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Program Information

Advances in Model-Based 3D Image Reconstruction

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G Chen
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X Pan
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J Stayman

E Samei

G Chen1*, X Pan2*, J Stayman3*, E Samei4*, (1) University of Wisconsin, Madison, WI, (2) University Chicago, Chicago, IL, (3) Johns Hopkins University, Baltimore, MD, (4) Duke University Medical Center, Durham, NC


MO-C-18A-1 Monday 10:15AM - 12:15PM Room: 18A

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within the reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications.

Learning Objectives:
1. Learn the general methodologies associated with model-based 3D image reconstruction.
2. Learn the potential advantages in image quality and dose associated with model-based image reconstruction.
3. Learn the challenges associated with computational load and image quality assessment for such reconstruction methods.
4. Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques.
5. Learn how model-based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.


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