Optical Tomography Imaging System Research Articles

Biomimicking Atherosclerotic Vessels: A Relevant and (Yet) Sub-Explored Topic.

Atherosclerosis represents the etiologic source of several cardiovascular events, including myocardial infarction, cerebrovascular accidents, and peripheral artery disease, which remain the leading cause of mortality in the world. Numerous strategies are being delineated to revert the non-optimal projections of the World Health Organization, by both designing new diagnostic and therapeutic approaches or improving the interventional procedures performed by physicians. Deeply understanding the pathological process of atherosclerosis is, therefore, mandatory to accomplish improved results in these trials. Due to their availability, reproducibility, low expensiveness, and rapid production, biomimicking physical models are preferred over animal experimentation because they can overcome some limitations, mainly related to replicability and ethical issues. Their capability to represent any atherosclerotic stage and/or plaque type makes them valuable tools to investigate hemodynamical, pharmacodynamical, and biomechanical behaviors, as well as to optimize imaging systems and, thus, obtain meaningful prospects to improve the efficacy and effectiveness of treatment on a patient-specific basis. However, the broadness of possible applications in which these biomodels can be used is associated with a wide range of tissue-mimicking materials that are selected depending on the final purpose of the model and, consequently, prioritizing some materials' properties over others. This review aims to summarize the progress in fabricating biomimicking atherosclerotic models, mainly focusing on using materials according to the intended application.

Developing an Analytical Model for Knife-Edge Slit Collimator Optimization for Prompt Gamma Imaging in Proton Therapy

Purpose: Gamma cameras are one of the most promising technologies for in-vivo range monitoring in proton therapy. Monte Carlo (MC) simulation is a common calculation-based technique to design and optimize gamma cameras. However, it is prohibitively time-consuming. Analytical modeling speeds up the process of finding the optimal design.
 Materials and Methods: We proposed an analytical method using the efficiency-resolution trade-off for optimizing a knife-edge collimator based on the range retrieval precision of protons. Monte Carlo simulation was used for validation of obtained collimator efficiencies.
 Results: The model predicts that for the optimal range retrieval precision, the ratio of the source-to-detector distance to the source-to-collimator distance should be ranging from . For a special case, it was found that assuming an ideal detector , the falloff retrieval precision is optimal at independent of the collimator resolution. Moreover, using the optimized camera, the difference between the MC calculated range and the absolute range was 0.5 cm (the relative error is about 3%).
 Conclusion: It was found that the collimator parameters are in good agreement in comparison with that of the MC results reported in the literature. The analytical method studied in this work can be used to design and optimize imaging systems based on KE collimators in combination with new detectors in a fast and reliable way.

Simulation of breast lesions based upon fractal Perlin noise

PurposeSteadily increasing use of computational/virtual phantoms in medical physics has motivated expanding development of new simulation methods and data representations for modelling human anatomy. This has emphasized the need for increased realism, user control, and availability. In breast cancer research, virtual phantoms have gained an important role in evaluating and optimizing imaging systems. For this paper, we have developed an algorithm to model breast abnormalities based on fractal Perlin noise. We demonstrate and characterize the extension of this approach to simulate breast lesions of various sizes, shapes, and complexity. Materials and methodRecently, we developed an algorithm for simulating the 3D arrangement of breast anatomy based on Perlin noise. In this paper, we have expanded the method to also model soft tissue breast lesions. We simulated lesions within the size range of clinically representative breast lesions (masses, 5–20 mm in size). Simulated lesions were blended into simulated breast tissue backgrounds and visualized as virtual digital mammography images. The lesions were evaluated by observers following the BI-RADS assessment criteria. ResultsObservers categorized the lesions as round, oval or irregular, with circumscribed, microlobulated, indistinct or obscured margins. The majority of the simulated lesions were considered by the observers to have a realism score of moderate to well. The simulation method provides almost real-time lesion generation (average time and standard deviation: 1.4 ± 1.0 s). ConclusionWe presented a novel algorithm for computer simulation of breast lesions using Perlin noise. The algorithm enables efficient simulation of lesions, with different sizes and appearances.

