Abstract
Digital breast tomosynthesis (DBT) is an emerging imaging modality for improved breast cancer detection and diagnosis [1-5]. Numerous efforts have been made to find quantitative metrics associated with mammographic image quality assessment, such as the exponent β of anatomical noise power spectrum, glandularity, contrast noise ratio, etc. [6-8]. In addition, with the use of Fourier-domain detectability for a task-based assessment of DBT, a stationarity assumption on reconstructed image statistics was often made [9-11], resulting in the use of multiple regions-of-interest (ROIs) from different locations in order to increase sample size. While all these metrics provide some information on mammographic image characteristics and signal detection, the relationship between these metrics and detectability in DBT evaluation has not been fully understood. In this work, we investigated spatial-domain detectability trends and levels as a function of the number of slices Ns at three different ROI locations on the same image slice, where background statistics differ in terms of the aforementioned metrics. Detectabilities for the three ROI locations were calculated using multi-slice channelized Hotelling observers with 2D/3D Laguerre-Gauss channels. Our simulation results show that detectability levels and trends as a function of Ns vary across these three ROI locations. They also show that the exponent β, mean glandularity, and mean attenuation coefficient vary across the three ROI locations but they do not necessarily predict the ranking of detectability levels and trends across these ROI locations.
| Original language | English |
|---|---|
| Title of host publication | Medical Imaging 2016 |
| Subtitle of host publication | Image Perception, Observer Performance, and Technology Assessment |
| Publisher | SPIE |
| Volume | 9787 |
| ISBN (Electronic) | 9781510600225 |
| DOIs | |
| Publication status | Published - 2016 Jan 1 |
| Event | Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment - San Diego, United States Duration: 2016 Mar 2 → 2016 Mar 3 |
Other
| Other | Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 16/3/2 → 16/3/3 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Biomaterials
- Radiology Nuclear Medicine and imaging
Cite this
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Investigation on location-dependent detectability of a small mass for digital breast tomosynthesis evaluation. / Lee, Changwoo; Baek, Jongduk; Park, Subok.
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment. Vol. 9787 SPIE, 2016. 97870V.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Investigation on location-dependent detectability of a small mass for digital breast tomosynthesis evaluation
AU - Lee, Changwoo
AU - Baek, Jongduk
AU - Park, Subok
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Digital breast tomosynthesis (DBT) is an emerging imaging modality for improved breast cancer detection and diagnosis [1-5]. Numerous efforts have been made to find quantitative metrics associated with mammographic image quality assessment, such as the exponent β of anatomical noise power spectrum, glandularity, contrast noise ratio, etc. [6-8]. In addition, with the use of Fourier-domain detectability for a task-based assessment of DBT, a stationarity assumption on reconstructed image statistics was often made [9-11], resulting in the use of multiple regions-of-interest (ROIs) from different locations in order to increase sample size. While all these metrics provide some information on mammographic image characteristics and signal detection, the relationship between these metrics and detectability in DBT evaluation has not been fully understood. In this work, we investigated spatial-domain detectability trends and levels as a function of the number of slices Ns at three different ROI locations on the same image slice, where background statistics differ in terms of the aforementioned metrics. Detectabilities for the three ROI locations were calculated using multi-slice channelized Hotelling observers with 2D/3D Laguerre-Gauss channels. Our simulation results show that detectability levels and trends as a function of Ns vary across these three ROI locations. They also show that the exponent β, mean glandularity, and mean attenuation coefficient vary across the three ROI locations but they do not necessarily predict the ranking of detectability levels and trends across these ROI locations.
AB - Digital breast tomosynthesis (DBT) is an emerging imaging modality for improved breast cancer detection and diagnosis [1-5]. Numerous efforts have been made to find quantitative metrics associated with mammographic image quality assessment, such as the exponent β of anatomical noise power spectrum, glandularity, contrast noise ratio, etc. [6-8]. In addition, with the use of Fourier-domain detectability for a task-based assessment of DBT, a stationarity assumption on reconstructed image statistics was often made [9-11], resulting in the use of multiple regions-of-interest (ROIs) from different locations in order to increase sample size. While all these metrics provide some information on mammographic image characteristics and signal detection, the relationship between these metrics and detectability in DBT evaluation has not been fully understood. In this work, we investigated spatial-domain detectability trends and levels as a function of the number of slices Ns at three different ROI locations on the same image slice, where background statistics differ in terms of the aforementioned metrics. Detectabilities for the three ROI locations were calculated using multi-slice channelized Hotelling observers with 2D/3D Laguerre-Gauss channels. Our simulation results show that detectability levels and trends as a function of Ns vary across these three ROI locations. They also show that the exponent β, mean glandularity, and mean attenuation coefficient vary across the three ROI locations but they do not necessarily predict the ranking of detectability levels and trends across these ROI locations.
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U2 - 10.1117/12.2216579
DO - 10.1117/12.2216579
M3 - Conference contribution
AN - SCOPUS:84976334357
VL - 9787
BT - Medical Imaging 2016
PB - SPIE
ER -