Quality control in digital breast tomosynthesis: compliance of two phantoms with the EUREF protocol

Ever since the integration of Digital Breast Tomosynthesis (DBT) into breast cancer screening programmes, it has been a European endeavour to draw up standard guidelines for the assessment of the imaging performance of DBT systems. The quantitative evaluation of the quality of reconstructed tomosynthesis images is still an active area of research. In fact, the current version of the EUREF DBT QC protocol represents a preliminary set of guidelines to be used at acceptance, and to establish baseline values for constancy testing. New phantoms for QC in DBT have also been developed. Together, Sun Nuclear's Mammo 3D Performance Kits and CIRS DBT QC Phantom, model 021, have been shown to provide adequate test objects and background material for the assessment of the Automatic Exposure Control system performance, image receptor response function and noise analysis, system sharpness measured in projection images, and in-plane and out-of-plane spatial resolution in the reconstructed tomosynthesis image. From the comparison with the available literature, the use of the two phantoms with the Hologic Selenia Dimensions and Fujifilm AMULET Innovality systems has been validated

A dual modality, DCE-MRI and x-ray, physical phantom for quantitative evaluation of breast imaging protocols

The current clinical standard for breast cancer screening is mammography. However, this technique has a low sensitivity which results in missed cancers. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has recently emerged as a promising technique for breast cancer diagnosis and has been reported as being superior to mammography for screening of high-risk women and evaluation of extent of disease. At the same time, low and variable specificity has been documented in the literature as well as a rising number of mastectomies possibly due to the increasing use of DCE-MRI. In this study, we developed and characterized a dual-modality, x-ray and DCE-MRI, anthropomorphic breast phantom for the quantitative assessment of breast imaging protocols. X-ray properties of the phantom were quantitatively compared with patient data, including attenuation coefficients, which matched human values to within the measurement error, and tissue structure using spatial covariance matrices of image data, which were found to be similar in size to patient data. Simulations of the phantom scatter-to-primary ratio (SPR) were produced and experimentally validated then compared with published SPR predictions for homogeneous phantoms. SPR values were as high as 85% in some areas and were heavily influenced by the heterogeneous tissue structure. MRI properties of the phantom, T1 and T2 relaxation values and tissue structure, were also quantitatively compared with patient data and found to match within two error bars. Finally, a dynamic lesion that mimics lesion border shape and washout curve shape was included in the phantom. High spatial and temporal resolution x-ray measurements of the washout curve shape were performed to determine the true contrast agent concentration as a function of time. DCE-MRI phantom measurements using a clinical imaging protocol were compared against the x-ray truth measurements. MRI signal intensity curves were shown to be less specific to lesion type than the x-ray derived contrast agent concentration curves. This phantom allows, for the first time, for quantitative evaluation of and direct comparisons between x-ray and MRI breast imaging modalities in the context of lesion detection and characterization

Model-Based Iterative Reconstruction in Cone-Beam Computed Tomography: Advanced Models of Imaging Physics and Prior Information

Cone-beam computed tomography (CBCT) represents a rapidly developing imaging modality that provides three-dimensional (3D) volumetric images with sub-millimeter spatial resolution and soft-tissue visibility from a single gantry rotation. CBCT tends to have small footprint, mechanical simplicity, open geometry, and low cost compared to conventional diagnostic CT. Because of these features, CBCT has been used in a variety of specialty diagnostic applications, image-guided radiation therapy (on-board CBCT), and surgical guidance (e.g., C-arm based CBCT). However, the current generation of CBCT systems face major challenges in low-contrast, soft-tissue image quality – for example, in the detection of acute intracranial hemorrhage (ICH), which requires a fairly high level of image uniformity, spatial resolution, and contrast resolution. Moreover, conventional approaches in both diagnostic and image-guided interventions that involve a series of imaging studies fail to leverage the wealth of patient-specific anatomical information available from previous scans. Leveraging the knowledge gained from prior images holds the potential for major gains in image quality and dose reduction. Model-based iterative reconstruction (MBIR) attempts to make more efficient use of the measurement data by incorporating a forward model of physical detection processes. Moreover, MBIR allows incorporation of various forms of prior information into image reconstruction, ranging from image smoothness and sharpness to patient-specific anatomical information. By leveraging such advantages, MBIR has demonstrated improved tradeoffs between image quality and radiation dose in multi-detector CT in the past decade and has recently shown similar promise in CBCT. However, the full potential of MBIR in CBCT is yet to be realized. This dissertation explores the capabilities of MBIR in improving image quality (especially low-contrast, soft-tissue image quality) and reducing radiation dose in CBCT. The presented work encompasses new MBIR methods that: i) modify the noise model in MBIR to compensate for noise amplification from artifact correction; ii) design regularization by explicitly incorporating task-based imaging performance as the objective; iii) mitigate truncation effects in a computationally efficient manner; iv) leverage a wealth of patient-specific anatomical information from a previously acquired image; and v) prospectively estimate the optimal amount of prior image information for accurate admission of specific anatomical changes. Specific clinical challenges are investigated in the detection of acute ICH and surveillance of lung nodules. The results show that MBIR can substantially improve low-contrast, soft-tissue image quality in CBCT and enable dose reduction techniques in sequential imaging studies. The thesis demonstrates that novel MBIR methods hold strong potential to overcome conventional barriers to CBCT image quality and open new clinical applications that would benefit from high-quality 3D imaging

