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Research exploring CycleGAN-based synthetic image generation has recently accelerated in the medical community due to its ability to leverage unpaired images effectively. However, a commonly established drawback of the CycleGAN, t...
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Research exploring CycleGAN-based synthetic image generation has recently accelerated in the medical community due to its ability to leverage unpaired images effectively. However, a commonly established drawback of the CycleGAN, the introduction of artifacts in generated images, makes it unreliable for medical imaging use cases. In an attempt to address this, we explore the effect of structure losses on the CycleGAN and propose a generalized frequency-based loss that aims at preserving the content in the frequency domain. We apply this loss to the use-case of cone-beam computed tomography (CBCT) translation to computed tomography (CT)-like quality. Synthetic CT (sCT) images generated from our methods are compared against baseline CycleGAN along with other existing structure losses proposed in the literature. Our methods (MAE: 85.5, MSE: 20433, NMSE: 0.026, PSNR: 30.02, SSIM: 0.935) quantitatively and qualitatively improve over the baseline CycleGAN (MAE: 88.8, MSE: 24244, NMSE: 0.03, PSNR: 29.37, SSIM: 0.935) across all investigated metrics and are more robust than existing methods. Furthermore, no observable artifacts or loss in image quality were observed. Finally, we demonstrated that sCTs generated using our methods have superior performance compared to the original CBCT images on selected downstream tasks.
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With more and more engineered nanoparticles (NPs) being translated to the clinic, the United States Food and Drug Administration (FDA) has recently issued the latest draft guidance on nanomaterial-containing drug products with an ...
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With more and more engineered nanoparticles (NPs) being translated to the clinic, the United States Food and Drug Administration (FDA) has recently issued the latest draft guidance on nanomaterial-containing drug products with an emphasis on understanding their invivo transport and nano-bio interactions. Following these guidelines, NPs can be designed to target and treat diseases more efficiently than small molecules, have minimum accumulation in normal tissues, and induce minimum toxicity. In this Minireview, we integrate this guidance with our ten-year studies on developing renal clearable luminescent gold NPs. These gold NPs resist serum protein adsorption, escape liver uptake, target cancerous tissues, and report kidney dysfunction at early stages. At the same time, off-target gold NPs can be eliminated by the kidneys with minimum accumulation in the body. Additionally, we identify challenges to the translation of renal clearable gold NPs from the bench to the clinic.
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BACKGROUND: Clinical protocols aimed to characterize the post-myocardial infarction (MI) heart by cardiac magnetic resonance (CMR) need to be standardized to take account of dynamic biological phenomena evolving early after the in...
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BACKGROUND: Clinical protocols aimed to characterize the post-myocardial infarction (MI) heart by cardiac magnetic resonance (CMR) need to be standardized to take account of dynamic biological phenomena evolving early after the index ischemic event. Here, we evaluated the time course of edema reaction in patients with ST-segment-elevation MI by CMR and assessed its implications for myocardium-at-risk (MaR) quantification both in patients and in a large-animal model.
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Image-to-image translation (I2I) has broad application prospects for assisting physicians in diagnosis of medical image missing scenarios. Considering that there is no medical I2I model constructed from a geometric view of simulta...
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Image-to-image translation (I2I) has broad application prospects for assisting physicians in diagnosis of medical image missing scenarios. Considering that there is no medical I2I model constructed from a geometric view of simultaneously preserving local manifold-value and global manifold structure, we propose an I2I model based on manifold-value correction and manifold matching (MMNet) to translate one modal image to another in a paired and unpaired fashion and preserve the texture details of the target model image. For local manifold-value preservation, each manifold-value of the generated image is aligned with the corresponding real image as much as possible by jointly optimizing the distribution corrector and the distribution generator. For global manifold structure preservation, three distance metrics are defined to globally reduce the difference between the manifold of the generated images and the manifold of the real images through optimizing the manifold matching loss. Experimental results demonstrate that the proposed MMNet outperforms multiple state-of-the-art GANs-based methods for MR image translation in both qualitative and quantitative measures.
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Radiology continues to benefit from constant innovation and technological advances. However, for promising new imaging technologies to reach widespread clinical practice, several milestones must be met. These include regulatory ap...
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Radiology continues to benefit from constant innovation and technological advances. However, for promising new imaging technologies to reach widespread clinical practice, several milestones must be met. These include regulatory approval, early clinical evaluation, payer reimbursement, and broader marketplace adoption. Successful implementation of new imaging tests into clinical practice re, quires active stakeholder engagement and a focus on demonstrating clinical value during each phase of translation.
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Image registration is a crucial problem in medical image analysis. A new automatic registration algorithm is presented for complex multimodality medical image registration. The proposed method starts by finding edge elements local...