Convex optical freeforms using fringe Zernike overlay approach for two-mirror and three-mirror telescopes for space applications

The challenge in manufacturing and testing of convex optical parts is well known in comparison to its concave counterpart and an optimized imaging system invariably features a combination of both. Of late, freeform mirrors find lot of interest in space applications as the optical systems remain compact within the allocated envelope. As is well known, these freeform mirrors are difficult to manufacture and test due to their lack of rotational or translational symmetry and poses additional challenges if it has convex profile. In the current investigations, two new convex freeform optical surfaces are proposed which is based on fringe Zernike overlay technique. The first convex freeform envisaged is for a two-mirror off-axis telescope where the secondary mirror is an off-axis freeform and the second convex freeform for a three-mirror anastigmat telescope where the secondary mirror is an on-axis convex freeform. Substitution of these convex freeform surfaces in the two-mirror and three-mirror telescope systems aid in the correction of asymmetrical aberrations to a wider view field, thereby enhancing the performances of the de-centred and tilted systems in the respective configurations. The procedure adopted for the manufacturing of these convex freeform surfaces using bonnet polishing methods and the interferometric tests using modified-Hindle null corrector and subaperture-stitching techniques for evaluation are described in detail.

Delayed-onset and atypical left abdominal pain.

A 42-year-old man was transferred by an ambulance with worsening intermittent pain in his left lateral abdomen. On arrival at the hospital, the patient showed no symptoms, and no abnormalities were found on the physical examination. The hemodynamic states were stable. The pain had persisted for more than a few years and other hospitals had been visited, but the cause was unclear. A simple abdominal radiograph was obtained (Figure 1). He had been injured in an accident over 2 decades ago; however, the details were unknown. Abdominal computed tomography (CT) revealed an abdominal wall hernia with the intestine and interstitial membrane interposition around the pelvic fracture (Figure 2). Subsequently, the patient was admitted to a hospital where surgery was possible. Sahdev et al reported the diagnostic standard that abdominal wall hernia occurs later in the history of trauma.1 Having a history of trauma (such as experiencing an accident that occurred several years earlier) and confirmed pelvic fracture led to the consideration of this diagnosis in the absence of symptoms, and an abdominal CT was performed. CT is the simplest, fastest, and most optimal imaging system during emergencies.

Lighting up RNA-specific multi-photon and super-resolution imaging using a novel zinc complex.

Ribonucleic acid (RNA) probes are critical for understanding the role of RNA dynamics in cellular function but are in short supply due to the lack of optimized imaging systems and excellent fluorescence emission performance. Here, the terpyridine Zn(II) complex (Zn-T) with D-π-A configuration and bright aggregation-induced fluorescence emission (AIE) has been fabricated for the selective detection and real-time monitoring of RNA. Impressively, Zn-T exhibits a large Stokes shift and three-photon absorption (3PA) activity and responds specifically through hydrophobic interactions with an RNA pocket. The combination of AIE-assisted two-photon fluorescence and stimulated emission depletion (STED) microscopy of Zn-T for imaging nuclear RNA has higher spatial resolution and brightness, thus providing an imaging platform for studying RNA-related physiological or pathological processes.