Predicting Task-­specific Performance for Iterative Reconstruction in Computed Tomography

<p>The cross-sectional images of computed tomography (CT) are calculated from a series of projections using reconstruction methods. Recently introduced on clinical CT scanners, iterative reconstruction (IR) method enables potential patient dose reduction with significantly reduced image noise, but is limited by its "waxy" texture and nonlinear nature. To balance the advantages and disadvantages of IR, evaluations are needed with diagnostic accuracy as the endpoint. Moreover, evaluations need to take into consideration the type of the imaging task (detection and quantification), the properties of the task (lesion size, contrast, edge profile, etc.), and other acquisition and reconstruction parameters. </p><p>To evaluate detection tasks, the more acceptable method is observer studies, which involve image preparation, graphical user interface setup, manual detection and scoring, and statistical analyses. Because such evaluation can be time consuming, mathematical models have been proposed to efficiently predict observer performance in terms of a detectability index (d'). However, certain assumptions such as system linearity may need to be made, thus limiting the application of the models to potentially nonlinear IR. For evaluating quantification tasks, conventional method can also be time consuming as it usually involves experiments with anthropomorphic phantoms. A mathematical model similar to d' was therefore proposed for the prediction of volume quantification performance, named the estimability index (e'). However, this prior model was limited in its modeling of the task, modeling of the volume segmentation process, and assumption of system linearity.</p><p>To expand prior d' and e' models to the evaluations of IR performance, the first part of this dissertation developed an experimental methodology to characterize image noise and resolution in a manner that was relevant to nonlinear IR. Results showed that this method was efficient and meaningful in characterizing the system performance accounting for the non-linearity of IR at multiple contrast and noise levels. It was also shown that when certain criteria were met, the measurement error could be controlled to be less than 10% to allow challenging measuring conditions with low object contrast and high image noise.</p><p>The second part of this dissertation incorporated the noise and resolution characterizations developed in the first part into the d' calculations, and evaluated the performance of IR and conventional filtered backprojection (FBP) for detection tasks. Results showed that compared to FBP, IR required less dose to achieve a threshold performance accuracy level, therefore potentially reducing the required dose. The dose saving potential of IR was not constant, but dependent on the task properties, with subtle tasks (small size and low contrast) enabling more dose saving than conspicuous tasks. Results also showed that at a fixed dose level, IR allowed more subtle tasks to exceed a threshold performance level, demonstrating the overall superior performance of IR for detection tasks.</p><p>The third part of this dissertation evaluated IR performance in volume quantification tasks with conventional experimental method. The volume quantification performance of IR was measured using an anthropomorphic chest phantom and compared to FBP in terms of accuracy and precision. Results showed that across a wide range of dose and slice thickness, IR led to accuracy significantly different from that of FBP, highlighting the importance of calibrating or expanding current segmentation software to incorporate the image characteristics of IR. Results also showed that despite IR's great noise reduction in uniform regions, IR in general had quantification precision similar to that of FBP, possibly due to IR's diminished noise reduction at edges (such as nodule boundaries) and IR's loss of resolution at low dose levels. </p><p>The last part of this dissertation mathematically predicted IR performance in volume quantification tasks with an e' model that was extended in three respects, including the task modeling, the segmentation software modeling, and the characterizations of noise and resolution properties. Results showed that the extended e' model correlated with experimental precision across a range of image acquisition protocols, nodule sizes, and segmentation software. In addition, compared to experimental assessments of quantification performance, e' was significantly reduced in computational time, such that it can be easily employed in clinical studies to verify quantitative compliance and to optimize clinical protocols for CT volumetry.</p><p>The research in this dissertation has two important clinical implications. First, because d' values reflect the percent of detection accuracy and e' values reflect the quantification precision, this work provides a framework for evaluating IR with diagnostic accuracy as the endpoint. Second, because the calculations of d' and e' models are much more efficient compared to conventional observer studies, the clinical protocols with IR can be optimized in a timely fashion, and the compliance of clinical performance can be examined routinely.</p>Dissertatio

