1
|
Liao P, Zhang X, Wu Y, Chen H, Du W, Liu H, Yang H, Zhang Y. Weakly supervised low-dose computed tomography denoising based on generative adversarial networks. Quant Imaging Med Surg 2024; 14:5571-5590. [PMID: 39144020 PMCID: PMC11320552 DOI: 10.21037/qims-24-68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 06/17/2024] [Indexed: 08/16/2024]
Abstract
Background Low-dose computed tomography (LDCT) is a diagnostic imaging technique designed to minimize radiation exposure to the patient. However, this reduction in radiation may compromise computed tomography (CT) image quality, adversely impacting clinical diagnoses. Various advanced LDCT methods have emerged to mitigate this challenge, relying on well-matched LDCT and normal-dose CT (NDCT) image pairs for training. Nevertheless, these methods often face difficulties in distinguishing image details from nonuniformly distributed noise, limiting their denoising efficacy. Additionally, acquiring suitably paired datasets in the medical domain poses challenges, further constraining their applicability. Hence, the objective of this study was to develop an innovative denoising framework for LDCT images employing unpaired data. Methods In this paper, we propose a LDCT denoising network (DNCNN) that alleviates the need for aligning LDCT and NDCT images. Our approach employs generative adversarial networks (GANs) to learn and model the noise present in LDCT images, establishing a mapping from the pseudo-LDCT to the actual NDCT domain without the need for paired CT images. Results Within the domain of weakly supervised methods, our proposed model exhibited superior objective metrics on the simulated dataset when compared to CycleGAN and selective kernel-based cycle-consistent GAN (SKFCycleGAN): the peak signal-to-noise ratio (PSNR) was 43.9441, the structural similarity index measure (SSIM) was 0.9660, and the visual information fidelity (VIF) was 0.7707. In the clinical dataset, we conducted a visual effect analysis by observing various tissues through different observation windows. Our proposed method achieved a no-reference structural sharpness (NRSS) value of 0.6171, which was closest to that of the NDCT images (NRSS =0.6049), demonstrating its superiority over other denoising techniques in preserving details, maintaining structural integrity, and enhancing edge contrast. Conclusions Through extensive experiments on both simulated and clinical datasets, we demonstrated the superior efficacy of our proposed method in terms of denoising quality and quantity. Our method exhibits superiority over both supervised techniques, including block-matching and 3D filtering (BM3D), residual encoder-decoder convolutional neural network (RED-CNN), and Wasserstein generative adversarial network-VGG (WGAN-VGG), and over weakly supervised approaches, including CycleGAN and SKFCycleGAN.
Collapse
Affiliation(s)
- Peixi Liao
- Department of Stomatology, The Sixth People’s Hospital of Chengdu, Chengdu, China
| | - Xucan Zhang
- The National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China
| | - Yaoyao Wu
- The School of Computer Science, Sichuan University, Chengdu, China
| | - Hu Chen
- The College of Computer Science, Sichuan University, Chengdu, China
| | - Wenchao Du
- The College of Computer Science, Sichuan University, Chengdu, China
| | - Hong Liu
- The College of Computer Science, Sichuan University, Chengdu, China
| | - Hongyu Yang
- The College of Computer Science, Sichuan University, Chengdu, China
| | - Yi Zhang
- The School of Cyber Science and Engineering, Sichuan University, Chengdu, China
| |
Collapse
|
2
|
Winfree T, McCollough C, Yu L. Development and validation of a noise insertion algorithm for photon-counting-detector CT. Med Phys 2024. [PMID: 38923526 DOI: 10.1002/mp.17263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Inserting noise into existing patient projection data to simulate lower-radiation-dose exams has been frequently used in traditional energy-integrating-detector (EID)-CT to optimize radiation dose in clinical protocols and to generate paired images for training deep-learning-based reconstruction and noise reduction methods. Recent introduction of photon counting detector CT (PCD-CT) also requires such a method to accomplish these tasks. However, clinical PCD-CT scanners often restrict the users access to the raw count data, exporting only the preprocessed, log-normalized sinogram. Therefore, it remains a challenge to employ projection domain noise insertion algorithms on PCD-CT. PURPOSE To develop and validate a projection domain noise insertion algorithm for PCD-CT that does not require access to the raw count data. MATERIALS AND METHODS A projection-domain noise model developed originally for EID-CT was adapted for PCD-CT. This model requires, as input, a map of the incident number of photons at each detector pixel when no object is in the beam. To obtain the map of incident number of photons, air scans were acquired on a PCD-CT scanner, then the noise equivalent photon number (NEPN) was calculated from the variance in the log normalized projection data of each scan. Additional air scans were acquired at various mA settings to investigate the impact of pulse pileup on the linearity of NEPN measurement. To validate the noise insertion algorithm, Noise Power Spectra (NPS) were generated from a 30 cm water tank scan and used to compare the noise texture and noise level of measured and simulated half dose and quarter dose images. An anthropomorphic thorax phantom was scanned with automatic exposure control, and noise levels at different slice locations were compared between simulated and measured half dose and quarter dose images. Spectral correlation between energy thresholds T1 and T2, and energy bins, B1 and B2, was compared between simulated and measured data across a wide range of tube current. Additionally, noise insertion was performed on a clinical patient case for qualitative assessment. RESULTS The NPS generated from simulated low dose water tank images showed similar shape and amplitude to that generated from the measured low dose images, differing by a maximum of 5.0% for half dose (HD) T1 images, 6.3% for HD T2 images, 4.1% for quarter dose (QD) T1 images, and 6.1% for QD T2 images. Noise versus slice measurements of the lung phantom showed comparable results between measured and simulated low dose images, with root mean square percent errors of 5.9%, 5.4%, 5.0%, and 4.6% for QD T1, HD T1, QD T2, and HD T2, respectively. NEPN measurements in air were linear up until 112 mA, after which pulse pileup effects significantly distort the air scan NEPN profile. Spectral correlation between T1 and T2 in simulation agreed well with that in the measured data in typical dose ranges. CONCLUSIONS A projection-domain noise insertion algorithm was developed and validated for PCD-CT to synthesize low-dose images from existing scans. It can be used for optimizing scanning protocols and generating paired images for training deep-learning-based methods.
Collapse
Affiliation(s)
- Timothy Winfree
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
3
|
Kimura Y, Suyama TQ, Shimamura Y, Suzuki J, Watanabe M, Igei H, Otera Y, Kaneko T, Suzukawa M, Matsui H, Kudo H. Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm. Radiol Phys Technol 2024; 17:367-374. [PMID: 38413510 DOI: 10.1007/s12194-024-00786-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/11/2024] [Accepted: 01/25/2024] [Indexed: 02/29/2024]
Abstract
This study aimed to assess the subjective and objective image quality of low-dose computed tomography (CT) images processed using a self-supervised denoising algorithm with deep learning. We trained the self-supervised denoising model using low-dose CT images of 40 patients and applied this model to CT images of another 30 patients. Image quality, in terms of noise and edge sharpness, was rated on a 5-point scale by two radiologists. The coefficient of variation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated. The values for the self-supervised denoising model were compared with those for the original low-dose CT images and CT images processed using other conventional denoising algorithms (non-local means, block-matching and 3D filtering, and total variation minimization-based algorithms). The mean (standard deviation) scores of local and overall noise levels for the self-supervised denoising algorithm were 3.90 (0.40) and 3.93 (0.51), respectively, outperforming the original image and other algorithms. Similarly, the mean scores of local and overall edge sharpness for the self-supervised denoising algorithm were 3.90 (0.40) and 3.75 (0.47), respectively, surpassing the scores of the original image and other algorithms. The CNR and SNR for the self-supervised denoising algorithm were higher than those for the original images but slightly lower than those for the other algorithms. Our findings indicate the potential clinical applicability of the self-supervised denoising algorithm for low-dose CT images in clinical settings.
Collapse
Affiliation(s)
- Yuya Kimura
- Clinical Research Center, National Hospital Organization Tokyo National Hospital, Tokyo, Japan.
- Department of Clinical Epidemiology and Health Economics, School of Public Health, University of Tokyo, Tokyo, Japan.
| | - Takeru Q Suyama
- Nadogaya Research Institute, Nadogaya Hospital, Chiba, Japan
| | | | - Jun Suzuki
- Department of Respiratory Medicine, National Hospital Organization Tokyo Hospital, Tokyo, Japan
- Department of Radiology, Saitama Medical University International Medical Center, Saitama, Japan
| | - Masato Watanabe
- Department of Respiratory Medicine, National Hospital Organization Tokyo Hospital, Tokyo, Japan
| | - Hiroshi Igei
- Department of Respiratory Medicine, National Hospital Organization Tokyo Hospital, Tokyo, Japan
| | - Yuya Otera
- Department of Radiology, National Hospital Organization Tokyo Hospital, Tokyo, Japan
| | - Takayuki Kaneko
- Radiological Physics and Technology Department, National Center for Global Health and Medicine, Tokyo, Japan
| | - Maho Suzukawa
- Clinical Research Center, National Hospital Organization Tokyo National Hospital, Tokyo, Japan
| | - Hirotoshi Matsui
- Department of Respiratory Medicine, National Hospital Organization Tokyo Hospital, Tokyo, Japan
| | - Hiroyuki Kudo
- Institute of Systems and Information Engineering, University of Tsukuba, Ibaraki, Japan
| |
Collapse
|
4
|
Oh J, Wu D, Hong B, Lee D, Kang M, Li Q, Kim K. Texture-preserving low dose CT image denoising using Pearson divergence. Phys Med Biol 2024; 69:115021. [PMID: 38688292 DOI: 10.1088/1361-6560/ad45a4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/30/2024] [Indexed: 05/02/2024]
Abstract
Objective.The mean squared error (MSE), also known asL2loss, has been widely used as a loss function to optimize image denoising models due to its strong performance as a mean estimator of the Gaussian noise model. Recently, various low-dose computed tomography (LDCT) image denoising methods using deep learning combined with the MSE loss have been developed; however, this approach has been observed to suffer from the regression-to-the-mean problem, leading to over-smoothed edges and degradation of texture in the image.Approach.To overcome this issue, we propose a stochastic function in the loss function to improve the texture of the denoised CT images, rather than relying on complicated networks or feature space losses. The proposed loss function includes the MSE loss to learn the mean distribution and the Pearson divergence loss to learn feature textures. Specifically, the Pearson divergence loss is computed in an image space to measure the distance between two intensity measures of denoised low-dose and normal-dose CT images. The evaluation of the proposed model employs a novel approach of multi-metric quantitative analysis utilizing relative texture feature distance.Results.Our experimental results show that the proposed Pearson divergence loss leads to a significant improvement in texture compared to the conventional MSE loss and generative adversarial network (GAN), both qualitatively and quantitatively.Significance.Achieving consistent texture preservation in LDCT is a challenge in conventional GAN-type methods due to adversarial aspects aimed at minimizing noise while preserving texture. By incorporating the Pearson regularizer in the loss function, we can easily achieve a balance between two conflicting properties. Consistent high-quality CT images can significantly help clinicians in diagnoses and supporting researchers in the development of AI-diagnostic models.
