Comparison of Channel Methods and Observer Models for the Task-Based Assessment of Multi-Projection Imaging in the Presence of Structured Anatomical Noise

Although Laguerre-Gauss (LG) channels are often used for the task-based assessment of multi-projection imaging, LG channels may not be the most reliable in providing performance trends as a function of system or object parameters for all situations. Partial least squares (PLS) channels are more flexible in adapting to background and signal data statistics and were shown to be more efficient for detection tasks involving 2D non-Gaussian random backgrounds (Witten , 2010). In this work, we investigate ways of incorporating spatial correlations in the multi-projection data space using 2D LG channels and two implementations of PLS in the channelized version of the 3D projection Hotelling observer (Park , 2010) (3Dp CHO). Our task is to detect spherical and elliptical 3D signals in the angular projections of a structured breast phantom ensemble. The single PLS (sPLS) incorporates the spatial correlation within each projection, whereas the combined PLS (cPLS) incorporates the spatial correlations both within each of and across the projections. The 3Dp CHO-R indirectly incorporates the spatial correlation from the response space (R), whereas the 3Dp CHO-C from the channel space (C). The 3Dp CHO-R-sPLS has potential to be a good surrogate observer when either sample size is small or one training set is used for training both PLS channels and observer. So does the 3Dp CHO-C-cPLS when the sample size is large enough to have a good sized independent set for training PLS channels. Lastly a stack of 2D LG channels used as 3D channels in the CHO-C model showed the capability of incorporating the spatial correlation between the multiple angular projections.

[1]  Rongping Zeng,et al.  Evaluating the sensitivity of the optimization of acquisition geometry to the choice of reconstruction algorithm in digital breast tomosynthesis through a simulation study. , 2015, Physics in medicine and biology.

[2]  Jeffrey A. Fessler,et al.  3D Forward and Back-Projection for X-Ray CT Using Separable Footprints , 2010, IEEE Transactions on Medical Imaging.

[3]  A. Höskuldsson.  PLS regression methods , 1988 .

[4]  John M Boone,et al.  Contrast-enhanced dedicated breast CT: initial clinical experience. , 2010, Radiology.

[5]  Kyle J Myers,et al.  A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data. , 2013, Medical physics.

[6]  H H Barrett,et al.  Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  Andrew D. A. Maidment,et al.  Mammogram synthesis using a 3D simulation. I. Breast tissue model and image acquisition simulation. , 2002, Medical physics.

[8]  Xin He,et al.  Model Observers in Medical Imaging Research , 2013, Theranostics.

[9]  Ewout Vansteenkiste,et al.  Channelized Hotelling observers for the assessment of volumetric imaging data sets. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  Kyle J. Myers,et al.  Investigating the feasibility of using partial least squares as a method of extracting salient information for the evaluation of digital breast tomosynthesis , 2013, Medical Imaging.

[11]  Kyle J. Myers,et al.  A Task-Specific Argument for Variable-Exposure Breast Tomosynthesis , 2012, Digital Mammography / IWDM.

[12]  Harrison H Barrett,et al.  Validating the use of channels to estimate the ideal linear observer. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  Kyle J. Myers,et al.  Partial Least Squares: A Method to Estimate Efficient Channels for the Ideal Observers , 2010, IEEE Transactions on Medical Imaging.

[14]  M. C. Denham,et al.  Implementing partial least squares , 1995 .

[15]  Ehsan Samei,et al.  Optimized image acquisition for breast tomosynthesis in projection and reconstruction space. , 2009, Medical physics.

[16]  Ingrid Reiser,et al.  Spatial-domain model observers for optimizing tomosynthesis Subok Park , 2016 .

[17]  Kyle J. Myers,et al.  Is the Outcome of Optimizing the System Acquisition Parameters Sensitive to the Reconstruction Algorithm in Digital Breast Tomosynthesis? , 2012, Digital Mammography / IWDM.

[18]  E. Halpern,et al.  Assessing radiologist performance using combined digital mammography and breast tomosynthesis compared with digital mammography alone: results of a multicenter, multireader trial. , 2013, Radiology.

[19]  Aldo Badano,et al.  A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms. , 2010, Medical physics.

[20]  H H Barrett,et al.  Addition of a channel mechanism to the ideal-observer model. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[21]  Miguel P. Eckstein,et al.  The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds , 2006, IEEE Transactions on Medical Imaging.

[22]  Kai Yang,et al.  The characterization of breast anatomical metrics using dedicated breast CT. , 2011, Medical physics.

[23]  Rebecca Fahrig,et al.  Optimization of a tomosynthesis system for the detection of lung nodules. , 2006, Medical physics.

[24]  J. Boone,et al.  Non-Gaussian statistical properties of breast images. , 2012, Medical physics.

[25]  Andrew D. A. Maidment,et al.  Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm. , 2011, Medical physics.