Research Awards at AAPM 2014 (Austin TX)

Grace Gang and Adam Wang – both postdoctoral fellows in the I-STAR Lab at Hopkins – won awards for outstanding research at the annual meeting of the AAPM in Austin TX, July 24, 2014. Grace was awarded an AAPM Research Seed Funding Grant for her project entitled “Task-Driven, Patient-Specific Imaging for CT and Cone-Beam CT,” work that combines the fundamental physical models of image quality with statistical reconstruction to improve imaging performance and reduce dose. Adam Wang was awarded the Junior Investigator Award for his abstract entitled, “Low-Dose C-arm Cone-Beam CT with Model-Based Image Reconstruction for High-Quality Guidance of Neurosurgical Intervention.” Adam’s work demonstrates the powerful role that advanced 3D imaging methods hold for image-guided procedures, providing high-quality images in the OR for guidance, quality assurance, and improved patient safety.

Six Talks at AAPM 2014 – The Latest in Imaging Physics

The I-STAR Lab presented 6 talks at the annual meeting of the American Association of Physicists in Medicine (AAPM) in Austin TX, July 20 – 25, 2014, featuring the latest in imaging physics, analysis, reconstruction, and Monte Carlo simulation.

- Wojciech Zbijewski presented an overview and update in an educational symposium on Monte Carlo simulation, including variance reduction and kernel smoothing acceleration methods. His work shows such methods to provide accurate MC simulation on timescales consistent with real applications in diagnostic imaging and image-guided procedures.

– Alejandro Sisniega presented a framework for high-quality cone-beam CT of traumatic brain injury (TBI) including high-fidelity artifact correction. Using GPU-accelerated Monte Carlo scatter correction combined with parametric models of beam-hardening, lag, and veiling glare, his work demonstrates major improvement in CBCT image quality consistent with the challenging tasks of TBI imaging.

– Qian Cao presented a model for 3D image analysis in cone-beam CT of the joints. Using an electrostatic model that envisions the joint as a capacitor, his work overcomes conventional limitations of simple joint space measures and demonstrated significant improvement in the ability to detect osteoarthritis associated with subtle changes in joint space morphology.

– Adam Wang presented a new method for high-speed statistical image reconstruction using accelerated convergence methods based on Nesterov’s method. Without loss in image quality, Adam’s work shows that methods conventionally requiring an hour or more to reconstruct can be performed in 2 minutes, bringing advanced iterative reconstruction methods to a practical timescale for image-guided surgery.

– Web Stayman presented the latest advances in 3D image reconstruction in an invited symposium, including how specification of the imaging task can be rigorously incorporated in the image acquisition and reconstruction process. His work shows how advanced imaging platforms such as a robotic C-arm can be used to carry out noncircular orbits that are optimal in image quality and dose for image-guided procedures.

– Jeff Siewerdsen presented a plenary in the President’s Symposium entitled “Innovation in the Medical Physics Enterprise.” He addressed the central role that medical physicists play in advancing the state of care through identification of pertinent clinical needs, development of innovative solutions, and translation to clinical care. Particularly in a changing, cost-sensitive landscape, the role of medical physicists in multi-disciplinary research research and innovation is greater than ever.

Four Presentations from The I-STAR Lab at the 2014 International CT Meeting, Salt Lake City

The latest research in advanced CT image reconstruction methods, modeling, and image quality are presented in four presentations from The I-STAR Lab at the 2014 International CT Meeting in Salt Lake City (June 23 – 26). Highlights include:
- W. Zbijewski et al., “A Sparse Monte Carlo Method for High-Speed, High-Accuracy Scatter Correction for Soft-Tissue Imaging in Cone-Beam CT.” As illustrated in the animation at left, cone-beam CT can suffer from image artifacts that pose a major challenge to soft tissue visibility and diagnostic accuracy in imaging of the head. Research presented by Dr. Zbijewski shows that high-speed Monte Carlo methods can be used for high-quality scatter correction, and combined with a comprehensive framework for correction of artifacts arising from lag, beam hardening, veiling glare, and other sources of image degradation, can yield image quality suitable to imaging of subtle pathology such as intracranial hemorrhage and traumatic brain in jury.
- J. Web Stayman et al., “Integration of Component Knowledge in Penalized Likelihood Reconstruction with Morphological and Spectral Uncertainties.” By extending the framework for Known-Component Reconstruction (KCR) to deformable objects and a polyenergetic x-ray beam, research presented by Dr. Stayman offers to improve image quality and reduce radiation dose in CT-guided procedures such as needle biopsy.
- S. Tilley et al., “Iterative CT Reconstruction Using Models of Source and Detector Blur and Correlated Noise.” Research presented by Steve Tilley shows how model-based reconstruction can be improved by incorporating models for blur and noise correlation, showing particular advantage over conventional models for scanner configurations in which focal spot blur is a significant source of image degradatation.
- A. S. Wang et al., “Nesterov’s Method for Accelerated Penalized Likelihood Statistical Reconstruction for C-arm Cone-Beam CT.” For image-guided surgery, the ability to form high-quality cone-beam CT using a mobile C-arm offers important advances in surgical precision and safety. Research presented by Adam Wang shows that not only can advanced reconstruction methods be used to improve CBCT image quality for soft tissue imaging and reduce radiation dose, but also that such images can be formed on practical time scales in the operating room (less than 2 minutes) using Nesterov’s method for accelerated convergence.