Collapse
Affiliation(s)
- Jieun Oh
- Center for Advanced Medical Computing and Analysis (CAMCA), Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
- Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, 35015, Republic of Korea
| | - Dufan Wu
- Center for Advanced Medical Computing and Analysis (CAMCA), Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Boohwi Hong
- Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, 35015, Republic of Korea
| | - Dongheon Lee
- Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, 35015, Republic of Korea
| | - Minwoong Kang
- Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, 35015, Republic of Korea
| | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis (CAMCA), Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Kyungsang Kim
- Center for Advanced Medical Computing and Analysis (CAMCA), Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| |
Collapse
|
5
|
Kang J, Liu Y, Zhang P, Guo N, Wang L, Du Y, Gui Z. FSformer: A combined frequency separation network and transformer for LDCT denoising. Comput Biol Med 2024; 173:108378. [PMID: 38554660 DOI: 10.1016/j.compbiomed.2024.108378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/01/2024] [Accepted: 03/24/2024] [Indexed: 04/02/2024]
Abstract
Low-dose computed tomography (LDCT) has been widely concerned in the field of medical imaging because of its low radiation hazard to humans. However, under low-dose radiation scenarios, a large amount of noise/artifacts are present in the reconstructed image, which reduces the clarity of the image and is not conducive to diagnosis. To improve the LDCT image quality, we proposed a combined frequency separation network and Transformer (FSformer) for LDCT denoising. Firstly, FSformer decomposes the LDCT images into low-frequency images and multi-layer high-frequency images by frequency separation blocks. Then, the low-frequency components are fused with the high-frequency components of different layers to remove the noise in the high-frequency components with the help of the potential texture of low-frequency parts. Next, the estimated noise images can be obtained by using Transformer stage in the frequency aggregation denoising block. Finally, they are fed into the reconstruction prediction block to obtain improved quality images. In addition, a compound loss function with frequency loss and Charbonnier loss is used to guide the training of the network. The performance of FSformer has been validated and evaluated on AAPM Mayo dataset, real Piglet dataset and clinical dataset. Compared with previous representative models in different architectures, FSformer achieves the optimal metrics with PSNR of 33.7714 dB and SSIM of 0.9254 on Mayo dataset, the testing time is 1.825 s. The experimental results show that FSformer is a state-of-the-art (SOTA) model with noise/artifact suppression and texture/organization preservation. Moreover, the model has certain robustness and can effectively improve LDCT image quality.
Collapse
Affiliation(s)
- Jiaqi Kang
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, 030051, China; School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China
| | - Yi Liu
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, 030051, China; School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China
| | - Pengcheng Zhang
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, 030051, China; School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China
| | - Niu Guo
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, 030051, China; School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China
| | - Lei Wang
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, 030051, China; School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China
| | - Yinglin Du
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, 030051, China; School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China
| | - Zhiguo Gui
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, 030051, China; School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China.
| |
Collapse
|
6
|
Sherwani MK, Gopalakrishnan S. A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy. FRONTIERS IN RADIOLOGY 2024; 4:1385742. [PMID: 38601888 PMCID: PMC11004271 DOI: 10.3389/fradi.2024.1385742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 03/11/2024] [Indexed: 04/12/2024]
Abstract
The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.
Collapse
Affiliation(s)
- Moiz Khan Sherwani
- Section for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | | |
Collapse
|
7
|
Zhang J, Gong W, Ye L, Wang F, Shangguan Z, Cheng Y. A Review of deep learning methods for denoising of medical low-dose CT images. Comput Biol Med 2024; 171:108112. [PMID: 38387380 DOI: 10.1016/j.compbiomed.2024.108112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/18/2024] [Accepted: 02/04/2024] [Indexed: 02/24/2024]
Abstract
To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing the X-ray, and thus the quality of the resulting low-dose CT images (LDCT) is degraded, as evidenced by more noise and streaking artifacts. Therefore, it is important to maintain high quality CT image while effectively reducing radiation dose. In recent years, with the rapid development of deep learning technology, deep learning-based LDCT denoising methods have become quite popular because of their data-driven and high-performance features to achieve excellent denoising results. However, to our knowledge, no relevant article has so far comprehensively introduced and reviewed advanced deep learning denoising methods such as Transformer structures in LDCT denoising tasks. Therefore, based on the literatures related to LDCT image denoising published from year 2016-2023, and in particular from 2020 to 2023, this study presents a systematic survey of current situation, and challenges and future research directions in LDCT image denoising field. Four types of denoising networks are classified according to the network structure: CNN-based, Encoder-Decoder-based, GAN-based, and Transformer-based denoising networks, and each type of denoising network is described and summarized from the perspectives of structural features and denoising performances. Representative deep-learning denoising methods for LDCT are experimentally compared and analyzed. The study results show that CNN-based denoising methods capture image details efficiently through multi-level convolution operation, demonstrating superior denoising effects and adaptivity. Encoder-decoder networks with MSE loss, achieve outstanding results in objective metrics. GANs based methods, employing innovative generators and discriminators, obtain denoised images that exhibit perceptually a closeness to NDCT. Transformer-based methods have potential for improving denoising performances due to their powerful capability in capturing global information. Challenges and opportunities for deep learning based LDCT denoising are analyzed, and future directions are also presented.
Collapse
Affiliation(s)
- Ju Zhang
- College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
| | - Weiwei Gong
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Lieli Ye
- College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
| | - Fanghong Wang
- Zhijiang College, Zhejiang University of Technology, Shaoxing, China.
| | - Zhibo Shangguan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Yun Cheng
- Department of Medical Imaging, Zhejiang Hospital, Hangzhou, China.
| |
Collapse
|
8
|
Sadia RT, Chen J, Zhang J. CT image denoising methods for image quality improvement and radiation dose reduction. J Appl Clin Med Phys 2024; 25:e14270. [PMID: 38240466 PMCID: PMC10860577 DOI: 10.1002/acm2.14270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/15/2023] [Accepted: 12/28/2023] [Indexed: 02/13/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Rabeya Tus Sadia
- Department of Computer ScienceUniversity of KentuckyLexingtonKentuckyUSA
| | - Jin Chen
- Department of Medicine‐NephrologyUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Jie Zhang
- Department of RadiologyUniversity of KentuckyLexingtonKentuckyUSA
| |
Collapse
|
9
|
Zhang J, Huang X, Liu Y, Han Y, Xiang Z. GAN-based medical image small region forgery detection via a two-stage cascade framework. PLoS One 2024; 19:e0290303. [PMID: 38166011 PMCID: PMC10760893 DOI: 10.1371/journal.pone.0290303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/06/2023] [Indexed: 01/04/2024] Open
Abstract
Using generative adversarial network (GAN) Goodfellow et al. (2014) for data enhancement of medical images is significantly helpful for many computer-aided diagnosis (CAD) tasks. A new GAN-based automated tampering attack, like CT-GAN Mirsky et al. (2019), has emerged. It can inject or remove lung cancer lesions to CT scans. Because the tampering region may even account for less than 1% of the original image, even state-of-the-art methods are challenging to detect the traces of such tampering. This paper proposes a two-stage cascade framework to detect GAN-based medical image small region forgery like CT-GAN. In the local detection stage, we train the detector network with small sub-images so that interference information in authentic regions will not affect the detector. We use depthwise separable convolution and residual networks to prevent the detector from over-fitting and enhance the ability to find forged regions through the attention mechanism. The detection results of all sub-images in the same image will be combined into a heatmap. In the global classification stage, using gray-level co-occurrence matrix (GLCM) can better extract features of the heatmap. Because the shape and size of the tampered region are uncertain, we use hyperplanes in an infinite-dimensional space for classification. Our method can classify whether a CT image has been tampered and locate the tampered position. Sufficient experiments show that our method can achieve excellent performance than the state-of-the-art detection methods.
Collapse
Affiliation(s)
- Jianyi Zhang
- Beijing Electronic Science and Technology Institute, Beijing, China
- University of Louisiana at Lafayette, Lafayette, Louisiana, United States of America
| | - Xuanxi Huang
- Beijing Electronic Science and Technology Institute, Beijing, China
| | - Yaqi Liu
- Beijing Electronic Science and Technology Institute, Beijing, China
| | - Yuyang Han
- Beijing Electronic Science and Technology Institute, Beijing, China
| | - Zixiao Xiang
- Beijing Electronic Science and Technology Institute, Beijing, China
| |
Collapse
|
10
|
Kim DS, Lau LN, Kim JW, Yeo ISL. Measurement of proximal contact of single crowns to assess interproximal relief: A pilot study. Heliyon 2023; 9:e20403. [PMID: 37767497 PMCID: PMC10520794 DOI: 10.1016/j.heliyon.2023.e20403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 08/23/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
Background It is common for dental technicians to adjust the proximal surface of adjacent teeth on casts when fabricating single crowns. However, whether the accuracy of the proximal contact is affected if this step is eliminated is unclear. Objective To evaluate the accuracy of the proximal contact of single crowns for mandibular first molars fabricated from four different restorative materials, without adjustment of the proximal surface of the adjacent teeth by the laboratory/dental technician. Methods This study was in vitro; all the clinical procedures were conducted on a dentoform. The mandibular first molar tooth on the dentoform was prepared using diamond burs and a high speed handpiece. Twenty single crowns were fabricated, five for each group (monolithic zirconia, lithium disilicate, metal ceramic, and cast gold). No proximal surface adjacent to the definitive crowns was adjusted for tight contact in the dental laboratory. Both the qualitative analyses, using dental floss and shimstock, and the quantitative analyses, using a stereo microscope, were performed to evaluate the accuracy of the proximal contact of the restoration with the adjacent teeth. In the quantitative analysis, one-way analysis of variance was used to compare mean values at a significance level of 0.05. Results In quantitative analysis, the differences between the proximal contact tightness of the four groups was not statistically significant (P = 0.802 for mesial contacts, P = 0.354 for distal contacts). In qualitative analysis, in most crowns, dental floss passed through the contact with tight resistance and only one film of shimstock could be inserted between the adjacent teeth and the restoration. However, one specimen from the cast gold crown had open contact. Conclusions Even without proximal surface adjustment of the adjacent teeth during the crown fabrication process, adequate proximal contact tightness between the restoration and adjacent teeth could be achieved.