Large-depth three-photon fluorescence microscopy imaging of cortical microvasculature on nonhuman primates with bright AIE probe In vivo

Multiphoton microscopy has been a powerful tool in brain research, three-photon fluorescence microscopy is increasingly becoming an emerging technique for neurological research of the cortex in depth. Nonhuman primates play important roles in the study of brain science because of their neural and vascular similarity to humans. However, there are few research results of three-photon fluorescence microscopy on the brain of nonhuman primates due to the lack of optimized imaging systems and excellent fluorescent probes. Here we introduced a bright aggregation-induced emission (AIE) probe with excellent three-photon fluorescence efficiency as well as facile synthesis process and we validated its biocompatibility in the macaque monkey. We achieved a large-depth vascular imaging of approximately 1 mm in the cerebral cortex of macaque monkey with our lab-modified three-photon fluorescence microscopy system and the AIE probe. Functional measurement of blood velocity in deep cortex capillaries was also performed. Furthermore, the comparison of cortical deep vascular structure parameters across species was presented on the monkey and mouse cortex. This work is the first in vivo three-photon fluorescence microscopic imaging research on the macaque monkey cortex reaching the imaging depth of ∼1 mm with the bright AIE probe. The results demonstrate the potential of three-photon microscopy as primate-compatible method for imaging fine vascular networks and will advance our understanding of vascular function in normal and disease in humans.

Monte Carlo modeling of gold nanoparticles detection limits of benchtop three‐dimensional L‐ and K‐shell X‐ray fluorescence mapping systems

AbstractThis study aims to investigate the detection limits of gold nanoparticle (GNP) concentrations by Monte Carlo (MC) modeling of benchtop polychromatic K‐ and L‐shell X‐ray fluorescence mapping system. In Monte Carlo N‐Particle (MCNP version 6.1) simulations, a 0.25‐cm‐diameter cylinder containing GNPs of various concentrations (i.e., 0.005%–1.0% gold by weight, wt%) was assumed to be located at the center of a cylindrical water phantom of various diameters (1.0–10 cm). Two different sets of incident pencil beam X‐rays and detectors were modeled to stimulate X‐ray fluorescence (XRF) of GNPs: (1) 62 kVp and silicon drift detector for L‐XRF, (2) 105 kVp and cadmium telluride detector for K‐XRF. The detection limits were calculated for given radiation doses to the center of phantom (375–1500 mGy). When the diameter of the phantom was 1 cm, the detection limits for L‐XRF and K‐XRF were an order of 0.001 wt% and of 0.01 wt%, respectively. The detectability of K‐XRF turned out to be superior to that of L‐XRF for the phantoms greater than or equal to 3 cm in diameter. The MC results will provide a guide for developing an optimal benchtop XRF imaging system for in vivo preclinical imaging, depending on the sizes of GNP‐loaded objects, GNP concentrations, and radiation doses.

A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods.

The ideal observer (IO) sets an upper performance limit among all observers and has been advocated for assessing and optimizing imaging systems. For general joint detection and estimation (detection-estimation) tasks, estimation ROC (EROC) analysis has been established for evaluating the performance of observers. However, in general, it is difficult to accurately approximate the IO that maximizes the area under the EROC curve. In this study, a hybrid method that employs machine learning is proposed to accomplish this. Specifically, a hybrid approach is developed that combines a multi-task convolutional neural network and a Markov-Chain Monte Carlo (MCMC) method in order to approximate the IO for detection-estimation tasks. Unlike traditional MCMC methods, the hybrid method is not limited to use of specific utility functions. In addition, a purely supervised learning-based sub-ideal observer is proposed. Computer-simulation studies are conducted to validate the proposed method, which include signal-known-statistically/background-known-exactly and signal-known-statistically/background-known-statistically tasks. The EROC curves produced by the proposed method are compared to those produced by the MCMC approach or analytical computation when feasible. The proposed method provides a new approach for approximating the IO and may advance the application of EROC analysis for optimizing imaging systems.

