Minah Han
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View article: Low-dose computed tomography perceptual image quality assessment
Low-dose computed tomography perceptual image quality assessment Open
In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered…
View article: Impact of using sinogram domain data in the super‐resolution of CT images on diagnostic information
Impact of using sinogram domain data in the super‐resolution of CT images on diagnostic information Open
Background In recent times, deep‐learning‐based super‐resolution (DL‐SR) techniques for computed tomography (CT) images have shown outstanding results in terms of full‐reference image quality (FR‐IQ) metrics (e.g., root mean square error a…
View article: Strategy to implement a convolutional neural network based ideal model observer via transfer learning for multi-slice simulated breast CT images
Strategy to implement a convolutional neural network based ideal model observer via transfer learning for multi-slice simulated breast CT images Open
Objective. In this work, we propose a convolutional neural network (CNN)-based multi-slice ideal model observer using transfer learning (TL-CNN) to reduce the required number of training samples. Approach. To train model observers, we gene…
View article: Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images
Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images Open
In digital breast tomosynthesis (DBT) systems, projection data are acquired from a limited number of angles. Consequently, the reconstructed images contain severe blurring artifacts that might heavily degrade the DBT image quality and caus…
View article: Perceptual CT Loss: Implementing CT Image Specific Perceptual Loss for CNN-Based Low-Dose CT Denoiser
Perceptual CT Loss: Implementing CT Image Specific Perceptual Loss for CNN-Based Low-Dose CT Denoiser Open
Convolutional neural network (CNN)-based denoisers have been successful in low-dose CT (LDCT) denoising tasks. However, image blurring in the denoised images remains a problem, and it is mainly caused by pixel-level losses during the train…
View article: Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection
Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection Open
For digital breast tomosynthesis (DBT) systems, we investigate the effects of the reconstruction filters for different data acquisition angles on signal detection. We simulated a breast phantom with a 30% volume glandular fraction (VGF) of…
View article: A Convolutional Neural Network-Based Anthropomorphic Model Observer for Signal Detection in Breast CT Images Without Human-Labeled Data
A Convolutional Neural Network-Based Anthropomorphic Model Observer for Signal Detection in Breast CT Images Without Human-Labeled Data Open
Various imaging parameters in X-ray computed tomography (CT) should be examined and optimized by task-based assessment of human observer performance. Recently, convolutional neural networks (CNNs) have been introduced as anthropomorphic mo…
View article: A performance comparison of convolutional neural network‐based image denoising methods: The effect of loss functions on low‐dose CT images
A performance comparison of convolutional neural network‐based image denoising methods: The effect of loss functions on low‐dose CT images Open
Purpose Convolutional neural network (CNN)‐based image denoising techniques have shown promising results in low‐dose CT denoising. However, CNN often introduces blurring in denoised images when trained with a widely used pixel‐level loss f…
View article: Evaluation of human observer performance on lesion detectability in single‐slice and multislice dedicated breast cone beam CT images with breast anatomical background
Evaluation of human observer performance on lesion detectability in single‐slice and multislice dedicated breast cone beam CT images with breast anatomical background Open
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 compar…
View article: Inter‐laboratory comparison of channelized hotelling observer computation
Inter‐laboratory comparison of channelized hotelling observer computation Open
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 we…
View article: Human and model observer performance for lesion detection in breast cone beam CT images with the FDK reconstruction
Human and model observer performance for lesion detection in breast cone beam CT images with the FDK reconstruction Open
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 metho…
View article: Effect of anatomical noise on the detectability of cone beam CT images with different slice direction, slice thickness, and volume glandular fraction
Effect of anatomical noise on the detectability of cone beam CT images with different slice direction, slice thickness, and volume glandular fraction Open
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 spe…
View article: Investigation on slice direction dependent detectability of volumetric cone beam CT images
Investigation on slice direction dependent detectability of volumetric cone beam CT images Open
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 …