Mr. Nabeel Younus Khan Profile

Nabeel Younus Khan

Imaging Scientist at Volpara Solutions Ltd

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Proceedings Article | 22 May 2020 Paper

Mammographic image quality assessment by a contrast-to-noise ratio for clinical images

Melissa Hill, Alistair Mackenzie, Lucy Warren, Nabeel Khan, Kaier Wang, Ralph Highnam

Proceedings Volume 11513, 115130I (2020) https://doi.org/10.1117/12.2564355

KEYWORDS: Breast, Image quality, Mammography, Tissues, Image analysis, Gold, Spatial frequencies

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Purpose: To test the association between contrast-to-noise ratio (CNR) measurements made on digital mammograms (DM), human reader performance in a lesion detection task using the same images, and image quality (IQ) as predicted by phantom measurements. Methods: DM from 162 women were evaluated for their CNR using a novel metric for application on clinical images. The original unprocessed images were tested (100% dose), as well as the same images after processing to simulate a 50% and 25% relative dose level. IQ measurements from a CDMAM phantom images, as well as human reader calcification cluster detectability ratings on the clinical image set for the three treatments were used to provide ground truth for human lesion detection performance. Analysis was performed to test for association between DM image CNR at the three dose levels, the CDMAM measurements, and reader performance as quantified by a reader-averaged jack-knifed free response operating characteristic (JAFROC) figure of merit (FoM). Results: The clinical image CNR was strongly correlated with the JAFROC FoM and CDMAM threshold gold thicknesses (r=0.98, and r=0.99 @ 0.25 mm, r=0.94 @ 0.1 mm discs, respectively). On a per-image basis, strong associations between CNR and measures of beam quality and exposure were also found that indicate sensitivity to imaging technique factors while remaining independent of signal variations due to breast parenchyma. Conclusions: Using a clinical image CNR it is possible to objectively predict IQ in mammographic images. As such, this metric could provide a means to perform a practical continuous DM system performance monitoring.

Proceedings Article | 9 March 2018 Paper

Validation of noise estimation for a clinical contrast-to-noise ratio for digital mammographic imaging

Melissa Hill, Alistair Mackenzie, Nabeel Khan, Kaier Wang, Ralph Highnam

Proceedings Volume 10573, 105735D (2018) https://doi.org/10.1117/12.2293866

KEYWORDS: Breast imaging, Digital mammography, Digital x-ray imaging, Digital breast tomosynthesis, Image analysis, Image quality, Mammography, Spatial frequencies

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Mammographic image quality is important to monitor to maximize diagnostic performance while minimizing patient exposure to ionizing radiation. Phantom imaging for quality control permits practical monitoring of signal and noise, and to optimize use of dose via the contrast-to-noise ratio (CNR). However, it remains a challenge to directly and objectively evaluate CNR in clinical images due to subject variability. A novel clinical image CNR metric has been developed that derives an estimate of system-dependent image noise and references contrast to tissue composition. The present work uses phantom images to validate the noise estimates and to demonstrate sensitivity to imaging conditions. Images of 1 cm adipose-equivalent blocks with 2, 3, and 5 cm 50/50 swirl phantoms and uniform 50/50 blocks were acquired using AEC-selected parameters, and at 0.33 and 0.5 of the AEC-selected mAs at 6 cm. Digital mammograms (DM) were acquired on a GE Essential with and without FineView processing, and in conventional and digital breast tomosynthesis (DBT) views on a Hologic Selenia Dimensions. The CNR was computed using contrast between a 0.4 mm CaCO₃ speck in a target slab and adjacent background signal, and noise derived from paired raw and subtracted swirl phantom images. Swirl phantom CNR was estimated to within ±10% of uniform image CNR for GE and Hologic DM, and ±3% for Hologic DBT, and showed good sensitivity to acquisition technique. These results demonstrate promise for objective and efficient image quality evaluation from patient images, using noise estimates that effectively avoid signal related to tissue structure.

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