New 3D Image Reconstruction Methods Improve Image Quality and Reduce Dose

Research underway in The I-STAR Lab spearheaded by Dr. Web Stayman offers to advance the performance of CT and cone-beam CT in diagnostic and image-guided procedures. Among the breakthroughs  are three forms of penalized likelihood (PL) model-based image reconstruction that overcome conventional barriers to image quality and dose and present potentially new paradigms for CT image acquisition and reconstruction that knowledgeably include prior information and a specification of the imaging task.

Task-Driven Imaging. Over the last decade, task-based image quality assessment has formed an area of active research, including the use of task-based detectability index for the design and optimization of new cone-beam CT systems for diagnostic and image-guided procedures. Recent research takes task-based metrics from the realm of image quality assessment to a position directly within the imaging chain – as the objective function to be optimized in the process of image acquisition and reconstruction. The resulting Task-Driven Imaging method presents a new paradigm for technique optimization with implications for new acquisition and reconstruction techniques — e.g., optimal source-detector orbits, as reported at The Fully 3D Meeting 2013). Recent research includes testing and evaluation of Task-Driven Imaging on a robotic C-arm for image-guided interventions.

PIRPLE. Many imaging scenarios – especially in image-guided interventions – involve repeat image acquisitions. For example, in image-guided radiotherapy or surgery, the patient receives a planning CT followed by a number of images acquired for interventional guidance. The PIRPLE algorithm (Prior-Image-Registered Penalized Likelihood Estimation) incorporates the prior image within the up-to-date image reconstruction process via an additional penalty and regularization term. The approach demonstrates the potential for major improvement in image quality and reduction of dose. Recent work includes extension to deformable prior images (dPIRPLE) and evaluation in clinical studies.

KCR. The “Known-Component Reconstruction” (KCR) framework forms the 3D image in two parts – an unknown background component (e.g., patient anatomy) and a known component (e.g., implant or interventional device) whose shape and content are specified either exactly or parametrically. In solving a joint registration (of the known component) and reconstruction (of the component and background), KCR demonstrates major reduction in noise and artifacts that plague conventional imaging methods, especially in the presence of heavy metal components, such as screws, plates, and prosthetics. Original findings were reported in IEEE-TMI, and recent advances extend KCR to deformable components (dKCR, such as needles or cochlear implants) and account for effects of the polyenergetic x-ray beam.

1st and 2nd Place Paper Awards at SPIE 2014

Students from the I-STAR Lab earned 1st and 2nd place paper awards at the SPIE 2014 Medical Imaging conference in San Diego. 

Sureerat (“Ja”) Reaungamornrat won the 1st-place student paper award in the Image-Guided Procedures conference as well as the SPIE Young Scientist Award. Her paper entitled “Deformable registration for image-guided spine surgery: preserving rigid body vertebral morphology in free-form transformations.” described a method for constraining 3D deformable image registration in a manner that preserves the morphology of rigid bodies (e.g., spinal vertebrae) moving within a context of surrounding soft tissues. (SPIE Abstract 9036-27, Page 138) 

Jennifer Xu won the 2nd-place student paper award in the Physics of Medical Imaging conference. Her paper entitled “Cascaded Systems Analysis of Photon Counting Detectors.” described a cascaded systems model for noise, spatial resolution, and detective quantum efficiency (DQE) of photon counting detectors, providing an analytical basis for understanding the effects of charge sharing and detector threshold and validating the theoretical predictions in comparison to measurements with the Philips MicroDose photon counting system. (SPIE Abstract 9033-70, Page 20)