aLow-dose CT via convolutional neural network

Chen, Hu; Zhang, Yi; Zhang, Weihua; Liao, Peixi; Li, Ké; Zhou, Jiliu; Wang, Ge

doi:10.1364/boe.8.000679

Cited by 661 publications

(471 citation statements)

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“…Patched images are often used for training because of memory limitations of GPUs. 22,31 While patch-based training can effectively increase the sample size, the anatomical relationship is missing. In contrast, our approach could remove the iodine components through consideration of the surrounding anatomical structures.…”

Section: Discussionmentioning

confidence: 99%

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Koike¹,

Ohira²,

Akino

et al. 2019

Medical Physics

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Purpose The aim of this study was to develop a deep learning (DL) method for generating virtual noncontrast (VNC) computed tomography (CT) images from contrast‐enhanced (CE) CT images (VNCDL) and to evaluate its performance in dose calculations for head and neck radiotherapy in comparison with VNC images derived from a dual‐energy CT (DECT) scanner (VNCDECT). Methods This retrospective study included data for 61 patients who underwent head and neck radiotherapy. All planning CT images were obtained with a single‐source DECT scanner (80 and 140 kVp) with rapid kVp switching. The DL‐based method used a pair of virtual monochromatic images (VMIs) at 70 keV with and without contrast materials. VMIs without contrast materials were used as reference true noncontrast (TNC) images. Deformable image registration was used between the TNC and CE images. We used the data of 45 patients, chosen randomly, for training (7922 paired images), and data from the other 16 patients as test data. We generated the VNCDL images with a densely connected convolutional network. As the VNCDECT images, we used VMIs with the iodine signal suppressed, reconstructed from the CE images of the 16 test patients. The CT numbers of the tumor, common carotid artery, internal jugular vein, muscle, fat, bone marrow, cortical bone, and mandible of each VNC image were compared with those of the TNC image. The dose of the reference TNC plan was recalculated using the CE, VNCDL, and VNCDECT images. Difference maps of the dose distributions and dose–volume histograms were evaluated. Results The mean prediction time for the VNCDL images was 3.4 s per patient, and the mean number of slices was 204. The absolute differences in CT numbers of the VNCDL images were significantly smaller than those of the VNCDECT images for the bone marrow (8.0 ± 6.5 vs 175.1 ± 40.9 HU; P < 0.001) and mandible (20.3 ± 19.3 vs 106.2 ± 80.5 HU; P = 0.002). The DL‐based model provided the dose distribution most similar to that of the TNC plan. With the VNCDECT plans, dose errors >1.0% were observed in bone regions. The dose–volume histogram analysis showed that the VNCDL plans yielded the smallest errors for the primary target, although dose differences were <1.0% for all the approaches. For the maximum dose to the mandible, the mean ± SD errors for the CE, VNCDL, and VNCDECT plans were –0.13% ± 0.23% (range: −0.46% to 0.31%; P = 0.037), –0.01% ± 0.22% (range: −0.40% to 0.36%; P = 1.0), and 0.53% ± 0.47% (range: −0.21% to 1.41%; P < 0.001), respectively. Conclusions In this study, we developed a method based on DL that can rapidly generate VNC images from CE images without a DECT scanner. Compared with the DECT approach, the DL‐based method improved the prediction accuracy of CT numbers in bone regions. Consequently, there was greater agreement between the VNCDL and TNC plan dose distributions than with the CE and VNCDECT plans, achieved by suppressing the contrast material signals while retaining the CT numbers of bone structures.

