MO‐A‐ValB‐01: Tradeoffs in Image Quality and Radiation Dose for CT

McNitt‐Gray, M

doi:10.1118/1.2241390

Cited by 14 publications

(6 citation statements)

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“…In CT imaging, many technical parameters and components impact the image quality and radiation dose (McNitt-Gray 2003). The reconstructed slice thickness of an image relates to not only the Z-axis resolution, but also the noise, which is influenced by the x-ray tube current.…”

Section: Introductionmentioning

confidence: 99%

Computed tomography super-resolution using deep convolutional neural network

et al. 2018

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The objective of this study is to develop a convolutional neural network (CNN) for computed tomography (CT) image super-resolution. The network learns an end-to-end mapping between low (thick-slice thickness) and high (thin-slice thickness) resolution images using the modified U-Net. To verify the proposed method, we train and test the CNN using axially averaged data of existing thin-slice CT images as input and their middle slice as the label. Fifty-two CT studies are used as the CNN training set, and 13 CT studies are used as the test set. We perform five-fold cross-validation to confirm the performance consistency. Because all input and output images are used in two-dimensional slice format, the total number of slices for training the CNN is 7670. We assess the performance of the proposed method with respect to the resolution and contrast, as well as the noise properties. The CNN generates output images that are virtually equivalent to the ground truth. The most remarkable image-recovery improvement by the CNN is deblurring of boundaries of bone structures and air cavities. The CNN output yields an approximately 10% higher peak signal-to-noise ratio and lower normalized root mean square error than the input (thicker slices). The CNN output noise level is lower than the ground truth and equivalent to the iterative image reconstruction result. The proposed deep learning method is useful for both super-resolution and de-noising.

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Section: Introductionmentioning

confidence: 99%

Computed tomography super-resolution using deep convolutional neural network

et al. 2018

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“…All the series were acquired for the Catphan 604 quality control phantom. We applied the dose constancy restriction CTDI W = 17 mGy in all the series because this is the dose administered in our reference series at 120 kV p , and this way we could control for the dose effects on image quality 7 …”

Section: Methodsmentioning

confidence: 99%

“…We applied the dose constancy restriction CTDI W = 17 mGy in all the series because this is the dose administered in our reference series at 120 kV p , and this way we could control for the dose effects on image quality. 7 The VMI series were obtained using the Siemens Syngo Via software with the algorithm Monoenergetic+ for noise reduction at low energies, based on the SECT series at 80 and 140 kV p . Monoenergetic images at energy E are calculated as a linear combination of the acquisitions at high and low energy according to the formula developed by Yu et al 8 : where x is the specific pixel; HU x,low and HU x,high , the Hounsfield units of the pixel in the low-and high-energy series, respectively; and ω x , the weighting factor, which can be calculated from the attenuation coefficients of any two known materials, in this case, water and iodine, μ 1 and μ 2 , so that:…”

Section: Introductionmentioning

confidence: 99%

Determination of the optimal range for virtual monoenergetic images in dual‐energy CT based on physical quality parameters