Collapse
Affiliation(s)
| | - Le Na Lau
- Department of Prosthodontics, Seoul National University School of Dentistry, Seoul, Korea
| | - Jong-Woong Kim
- Department of Prosthodontics, Seoul National University School of Dentistry, Seoul, Korea
| | - In-Sung Luke Yeo
- Department of Prosthodontics, School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Korea
| |
Collapse
|
11
|
Huang Z, Li W, Wang Y, Liu Z, Zhang Q, Jin Y, Wu R, Quan G, Liang D, Hu Z, Zhang N. MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks. Artif Intell Med 2023; 143:102609. [PMID: 37673577 DOI: 10.1016/j.artmed.2023.102609] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 05/17/2023] [Accepted: 06/06/2023] [Indexed: 09/08/2023]
Abstract
Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works.
Collapse
Affiliation(s)
- Zhenxing Huang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wenbo Li
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Yunling Wang
- Department of Radiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China.
| | - Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Qiyang Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuxi Jin
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruodai Wu
- Department of Radiology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen 518055, China
| | - Guotao Quan
- Shanghai United Imaging Healthcare, Shanghai 201807, China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| |
Collapse
|
12
|
Wang H, Chi J, Wu C, Yu X, Wu H. Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising. J Digit Imaging 2023; 36:1894-1909. [PMID: 37118101 PMCID: PMC10407009 DOI: 10.1007/s10278-023-00831-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/13/2023] [Accepted: 04/06/2023] [Indexed: 04/30/2023] Open
Abstract
Computer tomography (CT) has played an essential role in the field of medical diagnosis. Considering the potential risk of exposing patients to X-ray radiations, low-dose CT (LDCT) images have been widely applied in the medical imaging field. Since reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures, or false lesions derived from noise. To tackle these issues, we propose a novel degradation adaption local-to-global transformer (DALG-Transformer) for restoring the LDCT image. Specifically, the DALG-Transformer is built on self-attention modules which excel at modeling long-range information between image patch sequences. Meanwhile, an unsupervised degradation representation learning scheme is first developed in medical image processing to learn abstract degradation representations of the LDCT images, which can distinguish various degradations in the representation space rather than the pixel space. Then, we introduce a degradation-aware modulated convolution and gated mechanism into the building modules (i.e., multi-head attention and feed-forward network) of each Transformer block, which can bring in the complementary strength of convolution operation to emphasize on the spatially local context. The experimental results show that the DALG-Transformer can provide superior performance in noise removal, structure preservation, and false lesions elimination compared with five existing representative deep networks. The proposed networks may be readily applied to other image processing tasks including image reconstruction, image deblurring, and image super-resolution.
Collapse
Affiliation(s)
- Huan Wang
- Northeastern University, NO. 195, Chuangxin Road, Shenyang, China
| | - Jianning Chi
- Northeastern University, NO. 195, Chuangxin Road, Shenyang, China
| | - Chengdong Wu
- Northeastern University, NO. 195, Chuangxin Road, Shenyang, China.
| | - Xiaosheng Yu
- Northeastern University, NO. 195, Chuangxin Road, Shenyang, China
| | - Hao Wu
- University of Sydney, Sydney, NSW, 2006, Australia
| |
Collapse
|
13
|
Hirairi T, Ichikawa K, Urikura A, Kawashima H, Tabata T, Matsunami T. Improvement of diagnostic performance of hyperacute ischemic stroke in head CT using an image-based noise reduction technique with non-black-boxed process. Phys Med 2023; 112:102646. [PMID: 37549457 DOI: 10.1016/j.ejmp.2023.102646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/05/2023] [Accepted: 07/28/2023] [Indexed: 08/09/2023] Open
Abstract
PURPOSE This study aims to investigate whether an image-based noise reduction (INR) technique with a conventional rule-based algorithm involving no black-boxed processes can outperform an existing hybrid-type iterative reconstruction (HIR) technique, when applied to brain CT images for diagnosis of early CT signs, which generally exhibit low-contrast lesions that are difficult to detect. METHODS The subjects comprised 27 patients having infarctions within 4.5 h of onset and 27 patients with no change in brain parenchyma. Images with thicknesses of 5 mm and 0.625 mm were reconstructed by HIR. Images with a thickness of 0.625 mm reconstructed by filter back projection (FBP) were processed by INR. The contrast-to-noise ratios (CNRs) were calculated between gray and white matters; lentiform nucleus and internal capsule; infarcted and non-infarcted areas. Two radiologists subjectively evaluated the presence of hyperdense artery signs (HASs) and infarctions and visually scored three properties regarding image quality (0.625-mm HIR images were excluded because of their notably worse noise appearances). RESULTS The CNRs of INR were significantly better than those of HIR with P < 0.001 for all the indicators. INR yielded significantly higher areas under the curve for both infarction and HAS detections than HIR (P < 0.001). Also, INR significantly improved the visual scores of all the three indicators. CONCLUSION The INR incorporating a simple and reproducible algorithm was more effective than HIR in detecting early CT signs and can be potentially applied to CT images from a large variety of CT systems.
Collapse
Affiliation(s)
- Tetsuya Hirairi
- Department of Radiological Technology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
| | - Katsuhiro Ichikawa
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Atsushi Urikura
- Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuuouku, Tokyo, 104-0045, Japan.
| | - Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan.
| | - Takasumi Tabata
- Department of Radiology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
| | - Tamaki Matsunami
- Department of Radiology, Juntendo University Shizuoka Hospital, 1129 Nagaoka, Izunokuni, Shizuoka, 410-2295, Japan.
| |
Collapse
|
14
|
Zhou Z, Inoue A, McCollough CH, Yu L. Self-trained deep convolutional neural network for noise reduction in CT. J Med Imaging (Bellingham) 2023; 10:044008. [PMID: 37636895 PMCID: PMC10449263 DOI: 10.1117/1.jmi.10.4.044008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/29/2023] Open
Abstract
Purpose Supervised deep convolutional neural network (CNN)-based methods have been actively used in clinical CT to reduce image noise. The networks of these methods are typically trained using paired high- and low-quality data from a massive number of patients and/or phantom images. This training process is tedious, and the network trained under a given condition may not be generalizable to patient images acquired and reconstructed under different conditions. We propose a self-trained deep CNN (ST_CNN) method for noise reduction in CT that does not rely on pre-existing training datasets. Approach The ST_CNN training was accomplished using extensive data augmentation in the projection domain, and the inference was applied to the data itself. Specifically, multiple independent noise insertions were applied to the original patient projection data to generate multiple realizations of low-quality projection data. Then, rotation augmentation was adopted for both the original and low-quality projection data by applying the rotation angle directly on the projection data so that images were rotated at arbitrary angles without introducing additional bias. A large number of paired low- and high-quality images from the same patient were reconstructed and paired for training the ST_CNN model. Results No significant difference was found between the ST_CNN and conventional CNN models in terms of the peak signal-to-noise ratio and structural similarity index measure. The ST_CNN model outperformed the conventional CNN model in terms of noise texture and homogeneity in liver parenchyma as well as better subjective visualization of liver lesions. The ST_CNN may sacrifice the sharpness of vessels slightly compared to the conventional CNN model but without affecting the visibility of peripheral vessels or diagnosis of vascular pathology. Conclusions The proposed ST_CNN method trained from the data itself may achieve similar image quality in comparison with conventional deep CNN denoising methods pre-trained on external datasets.
Collapse
Affiliation(s)
- Zhongxing Zhou
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Akitoshi Inoue
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | | | - Lifeng Yu
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| |
Collapse
|
15
|
Lee J, Jeon J, Hong Y, Jeong D, Jang Y, Jeon B, Baek HJ, Cho E, Shim H, Chang HJ. Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising. Comput Biol Med 2023; 159:106931. [PMID: 37116238 DOI: 10.1016/j.compbiomed.2023.106931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 04/04/2023] [Accepted: 04/13/2023] [Indexed: 04/30/2023]
Abstract
BACKGROUND Most computed tomography (CT) denoising algorithms have been evaluated using image quality analysis (IQA) methods developed for natural image, which do not adequately capture the texture details in medical imaging. Radiomics is an emerging image analysis technique that extracts texture information to provide a more objective basis for medical imaging diagnostics, overcoming the subjective nature of traditional methods. By utilizing the difficulty of reproducing radiomics features under different imaging protocols, we can more accurately evaluate the performance of CT denoising algorithms. METHOD We introduced radiomic feature reproducibility analysis as an evaluation metric for a denoising algorithm. Also, we proposed a low-dose CT denoising method based on a generative adversarial network (GAN), which outperformed well-known CT denoising methods. RESULTS Although the proposed model produced excellent results visually, the traditional image assessment metrics such as peak signal-to-noise ratio and structural similarity failed to show distinctive performance differences between the proposed method and the conventional ones. However, radiomic feature reproducibility analysis provided a distinctive assessment of the CT denoising performance. Furthermore, radiomic feature reproducibility analysis allowed fine-tuning of the hyper-parameters of the GAN. CONCLUSION We demonstrated that the well-tuned GAN architecture outperforms the well-known CT denoising methods. Our study is the first to introduce radiomics reproducibility analysis as an evaluation metric for CT denoising. We look forward that the study may bridge the gap between traditional objective and subjective evaluations in the clinical medical imaging field.
Collapse
Affiliation(s)
- Jina Lee
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, 03722, South Korea
| | - Jaeik Jeon
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea
| | - Youngtaek Hong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Ontact Health, Seoul, 03764, South Korea.
| | - Dawun Jeong
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, 03722, South Korea
| | - Yeonggul Jang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, 03722, South Korea
| | - Byunghwan Jeon
- Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, 17035, South Korea
| | - Hye Jin Baek
- Department of Radiology, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Changwon, 51472, South Korea
| | - Eun Cho
- Department of Radiology, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Changwon, 51472, South Korea
| | - Hackjoon Shim
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea
| | - Hyuk-Jae Chang
- CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03764, South Korea; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, 03722, South Korea
| |
Collapse
|
16
|
Pierre K, Haneberg AG, Kwak S, Peters KR, Hochhegger B, Sananmuang T, Tunlayadechanont P, Tighe PJ, Mancuso A, Forghani R. Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond. Semin Roentgenol 2023; 58:158-169. [PMID: 37087136 DOI: 10.1053/j.ro.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 04/24/2023]
Abstract
There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication. These non-diagnostic workflow and process optimization tasks are an important part of the arsenal of potential AI tools that can streamline day to day clinical practice and patient care.