Optimization of proton imaging system including fringe field of magnetic lens

The proton imaging system is composed of four quadrupole magnetic lenses and a collimator. The quadrupole magnetic lenses can realize point-to-point imaging, and the collimator can improve image quality by controlling proton flux and realize material diagnosis. The magnetic field gradient of an ideal quadrupole lens becomes zero at the edge. Inside the lens, the magnetic field gradient is constant along the axis, while the magnetic field boundary of the actual lens extends outward. In the proton imaging system, the fringing field will affect the proton transport state and the performance of the imaging system as well. In this paper, a method to optimize the system is presented when the fringe field is considered. A proton imaging system of 1.6 GeV is established with the Geant 4 program, in which the magnetic field gradient distribution of the actual lens is approximated by the Bell function. In an ideal imaging system, the external drift length is 1.2 m, the internal drift length is 0.5 m, the length of the magnet is 0.8 m, and the magnetic field gradient is 8.09 T/m. The parameters of the practical imaging system can be obtained by using the optimization method: when the integral difference in magnetic field gradient distribution between the actual lens and the ideal lens is equal to zero, the outer drift length of the imaging system is 1.203 m and the inner drift length is 0.506 m; when the integral difference in the magnetic field gradient distribution between the actual lens and the ideal lens is equal to 1%, the outer drift length is 1.208 m and the inner drift length is 0.516 m. In the numerical simulation, a 1mm-thick copper plate and a concentric ball are chosen as the objects, and the influence of the fringing field on the collimator aperture and that on the proton flux error are studied. The results show that the optimized imaging system can reduce the flux error of protons passing through the object, and the difference in the aperture of collimator is on the order of 10<sup>–2</sup> when the integral difference is on the order of 10<sup>–2</sup> in magnitude.

Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods.

Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems because the IO sets an upper performance limit among all observers. When joint signal detection and localization tasks are considered, the IO that employs a modified generalized likelihood ratio test maximizes observer performance as characterized by the localization receiver operating characteristic (LROC) curve. Computations of likelihood ratios are analytically intractable in the majority of cases. Therefore, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed to approximate the likelihood ratios. However, the applications of MCMC methods have been limited to relatively simple object models. Supervised learning-based methods that employ convolutional neural networks have been recently developed to approximate the IO for binary signal detection tasks. In this paper, the ability of supervised learning-based methods to approximate the IO for joint signal detection and localization tasks is explored. Both background-known-exactly and background-known-statistically signal detection and localization tasks are considered. The considered object models include a lumpy object model and a clustered lumpy model, and the considered measurement noise models include Laplacian noise, Gaussian noise, and mixed Poisson-Gaussian noise. The LROC curves produced by the supervised learning-based method are compared to those produced by the MCMC approach or analytical computation when feasible. The potential utility of the proposed method for computing objective measures of IQ for optimizing imaging system performance is explored.

Development of the optimal gamma-ray imaging system design

This article covers solutions in the field of the gamma-ray imaging systems, as well as proposes an optimal solution for design of such system.Gamma-ray imaging systems are proving their utility as a useful tool for radio-ecological research, investigation of isotope mishandling, nuclear incidents liquidation and so on. They allow viewing gamma-ray fields as if they were visible. For example, this kind of system is capable of pointing to radiation sources on top of video stream, which allows localization of radiation sources without the need of getting close to them with a dosimeter.Overview of the existing solutions covers directional imagers, collimator-based imagers, coded aperture imagers and rotation modulation systems. Each section of this paragraph outlines main advantages and disadvantages of the given solution.Based on a solution overview, a proposition of an optimal solution for this kind of system is made. Proposition covers a creation of the one-dimensional coded aperture system with modified uniformly-redundant array mask, scintillation detectors and FPGA-based data acquisition system. This kind of system uses a thick screen, also called mask, with holes. This screen casts a gamma-ray shadow onto an array of detectors. Then a data acquisition system is used to run a correlation algorithm, which finds a shadow pattern in detector’s readouts. This allows determining from what angle this shadow has been cast. The mask is formed by a modified uniformly-redundant array pattern to minimize the chance of incorrect correlation algorithm result and to use it’s ability to turn coded aperture into an inverse one. Performing measurements with and inverse mask allows compensation for irregular background radiation and irregularity between detector sensitivity in the array of sensors. It should also be noted, that using of such ability requires to physically turn the mask around it’s vertical axis.This paragraph outlines a number of design principles and requirements, such as avoidance of irregularities in radiation transparency of the mechanical construction, benefits of making the whole system remotely operated, overall requirements for supply and readout electronics and a proposal to use programmable logic integrated circuits as a data acquisition measure. This section also covers possible steps to improve system performance, via turning it around vertical axle, rotation of the viewing frame to create 2-dimensional images and performing correlation algorithm on the spectral data to distinguish different nuclides amongst gamma-ray sources.Overall conclusions of this article suggests an implementation of the test device to verify and field-test proposed technical solutions and to promote further research and development in this field.

