Minah Han

Effect of anatomical noise on the detectability of cone beam CT images with different slice direction, slice thickness, and volume glandular fraction.

We investigate the effect of anatomical noise on the detectability of cone beam CT (CBCT) images with different slice directions, slice thicknesses, and volume glandular fractions (VGFs). Anatomical noise is generated using a power law spectrum of breast anatomy, and spherical objects with diameters from 1mm to 11mm are used as breast masses. CBCT projection images are simulated and reconstructed using the FDK algorithm. A channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels is used to evaluate detectability for the signal-known-exactly (SKE) binary detection task. Detectability is calculated for various slice thicknesses in the transverse and longitudinal planes for 15%, 30% and 60% VGFs. The optimal slice thicknesses that maximize the detectability of the objects are determined. The results show that the β value increases as the slice thickness increases, but that thicker slices yield higher detectability in the transverse and longitudinal planes, except for the case of a 1mm diameter spherical object. It is also shown that the longitudinal plane with a 0.1mm slice thickness provides higher detectability than the transverse plane, despite its higher β value. With optimal slice thicknesses, the longitudinal plane exhibits better detectability for all VGFs and spherical objects.

Investigation on slice direction dependent detectability of volumetric cone beam CT images

We investigate the detection performance of transverse and longitudinal planes for various signal sizes (i.e., 1 mm to 8 mm diameter spheres) in cone beam computed tomography (CBCT) images. CBCT images are generated by computer simulation and images are reconstructed using an FDK algorithm. For each slice direction and signal size, a human observer study is conducted with a signal-known-exactly/background-known-exactly (SKE/BKE) binary detection task. The detection performance of human observers is compared with that of a channelized Hotelling observer (CHO). The detection performance of an ideal linear observer is also calculated using a CHO with Laguerre-Gauss (LG) channels. The detectability of high contrast small signals (i.e., up to 4-mm-diameter spheres) is higher in the longitudinal plane than the transverse plane. It is also shown that CHO performance correlates well with human observer performance in both transverse and longitudinal plane images.

Human and model observer performance for lesion detection in breast cone beam CT images with the FDK reconstruction

We investigate the detectability of breast cone beam computed tomography images using human and model observers and the variations of exponent, β, of the inverse power-law spectrum for various reconstruction filters and interpolation methods in the Feldkamp-Davis-Kress (FDK) reconstruction. Using computer simulation, a breast volume with a 50% volume glandular fraction and a 2mm diameter lesion are generated and projection data are acquired. In the FDK reconstruction, projection data are apodized using one of three reconstruction filters; Hanning, Shepp-Logan, or Ram-Lak, and back-projection is performed with and without Fourier interpolation. We conduct signal-known-exactly and background-known-statistically detection tasks. Detectability is evaluated by human observers and their performance is compared with anthropomorphic model observers (a non-prewhitening observer with eye filter (NPWE) and a channelized Hotelling observer with either Gabor channels or dense difference-of-Gaussian channels). Our results show that the NPWE observer with a peak frequency of 7cyc/degree attains the best correlation with human observers for the various reconstruction filters and interpolation methods. We also discover that breast images with smaller β do not yield higher detectability in the presence of quantum noise.

SU‐E‐I‐10: Investigation On Detectability of a Small Target for Different Slice Direction of a Volumetric Cone Beam CT Image

Purpose: To investigate the detectability of a small target for different slice direction of a volumetric cone beam CT image and its impact on dose reduction. Methods: Analytic projection data of a sphere object (1 mm diameter, 0.2/cm attenuation coefficient) were generated and reconstructed by FDK algorithm. In this work, we compared the detectability of the small target from four different backprojection Methods: hanning weighted ramp filter with linear interpolation (RECON 1), hanning weighted ramp filter with Fourier interpolation (RECON2), ramp filter with linear interpolation (RECON 3), and ramp filter with Fourier interpolation (RECON4), respectively. For noise simulation, 200 photons per measurement were used, and the noise only data were reconstructed using FDK algorithm. For each reconstructed volume, axial and coronal slice were extracted and detection-SNR was calculated using channelized Hotelling observer (CHO) with dense difference-of-Gaussian (D-DOG) channels. Results: Detection-SNR of coronal images varies for different backprojection methods, while axial images have a similar detection-SNR. Detection-SNR2 ratios of coronal and axial images in RECON1 and RECON2 are 1.33 and 1.15, implying that the coronal image has a better detectability than axial image. In other words, using coronal slices for the small target detection can reduce the patient dose about 33% and 15% compared to using axial slices in RECON 1 and RECON 2. Conclusion: In this work, we investigated slice direction dependent detectability of a volumetric cone beam CT image. RECON 1 and RECON 2 produced the highest detection-SNR, with better detectability in coronal slices. These results indicate that it is more beneficial to use coronal slice to improve detectability of a small target in a volumetric cone beam CT image. This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the IT Consilience Creative Program (NIPA-2014-H0201-14-1002) supervised by the NIPA (National IT Industry Promotion Agency). Authors declares that s/he has no conflict of Interest in relation to the work in this abstract.