Collapse
Affiliation(s)
- Kevin Pierre
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Adam G Haneberg
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Sean Kwak
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL
| | - Keith R Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Thiparom Sananmuang
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Padcha Tunlayadechanont
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Patrick J Tighe
- Departments of Anesthesiology & Orthopaedic Surgery, University of Florida College of Medicine, Gainesville, FL
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
| |
Collapse
|
17
|
Yang M, Wang J, Zhang Z, Li J, Liu L. Transfer learning framework for low-dose CT reconstruction based on marginal distribution adaptation in multiscale. Med Phys 2023; 50:1450-1465. [PMID: 36321246 DOI: 10.1002/mp.16027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND With the increasing use of computed tomography (CT) in clinical practice, limiting CT radiation exposure to reduce potential cancer risks has become one of the important directions of medical imaging research. As the dose decreases, the reconstructed CT image will be severely degraded by projection noise. PURPOSE As an important method of image processing, supervised deep learning has been widely used in the restoration of low-dose CT (LDCT) in recent years. However, the normal-dose CT (NDCT) corresponding to a specific LDCT (it is regarded as the label of the LDCT, which is necessary for supervised learning) is very difficult to obtain so that the application of supervised learning methods in LDCT reconstruction is limited. It is necessary to construct a unsupervised deep learning framework for LDCT reconstruction that does not depend on paired LDCT-NDCT datasets. METHODS We presented an unsupervised learning framework for the transferring from the identity mapping to the low-dose reconstruction task, called marginal distribution adaptation in multiscale (MDAM). For NDCTs as source domain data, MDAM is an identity map with two parts: firstly, it establishes a dimensionality reduction mapping, which can obtain the same feature distribution from NDCTs and LDCTs; and then NDCTs is retrieved by reconstructing the image overview and details from the low-dimensional features. For the purpose of the feature transfer between source domain and target domain (LDCTs), we introduce the multiscale feature extraction in the MDAM, and then eliminate differences in probability distributions of these multiscale features between NDCTs and LDCTs through wavelet decomposition and domain adaptation learning. RESULTS Image quality evaluation metrics and subjective quality scores show that, as an unsupervised method, the performance of the MDAM approaches or even surpasses some state-of-the-art supervised methods. Especially, MDAM has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection. CONCLUSIONS We demonstrated that, the MDAM framework can reconstruct corresponding NDCTs from LDCTs with high accuracy, and without relying on any labeles. Moreover, it is more suitable for clinical application compared with supervised learning methods.
Collapse
Affiliation(s)
- Minghan Yang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Jianye Wang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Ziheng Zhang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Jie Li
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Lingling Liu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui, China
| |
Collapse
|
18
|
Yi Z, Wang J, Li M. Deep image and feature prior algorithm based on U-ConformerNet structure. Phys Med 2023; 107:102535. [PMID: 36764130 DOI: 10.1016/j.ejmp.2023.102535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 01/04/2023] [Accepted: 01/25/2023] [Indexed: 02/10/2023] Open
Abstract
PURPOSE The reconstruction performance of the deep image prior (DIP) approach is limited by the conventional convolutional layer structure and it is difficult to enhance its potential. In order to improve the quality of image reconstruction and suppress artifacts, we propose a DIP algorithm with better performance, and verify its superiority in the latest case. METHODS We construct a new U-ConformerNet structure as the DIP algorithm's network, replacing the traditional convolutional layer-based U-net structure, and introduce the 'lpips' deep network based feature distance regularization method. Our algorithm can switch between supervised and unsupervised modes at will to meet different needs. RESULTS The reconstruction was performed on the low dose CT dataset (LoDoPaB). Our algorithm attained a PSNR of more than 35 dB under unsupervised conditions, and the PSNR under the supervised condition is greater than 36 dB. Both of which are better than the performance of the DIP-TV. Furthermore, the accuracy of this method is positively connected with the quality of the a priori image with the help of deep networks. In terms of noise eradication and artifact suppression, the DIP algorithm with U-ConformerNet structure outperforms the standard DIP method based on convolutional structure. CONCLUSIONS It is known by experimental verification that, in unsupervised mode, the algorithm improves the output PSNR by at least 2-3 dB when compared to the DIP-TV algorithm (proposed in 2020). In supervised mode, our algorithm approaches that of the state-of-the-art end-to-end deep learning algorithms.
Collapse
Affiliation(s)
- Zhengming Yi
- The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Key Laboratory for Ferrous Metallurgy and Resources Utilization of Metallurgy and Resources Utilization of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China
| | - Junjie Wang
- The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Key Laboratory for Ferrous Metallurgy and Resources Utilization of Metallurgy and Resources Utilization of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China
| | - Mingjie Li
- The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Key Laboratory for Ferrous Metallurgy and Resources Utilization of Metallurgy and Resources Utilization of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China.
| |
Collapse
|
19
|
Zhang P, Ren S, Liu Y, Gui Z, Shangguan H, Wang Y, Shu H, Chen Y. A total variation prior unrolling approach for computed tomography reconstruction. Med Phys 2023; 50:2816-2834. [PMID: 36791315 DOI: 10.1002/mp.16307] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 01/09/2023] [Accepted: 02/06/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND With the rapid development of deep learning technology, deep neural networks can effectively enhance the performance of computed tomography (CT) reconstructions. One kind of commonly used method to construct CT reconstruction networks is to unroll the conventional iterative reconstruction (IR) methods to convolutional neural networks (CNNs). However, most unrolling methods primarily unroll the fidelity term of IR methods to CNNs, without unrolling the prior terms. The prior terms are always directly replaced by neural networks. PURPOSE In conventional IR methods, the prior terms play a vital role in improving the visual quality of reconstructed images. Unrolling the hand-crafted prior terms to CNNs may provide a more specialized unrolling approach to further improve the performance of CT reconstruction. In this work, a primal-dual network (PD-Net) was proposed by unrolling both the data fidelity term and the total variation (TV) prior term, which effectively preserves the image edges and textures in the reconstructed images. METHODS By further deriving the Chambolle-Pock (CP) algorithm instance for CT reconstruction, we discovered that the TV prior updates the reconstructed images with its divergences in each iteration of the solution process. Based on this discovery, CNNs were applied to yield the divergences of the feature maps for the reconstructed image generated in each iteration. Additionally, a loss function was applied to the predicted divergences of the reconstructed image to guarantee that the CNNs' results were the divergences of the corresponding feature maps in the iteration. In this manner, the proposed CNNs seem to play the same roles in the PD-Net as the TV prior in the IR methods. Thus, the TV prior in the CP algorithm instance can be directly unrolled to CNNs. RESULTS The datasets from the Low-Dose CT Image and Projection Data and the Piglet dataset were employed to assess the effectiveness of our proposed PD-Net. Compared with conventional CT reconstruction methods, our proposed method effectively preserves the structural and textural information in reference to ground truth. CONCLUSIONS The experimental results show that our proposed PD-Net framework is feasible for the implementation of CT reconstruction tasks. Owing to the promising results yielded by our proposed neural network, this study is intended to inspire further development of unrolling approaches by enabling the direct unrolling of hand-crafted prior terms to CNNs.
Collapse
Affiliation(s)
- Pengcheng Zhang
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China
| | - Shuhui Ren
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China
| | - Yi Liu
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China
| | - Zhiguo Gui
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China
| | - Hong Shangguan
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, China
| | - Yanling Wang
- School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, China
| | - Yang Chen
- Laboratory of Image Science and Technology, Southeast University, Nanjing, China.,Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France.,Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China.,Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, China
| |
Collapse
|
20
|
Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network. Sci Rep 2023; 13:2356. [PMID: 36759636 PMCID: PMC9911730 DOI: 10.1038/s41598-023-28175-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 01/13/2023] [Indexed: 02/11/2023] Open
Abstract
The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGGAN models using normal and abnormal CXRs, solely relying on normal CXRs to demonstrate the quality of synthetic CXRs that were 1000 × 1000 pixels in size. Image Turing tests were evaluated by six radiologists in a binary fashion using two independent validation sets to judge the authenticity of each CXR, with a mean accuracy of 67.42% and 69.92% for the first and second trials, respectively. Inter-reader agreements were poor for the first (κ = 0.10) and second (κ = 0.14) Turing tests. Additionally, a convolutional neural network (CNN) was used to classify normal or abnormal CXR using only real images and/or synthetic images mixed datasets. The accuracy of the CNN model trained using a mixed dataset of synthetic and real data was 93.3%, compared to 91.0% for the model built using only the real data. PGGAN was able to generate CXRs that were identical to real CXRs, and this showed promise to overcome imbalances between classes in CNN training.
Collapse
|
21
|
Tariq A, Patel BN, Sensakovic WF, Fahrenholtz SJ, Banerjee I. Opportunistic screening for low bone density using abdominopelvic computed tomography scans. Med Phys 2023. [PMID: 36748265 DOI: 10.1002/mp.16230] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/08/2022] [Accepted: 12/21/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND While low bone density is a major burden on US health system, current osteoporosis screening guidelines by the US Preventive Services Task Force are limited to women aged ≥65 and all postmenopausal women with certain risk factors. Even within recommended screening groups, actual screening rates are low (<26%) and vary across socioeconomic groups. The proposed model can opportunistically screen patients using abdominal CT studies for low bone density who may otherwise go undiagnosed. PURPOSE To develop an artificial intelligence (AI) model for opportunistic screening of low bone density using both contrast and non-contrast abdominopelvic computed tomography (CT) exams, for the purpose of referral to traditional bone health management, which typically begins with dual energy X-ray absorptiometry (DXA). METHODS We collected 6083 contrast-enhanced CT imaging exams paired with DXA exams within ±6 months documented between May 2015 and August 2021 in a single institution with four major healthcare practice regions. Our fusion AI pipeline receives the coronal and axial plane images of a contrast enhanced abdominopelvic CT exam and basic patient demographics (age, gender, body cross section lengths) to predict risk of low bone mass. The models were trained on lumbar spine T-scores from DXA exams and tested on multi-site imaging exams. The model was again tested in a prospective group (N = 344) contrast-enhanced and non-contrast-enhanced studies. RESULTS The models were evaluated on the same test set (1208 exams)-(1) Baseline model using demographic factors from electronic medical records (EMR) - 0.7 area under the curve of receiver operator characteristic (AUROC); Imaging based models: (2) axial view - 0.83 AUROC; (3) coronal view- 0.83 AUROC; (4) Fusion model-Imaging + demographic factors - 0.86 AUROC. The prospective test yielded one missed positive DXA case with a hip prosthesis among 23 positive contrast-enhanced CT exams and 0% false positive rate for non-contrast studies. Both positive cases among non-contrast enhanced CT exams were successfully detected. While only about 8% patients from prospective study received a DXA exam within 2 years, about 30% were detected with low bone mass by the fusion model, highlighting the need for opportunistic screening. CONCLUSIONS The fusion model, which combines two planes of CT images and EMRs data, outperformed individual models and provided a high, robust diagnostic performance for opportunistic screening of low bone density using contrast and non-contrast CT exams. This model could potentially improve bone health risk assessment with no additional cost. The model's handling of metal implants is an ongoing effort.
