage were retrieved. Topic areas included outlining, planning and IGRT; diagnosis, follow-up and staging-related papers were excluded. After selection and further text analysis, papers were grouped by tumour site and year of publication.ResultsThe literature search and filtering resulted in a total of 123 papers, of which 66 were related to ‘outlining’, 37 to ‘planning’ and 20 to ‘IGRT’. The main sites of existing MRI expertise in RT were brain, central nervous system, prostate, and head and neck tumours. Expertise was clearly related to regions where MRI offered improved soft-tissue contrast. MRI studies within RT have been published from 2007 onwards at a steadily increasing rate.ConclusionCurrent use of MRI in RT is mainly restricted to sites where MRI offers a considerable imaging advantage over computed tomography. Given the changing use of MRI for image guidance, emerging therapeutic radiographers will require training in MRI interpretation across a wider range of anatomical regions.</jats:sec

Assessment and preliminary model development of shape memory polymers mechanical counter pressure space suits

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 39-41).This thesis seeks to assess the viability of a space qualified shape memory polymer (SMP) mechanical counter pressure (MCP) suit. A key development objective identified by the International Space Exploration Coordination Group, the development of a superior space suit with greater mobility and environmental robustness is necessary to support long-range human space exploration, specifically a mission to Mars. Conceptualized in 1971, a spacesuit utilizing MCP would fulfill these goals but its development was halted due to inadequate mechanical analysis and material limitations at the time. Since then, new active materials have been assessed to potentially further the development of a space qualified MCP space suit, which include quantitative thresholds for minimum pressure production, durability, pressure distribution, mobility range, and ease of garment donning and doffing. Guided by these criteria, a SMP biaxial tubular braid applying MCP through active compression was designed and the prototype manufacturing processes were outlined. To predict the pressure production of this garment, the thermo-mechanics of a SMP was combined with the textile mechanics of a biaxial tubular braid and simulated within design parameter ranges consistent with the design criteria and practical considerations. The pressure production was controllable with the design parameters SMP elastic modulus, garment radial deformation, textile fiber spacing, and operational temperature. Assuming reasonable model accuracy, a SMP garment could achieve the necessary pressure production for a space qualified MCP suit, however, the durability of such a garment would be questionable considering the creep sustained from consecutive spacewalks of four to eight hours. Recommendations are made for methods to increase model accuracy, suggested SMP actuation mechanisms, and alternative textile architectures.by Brian Wee.S.B

3D Dynamic Scene Reconstruction from Multi-View Image Sequences

A confirmation report outlining my PhD research plan is presented. The PhD research topic is 3D dynamic scene reconstruction from multiple view image sequences. Chapter 1 describes the motivation and research aims. An overview of the progress in the past year is included. Chapter 2 is a review of volumetric scene reconstruction techniques and Chapter 3 is an in-depth description of my proposed reconstruction method. The theory behind the proposed volumetric scene reconstruction method is also presented, including topics in projective geometry, camera calibration and energy minimization. Chapter 4 presents the research plan and outlines the future work planned for the next two years

A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes

Comparison of manual and semi-automated delineation of regions of interest for radioligand PET imaging analysis

BACKGROUND As imaging centers produce higher resolution research scans, the number of man-hours required to process regional data has become a major concern. Comparison of automated vs. manual methodology has not been reported for functional imaging. We explored validation of using automation to delineate regions of interest on positron emission tomography (PET) scans. The purpose of this study was to ascertain improvements in image processing time and reproducibility of a semi-automated brain region extraction (SABRE) method over manual delineation of regions of interest (ROIs). METHODS We compared 2 sets of partial volume corrected serotonin 1a receptor binding potentials (BPs) resulting from manual vs. semi-automated methods. BPs were obtained from subjects meeting consensus criteria for frontotemporal degeneration and from age- and gender-matched healthy controls. Two trained raters provided each set of data to conduct comparisons of inter-rater mean image processing time, rank order of BPs for 9 PET scans, intra- and inter-rater intraclass correlation coefficients (ICC), repeatability coefficients (RC), percentages of the average parameter value (RM%), and effect sizes of either method. RESULTS SABRE saved approximately 3 hours of processing time per PET subject over manual delineation (p 0.8) for both methods. RC and RM% were lower for the manual method across all ROIs, indicating less intra-rater variance across PET subjects' BPs. CONCLUSION SABRE demonstrated significant time savings and no significant difference in reproducibility over manual methods, justifying the use of SABRE in serotonin 1a receptor radioligand PET imaging analysis. This implies that semi-automated ROI delineation is a valid methodology for future PET imaging analysis

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

A method for mapping and quantifying whole organ diffusion-weighted image distortion in MR imaging of the prostate.

A computational algorithm was designed to produce a measure of DW image distortion across the prostate. This algorithm was tested and validated on virtual phantoms incorporating known degrees and distributions of distortion. A study was then carried out on DW image volumes from three sets of 10 patients who had been imaged previously. These volumes had been radiologically assessed to have, respectively, 'no distortion' or 'significant distortion' or the potential for 'significant distortion' due to susceptibility effects from hip prostheses. Prostate outlines were drawn on a T2-weighted (T2W) image 'gold-standard' volume and on an ADC image volume derived from DW images acquired over the same region. The algorithm was then applied to these outlines to quantify and map image distortion. The proposed method correctly reproduced known distortion values and distributions in virtual phantoms. It also successfully distinguished between the three groups of patients: mean distortion in 'non-distorted' image volumes, 1.942 ± 0.582 mm; 'distorted', 4.402 ± 1.098 mm; and 'hip patients' 8.083 ± 4.653 mm; P < 0.001. This work has demonstrated and validated a means of quantifying and mapping image distortion in clinical prostate MRI cases

Level-Set Based Artery-Vein Separation in Blood Pool Agent CE-MR Angiograms

Blood pool agents (BPAs) for contrast-enhanced (CE) magnetic-resonance angiography (MRA) allow prolonged imaging times for higher contrast and resolution. Imaging is performed during the steady state when the contrast agent is distributed through the complete vascular system. However, simultaneous venous and arterial enhancement in this steady state hampers interpretation. In order to improve visualization of the arteries and veins from steady-state BPA data, a semiautomated method for artery-vein separation is presented. In this method, the central arterial axis and central venous axis are used as initializations for two surfaces that simultaneously evolve in order to capture the arterial and venous parts of the vasculature using the level-set framework. Since arteries and veins can be in close proximity of each other, leakage from the evolving arterial (venous) surface into the venous (arterial) part of the vasculature is inevitable. In these situations, voxels are labeled arterial or venous based on the arrival time of the respective surface. The evolution is steered by external forces related to feature images derived from the image data and by internal forces related to the geometry of the level sets. In this paper, the robustness and accuracy of three external forces (based on image intensity, image gradient, and vessel-enhancement filtering) and combinations of them are investigated and tested on seven patient datasets. To this end, results with the level-set-based segmentation are compared to the reference-standard manually obtained segmentations. Best results are achieved by applying a combination of intensity- and gradient-based forces and a smoothness constraint based on the curvature of the surface. By applying this combination to the seven datasets, it is shown that, with minimal user interaction, artery-vein separation for improved arterial and venous visualization in BPA CE-MRA can be achieved