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Image registration is a crucial problem in medical image analysis. A new automatic registration algorithm is presented for complex multimodality medical image registration. The proposed method starts by finding edge elements locally, and then aggregates them globally to find straight lines segments. Based on the consistency of the characteristics such as orientation and length of the straight-line segments, the relations of these segments are first estimated. Afterwards, the distributions of scaling and translation differences are used to estimate the remaining transformation coefficients. An excellent result has been obtained by the proposed algorithm when it tested over the real medical images taken from the King Fahad Specialist Hospital-Dammam, Saudi Arabia.
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Multi-modal image registration is an essential step for many medical image analysis applications. Recent advances in multi-modal image registration rely on image-to-image translation to achieve good performance. However, the perfo...
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Multi-modal image registration is an essential step for many medical image analysis applications. Recent advances in multi-modal image registration rely on image-to-image translation to achieve good performance. However, the performance is still limited owing to the poor use of complementary regularization between image registration and translation, which is able to simultaneously enhance both parts' accuracy. To this end, we propose CoCycleReg, a novel method that formulates image registration and translation in a Collaborative Cycle -consistency manner. Instead of dividing into two discrete stages, we unify the image registration and translation via cycle-consistency in an end-to-end training process, such that each part can benefit from the other one. To ensure the deformation fields' reversibility in the cycle, we extensively introduce a novel dual-head registration network, consisting of one single backbone to extract the features and two heads to respectively predict the deformation fields. The experiments on T1-T2(MRI) and CT-MRI datasets validate that the proposed CoCycleReg surpasses the other state-ofthe-art conventional and deep learning approaches comprehensively considering the speed, accuracy, and regularity of deformation fields. In the ablation analysis, a method that sets the cycle-consistency Corresponding authors at: Department of Computer Science at School of Informatics, Xiamen University, Xiamen 361005, Chinaconstraints of registration and image-to-image translation separately is compared, and the results demonstrate the effectiveness of collaborative cycle-consistency. In addition, the improvement of image-to-image translation is also verified in further analysis. The code is publicly available at https://github.com/DopamineLcy/cocycle-reg/.(c) 2022 Elsevier B.V. All rights reserved.
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Objectives: Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used to complement ultrasound examinations and x-ray mammography for early detection and diagnosis of breast cancer. However, images generated by...
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Objectives: Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used to complement ultrasound examinations and x-ray mammography for early detection and diagnosis of breast cancer. However, images generated by various MRI scanners (e.g., GE Healthcare, and Siemens) differ both in intensity and noise distribution, preventing algorithms trained on MRIs from one scanner to generalize to data from other scanners. In this work, we propose a method to solve this problem by normalizing images between various scanners. Methods: MRI normalization is challenging because it requires normalizing intensity values and mapping noise distributions between scanners. We utilize a cycle-consistent generative adversarial network to learn a bidirectional mapping and perform normalization between MRIs produced by GE Healthcare and Siemens scanners in an unpaired setting. Initial experiments demonstrate that the traditional CycleGAN architecture struggles to preserve the anatomical structures of the breast during normalization. Thus, we propose two technical innovations in order to preserve both the shape of the breast as well as the tissue structures within the breast. First, we incorporate mutual information loss during training in order to ensure anatomical consistency. Second, we propose a modified discriminator architecture that utilizes a smaller field-of-view to ensure the preservation of finer details in the breast tissue. Results: Quantitative and qualitative evaluations show that the second innovation consistently preserves the breast shape and tissue structures while also performing the proper intensity normalization and noise distribution mapping. Conclusion: Our results demonstrate that the proposed model can successfully learn a bidirectional mapping and perform normalization between MRIs produced by different vendors, potentially enabling improved diagnosis and detection of breast cancer. All the data used in this study are publicly available at https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903 . (c) 2021 Elsevier B.V. All rights reserved.
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Background: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily a...
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Background: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by other researchers or clinicians. Even if developers publish their code and pre-trained models on the internet, integration in stand-alone applications and existing workflows is often not straightforward, especially for clinical research partners. In this paper, we propose an open-source framework to provide AI-enabled medical image analysis through the network. Methods: TOMAAT provides a cloud environment for general medical image analysis, composed of three basic components: (i) an announcement service, maintaining a public registry of (ii) multiple distributed server nodes offering various medical image analysis solutions, and (iii) client software offering simple interfaces for users. Deployment is realized through HTTP-based communication, along with an API and wrappers for common image manipulations during pre- and post-processing. Results: We demonstrate the utility and versatility of TOMAAT on several hallmark medical image analysis tasks: segmentation, diffeomorphic deformable atlas registration, landmark localization, and workflow integration. Through TOMAAT, the high hardware demands, setup and model complexity of demonstrated approaches are transparent to users, who are provided with simple client interfaces. We present example clients in three-dimensional Slicer, in the web browser, on iOS devices and in a commercially available, certified medical image analysis suite. Conclusion: TOMAAT enables deployment of state-of-the-art image segmentation in the cloud, fostering interaction among deep learning researchers and medical collaborators in the clinic. Currently, a public announcement service is hosted by the authors, and several ready-to-use services are registered and enlisted at http://tomaat.cloud.
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