Development and Preclinical Evaluation of A Compact Image-guided Microbeam Radiation Therapy System

Microbeam radiation therapy (MRT) is a novel and experimental cancer treatment modality. It has received increasing emphasis worldwide in recent years due to the demonstrated high therapeutic ratio in preclinical studies. MRT uses arrays of quasi-parallel radiation beams that are up to a few hundred microns wide and separated by several times of its beamwidth. Extensive preclinical experiments conducted at European Synchrotron Radiation Facility and several other national synchrotron facilities have shown that microbeams with doses of several hundreds of grays are well tolerated by healthy brain tissues while causing preferential damage in tumors. As the effort now moves towards large animal and clinical trials, there are eminent needs to develop compact and economically-viable microbeam irradiators for MRT radiobiology research and clinical installation eventually. Our research group has invented the carbon nanotube (CNT) field emission based X-ray source technology and has been dedicated to CNT-based medical device research over the past decade. A laboratory-scale microbeam irradiator has been recently developed with the CNT source array technology. The unique nature of CNT X-ray cathode allows for optimization of the anode focal spot shape and size, and therefore overcomes the obstacles of producing high flux microbeam radiation with conventional X-ray tubes. Preliminary studies have shown that the CNT-based MRT prototype is capable of generating orthovoltage radiation with all essential dosimetric characteristics of microbeam radiation therapy. The goals of this dissertation are to characterize and to optimize the system performance, to implement image guidance for dose delivery, and to evaluate the treatment efficacy in preclinical studies. Characterization of radiation source and dosimetric parameters was performed and described in detail. An on-board imaging system was constructed and integrated with the microbeam irradiating system. Dedicated image-guidance protocols were developed for high accuracy microbeam delivery in small animal models. Therapeutic assessment of brain tumor bearing mice was conducted with the CNT-MRT prototype. Preliminary results included encouraging treatment effects in terms of tumor local control and mean survival time extension. MRT radiobiological evaluations were carried out, for the first time, using a non-synchrotron-based compact radiation source. Additionally, feasibility of delivering multi-arrays of microbeams cross-firing geometry at the brain tumor target was successfully demonstrated facilitated by multi-modality 3D image guidance. The results in this work demonstrate the advantages of CNT-based MRT system as an attractive alternative for microbeam generation and delivery. With continued effort in system development and optimization, this nanotechnology-based compact MRT system could become a powerful research tool that can be installed in a laboratory environment for elucidating the still poorly understood therapeutic mechanism of MRT without the need of synchrotron light sources. The feasibility studies also showed that the CNT-based MRT technology offers a promising pathway for clinical implementation in the near future.Doctor of Philosoph

Applications of Medical Physics

Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology

Image-Guided Interventions Using Cone-Beam CT: Improving Image Quality with Motion Compensation and Task-Based Modeling

Cone-beam CT (CBCT) is an increasingly important modality for intraoperative 3D imaging in interventional radiology (IR). However, CBCT exhibits several factors that diminish image quality — notably, the major challenges of patient motion and detectability of low-contrast structures — which motivate the work undertaken in this thesis. A 3D–2D registration method is presented to compensate for rigid patient motion. The method is fiducial-free, works naturally within standard clinical workflow, and is applicable to image-guided interventions in locally rigid anatomy, such as the head and pelvis. A second method is presented to address the challenge of deformable motion, presenting a 3D autofocus concept that is purely image-based and does not require additional fiducials, tracking hardware, or prior images. The proposed method is intended to improve interventional CBCT in scenarios where patient motion may not be sufficiently managed by immobilization and breath-hold, such as the prostate, liver, and lungs. Furthermore, the work aims to improve the detectability of low-contrast structures by computing source–detector trajectories that are optimal to a particular imaging task. The approach is applicable to CBCT systems with the capability for general source–detector positioning, as with a robotic C-arm. A “task-driven” analytical framework is introduced, various objective functions and optimization methods are described, and the method is investigated via simulation and phantom experiments and translated to task-driven source–detector trajectories on a clinical robotic C-arm to demonstrate the potential for improved image quality in intraoperative CBCT. Overall, the work demonstrates how novel optimization-based imaging techniques can address major challenges to CBCT image quality