Collapse
Affiliation(s)
- Amara Tariq
- Department of Administration, Mayo Clinic, Phoenix, Arizona, USA
| | - Bhavik N Patel
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA.,Department of Computer Engineering, Ira A. Fulton School of Engineering, Arizona State University, Phoenix, Arizona, USA
| | | | | | - Imon Banerjee
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA.,Department of Computer Engineering, Ira A. Fulton School of Engineering, Arizona State University, Phoenix, Arizona, USA
| |
Collapse
|
22
|
Li G, Chen X, You C, Huang X, Deng Z, Luo S. A nonconvex model-based combined geometric calibration scheme for micro cone-beam CT with irregular trajectories. Med Phys 2023; 50:2759-2774. [PMID: 36718546 DOI: 10.1002/mp.16257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/21/2022] [Accepted: 01/17/2023] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Many dedicated cone-beam CT (CBCT) systems have irregular scanning trajectories. Compared with the standard CBCT calibration, accurate calibration for CBCT systems with irregular trajectories is a more complex task, since the geometric parameters for each scanning view are variable. Most of the existing calibration methods assume that the intrinsic geometric relationship of the fiducials in the phantom is precisely known, and rarely delve deeper into the issue of whether the phantom accuracy is adapted to the calibration model. PURPOSE A high-precision phantom and a highly robust calibration model are interdependent and mutually supportive, and they are both important for calibration accuracy, especially for the high-resolution CBCT. Therefore, we propose a calibration scheme that considers both accurate phantom measurement and robust geometric calibration. METHODS Our proposed scheme consists of two parts: (1) introducing a measurement model to acquire the accurate intrinsic geometric relationship of the fiducials in the phantom; (2) developing a highly noise-robust nonconvex model-based calibration method. The measurement model in the first part is achieved by extending our previous high-precision geometric calibration model suitable for CBCT with circular trajectories. In the second part, a novel iterative method with optimization constraints based on a back-projection model is developed to solve the geometric parameters of each view. RESULTS The simulations and real experiments show that the measurement errors of the fiducial ball bearings (BBs) are within the subpixel level. With the help of the geometric relationship of the BBs obtained by our measurement method, the classic calibration method can achieve good calibration based on far fewer BBs. All metrics obtained in simulated experiments as well as in real experiments on Micro CT systems with resolutions of 9 and 4.5 μm show that the proposed calibration method has higher calibration accuracy than the competing classic method. It is particularly worth noting that although our measurement model proves to be very accurate, the classic calibration method based on this measurement model can only achieve good calibration results when the resolution of the measurement system is close to that of the system to be calibrated, but our calibration scheme enables high-accuracy calibration even when the resolution of the system to be calibrated is twice that of the measurement system. CONCLUSIONS The proposed combined geometrical calibration scheme does not rely on a phantom with an intricate pattern of fiducials, so it is applicable in Micro CT with high resolution. The two parts of the scheme, the "measurement model" and the "calibration model," prove to be of high accuracy. The combination of these two models can effectively improve the calibration accuracy, especially in some extreme cases.
Collapse
Affiliation(s)
- Guang Li
- Jiangsu Key Laboratory for Biomaterials and Devices, Department of Biomedical Engineering, Southeast University, Nanjing, China
| | - Xue Chen
- Jiangsu Key Laboratory for Biomaterials and Devices, Department of Biomedical Engineering, Southeast University, Nanjing, China
| | - Chenyu You
- Image Processing and Analysis Group (IPAG), Yale University, New Haven, Connecticut, USA
| | - Xinhai Huang
- Jiangsu Key Laboratory for Biomaterials and Devices, Department of Biomedical Engineering, Southeast University, Nanjing, China
| | - Zhenhao Deng
- Jiangsu Key Laboratory for Biomaterials and Devices, Department of Biomedical Engineering, Southeast University, Nanjing, China
| | - Shouhua Luo
- Jiangsu Key Laboratory for Biomaterials and Devices, Department of Biomedical Engineering, Southeast University, Nanjing, China
| |
Collapse
|
23
|
Shi L, Zhang J, Toyonaga T, Shao D, Onofrey JA, Lu Y. Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application. Phys Med Biol 2023; 68. [PMID: 36584395 DOI: 10.1088/1361-6560/acaf49] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 12/30/2022] [Indexed: 12/31/2022]
Abstract
Objective. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET activity (λ-MLAA) and attenuation (μ-MLAA) maps was proposed to solve those issues using the time-of-flight PET raw data only. However,λ-MLAA still suffers from quantification error as compared to reconstruction using the gold-standard CT-based attenuation map (μ-CT). Recently, a deep learning (DL)-based framework was proposed to improve MLAA by predictingμ-DL fromλ-MLAA andμ-MLAA using an image domain loss function (IM-loss). However, IM-loss does not directly measure the AC errors according to the PET attenuation physics. Our preliminary studies showed that an additional physics-based loss function can lead to more accurate PET AC. The main objective of this study is to optimize the attenuation map generation framework for clinical full-dose18F-FDG studies. We also investigate the effectiveness of the optimized network on predicting attenuation maps for synthetic low-dose oncological PET studies.Approach. We optimized the proposed DL framework by applying different preprocessing steps and hyperparameter optimization, including patch size, weights of the loss terms and number of angles in the projection-domain loss term. The optimization was performed based on 100 skull-to-toe18F-FDG PET/CT scans with minimal misalignment. The optimized framework was further evaluated on 85 clinical full-dose neck-to-thigh18F-FDG cancer datasets as well as synthetic low-dose studies with only 10% of the full-dose raw data.Main results. Clinical evaluation of tumor quantification as well as physics-based figure-of-merit metric evaluation validated the promising performance of our proposed method. For both full-dose and low-dose studies, the proposed framework achieved <1% error in tumor standardized uptake value measures.Significance. It is of great clinical interest to achieve CT-less PET reconstruction, especially for low-dose PET studies.
Collapse
Affiliation(s)
- Luyao Shi
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
| | - Jiazhen Zhang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| | - Dan Shao
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America.,Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, People's Republic of China
| | - John A Onofrey
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America.,Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America.,Department of Urology, Yale University, New Haven, CT, United States of America
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States of America
| |
Collapse
|
24
|
Zhou Z, Huber NR, Inoue A, McCollough CH, Yu L. Multislice input for 2D and 3D residual convolutional neural network noise reduction in CT. J Med Imaging (Bellingham) 2023; 10:014003. [PMID: 36743869 PMCID: PMC9888548 DOI: 10.1117/1.jmi.10.1.014003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 01/09/2023] [Indexed: 02/03/2023] Open
Abstract
Purpose Deep convolutional neural network (CNN)-based methods are increasingly used for reducing image noise in computed tomography (CT). Current attempts at CNN denoising are based on 2D or 3D CNN models with a single- or multiple-slice input. Our work aims to investigate if the multiple-slice input improves the denoising performance compared with the single-slice input and if a 3D network architecture is better than a 2D version at utilizing the multislice input. Approach Two categories of network architectures can be used for the multislice input. First, multislice images can be stacked channel-wise as the multichannel input to a 2D CNN model. Second, multislice images can be employed as the 3D volumetric input to a 3D CNN model, in which the 3D convolution layers are adopted. We make performance comparisons among 2D CNN models with one, three, and seven input slices and two versions of 3D CNN models with seven input slices and one or three output slices. Evaluation was performed on liver CT images using three quantitative metrics with full-dose images as reference. Visual assessment was made by an experienced radiologist. Results When the input channels of the 2D CNN model increases from one to three to seven, a trend of improved performance was observed. Comparing the three models with the seven-slice input, the 3D CNN model with a one-slice output outperforms the other models in terms of noise texture and homogeneity in liver parenchyma as well as subjective visualization of vessels. Conclusions We conclude the that multislice input is an effective strategy for improving performance for 2D deep CNN denoising models. The pure 3D CNN model tends to have a better performance than the other models in terms of continuity across axial slices, but the difference was not significant compared with the 2D CNN model with the same number of slices as the input.
Collapse
Affiliation(s)
- Zhongxing Zhou
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Nathan R. Huber
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Akitoshi Inoue
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | | | - Lifeng Yu
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| |
Collapse
|
25
|
Yang S, Pu Q, Lei C, Zhang Q, Jeon S, Yang X. Low-dose CT denoising with a high-level feature refinement and dynamic convolution network. Med Phys 2022. [PMID: 36542402 DOI: 10.1002/mp.16175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 10/31/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Since the potential health risks of the radiation generated by computer tomography (CT), concerns have been expressed on reducing the radiation dose. However, low-dose CT (LDCT) images contain complex noise and artifacts, bringing uncertainty to medical diagnosis. PURPOSE Existing deep learning (DL)-based denoising methods are difficult to fully exploit hierarchical features of different levels, limiting the effect of denoising. Moreover, the standard convolution kernel is parameter sharing and cannot be adjusted dynamically with input change. This paper proposes an LDCT denoising network using high-level feature refinement and multiscale dynamic convolution to mitigate these problems. METHODS The dual network structure proposed in this paper consists of the feature refinement network (FRN) and the dynamic perception network (DPN). The FDN extracts features of different levels through residual dense connections. The high-level hierarchical information is transmitted to DPN to improve the low-level representations. In DPN, the two networks' features are fused by local channel attention (LCA) to assign weights in different regions and handle CT images' delicate tissues better. Then, the dynamic dilated convolution (DDC) with multibranch and multiscale receptive fields is proposed to enhance the expression and processing ability of the denoising network. The experiments were trained and tested on the dataset "NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge," consisting of 10 anonymous patients with normal-dose abdominal CT and LDCT at 25% dose. In addition, external validation was performed on the dataset "Low Dose CT Image and Projection Data," which included 300 chest CT images at 10% dose and 300 head CT images at 25% dose. RESULTS Proposed method compared with seven mainstream LDCT denoising algorithms. On the Mayo dataset, achieved peak signal-to-noise ratio (PSNR): 46.3526 dB (95% CI: 46.0121-46.6931 dB) and structural similarity (SSIM): 0.9844 (95% CI: 0.9834-0.9854). Compared with LDCT, the average increase was 3.4159 dB and 0.0239, respectively. The results are relatively optimal and statistically significant compared with other methods. In external verification, our algorithm can cope well with ultra-low-dose chest CT images at 10% dose and obtain PSNR: 28.6130 (95% CI: 28.1680-29.0580 dB) and SSIM: 0.7201 (95% CI: 0.7101-0.7301). Compared with LDCT, PSNR/SSIM is increased by 3.6536dB and 0.2132, respectively. In addition, the quality of LDCT can also be improved in head CT denoising. CONCLUSIONS This paper proposes a DL-based LDCT denoising algorithm, which utilizes high-level features and multiscale dynamic convolution to optimize the network's denoising effect. This method can realize speedy denoising and performs well in noise suppression and detail preservation, which can be helpful for the diagnosis of LDCT.
