Anatomical background and generalized detectability in tomosynthesis and cone‐beam CT

Gang, Grace J.; Tward, Daniel J.; Lee, J; Siewerdsen, Jeffrey H.

doi:10.1118/1.3352586

Cited by 117 publications

(152 citation statements)

References 49 publications

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“…3 decades (78,79). These model observers have been applied to many different imaging modalities to narrow the range of acceptable imaging conditions and to improve the efficiency of system optimization (80), including nuclear medicine imaging (81)(82)(83), mammography (84-87), dual-energy radiography (88), tomosynthesis and flat-panel cone-beam CT (89)(90)(91), and magnetic resonance imaging (92). However, relatively few studies have been performed with clinical CT (93,94).…”

Section: Acknowledgmentmentioning

confidence: 99%

Achieving Routine Submillisievert CT Scanning: Report from the Summit on Management of Radiation Dose in CT

McCollough¹,

Chen²,

Kalender³

et al. 2012

Radiology

249

160

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Section: Acknowledgmentmentioning

confidence: 99%

Achieving Routine Submillisievert CT Scanning: Report from the Summit on Management of Radiation Dose in CT

McCollough¹,

Chen²,

Kalender³

et al. 2012

Radiology

249

160

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“…The approach provides a general framework that has been applied fairly broadly for modeling and optimization of 2D imaging systems, [15][16][17][18] as well as 3D modalities such as CBCT. 14,[19][20][21] The complete model of a CBCT imaging system 20,22 consists of 13 stages, including: a 2D projection cascade describing the physical processes from interaction of x-rays in the converter to sampling and readout of the detector with additive noise (and optional pixel binning 22 ); and a 3D cascade describing the mathematical processes of filtered backprojection, from log-transform of the projection data to discrete sampling of the 3D reconstruction matrix. 22 The studies reported below were limited to an investigation of the resolution (presampling detector MTF) and DQE inherent to the 2D image acquisition component of the proposed system via the 2D projection model.…”

Section: Iic1 Cascaded Systems Modelmentioning

confidence: 99%

A dedicated cone‐beam CT system for musculoskeletal extremities imaging: Design, optimization, and initial performance characterization

et al. 2011

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Purpose: This paper reports on the design and initial imaging performance of a dedicated conebeam CT (CBCT) system for musculoskeletal (MSK) extremities. The system complements conventional CT and MR and offers a variety of potential clinical and logistical advantages that are likely to be of benefit to diagnosis, treatment planning, and assessment of therapy response in MSK radiology, orthopaedic surgery, and rheumatology. Methods: The scanner design incorporated a host of clinical requirements (e.g., ability to scan the weight-bearing knee in a natural stance) and was guided by theoretical and experimental analysis of image quality and dose. Such criteria identified the following basic scanner components and system configuration: a flat-panel detector (FPD, Varian 3030þ, 0.194 mm pixels); and a low-power, fixed anode x-ray source with 0.5 mm focal spot (SourceRay XRS-125-7K-P, 0.875 kW) mounted on a retractable C-arm allowing for two scanning orientations with the capability for side entry, viz. a standing configuration for imaging of weight-bearing lower extremities and a sitting configuration for imaging of tensioned upper extremity and unloaded lower extremity. Theoretical modeling employed cascaded systems analysis of modulation transfer function (MTF) and detective quantum efficiency (DQE) computed as a function of system geometry, kVp and filtration, dose, source power, etc. Physical experimentation utilized an imaging bench simulating the scanner geometry for verification of theoretical results and investigation of other factors, such as antiscatter grid selection and 3D image quality in phantom and cadaver, including qualitative comparison to conventional CT. Results: Theoretical modeling and benchtop experimentation confirmed the basic suitability of the FPD and x-ray source mentioned above. Clinical requirements combined with analysis of MTF and DQE yielded the following system geometry: a $55 cm source-to-detector distance; 1.3 magnification; a 20 cm diameter bore (20 Â 20 Â 20 cm 3 field of view); total acquisition arc of $240 . The system MTF declines to 50% at $1.3 mm À1 and to 10% at $2.7 mm À1, consistent with submillimeter spatial resolution. Analysis of DQE suggested a nominal technique of 90 kVp (þ0.3 mm Cu added filtration) to provide high imaging performance from $500 projections at less than $0.5 kW power, implying $6.4 mGy (0.064 mSv) for low-dose protocols and $15 mGy (0.15 mSv) for high-quality protocols. The experimental studies show improved image uniformity and contrast-tonoise ratio (without increase in dose) through incorporation of a custom 10:1 GR antiscatter grid. Cadaver images demonstrate exquisite bone detail, visualization of articular morphology, and softtissue visibility comparable to diagnostic CT (10-20 HU contrast resolution). Conclusions:The results indicate that the proposed system will deliver volumetric images of the extremities with soft-tissue contrast resolution comparable to diagnostic CT and improved spatial resolution at potentially reduced dose. Cascaded sys...

