Efficient evaluation of low-contrast detectability of deep-CNN-based CT reconstruction using channelized Hotelling observer on the ACR accreditation phantom

Fan, Mingdong; Zhou, Zhongxing; Vrieze, Thomas J.; Wang, Jia; McCollough, Cynthia H.; Yu, Lifeng

doi:10.1117/12.2612414

Cited by 1 publication

(7 citation statements)

References 8 publications

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“…The predominant DL models for CT denoising are GANs and CNNs. As shown in Figure 2a, out of 99 publications examined, 61 studies use the models based on CNN, 59–119 while 30 studies are based on GAN 120–149 . Additionally, two studies adopt Transformer‐based approaches 150,151 .…”

Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%

“…The predominant DL models for CT denoising are GANs and CNNs. As shown in Figure 2a , out of 99 publications examined, 61 studies use the models based on CNN, 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , …”

Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%

“…74,77,79,82,90,92,93,95,103,104,113,114,147 Additionally, the implementation of DL for metal artifact reduction demonstrates superior results when compared to IR. 62,86,119,121 CT image denoising approaches show promising potential, but are not widely accepted in routine clinical practice. To date, three CT vendors have introduced DL-based reconstruction algorithms: TrueFidelity by GE Healthcare, AiCE by Canon Medical Systems, and Precise Image by Philips Healthcare.…”

Section: Applicationmentioning

confidence: 99%

“…Supervised DL‐based methods have been shown to produce high‐quality denoised CT images, and they are effective at removing various types of noise patterns. Out of 99 studies reviewed, 64 studies 42,59–68,71,72,74,76,77,79,80,83,84,86,88–93,97–104 , 106–109,111–119,123–126,132,134,137–139,142,147,153,159,161–163 apply supervised training method. CNNs are a common type of supervised DL‐based model used for CT image denoising.…”

Section: Training Validation and Evaluationmentioning

confidence: 99%

“…The majority of research focusing on the objective image quality evaluations of DL algorithms has consistently demonstrated remarkable noise reduction compared to FBP and IR at equivalent or lower radiation dose levels 74,77,79,82,90,92,93,95,103,104,113,114,147 . Additionally, the implementation of DL for metal artifact reduction demonstrates superior results when compared to IR 62,86,119,121 …”

Section: Applicationmentioning

confidence: 99%

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CT image denoising methods for image quality improvement and radiation dose reduction

Sadia,

Chen,

Zhang

2024

J Applied Clin Med Phys

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With the ever‐increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low‐dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)‐based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL‐based models.

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Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%