Inter‐laboratory comparison of channelized hotelling observer computation

Purpose The task‐based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well‐established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise. Materials and Methods Image samples to estimate model observer performance for detection tasks were generated from two‐dimensional CT image slices of a uniform water phantom. A common set of images was sent to participating laboratories to perform and document the following tasks: (a) estimate the detectability index of a well‐defined CHO and its uncertainty in three conditions involving different sized targets all at the same dose, and (b) apply this CHO to an image set where ground truth was unknown to participants (lower image dose). In addition, and on an optional basis, we asked the participating laboratories to (c) estimate the performance of real human observers from a psychophysical experiment of their choice. Each of the 13 participating laboratories was confidentially assigned a participant number and image sets could be downloaded through a secure server. Results were distributed with each participant recognizable by its number and then each laboratory was able to modify their results with justification as model observer calculation are not yet a routine and potentially error prone. Results Detectability index increased with signal size for all participants and was very consistent for 6 mm sized target while showing higher variability for 8 and 10 mm sized target. There was one order of magnitude between the lowest and the largest uncertainty estimation. Conclusions This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting.

Evaluation of human observer performance on lesion detectability in single-slice and multislice dedicated breast cone beam CT images with breast anatomical background

PURPOSE We evaluate the lesion detectability using human and model observer studies in single-slice and multislice cone beam computed tomography (CBCT) images with a breast anatomical background. The purposes of this work are (a) to compare human observer detectability between single-slice and multislice images for different signal sizes and noise structures, (b) to investigate the effect of different multislice viewing modes (i.e., sequential and simultaneous) on the detectability by a human observer, and (c) to predict the detectability by a human observer in single-slice and multislice images using single-slice channelized Hotelling observer (ssCHO) and multislice CHO (msCHO), respectively. METHODS Breast anatomical background is modeled using a power law spectrum of mammograms and the lesion is modeled with a spherical signal. We conduct signal-known-exactly and background-known-statistically detection tasks on transverse and longitudinal images reconstructed using the Feldkamp-Davis-Kress algorithm with Hanning and Ram-Lak weighted ramp filters. The human observer study is conducted on three different viewing modes: single-slice, and sequential and simultaneous multislice. To predict the detectability by a human observer, we use ssCHO and msCHO with anthropomorphic channels (i.e., dense difference-of-Gaussian (D-DOG) and Gabor channels) and internal noise. RESULTS The detectability by a human observer increases for multislice images compared to single-slice images. For multislice images, the sequential viewing mode yields higher detectability than the simultaneous viewing mode. However, the relative rank of detectability by a human observer for different signal sizes, image planes, and reconstruction filters is not much different between the viewing modes. Detectability by CHO with internal noise shows good correlation with that of the human observer for all viewing modes. CONCLUSIONS Detectability by a human observer in CBCT images with breast anatomical background is affected by the image viewing mode, and the effect of the viewing mode depends on the signal size and noise structure. D-DOG and Gabor CHO with internal noise predict the detectability by a human observer well for both the single-slice and multislice image viewing modes.

Lesion detection performance of cone beam CT images with anatomical background noise: single-slice vs multi-slice human and model observer study.

We investigate lesion detectability and its trends for different noise structures in single-slice and multislice CBCT images with anatomical background noise. Anatomical background noise is modeled using a power law spectrum of breast anatomy. Spherical signal with a 2 mm diameter is used for modeling a lesion. CT projection data are acquired by the forward projection and reconstructed by the Feldkamp-Davis-Kress algorithm. To generate different noise structures, two types of reconstruction filters (Hanning and Ram-Lak weighted ramp filters) are used in the reconstruction, and the transverse and longitudinal planes of reconstructed volume are used for detectability evaluation. To evaluate single-slice images, the central slice, which contains the maximum signal energy, is used. To evaluate multislice images, central nine slices are used. Detectability is evaluated using human and model observer studies. For model observer, channelized Hotelling observer (CHO) with dense difference-of-Gaussian (D-DOG) channels are used. For all noise structures, detectability by a human observer is higher for multislice images than single-slice images, and the degree of detectability increase in multislice images depends on the noise structure. Variation in detectability for different noise structures is reduced in multislice images, but detectability trends are not much different between single-slice and multislice images. The CHO with D-DOG channels predicts detectability by a human observer well for both single-slice and multislice images.