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“…Specifically, optimization of the x-ray source scan angle and angular sampling scheme is an area of interest in recent literature. [6][7][8][9][10][11][12][13][14][15][16][17] These studies could reduce the number of physical prototype iterations and eventually lead to task-and patient-specific optimization of tomosynthesis. But to achieve these goals, we first need accurate, task-based strategies for evaluating key DBT system parameters in simulation.…”

Section: Introductionmentioning

confidence: 99%

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

et al. 2013

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Purpose: Digital breast tomosynthesis (DBT) is a promising breast cancer screening tool that has already begun making inroads into clinical practice. However, there is ongoing debate over how to quantitatively evaluate and optimize these systems, because different definitions of image quality can lead to different optimal design strategies. Powerful and accurate tools are desired to extend our understanding of DBT system optimization and validate published design principles. Methods: The authors developed a virtual trial framework for task-specific DBT assessment that uses digital phantoms, open-source x-ray transport codes, and a projection-space, spatial-domain observer model for quantitative system evaluation. The authors considered evaluation of reconstruction algorithms as a separate problem and focused on the information content in the raw, unfiltered projection images. Specifically, the authors investigated the effects of scan angle and number of angular projections on detectability of a small (3 mm diameter) signal embedded in randomly-varying anatomical backgrounds. Detectability was measured by the area under the receiver-operating characteristic curve (AUC). Experiments were repeated for three test cases where the detectability-limiting factor was anatomical variability, quantum noise, or electronic noise. The authors also juxtaposed the virtual trial framework with other published studies to illustrate its advantages and disadvantages. Results: The large number of variables in a virtual DBT study make it difficult to directly compare different authors' results, so each result must be interpreted within the context of the specific virtual trial framework. The following results apply to 25% density phantoms with 5.15 cm compressed thickness and 500 μm 3 voxels (larger 500 μm 2 detector pixels were used to avoid voxel-edge artifacts): 1. For raw, unfiltered projection images in the anatomical-variability-limited regime, AUC appeared to remain constant or increase slightly with scan angle. 2. In the same regime, when the authors fixed the scan angle, AUC increased asymptotically with the number of projections. The threshold number of projections for asymptotic AUC performance depended on the scan angle. In the quantum-and electronic-noise dominant regimes, AUC behaviors as a function of scan angle and number of projections sometimes differed from the anatomy-limited regime. For example, with a fixed scan angle, AUC generally decreased with the number of projections in the electronic-noise dominant regime. These results are intended to demonstrate the capabilities of the virtual trial framework, not to be used as optimization rules for DBT. Conclusions:The authors have demonstrated a novel simulation framework and tools for evaluating DBT systems in an objective, task-specific manner. This framework facilitates further investigation of image quality tradeoffs in DBT.

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Anatomical background and generalized detectability in tomosynthesis and cone‐beam CT

Abstract: The complex tradeoffs among anatomical background, quantum noise, and electronic noise in projection imaging, tomosynthesis, and CBCT can be described by generalized cascaded systems analysis, providing a useful framework for system design and optimization.

Cited by 117 publications

References 49 publications

Achieving Routine Submillisievert CT Scanning: Report from the Summit on Management of Radiation Dose in CT

Achieving Routine Submillisievert CT Scanning: Report from the Summit on Management of Radiation Dose in CT

A dedicated cone‐beam CT system for musculoskeletal extremities imaging: Design, optimization, and initial performance characterization

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

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