SNR and noise measurements for medical imaging: I. A practical approach based on statistical decision theory

ABSTRACT. Magnification, which is considered to be a relatively high "dose cost" mammographic technique, is a complementary examination performed on women exhibiting breast complaints or abnormalities. Particular attention is given to the imaging procedure as the primary aim is to confirm the existence of suspected abnormalities, despite the additional dose. The introduction of post-processing capabilities and the widespread use of digital mammography promoted some controversy in the last decades on whether electronic zoom performed on the derived initial screening mammogram can effectively replace this technique. This study used Monte Carlo simulation methods to derive simulated screening mammograms produced under several exposure conditions, aiming to electronically magnify and compare them to the corresponding magnification mammograms. Comparison was based on quantitative measurements of image quality, namely contrast to noise ratio (CNR) and spatial resolution. Results demonstrated that CNR was higher for geometric magnification compared to the case of electronic zooming. The percentage difference was higher for lesions of smaller radius and achieved 29% for 0.10 mm details. Although spatial resolution is maintained high in the zoomed images, when investigating microcalcifications of 0.05 mm radius or less, only with geometric magnification can they be visualised.

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“…The CNR values for both geometric and electronic magnification modes were calculated using the formula proposed by Tapiovaara and Wagner [32]:…”

Section: Calculation Of Image Quality Metricsmentioning

confidence: 99%

Can electronic zoom replace magnification in mammography? A comparative Monte Carlo study

Koutalonis¹,

Delis²,

Pascoal³

et al. 2010

BJR

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“…Since our work focused on specific microcalcification enhancement and the more interesting work for radiologists is to enhance microcalcifications embedded in inhomogeneous and variable background, we defined two new evaluation indexes, the PSNR and the ASNR. These definitions were based on the general medical physics measurement and accepted by radiologists for the detection of microcalcifications [48], [49].…”

Section: A Evaluation Of Enhancementmentioning

confidence: 99%

Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms

Liu

Lo³

1997

IEEE Trans. Med. Imaging

154

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Abstract-The objective of this research is to model the mammographic parenchymal, ductal patterns and enhance the microcalcifications using deterministic fractal approach. According to the theory of deterministic fractal geometry, images can be modeled by deterministic fractal objects which are attractors of sets of two-dimensional (2-D) affine transformations. The iterated functions systems and the collage theorem are the mathematical foundations of fractal image modeling. In this paper, a methodology based on fractal image modeling is developed to analyze and model breast background structures. We show that general mammographic parenchymal and ductal patterns can be well modeled by a set of parameters of affine transformations. Therefore, microcalcifications can be enhanced by taking the difference between the original image and the modeled image. Our results are compared with those of the partial wavelet reconstruction and morphological operation approaches. The results demonstrate that the fractal modeling method is an effective way to enhance microcalcifications. It may also be able to improve the detection and classification of microcalcifications in a computer-aided diagnosis system.

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“…[38][39][40] A comprehensive description of this theory can be found in the publication by Barrett and Myers 41 To apply the theory to our photon-counting spectral imaging system, we assume two hypotheses, H 0 and H 1 , representing the absence and presence of an imaging target, respectively. The task is then defined as deciding whether the imaging target is present or not.…”

Section: Figure Of Meritmentioning

confidence: 99%

Optimization of beam quality for photon-counting spectral computed tomography in head imaging: simulation study

et al. 2015

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Abstract. Head computed tomography (CT) plays an important role in the comprehensive evaluation of acute stroke. Photon-counting spectral detectors, as promising candidates for use in the next generation of x-ray CT systems, allow for assigning more weight to low-energy x-rays that generally contain more contrast information. Most importantly, the spectral information can be utilized to decompose the original set of energy-selective images into several basis function images that are inherently free of beam-hardening artifacts, a potential advantage for further improving the diagnosis accuracy. We are developing a photon-counting spectral detector for CT applications. The purpose of this work is to determine the optimal beam quality for material decomposition in two head imaging cases: nonenhanced imaging and K-edge imaging. A cylindrical brain tissue of 16-cm diameter, coated by a 6-mm-thick bone layer and 2-mm-thick skin layer, was used as a head phantom. The imaging target was a 5-mm-thick blood vessel centered in the head phantom. In K-edge imaging, two contrast agents, iodine and gadolinium, with the same concentration (5 mg∕mL) were studied. Three parameters that affect beam quality were evaluated: kVp settings (50 to 130 kVp), filter materials (Z ¼ 13 to 83), and filter thicknesses [0 to 2 half-value layer (HVL)]. The image qualities resulting from the varying x-ray beams were compared in terms of two figures of merit (FOMs): squared signal-difference-to-noise ratio normalized by brain dose (SDNR 2 ∕BD) and that normalized by skin dose (SDNR 2 ∕SD). For nonenhanced imaging, the results show that the use of the 120-kVp spectrum filtered by 2 HVL copper (Z ¼ 29) provides the best performance in both FOMs. When iodine is used in K-edge imaging, the optimal filter is 2 HVL iodine (Z ¼ 53) and the optimal kVps are 60 kVp in terms of SDNR 2 ∕BD and 75 kVp in terms of SDNR 2 ∕SD. A tradeoff of 65 kVp was proposed to lower the potential risk of skin injuries if a relatively long exposure time is necessarily performed in the iodinated imaging. In the case of gadolinium imaging, both SD and BD can be minimized at 120 kVp filtered with 2 HVL thulium (Z ¼ 69). The results also indicate that with the same concentration and their respective optimal spectrum, the values of SDNR 2 ∕BD and SDNR 2 ∕SD in gadolinium imaging are, respectively, around 3 and 10 times larger than those in iodine imaging. However, since gadolinium is used in much lower concentrations than iodine in the clinic, iodine may be a preferable candidate for K-edge imaging.

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SNR and noise measurements for medical imaging: I. A practical approach based on statistical decision theory

Cited by 100 publications

References 57 publications

Can electronic zoom replace magnification in mammography? A comparative Monte Carlo study

Can electronic zoom replace magnification in mammography? A comparative Monte Carlo study

Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms

Optimization of beam quality for photon-counting spectral computed tomography in head imaging: simulation study

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