Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function

Li, Zhihua; Shi, Weili; Xing, Qiwei; Miao, Yu; Wei, He; Yang, Huamin; Jiang, Zhengang

doi:10.1155/2021/2973108

Cited by 20 publications

(11 citation statements)

References 53 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%

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%

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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“…Although hybrid loss functions have been used recently in various deep networks 30 – 32 , our loss function kept the same as the original Mask R-CNN due to its efficiency with the dataset. where and still follows the definition in Faster R-CNN 33 and is the average binary cross entropy loss proposed in Mask R-CNN 22 .…”

Section: Methodsmentioning

confidence: 99%

Mask R-CNN assisted 2.5D object detection pipeline of 68Ga-PSMA-11 PET/CT-positive metastatic pelvic lymph node after radical prostatectomy from solely CT imaging

Cao

et al. 2023

Sci Rep

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Prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) is a molecular and functional imaging modality with better restaging accuracy over conventional imaging for detecting prostate cancer in men suspected of lymph node (LN) progression after definitive therapy. However, the availability of PSMA PET/CT is limited in both low-resource settings and for repeating imaging surveillance. In contrast, CT is widely available, cost-effective, and routinely performed as part of patient follow-up or radiotherapy workflow. Compared with the molecular activities, the morphological and texture changes of subclinical LNs in CT are subtle, making manual detection of positive LNs infeasible. Instead, we harness the power of artificial intelligence for automated LN detection on CT. We examined 68Ga-PSMA-11 PET/CT images from 88 patients (including 739 PSMA PET/CT-positive pelvic LNs) who experienced a biochemical recurrence after radical prostatectomy and presented for salvage radiotherapy with prostate-specific antigen < 1 ng/mL. Scans were divided into a training set (nPatient = 52, nNode = 400), a validation set (nPatient = 18, nNode = 143), and a test set (nPatient = 18, nNodes = 196). Using PSMA PET/CT as the ground truth and consensus pelvic LN clinical target volumes as search regions, a 2.5-dimensional (2.5D) Mask R-CNN based object detection framework was trained. The entire framework contained whole slice imaging pretraining, masked-out region fine-tuning, prediction post-processing, and “window bagging”. Following an additional preprocessing step—pelvic LN clinical target volume extraction, our pipeline located positive pelvic LNs solely based on CT scans. Our pipeline could achieve a sensitivity of 83.351%, specificity of 58.621% out of 196 positive pelvic LNs from 18 patients in the test set, of which most of the false positives can be post-removable by radiologists. Our tool may aid CT-based detection of pelvic LN metastasis and triage patients most unlikely to benefit from the PSMA PET/CT scan.

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“…Due to the excellent performance of WGAN in generating faithful real-world CT images and the role of the perceptual loss in structural fidelity, this model alleviated the over-smoothness in the denoised images. Li et al employed a GAN armed with the structural similarity loss, the perceptual loss, the adversarial loss, and the sharpness loss to preserve structural details and sharp boundaries (Li et al 2021). Fan et al constructed a quadratic neuron-based autoencoder for LDCT image denoising with more robustness and efficiency as opposed to conventional CNN-based methods (Fan et al 2019).…”

Section: Related Workmentioning

confidence: 99%

CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising

Wang

Fan

et al. 2023

Phys. Med. Biol.

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Low-dose computed tomography (LDCT) denoising is an important problem in CT research. Compared to the normal dose CT (NDCT), LDCT images are subjected to severe noise and artifacts. Recently in many studies, vision transformers have shown superior feature representation ability over convolutional neural networks (CNNs). However, unlike CNNs, the potential of vision transformers in LDCT denoising was little explored so far. To fill this gap, we propose a Convolution-free Token2Token Dilated Vision Transformer (CTformer) for low-dose CT denoising. The CTformer uses a more powerful token rearrangement to encompass local contextual information and thus avoids convolution. It also dilates and shifts feature maps to capture longer-range interaction. We interpret the CTformer by statically inspecting patterns of its internal attention maps and dynamically tracing the hierarchical attention flow with an explanatory graph. Furthermore, an overlapped inference mechanism is introduced to effectively eliminate the boundary artifacts that are common for encoder-decoder-based denoising models. Experimental results on MayoLDCT dataset suggest that the CTformeroutperforms the state-of-the-art denoising methods with a low computation overhead.

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Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function

Cited by 20 publications

References 53 publications

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

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

Mask R-CNN assisted 2.5D object detection pipeline of 68Ga-PSMA-11 PET/CT-positive metastatic pelvic lymph node after radical prostatectomy from solely CT imaging

CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising

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