Collapse
Affiliation(s)
- Sihan Yang
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China.,School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan, China
| | - Qiang Pu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chunting Lei
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China.,School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan, China
| | - Qiao Zhang
- Macro Net Communication Co., Ltd., Chongqing, China
| | - Seunggil Jeon
- Samsung Electronics, Suwon-si, Gyeonggi-do, Republic of Korea
| | - Xiaomin Yang
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China.,School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan, China
| |
Collapse
|
26
|
Zhang P, Li K. A dual-domain neural network based on sinogram synthesis for sparse-view CT reconstruction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107168. [PMID: 36219892 DOI: 10.1016/j.cmpb.2022.107168] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The dual-domain deep learning-based reconstruction techniques have enjoyed many successful applications in the field of medical image reconstruction. Applying the analytical reconstruction based operator to transfer the data from the projection domain to the image domain, the dual-domain techniques may suffer from the insufficient suppression or removal of streak artifacts in areas with the missing view data, when addressing the sparse-view reconstruction problems. In this work, to overcome this problem, an intelligent sinogram synthesis based back-projection network (iSSBP-Net) was proposed for sparse-view computed tomography (CT) reconstruction. In the iSSBP-Net method, a convolutional neural network (CNN) was involved in the dual-domain method to inpaint the missing view data in the sinogram before CT reconstruction. METHODS The proposed iSSBP-Net method fused a sinogram synthesis sub-network (SS-Net), a sinogram filter sub-network (SF-Net), a back-projection layer, and a post-CNN into an end-to-end network. Firstly, to inpaint the missing view data, the SS-Net employed a CNN to synthesize the full-view sinogram in the projection domain. Secondly, to improve the visual quality of the sparse-view CT images, the synthesized sinogram was filtered by a CNN. Thirdly, the filtered sinogram was brought into the image domain through the back-projection layer. Finally, to yield images of high visual sensitivity, the post-CNN was applied to restore the desired images from the outputs of the back-projection layer. RESULTS The numerical experiments demonstrate that the proposed iSSBP-Net is superior to all competing algorithms under different scanning condintions for sparse-view CT reconstruction. Compared to the competing algorithms, the proposed iSSBP-Net method improved the peak signal-to-noise ratio of the reconstructed images about 1.21 dB, 0.26 dB, 0.01 dB, and 0.37 dB under the scanning conditions of 360, 180, 90, and 60 views, respectively. CONCLUSION The promising reconstruction results indicate that involving the SS-Net in the dual-domain method is could be an effective manner to suppress or remove the streak artifacts in sparse-view CT images. Due to the promising results reconstructed by the iSSBP-Net method, this study is intended to inspire the further development of sparse-view CT reconstruction by involving a SS-Net in the dual-domain method.
Collapse
Affiliation(s)
- Pengcheng Zhang
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan 030051, PR China.
| | - Kunpeng Li
- State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan 030051, PR China
| |
Collapse
|
27
|
Semi-supervised structure attentive temporal mixup coherence for medical image segmentation. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
28
|
Li Z, Liu Y, Li K, Chen Y, Shu H, Kang J, Lu J, Gui Z. Edge feature extraction-based dual CNN for LDCT denoising. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2022; 39:1929-1938. [PMID: 36215566 DOI: 10.1364/josaa.462923] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
In low-dose computed tomography (LDCT) denoising tasks, it is often difficult to balance edge/detail preservation and noise/artifact reduction. To solve this problem, we propose a dual convolutional neural network (CNN) based on edge feature extraction (Ed-DuCNN) for LDCT. Ed-DuCNN consists of two branches. One branch is the edge feature extraction subnet (Edge_Net) that can fully extract the edge details in the image. The other branch is the feature fusion subnet (Fusion_Net) that introduces an attention mechanism to fuse edge features and noisy image features. Specifically, first, shallow edge-specific detail features are extracted by trainable Sobel convolutional blocks and then are integrated into Edge_Net together with the LDCT images to obtain deep edge detail features. Finally, the input image, shallow edge detail, and deep edge detail features are fused in Fusion_Net to generate the final denoised image. The experimental results show that the proposed Ed-DuCNN can achieve competitive performance in terms of quantitative metrics and visual perceptual quality compared with that of state-of-the-art methods.
Collapse
|
29
|
Kim W, Lee J, Kang M, Kim JS, Choi JH. Wavelet subband-specific learning for low-dose computed tomography denoising. PLoS One 2022; 17:e0274308. [PMID: 36084002 PMCID: PMC9462582 DOI: 10.1371/journal.pone.0274308] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/25/2022] [Indexed: 11/19/2022] Open
Abstract
Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early algorithms were primarily optimized to obtain an accurate image with low distortion between the denoised image and reference full-dose image at the cost of yielding an overly smoothed unrealistic CT image. Recent research has sought to preserve the fine details of denoised images with high perceptual quality, which has been accompanied by a decrease in objective quality due to a trade-off between perceptual quality and distortion. We pursue a network that can generate accurate and realistic CT images with high objective and perceptual quality within one network, achieving a better perception-distortion trade-off. To achieve this goal, we propose a stationary wavelet transform-assisted network employing the characteristics of high- and low-frequency domains of the wavelet transform and frequency subband-specific losses defined in the wavelet domain. We first introduce a stationary wavelet transform for the network training procedure. Then, we train the network using objective loss functions defined for high- and low-frequency domains to enhance the objective quality of the denoised CT image. With this network design, we train the network again after replacing the objective loss functions with perceptual loss functions in high- and low-frequency domains. As a result, we acquired denoised CT images with high perceptual quality using this strategy while minimizing the objective quality loss. We evaluated our algorithms on the phantom and clinical images, and the quantitative and qualitative results indicate that ours outperform the existing state-of-the-art algorithms in terms of objective and perceptual quality.
Collapse
Affiliation(s)
- Wonjin Kim
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
| | - Jaayeon Lee
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
| | - Mihyun Kang
- Department of Cyber Security, Ewha Womans University, Seoul, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jang-Hwan Choi
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
- * E-mail:
| |
Collapse
|
30
|
A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning. OPHTHALMOLOGY SCIENCE 2022; 2:100169. [PMID: 36245755 PMCID: PMC9560561 DOI: 10.1016/j.xops.2022.100169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/02/2022] [Accepted: 05/09/2022] [Indexed: 11/22/2022]
|
31
|
Liu J, Jiang H, Ning F, Li M, Pang W. DFSNE-Net: Deviant feature sensitive noise estimate network for low-dose CT denoising. Comput Biol Med 2022; 149:106061. [DOI: 10.1016/j.compbiomed.2022.106061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/10/2022] [Accepted: 08/27/2022] [Indexed: 11/26/2022]
|
32
|
Three-dimensional conditional generative adversarial network-based virtual thin-slice technique for the morphological evaluation of the spine. Sci Rep 2022; 12:12176. [PMID: 35842451 PMCID: PMC9288435 DOI: 10.1038/s41598-022-16637-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 07/13/2022] [Indexed: 11/08/2022] Open
Abstract
Virtual thin-slice (VTS) technique is a generative adversarial network-based algorithm that can generate virtual 1-mm-thick CT images from images of 3–10-mm thickness. We evaluated the performance of VTS technique for assessment of the spine. VTS was applied to 4-mm-thick CT images of 73 patients, and the visibility of intervertebral spaces was evaluated on the 4-mm-thick and VTS images. The heights of vertebrae measured on sagittal images reconstructed from the 4-mm-thick images and VTS images were compared with those measured on images reconstructed from 1-mm-thick images. Diagnostic performance for the detection of compression fractures was also compared. The intervertebral spaces were significantly more visible on the VTS images than on the 4-mm-thick images (P < 0.001). The absolute value of the measured difference in mean vertebral height between the VTS and 1-mm-thick images was smaller than that between the 4-mm-thick and 1-mm-thick images (P < 0.01–0.54). The diagnostic performance of the VTS images for detecting compression fracture was significantly lower than that of the 4-mm-thick images for one reader (P = 0.02). VTS technique enabled the identification of each vertebral body, and enabled accurate measurement of vertebral height. However, this technique is not suitable for diagnosing compression fractures.
Collapse
|
33
|
Okamoto T, Kumakiri T, Haneishi H. Patch-based artifact reduction for three-dimensional volume projection data of sparse-view micro-computed tomography. Radiol Phys Technol 2022; 15:206-223. [PMID: 35622229 DOI: 10.1007/s12194-022-00661-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 11/27/2022]
Abstract
Micro-computed tomography (micro-CT) enables the non-destructive acquisition of three-dimensional (3D) morphological structures at the micrometer scale. Although it is expected to be used in pathology and histology to analyze the 3D microstructure of tissues, micro-CT imaging of tissue specimens requires a long scan time. A high-speed imaging method, sparse-view CT, can reduce the total scan time and radiation dose; however, it causes severe streak artifacts on tomographic images reconstructed with analytical algorithms due to insufficient sampling. In this paper, we propose an artifact reduction method for 3D volume projection data from sparse-view micro-CT. Specifically, we developed a patch-based lightweight fully convolutional network to estimate full-view 3D volume projection data from sparse-view 3D volume projection data. We evaluated the effectiveness of the proposed method using physically acquired datasets. The qualitative and quantitative results showed that the proposed method achieved high estimation accuracy and suppressed streak artifacts in the reconstructed images. In addition, we confirmed that the proposed method requires both short training and prediction times. Our study demonstrates that the proposed method has great potential for artifact reduction for 3D volume projection data under sparse-view conditions.
Collapse
Affiliation(s)
- Takayuki Okamoto
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan.
| | - Toshio Kumakiri
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan
| | - Hideaki Haneishi
- Center for Frontier Medical Engineering, Chiba University, Chiba, 263-8522, Japan
| |
Collapse
|
34
|
Gonzalez‐Montoro A, Vera‐Donoso CD, Konstantinou G, Sopena P, Martinez M, Ortiz JB, Carles M, Benlloch J, Gonzalez A. Nuclear‐medicine probes: where we are and where we are going. Med Phys 2022; 49:4372-4390. [PMID: 35526220 PMCID: PMC9545507 DOI: 10.1002/mp.15690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/08/2022] [Accepted: 04/26/2022] [Indexed: 11/10/2022] Open
Abstract
Nuclear medicine probes turned into the key for the identification and precise location of sentinel lymph nodes and other occult lesions (i.e., tumors) by using the systemic administration of radiotracers. Intraoperative nuclear probes are key in the surgical management of some malignancies as well as in the determination of positive surgical margins, thus reducing the extent and potential surgery morbidity. Depending on their application, nuclear probes are classified into two main categories, namely, counting and imaging. Although counting probes present a simple design, are handheld (to be moved rapidly), and provide only acoustic signals when detecting radiation, imaging probes, also known as cameras, are more hardware‐complex and also able to provide images but at the cost of an increased intervention time as displacing the camera has to be done slowly. This review article begins with an introductory section to highlight the relevance of nuclear‐based probes and their components as well as the main differences between ionization‐ (semiconductor) and scintillation‐based probes. Then, the most significant performance parameters of the probe are reviewed (i.e., sensitivity, contrast, count rate capabilities, shielding, energy, and spatial resolution), as well as the different types of probes based on the target radiation nature, namely: gamma (γ), beta (β) (positron and electron), and Cherenkov. Various available intraoperative nuclear probes are finally compared in terms of performance to discuss the state‐of‐the‐art of nuclear medicine probes. The manuscript concludes by discussing the ideal probe design and the aspects to be considered when selecting nuclear‐medicine probes.