Neutron imaging at the low flux training and research reactor AKR-2

Abstract Neutron imaging with thermal neutrons was performed at the training and research reactor AKR-2 of the Technische Universitat Dresden (TUD) with the aim to demonstrate the feasibility of this method in the sense of a proof of concept at a very low-flux neutron source . The L/D-ratio characterizing the effective beam divergence was measured at all available experimental channels using the cadmium-knife method which in turn allows the estimation of the modulation transfer function of the complete imaging setup. The objective of this investigation is to determine a useful L/D and exposure time for imaging at AKR-2. The measured L/D-ratio ranges between 125 and 500 depending on the used channel. This result motivates the continuation of the study with an optimized imaging system, taking into account the specific properties of AKR-2.

Optimal microwave imaging with random field illuminations

In the recent years, the theory and technologies of electromagnetic computational imaging have been well developed and several novel imaging methods have been proposed, one of which is known as the microwave imaging under random field illumination. In order to solve the matrix equation of imaging model, the key of such an imaging system is to generate the random electromagnetic radiation field distribution, implementing the independent measurements under random field illuminations. In this work, an optimal microwave imaging system for the desired imaging region and resolution is theoretically analyzed and experimentally implemented. In the randomness analysis, the correlation between different measurements is evaluated by the singular value decomposition, which is also adopted as a criterion for choosing the optimal parameters of the imaging system. Based on random field illuminations generated by the least number of antenna elements, a full-rank matrix equation can be used to reconstruct the object by direct matrix inversion, which can be completed in nearly real-time once the system calibration is implemented in advance. The numerical simulation and experimental investigation are performed, and the results prove the effectiveness of the proposed optimal imaging system. By using the traditional array theory, it is found that for an <i>N</i>-element phase array, <i>N</i> illuminations with each element excited by a single frequency, equal amplitude and randomized 0 or <inline-formula><tex-math id="M5000">\begin{document}${\text{π}}$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20182122_M5000.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20182122_M5000.png"/></alternatives></inline-formula> phase signal will result in <i>N</i> independent measurements. Theoretically, any additional measurement under random illumination will be correlated with the previous <i>N</i> measurements. Since the random field illumination is obtained by array antennas with 1-bit random phase modulation, the power radiated by each transmitting element is not sacrificed, resulting in an optimal power efficiency of the imaging system compared with those of earlier metasurface-based imaging systems. Besides, a single frequency signal source is used in the system, which also realizes the optimal spectrum efficiency. In conclusion, there are two major innovations of the proposed imaging system: 1) the completely random field illuminations based on 1-bit phase modulation; 2) the approach to optimizing the system on desired demand. The compact and low-cost imaging system promises to have various imaging applications, such as public security and indoor localization.

Non-contact dynamic diffuse optical tomography imaging system for evaluating lower extremity vasculature.

A novel multi-view non-contact dynamic diffuse optical tomographic imaging system for the clinical evaluation of vasculature in the lower extremities is presented. The system design and implementation are described in detail, including methods for simultaneously obtaining and reconstructing diffusely reflected and transmitted light using a system of mirrors and a single CCD camera. The system and its performance using numeric simulations and optical phantoms. Measurements of a healthy foot in vivo demonstrates the potential of the system in assessing perfusion within the foot.