et al. 2021

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Purpose Virtual monoenergetic images (VMI) obtained from Dual‐Energy Computed Tomography (DECT) with iodinated contrast are used in radiotherapy of the Head and Neck to improve the delineation of target volumes and organs at‐risk (OAR). The energies used to vary from 40 to 70 keV, but noise at low keV and the use of Single Energy CT (SECT) at low kVp settings may shrink this interval. There is no guide about how to find out the optimal range where VMI has a significant improvement related to SECT images. Our study proposes a procedure to determine this optimal range, based on common image quality parameters, and establishes this range in a Siemens Somatom Confidence and a Head and Neck protocol. Methods We compared the quality of the VMI series at 40–60 keV versus single X‐ray tube voltage computed tomography (SECT) at 80 and 120 kVp. Our reference was 120 kVp. DECT images were sequentially acquired using the Siemens Somatom Confidence RT Pro CT according to the head and neck protocol in our department. VMI series were constructed using the Syngo Via software Monoenergetic+ algorithm. Quality parameters were: image uniformity, high‐ and low‐contrast resolution, noise, and sensitivity to the iodinated contrast. We used the Catphan 604 phantom for quality control, except when assessing iodine sensitivity. To evaluate high contrast resolution, we calculated the modulation transfer function (MTF) using the point spread function estimation of a point bead and the slanted edge methods. For the low‐contrast resolution, we used a statistical method for assessing differences between contrast structures and local noise. To measure the absolute value of noise and compare its texture, we used the standard deviation and the noise power spectrum. We measured iodine sensitivity by dissolving the Optiray Ultraject iodinated contrast in water in concentrations of 0 to 4500 mg/l and then compared the contrast to noise ratio (CNR) and analyzed the linear correlation between concentration and HU. Results The entire series met the minimum quality requirements. However, the one at 40 keV presented uniformity at the limits of acceptability. The high‐ and low‐contrast resolutions were similar between series. The noise of the VMI series decreased with increasing energy, while sensitivity to the contrast displayed the opposite behavior. All series showed linearity of HUs from very low iodine concentrations. Images at 60 keV presented lower iodine sensitivity than SECT at 80 kVp, while those at 55 keV were similar to them. Conclusions Our method of image comparison based on standard quality parameters in phantom gave clear results about the optimal range and can be used as a guide to characterize any other DECT imaging protocols. The optimal range for using VMI images in iodinated contrasts in the Siemens system was 45–55 keV. Lower energies lacked noise and uniformity, while higher ones could be substituted by SECT images at low kilovoltage (80 kVp).

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“…If the voxel was partially occupied by the nodule (partial volume effect), the density of the voxel was proportional to the difference between nodule density and background density, and the fraction of the nodule occupying the voxel. The exact voxel density was determined using a cosine sensitivity profile, as described in prior work [8].…”

Section: Methodsmentioning

confidence: 99%

A Mathematical Simulation to Assess Variability in Lung Nodule Size Measurement Associated With Nodule-Slice Position

Juluru

Khori

et al. 2014

J Digit Imaging

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The purpose of this study is to assess the variance and error in nodule diameter measurement associated with variations in nodule-slice position in cross-sectional imaging. A computer program utilizing a standard geometric model was used to simulate theoretical slices through a perfectly spherical nodule of known size, position, and density within a background of "lung" of known fixed density. Assuming a threshold density, partial volume effect of a voxel was simulated using published slice and pixel sensitivity profiles. At a given slice thickness and nodule size, 100 scans were simulated differing only in scan start position, then repeated for multiple node sizes at three simulated slice thicknesses. Diameter was measured using a standard, automated algorithm. The frequency of measured diameters was tabulated; average errors and standard deviations (SD) were calculated. For a representative 5-mm nodule, average measurement error ranged from +10 to −23 % and SD ranged from 0.07 to 0.99 mm at slice thicknesses of 0.75 to 5 mm, respectively. At fixed slice thickness, average error and SD decreased from peak values as nodule size increased. At fixed nodule size, SD increased as slice thickness increased. Average error exhibited dependence on both slice thickness and threshold. Variance and error in nodule diameter measurement associated with nodule-slice position exists due to geometrical limitations. This can lead to false interpretations of nodule growth or stability that could affect clinical management. The variance is most pronounced at higher slice thicknesses and for small nodule sizes. Measurement error is slice thickness and threshold dependent.

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MO‐A‐ValB‐01: Tradeoffs in Image Quality and Radiation Dose for CT

Cited by 14 publications

References 0 publications

Computed tomography super-resolution using deep convolutional neural network

Computed tomography super-resolution using deep convolutional neural network

Determination of the optimal range for virtual monoenergetic images in dual‐energy CT based on physical quality parameters

A Mathematical Simulation to Assess Variability in Lung Nodule Size Measurement Associated With Nodule-Slice Position

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