Collapse
Affiliation(s)
- A. Gonzalez‐Montoro
- Instituto de Instrumentación para Imagen Molecular (I3M) Centro Mixto CSIC Universitat Politècnica de València Camino de Vera s/n Valencia 46022 Spain
| | | | | | - P. Sopena
- Servicio de Medicina Nuclear Área clínica de Imagen Médica, La Fe Hospital Valencia 46026 Spain
| | - M. Martinez
- Urology Department La Fe Hospital Valencia 46026 Spain
| | - J. B. Ortiz
- Urology Department La Fe Hospital Valencia 46026 Spain
| | - M. Carles
- Biomedical Imaging Research Group La Fe Hospital Valencia 46026 Spain
| | - J.M. Benlloch
- Instituto de Instrumentación para Imagen Molecular (I3M) Centro Mixto CSIC Universitat Politècnica de València Camino de Vera s/n Valencia 46022 Spain
| | - A.J. Gonzalez
- Instituto de Instrumentación para Imagen Molecular (I3M) Centro Mixto CSIC Universitat Politècnica de València Camino de Vera s/n Valencia 46022 Spain
| |
Collapse
|
35
|
Li S, Li Q, Li R, Wu W, Zhao J, Qiang Y, Tian Y. An adaptive self-guided wavelet convolutional neural network with compound loss for low-dose CT denoising. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
36
|
Zhou J, Xin H. Emerging artificial intelligence methods for fighting lung cancer: a survey. CLINICAL EHEALTH 2022. [DOI: 10.1016/j.ceh.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
|
37
|
Klug M, Shemesh J, Green M, Mayer A, Kerpel A, Konen E, Marom E. A deep-learning method for the denoising of ultra-low dose chest CT in coronary artery calcium score evaluation. Clin Radiol 2022; 77:e509-e517. [DOI: 10.1016/j.crad.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/11/2022] [Indexed: 11/03/2022]
|
38
|
Ye RZ, Noll C, Richard G, Lepage M, Turcotte ÉE, Carpentier AC. DeepImageTranslator: A free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis. SLAS Technol 2022; 27:76-84. [PMID: 35058205 DOI: 10.1016/j.slast.2021.10.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The advent of deep-learning has set new standards in an array of image translation applications. At present, the use of these methods often requires computer programming experience. Non-commercial programs with graphical interface usually do not allow users to fully customize their deep-learning pipeline. Therefore, our primary objective is to provide a simple graphical interface that allows researchers with no programming experience to easily create, train, and evaluate custom deep-learning models for image translation. We also aimed to test the applicability of our tool in CT image semantic segmentation and noise reduction. DeepImageTranslator was implemented using the Tkinter library, the standard Python interface to the Tk graphical user interface toolkit; backend computations were implemented using data augmentation packages such as Pillow, Numpy, OpenCV, Augmentor, Tensorflow, and Keras libraries. Convolutional neural networks (CNNs) were trained using DeepImageTranslator. The effects of data augmentation, deep-supervision, and sample size on model accuracy were also systematically assessed. The DeepImageTranslator a simple tool that allows users to customize all aspects of their deep-learning pipeline, including the CNN, training optimizer, loss function, and the types of training image augmentation scheme. We showed that DeepImageTranslator can be used to achieve state-of-the-art accuracy and generalizability in semantic segmentation and noise reduction. Highly accurate 3D segmentation models for body composition can be obtained using training sample sizes as small as 17 images. In conclusion, an open-source deep-learning tool for accurate image translation with a user-friendly graphical interface was presented and evaluated. This standalone software can be downloaded at: https://sourceforge.net/projects/deepimagetranslator/.
Collapse
Affiliation(s)
- Run Zhou Ye
- Division of Endocrinology, Department of Medicine, Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Christophe Noll
- Division of Endocrinology, Department of Medicine, Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Gabriel Richard
- Sherbrooke Molecular Imaging Center, Department of Nuclear Medicine and Radiobiology, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Martin Lepage
- Sherbrooke Molecular Imaging Center, Department of Nuclear Medicine and Radiobiology, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Éric E Turcotte
- Department of Nuclear Medicine and Radiobiology, Centre d'Imagerie Moléculaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - André C Carpentier
- Division of Endocrinology, Department of Medicine, Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada.
| |
Collapse
|
39
|
Wu X, Zhang Y, Zhang P, Hui H, Jing J, Tian F, Jiang J, Yang X, Chen Y, Tian J. Structure attention co-training neural network for neovascularization segmentation in intravascular optical coherence tomography. Med Phys 2022; 49:1723-1738. [PMID: 35061247 DOI: 10.1002/mp.15477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 01/09/2022] [Accepted: 01/10/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To development and validate a Neovascularization (NV) segmentation model in intravascular optical coherence tomography (IVOCT) through deep learning methods. METHODS AND MATERIALS A total of 1950 2D slices of 70 IVOCT pullbacks were used in our study. We randomly selected 1273 2D slices from 44 patients as the training set, 379 2D slices from 11 patients as the validation set, and 298 2D slices from the last 15 patients as the testing set. Automatic NV segmentation is quite challenging, as it must address issues of speckle noise, shadow artifacts, high distribution variation, etc. To meet these challenges, a new deep learning-based segmentation method is developed based on a co-training architecture with an integrated structural attention mechanism. Co-training is developed to exploit the features of three consecutive slices. The structural attention mechanism comprises spatial and channel attention modules and is integrated into the co-training architecture at each up-sampling step. A cascaded fixed network is further incorporated to achieve segmentation at the image level in a coarse-to-fine manner. RESULTS Extensive experiments were performed involving a comparison with several state-of-the-art deep learning-based segmentation methods. Moreover, the consistency of the results with those of manual segmentation was also investigated. Our proposed NV automatic segmentation method achieved the highest correlation with the manual delineation by interventional cardiologists (the Pearson correlation coefficient is 0.825). CONCLUSION In this work, we proposed a co-training architecture with an integrated structural attention mechanism to segment NV in IVOCT images. The good agreement between our segmentation results and manual segmentation indicates that the proposed method has great potential for application in the clinical investigation of NV-related plaque diagnosis and treatment. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Xiangjun Wu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100083, China.,CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China
| | - Yingqian Zhang
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, 100853, China
| | - Peng Zhang
- Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Jing Jing
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, 100853, China
| | - Feng Tian
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, 100853, China
| | - Jingying Jiang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100083, China
| | - Xin Yang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China
| | - Yundai Chen
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, 100853, China.,Southern Medical University, Guangzhou, 510515, China
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100083, China.,CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,Zhuhai Precision Medical Center, Zhuhai People's Hospital, affiliated with Jinan University, Zhuhai, 519000, China
| |
Collapse
|
40
|
Islam KT, Wijewickrema S, O’Leary S. A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:523. [PMID: 35062484 PMCID: PMC8780247 DOI: 10.3390/s22020523] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/26/2021] [Accepted: 12/28/2021] [Indexed: 06/14/2023]
Abstract
Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods.
Collapse
|
41
|
Cong W, De Man B, Wang G. Projection decomposition via univariate optimization for dual-energy CT. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:725-736. [PMID: 35634811 PMCID: PMC9427723 DOI: 10.3233/xst-221153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Dual-energy computed tomography (DECT) acquires two x-ray projection datasets with different x-ray energy spectra, performs material-specific image reconstruction based on the energy-dependent non-linear integral model, and provides more accurate quantification of attenuation coefficients than single energy spectrum CT. In the diagnostic energy range, x-ray energy-dependent attenuation is mainly caused by photoelectric absorption and Compton scattering. Theoretically, these two physical components of the x-ray attenuation mechanism can be determined from two projection datasets with distinct energy spectra. Practically, the solution of the non-linear integral equation is complicated due to spectral uncertainty, detector sensitivity, and data noise. Conventional multivariable optimization methods are prone to local minima. In this paper, we develop a new method for DECT image reconstruction in the projection domain. This method combines an analytic solution of a polynomial equation and a univariate optimization to solve the polychromatic non-linear integral equation. The polynomial equation of an odd order has a unique real solution with sufficient accuracy for image reconstruction, and the univariate optimization can achieve the global optimal solution, allowing accurate and stable projection decomposition for DECT. Numerical and physical phantom experiments are performed to demonstrate the effectiveness of the method in comparison with the state-of-the-art projection decomposition methods. As a result, the univariate optimization method yields a quality improvement of 15% for image reconstruction and substantial reduction of the computational time, as compared to the multivariable optimization methods.
Collapse
Affiliation(s)
- Wenxiang Cong
- Biomedical Imaging Center, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Bruno De Man
- GE Research, One Research Circle, Niskayuna, NY, USA
| | - Ge Wang
- Biomedical Imaging Center, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| |
Collapse
|
42
|
Cui X, Guo Y, Zhang X, Shangguan H, Liu B, Wang A. Artifact-Assisted multi-level and multi-scale feature fusion attention network for low-dose CT denoising. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:875-889. [PMID: 35694948 DOI: 10.3233/xst-221149] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Since low-dose computed tomography (LDCT) images typically have higher noise that may affect accuracy of disease diagnosis, the objective of this study is to develop and evaluate a new artifact-assisted feature fusion attention (AAFFA) network to extract and reduce image artifact and noise in LDCT images. METHODS In AAFFA network, a feature fusion attention block is constructed for local multi-scale artifact feature extraction and progressive fusion from coarse to fine. A multi-level fusion architecture based on skip connection and attention modules is also introduced for artifact feature extraction. Specifically, long-range skip connections are used to enhance and fuse artifact features with different depth levels. Then, the fused shallower features enter channel attention for better extraction of artifact features, and the fused deeper features are sent into pixel attention for focusing on the artifact pixel information. Besides, an artifact channel is designed to provide rich artifact features and guide the extraction of noise and artifact features. The AAPM LDCT Challenge dataset is used to train and test the network. The performance is evaluated by using both visual observation and quantitative metrics including peak signal-noise-ratio (PSNR), structural similarity index (SSIM) and visual information fidelity (VIF). RESULTS Using AAFFA network improves the averaged PSNR/SSIM/VIF values of AAPM LDCT images from 43.4961, 0.9595, 0.3926 to 48.2513, 0.9859, 0.4589, respectively. CONCLUSIONS The proposed AAFFA network is able to effectively reduce noise and artifacts while preserving object edges. Assessment of visual quality and quantitative index demonstrates the significant improvement compared with other image denoising methods.