Thermal Light Longitudinal Correlated Imaging with Random Orthogonal Matching Pursuit Algorithm

A thermal light correlated longitudinal imaging experiment is proposed. The quasi-thermal light beam is split into two beams, a test beam and a reference beam, respectively. The light in the test beam is scattered by two amplitude objects with a specific longitudinal distance between them, while the light of the reference beam travels uninterrupted. At the end of the test and reference beams, two charge-coupled detectors (CCDs) are used to measure the intensity of the optical field. Through intensity correlation measurement the images of the two detected objects can be achieved simultaneously, only if the distance between the objects is less than the longitudinal coherent length. The theoretical analysis shows that the longitudinal coherent length is determined by both the transverse size of the incoherent thermal light source and the length of the optical path. The quality of the correlated images of the two objects is improved greatly by making use of the orthogonal matching pursuit (OMP) and the proposed variant random orthogonal matching pursuit (Random-OMP) algorithms. The experimental results show that the Random-OMP algorithm is more effective than the OMP algorithm for increasing both the visibility and continuity of the images. The experimental scenario can mimic an optical tomography imaging system, and the two objects with longitudinal distance can be taken as the two transverse layers of a three-dimensional object. The proposed Random-OMP algorithm is effective for improving the quality of the tomography image and has potential value in optical tomography imaging technology using incoherent light sources.

Virtual phase conjugation based optical tomography for single-shot three-dimensional imaging.

We propose a virtual phase conjugation (VPC) based optical tomography (VPC-OT) for realizing single-shot optical tomographic imaging systems. Using a computer-based numerical beam propagation, the VPC combines pre-modulation and post-demodulation of the probe beam's wavefront, which provides an optical sectioning capability for resolving the depth coordinates. In VPC-OT, the physical optical microscope system and VPC are coupled using digital holography. Therefore, in contrast to conventional optical tomographic imaging (OTI) systems, this method does not require additional elements such as low-coherence light sources or confocal pinholes. It is challenging to obtain single-shot three-dimensional (3D) tomographic images using a conventional OTI system; however, this can be achieved using VPC-OT, which employs both digital holography and computer based numerical beam propagation. In addition, taking into account that VPC-OT is based on a complex amplitude detection using digital holography, this method allows us to simultaneously obtain quantitative phase contrast images. Using an objective lens with a numerical aperture (NA) of 0.8, we demonstrate a single-shot 3D imaging of frog blood cells with a depth resolution of 0.94 μm.

激光扫描光学断层成像系统的设计与应用

激光扫描光学断层成像系统的设计与应用

TU‐D‐207A‐00: Quantitative Assessment of CT Systems with Iterative Image Reconstruction Algorithms