Investigation on location dependent detectability in cone beam CT images with uniform and anatomical backgrounds

We investigate location dependent lesion detectability of cone beam computed tomography images for different background types (i.e., uniform and anatomical), image planes (i.e., transverse and longitudinal) and slice thicknesses. Anatomical backgrounds are generated using a power law spectrum of breast anatomy, 1/f3. Spherical object with a 5mm diameter is used as a signal. CT projection data are acquired by the forward projection of uniform and anatomical backgrounds with and without the signal. Then, projection data are reconstructed using the FDK algorithm. Detectability is evaluated by a channelized Hotelling observer with dense difference-of-Gaussian channels. For uniform background, off-centered images yield higher detectability than iso-centered images for the transverse plane, while for the longitudinal plane, detectability of iso-centered and off-centered images are similar. For anatomical background, off-centered images yield higher detectability for the transverse plane, while iso-centered images yield higher detectability for the longitudinal plane, when the slice thickness is smaller than 1.9mm. The optimal slice thickness is 3.8mm for all tasks, and the transverse plane at the off-center (iso-center and off-center) produces the highest detectability for uniform (anatomical) background.

Effect of anatomical backgrounds on detectability in volumetric cone beam CT images

As anatomical noise is often a dominating factor affecting signal detection in medical imaging, we investigate the effects of anatomical backgrounds on signal detection in volumetric cone beam CT images. Signal detection performances are compared between transverse and longitudinal planes with either uniform or anatomical backgrounds. Sphere objects with diameters of 1mm, 5mm, 8mm, and 11mm are used as the signals. Three-dimensional (3D) anatomical backgrounds are generated using an anatomical noise power spectrum, 1/fβ, with β=3, equivalent to mammographic background [1]. The mean voxel value of the 3D anatomical backgrounds is used as an attenuation coefficient of the uniform background. Noisy projection data are acquired by the forward projection of the uniform and anatomical 3D backgrounds with/without sphere lesions and by the addition of quantum noise. Then, images are reconstructed by an FDK algorithm [2]. For each signal size, signal detection performances in transverse and longitudinal planes are measured by calculating the task SNR of a channelized Hotelling observer with Laguerre-Gauss channels. In the uniform background case, transverse planes yield higher task SNR values for all sphere diameters but 1mm. In the anatomical background case, longitudinal planes yield higher task SNR values for all signal diameters. The results indicate that it is beneficial to use longitudinal planes to detect spherical signals in anatomical backgrounds.

SU-G-IeP2-01: A Method to Reduce Information Loss in JPEG Compressed Medical Images Using Richardson-Lucy Deconvolution

PURPOSE To minimize the information loss of medical images compressed by JPEG using Richardson-Lucy (RL) deconvolution. METHODS We first generated reference images having three different noise structures; low-passed noise, high-passed noise, and white noise, which mimic noise structures for different medical imaging modalities. Each reference image was filtered by a Gaussian function and compressed/decompressed by JPEG 2000 algorithm. After decompression, RL deconvolution was applied to recover the reference image using the same Gaussian function. The goal of the RL deconvolution is to restore the reference image from the decompressed image as similar as possible. For each reference image, the optimal kernel size of the Gaussian function and iteration number of the RL deconvolution were found by brute-force searching. The optimal parameters were chosen when the Structural Similarity Index Metric (SSIM) value between reference image and restored image by the proposed method was maximized. We also compared the SSIM value of decompressed conventional JPEG image. RESULTS For each reference image, proposed algorithm produces a higher SSIM value than conventional JPEG image, demonstrating the information loss by JPEG can be reduced by a simple deconvolution technique. CONCLUSION In this work, we present a simple method to prevent the information loss caused by JPEG algorithm in medical images. We only tested our method using a simple Gaussian function, but using different functions optimized for different noise structures would improve the performance of the proposed method, which will be a subject of our future research. This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the IT Consilience Creative Programs (IITP-2015-R0346-15-1008) supervised by the IITP (Institute for Information & Communications Technology Promotion), Basic Science Research Program through the National Research Foundation of Korea (NRF) unded by the MSIP(2015R1C1A1A01052268) and framework of international cooperation program managed by NRF(NRF- 2015K2A1A2067635).