Collapse
Affiliation(s)
- Xueying Cui
- School of Applied Science, Taiyuan University of Science and Technology, Taiyuan, China
| | - Yingting Guo
- School of Applied Science, Taiyuan University of Science and Technology, Taiyuan, China
| | - Xiong Zhang
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, China
| | - Hong Shangguan
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, China
| | - Bin Liu
- School of Applied Science, Taiyuan University of Science and Technology, Taiyuan, China
| | - Anhong Wang
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, China
| |
Collapse
|
43
|
Hu S, Lei B, Wang S, Wang Y, Feng Z, Shen Y. Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:145-157. [PMID: 34428138 DOI: 10.1109/tmi.2021.3107013] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Fusing multi-modality medical images, such as magnetic resonance (MR) imaging and positron emission tomography (PET), can provide various anatomical and functional information about the human body. However, PET data is not always available for several reasons, such as high cost, radiation hazard, and other limitations. This paper proposes a 3D end-to-end synthesis network called Bidirectional Mapping Generative Adversarial Networks (BMGAN). Image contexts and latent vectors are effectively used for brain MR-to-PET synthesis. Specifically, a bidirectional mapping mechanism is designed to embed the semantic information of PET images into the high-dimensional latent space. Moreover, the 3D Dense-UNet generator architecture and the hybrid loss functions are further constructed to improve the visual quality of cross-modality synthetic images. The most appealing part is that the proposed method can synthesize perceptually realistic PET images while preserving the diverse brain structures of different subjects. Experimental results demonstrate that the performance of the proposed method outperforms other competitive methods in terms of quantitative measures, qualitative displays, and evaluation metrics for classification.
Collapse
|
44
|
Bera S, Biswas PK. Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3663-3673. [PMID: 34224348 DOI: 10.1109/tmi.2021.3094525] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened public concern over the patient's associated radiation dose. On the other hand, reducing the radiation dose leads to increased noise and artifacts, which adversely degrades the scan's interpretability. In recent times, the deep learning-based technique has emerged as a promising method for low dose CT(LDCT) denoising. However, some common bottleneck still exists, which hinders deep learning-based techniques from furnishing the best performance. In this study, we attempted to mitigate these problems with three novel accretions. First, we propose a novel convolutional module as the first attempt to utilize neighborhood similarity of CT images for denoising tasks. Our proposed module assisted in boosting the denoising by a significant margin. Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising. The loss mentioned above also assisted to alleviate the laborious effort required while training CT denoising network using image patches. Lastly, we propose a novel discriminator function for CT denoising tasks. The conventional vanilla discriminator tends to overlook the fine structural details and focus on the global agreement. Our proposed discriminator leverage self-attention and pixel-wise GANs for restoring the diagnostic quality of LDCT images. Our method validated on a publicly available dataset of the 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge performed remarkably better than the existing state of the art method. The corresponding source code is available at: https://github.com/reach2sbera/ldct_nonlocal.
Collapse
|
45
|
Shi L, Lu Y, Dvornek N, Weyman CA, Miller EJ, Sinusas AJ, Liu C. Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3293-3304. [PMID: 34018932 PMCID: PMC8670362 DOI: 10.1109/tmi.2021.3082578] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Patient motion during dynamic PET imaging can induce errors in myocardial blood flow (MBF) estimation. Motion correction for dynamic cardiac PET is challenging because the rapid tracer kinetics of 82Rb leads to substantial tracer distribution change across different dynamic frames over time, which can cause difficulties for image registration-based motion correction, particularly for early dynamic frames. In this paper, we developed an automatic deep learning-based motion correction (DeepMC) method for dynamic cardiac PET. In this study we focused on the detection and correction of inter-frame rigid translational motion caused by voluntary body movement and pattern change of respiratory motion. A bidirectional-3D LSTM network was developed to fully utilize both local and nonlocal temporal information in the 4D dynamic image data for motion detection. The network was trained and evaluated over motion-free patient scans with simulated motion so that the motion ground-truths are available, where one million samples based on 65 patient scans were used in training, and 600 samples based on 20 patient scans were used in evaluation. The proposed method was also evaluated using additional 10 patient datasets with real motion. We demonstrated that the proposed DeepMC obtained superior performance compared to conventional registration-based methods and other convolutional neural networks (CNN), in terms of motion estimation and MBF quantification accuracy. Once trained, DeepMC is much faster than the registration-based methods and can be easily integrated into the clinical workflow. In the future work, additional investigation is needed to evaluate this approach in a clinical context with realistic patient motion.
Collapse
|
46
|
Dong S, Hangel G, Bogner W, Trattnig S, Rossler K, Widhalm G, De Feyter HM, De Graaf RA, Duncan JS. High-Resolution Magnetic Resonance Spectroscopic Imaging using a Multi-Encoder Attention U-Net with Structural and Adversarial Loss. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2891-2895. [PMID: 34891851 DOI: 10.1109/embc46164.2021.9630146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Common to most medical imaging techniques, the spatial resolution of Magnetic Resonance Spectroscopic Imaging (MRSI) is ultimately limited by the achievable SNR. This work presents a deep learning method for 1H-MRSI spatial resolution enhancement, based on the observation that multi-parametric MRI images provide relevant spatial priors for MRSI enhancement. A Multi-encoder Attention U-Net (MAU-Net) architecture was constructed to process a MRSI metabolic map and three different MRI modalities through separate encoding paths. Spatial attention modules were incorporated to automatically learn spatial weights that highlight salient features for each MRI modality. MAU-Net was trained based on in vivo brain imaging data from patients with high-grade gliomas, using a combined loss function consisting of pixel, structural and adversarial loss. Experimental results showed that the proposed method is able to reconstruct high-quality metabolic maps with a high-resolution of 64×64 from a low-resolution of 16 × 16, with better performance compared to several baseline methods.
Collapse
|
47
|
Bai T, Wang B, Nguyen D, Wang B, Dong B, Cong W, Kalra MK, Jiang S. Deep Interactive Denoiser (DID) for X-Ray Computed Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2965-2975. [PMID: 34329156 DOI: 10.1109/tmi.2021.3101241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Low-dose computed tomography (LDCT) is desirable for both diagnostic imaging and image-guided interventions. Denoisers are widely used to improve the quality of LDCT. Deep learning (DL)-based denoisers have shown state-of-the-art performance and are becoming mainstream methods. However, there are two challenges to using DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs, which are sometimes needed for different clinical tasks; and 2) the model's generalizability might be an issue when the noise level in the testing images differs from that in the training dataset. To address these two challenges, in this work, we introduce a lightweight optimization process that can run on top of any existing DL-based denoiser during the testing phase to generate multiple image candidates with different noise-resolution tradeoffs suitable for different clinical tasks in real time. Consequently, our method allows users to interact with the denoiser to efficiently review various image candidates and quickly pick the desired one; thus, we termed this method deep interactive denoiser (DID). Experimental results demonstrated that DID can deliver multiple image candidates with different noise-resolution tradeoffs and shows great generalizability across various network architectures, as well as training and testing datasets with various noise levels.
Collapse
|
48
|
Lin H, Li Z, Yang Z, Wang Y. Variance-aware attention U-Net for multi-organ segmentation. Med Phys 2021; 48:7864-7876. [PMID: 34716711 DOI: 10.1002/mp.15322] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/06/2021] [Accepted: 10/23/2021] [Indexed: 01/20/2023] Open
Abstract
PURPOSE With the continuous development of deep learning based medical image segmentation technology, it is expected to attain more robust and accurate performance for more challenging tasks, such as multi-organs, small/irregular areas, and ambiguous boundary issues. METHODS We propose a variance-aware attention U-Net to solve the problem of multi-organ segmentation. Specifically, a simple yet effective variance-based uncertainty mechanism is devised to evaluate the discrimination of each voxel via its prediction probability. The proposed variance uncertainty is further embedded into an attention architecture, which not only aggregates multi-level deep features in a global-level but also enforces the network to pay extra attention to voxels with uncertain predictions during training. RESULTS Extensive experiments on challenging abdominal multi-organ CT dataset show that our proposed method consistently outperforms cutting-edge attention networks with respect to the evaluation metrics of Dice index (DSC), 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD). CONCLUSIONS The proposed network provides an accurate and robust solution for multi-organ segmentation and has the potential to be used for improving other segmentation applications.
Collapse
Affiliation(s)
- Haoneng Lin
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Shenzhen University, Shenzhen, China
| | - Zongshang Li
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Shenzhen University, Shenzhen, China
| | - Zefan Yang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Shenzhen University, Shenzhen, China
| | - Yi Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Shenzhen University, Shenzhen, China.,Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China.,Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| |
Collapse
|
49
|
Chen K, Long K, Ren Y, Sun J, Pu X. Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection. PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA 2021. [DOI: 10.1145/3474085.3475480] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Kecheng Chen
- University of Electronic Science and Technology of China, Chengdu, China
| | - Kun Long
- University of Electronic Science and Technology of China, Chengdu, China
| | - Yazhou Ren
- University of Electronic Science and Technology of China, Chengdu, China
| | - Jiayu Sun
- West China Hospital of SiChuan University, Chengdu, China
| | - Xiaorong Pu
- University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
50
|
The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain. Sci Rep 2021; 11:20390. [PMID: 34650183 PMCID: PMC8516935 DOI: 10.1038/s41598-021-99896-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 09/30/2021] [Indexed: 12/04/2022] Open
Abstract
Our objective was to investigate the feasibility of deep learning-based synthetic contrast-enhanced CT (DL-SCE-CT) from nonenhanced CT (NECT) in patients who visited the emergency department (ED) with acute abdominal pain (AAP). We trained an algorithm generating DL-SCE-CT using NECT with paired precontrast/postcontrast images. For clinical application, 353 patients from three institutions who visited the ED with AAP were included. Six reviewers (experienced radiologists, ER1-3; training radiologists, TR1-3) made diagnostic and disposition decisions using NECT alone and then with NECT and DL-SCE-CT together. The radiologists’ confidence in decisions was graded using a 5-point scale. The diagnostic accuracy using DL-SCE-CT improved in three radiologists (50%, P = 0.023, 0.012, < 0.001, especially in 2/3 of TRs). The confidence of diagnosis and disposition improved significantly in five radiologists (83.3%, P < 0.001). Particularly, in subgroups with underlying malignancy and miscellaneous medical conditions (MMCs) and in CT-negative cases, more radiologists reported increased confidence in diagnosis (83.3% [5/6], 100.0% [6/6], and 83.3% [5/6], respectively) and disposition (66.7% [4/6], 83.3% [5/6] and 100% [6/6], respectively). In conclusion, DL-SCE-CT enhances the accuracy and confidence of diagnosis and disposition regarding patients with AAP in the ED, especially for less experienced radiologists, in CT-negative cases, and in certain disease subgroups with underlying malignancy and MMCs.
Collapse
|