In recent several years, motivated by the need to reduce radiation doses in CT exams, all of the major CT manufacturers have commercialized different iterative image reconstruction techniques and these innovative techniques were used in clinical routines with increasing popularity. However, due to the intrinsic nonlinearity of these new techniques, the well accepted quantitative image quality assessment metrics such as spatial resolution and contrast to noise ratio are not sufficient to provide the needed quantitative metrics for assessing image quality and for guiding the CT scan protocol optimization. This symposium aims at providing a thorough update to AAPM community on what we have understood in the past for linear CT imaging system, what are the new challenges and opportunities offered by the nonlinear iterative image reconstruction, and what would be the future directions in quantitative image quality assessment that the AAPM community can work together to address the challenges and to adapt the nonlinear image reconstruction methods to routine clinical practice to improve patient care. Three invited speakers will lead the discussions in this symposium.Dr. Ke Li from the University of Wisconsin‐Madison will be presenting the new challenges introduced in model‐based iterative reconstruction (MBIR) method with a focus on how the nonlinear nature of the MBIR reconstruction poses challenges in quantitative image quality metrics such as spatial resolution, noise power spectrum, and the limitations of CNR in protocol optimization which eventually leads to the need of task‐based detectability index as the metric.Dr. Shuai Leng from Mayo Clinic will then present how the CNR metric is insufficient for nonlinear reconstruction algorithms, and how task based model observers can be used to more accurately quantify image quality, and to optimize imaging system and scanning protocols for nonlinear iterative image reconstruction algorithms based on specific imaging task. Practical considerations regarding the application of task based image quality metrics will also be discussed.Dr. Ehsan Samei from Duke University will start the symposium by summarizing a to‐be‐released AAPM Task Group report (TG 233: Performance Evaluation of Computed Tomography Systems). He will cover what has been understood and accepted for linear CT imaging system, what are the new challenges in CT image quality assessment introduced by the nonlinear iterative reconstruction (IR) techniques, and summarize both taskneutral and task‐based metrologies developed in TG 233 to partially address these challenges, and foresee additional challenges in the future. Learning Objectives: Understand the challenges in quantitative image quality assessment introduced by nonlinear iterative reconstruction techniques Understand the challenges and methods in noise performance assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in spatial resolution assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in task‐based observer performance assessment for nonlinear iterative reconstruction techniquesFunding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; S. Leng, R01 EB071095; U01 EB017185; E. Samei, Research grant, Siemens; Research grant, GE; K. Li, Funding from NIH, DOD and AAPM

TU‐D‐207A‐03: Quantitative Assessment of CT Systems with Iterative Image Reconstruction Algorithms

In recent several years, motivated by the need to reduce radiation doses in CT exams, all of the major CT manufacturers have commercialized different iterative image reconstruction techniques and these innovative techniques were used in clinical routines with increasing popularity. However, due to the intrinsic nonlinearity of these new techniques, the well accepted quantitative image quality assessment metrics such as spatial resolution and contrast to noise ratio are not sufficient to provide the needed quantitative metrics for assessing image quality and for guiding the CT scan protocol optimization. This symposium aims at providing a thorough update to AAPM community on what we have understood in the past for linear CT imaging system, what are the new challenges and opportunities offered by the nonlinear iterative image reconstruction, and what would be the future directions in quantitative image quality assessment that the AAPM community can work together to address the challenges and to adapt the nonlinear image reconstruction methods to routine clinical practice to improve patient care. Three invited speakers will lead the discussions in this symposium.Dr. Ke Li from the University of Wisconsin‐Madison will be presenting the new challenges introduced in model‐based iterative reconstruction (MBIR) method with a focus on how the nonlinear nature of the MBIR reconstruction poses challenges in quantitative image quality metrics such as spatial resolution, noise power spectrum, and the limitations of CNR in protocol optimization which eventually leads to the need of task‐based detectability index as the metric.Dr. Shuai Leng from Mayo Clinic will then present how the CNR metric is insufficient for nonlinear reconstruction algorithms, and how task based model observers can be used to more accurately quantify image quality, and to optimize imaging system and scanning protocols for nonlinear iterative image reconstruction algorithms based on specific imaging task. Practical considerations regarding the application of task based image quality metrics will also be discussed.Dr. Ehsan Samei from Duke University will start the symposium by summarizing a to‐be‐released AAPM Task Group report (TG 233: Performance Evaluation of Computed Tomography Systems). He will cover what has been understood and accepted for linear CT imaging system, what are the new challenges in CT image quality assessment introduced by the nonlinear iterative reconstruction (IR) techniques, and summarize both taskneutral and task‐based metrologies developed in TG 233 to partially address these challenges, and foresee additional challenges in the future. Learning Objectives: Understand the challenges in quantitative image quality assessment introduced by nonlinear iterative reconstruction techniques Understand the challenges and methods in noise performance assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in spatial resolution assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in task‐based observer performance assessment for nonlinear iterative reconstruction techniquesFunding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; S. Leng, R01 EB071095; U01 EB017185; E. Samei, Research grant, Siemens; Research grant, GE; K. Li, Funding from NIH, DOD and AAPM