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Section: Discussionmentioning

confidence: 99%

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Koike¹,

Ohira²,

Akino

et al. 2019

Medical Physics

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“…For a couple of years, CNNs have been intensively investigated to improve performance in denoising or super-resolution tasks in computer vision 20,21 and medical imaging communities. 9,22,23 Resolution enhancement and deblurring are ill-posed problems, meaning there exists no unique solution. When CNNs are trained to minimize L2 loss between the full sampled images and the down-sampled images, the estimated pixel values approximate the average of all possible solutions, resulting in perceptually blurred images.…”

Section: A Related Workmentioning

confidence: 99%

“…For a couple of years, CNNs have been intensively investigated to improve performance in denoising or super‐resolution tasks in computer vision and medical imaging communities . Resolution enhancement and deblurring are ill‐posed problems, meaning there exists no unique solution.…”

Section: Introductionmentioning

confidence: 99%

Improving resolution of MR images with an adversarial network incorporating images with different contrast

Kim

Park

2018

Medical Physics

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“…In recent years, several convolutional neural network (CNN)‐based methods have been proposed for natural image denoising, and the application of three‐layer CNN for LDCT denoising has shown promising results. However, for certain imaging tasks, the three‐layer CNN introduces image blurring, thus a deeper CNN has been employed to increase the sharpness in LDCT denoising .…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the network designs and loss functions, reliable and objective image quality assessment is essential to derive a meaningful conclusion from a comparative study on different image denoising methods. The image quality metrics like the root‐mean‐squared error (RMSE) and the structural similarity index (SSIM) are widely used for the quantitative evaluation of LDCT denoising . However, regarding perceptual similarity, these simple metrics often do not match the qualitative evaluation .…”

Section: Introductionmentioning

confidence: 99%

A performance comparison of convolutional neural network‐based image denoising methods: The effect of loss functions on low‐dose CT images

et al. 2019

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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 function. Perceptual loss and adversarial loss have been proposed recently to further improve the image denoising performance. In this paper, we investigate the effect of different loss functions on image denoising performance using task‐based image quality assessment methods for various signals and dose levels. Methods We used a modified version of U‐net that was effective at reducing the correlated noise in CT images. The loss functions used for comparison were two pixel‐level losses (i.e., the mean‐squared error and the mean absolute error), Visual Geometry Group network‐based perceptual loss (VGG loss), adversarial loss used to train the Wasserstein generative adversarial network with gradient penalty (WGAN‐GP), and their weighted summation. Each image denoising method was applied to reconstructed images and sinogram images independently and validated using the extended cardiac‐torso (XCAT) simulation and Mayo Clinic datasets. In the XCAT simulation, we generated fan‐beam CT datasets with four different dose levels (25%, 50%, 75%, and 100% of a normal‐dose level) using 10 XCAT phantoms and inserted signals in a test set. The signals had two different shapes (spherical and spiculated), sizes (4 and 12 mm), and contrast levels (60 and 160 HU). To evaluate signal detectability, we used a detection task SNR (tSNR) calculated from a non‐prewhitening model observer with an eye filter. We also measured the noise power spectrum (NPS) and modulation transfer function (MTF) to compare the noise and signal transfer properties. Results Compared to CNNs without VGG loss, VGG‐loss‐based CNNs achieved a more similar tSNR to that of the normal‐dose CT for all signals at different dose levels except for a small signal at the 25% dose level. For a low‐contrast signal at 25% or 50% dose, adding other losses to the VGG loss showed more improved performance than only using VGG loss. The NPS shapes from VGG‐loss‐based CNN closely matched that of normal‐dose CT images while CNN without VGG loss overly reduced the mid‐high‐frequency noise power at all dose levels. MTF also showed VGG‐loss‐based CNN with better‐preserved high resolution for all dose and contrast levels. It is also observed that additional WGAN‐GP loss helps improve the noise and signal transfer properties of VGG‐loss‐based CNN. Conclusions The evaluation results using tSNR, NPS, and MTF indicate that VGG‐loss‐based CNNs are more effective than those without VGG loss for natural denoising of low‐dose images and WGAN‐GP loss improves the denoising performance of VGG‐loss‐based CNNs, which corresponds with the qualitative evaluation.

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aLow-dose CT via convolutional neural network

Cited by 661 publications

References 44 publications

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Improving resolution of MR images with an adversarial network incorporating images with different contrast

A performance comparison of convolutional neural network‐based image denoising methods: The effect of loss functions on low‐dose CT images

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