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Xu H, Shi W, Sun J, Zheng T, Xu X, Sun C, Yi S, Wang G, Wu D. A motion assessment method for reference stack selection in fetal brain MRI reconstruction based on tensor rank approximation. NMR IN BIOMEDICINE 2024:e5248. [PMID: 39231762 DOI: 10.1002/nbm.5248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 07/12/2024] [Accepted: 08/08/2024] [Indexed: 09/06/2024]
Abstract
Slice-to-volume registration and super-resolution reconstruction are commonly used to generate 3D volumes of the fetal brain from 2D stacks of slices acquired in multiple orientations. A critical initial step in this pipeline is to select one stack with the minimum motion among all input stacks as a reference for registration. An accurate and unbiased motion assessment (MA) is thus crucial for successful selection. Here, we presented an MA method that determines the minimum motion stack based on 3D low-rank approximation using CANDECOMP/PARAFAC (CP) decomposition. Compared to the current 2D singular value decomposition (SVD) based method that requires flattening stacks into matrices to obtain ranks, in which the spatial information is lost, the CP-based method can factorize 3D stack into low-rank and sparse components in a computationally efficient manner. The difference between the original stack and its low-rank approximation was proposed as the motion indicator. Experiments on linearly and randomly simulated motion illustrated that CP demonstrated higher sensitivity in detecting small motion with a lower baseline bias, and achieved a higher assessment accuracy of 95.45% in identifying the minimum motion stack, compared to the SVD-based method with 58.18%. CP also showed superior motion assessment capabilities in real-data evaluations. Additionally, combining CP with the existing SRR-SVR pipeline significantly improved 3D volume reconstruction. The results indicated that our proposed CP showed superior performance compared to SVD-based methods with higher sensitivity to motion, assessment accuracy, and lower baseline bias, and can be used as a prior step to improve fetal brain reconstruction.
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Affiliation(s)
- Haoan Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Wen Shi
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jiwei Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Tianshu Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Sun Yi
- MR Collaboration, Siemens Healthcare China, Shanghai, China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
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Ermak AD, Gavrilov DV, Novitskiy RE, Gusev AV, Andreychenko AE. Development, evaluation and validation of machine learning models to predict hospitalizations of patients with coronary artery disease within the next 12 months. Int J Med Inform 2024; 188:105476. [PMID: 38743996 DOI: 10.1016/j.ijmedinf.2024.105476] [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: 11/20/2023] [Revised: 04/18/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Improved survival of patients after acute coronary syndromes, population growth, and overall life expectancy rise have led to a significant increase in the proportion of patients with stable coronary artery disease (CAD), creating a significant load on the entire healthcare system. The disease often progresses with the development of many complications while significantly increasing the likelihood of hospitalization. Developing and applying a machine learning model for predicting hospitalizations of patients with CAD to an inpatient medical facility will allow for close monitoring of high-risk patients, early preventive interventions, and optimized medical care. AIMS Development and external validation of personalized models for predicting the preventable hospitalizations of patients with stable CAD and its complications using ML algorithms and data of real-world clinical practice. METHODS 135,873 depersonalized electronic health records of 49,103 patients with stable CAD were included in the study. Anthropometric measurements, physical examination results, laboratory, instrumental, anamnestic, and socio-demographic data, widely used in routine medical practice, were considered as potential predictors, a total of 73 features. Logistic regression, decision tree-based methods including gradient boosting (AdaBoost, LightGBM, XGBoost, CatBoost) and bagging (RandomForest and ExtraTrees), discriminant analysis (LinearDiscriminant, QuadraticDiscriminant), and naive Bayes classifier were compared. External validation was performed on the data of a separate region. RESULTS The best results and stability to external validation data were shown by the CatBoost model with an AUC of 0.875 (95% CI 0.865-0.885) for the internal testing and 0.872 (95% CI 0.856-0.886) for the external validation. The best model showed good performance evaluated through AUROC, Brier score and standardized net benefit (for the target NPV threshold) for the validation dataset that was only slightly similar to the train data. CONCLUSION The metrics of the best model were superior to previously published studies. The results of external validation demonstrated the relative stability of the model to new data from another region that confirms the possibility of the model's application in real clinical practice.
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Affiliation(s)
| | | | | | - Alexander V Gusev
- Federal Research Institute for Health Organization and Informatics, Moscow, Russia; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia
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Belton N, Hagos MT, Lawlor A, Curran KM. Towards a unified approach for unsupervised brain MRI Motion Artefact Detection with few shot Anomaly Detection. Comput Med Imaging Graph 2024; 115:102391. [PMID: 38718561 DOI: 10.1016/j.compmedimag.2024.102391] [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: 09/07/2023] [Revised: 03/19/2024] [Accepted: 04/26/2024] [Indexed: 06/03/2024]
Abstract
Automated Motion Artefact Detection (MAD) in Magnetic Resonance Imaging (MRI) is a field of study that aims to automatically flag motion artefacts in order to prevent the requirement for a repeat scan. In this paper, we identify and tackle the three current challenges in the field of automated MAD; (1) reliance on fully-supervised training, meaning they require specific examples of Motion Artefacts (MA), (2) inconsistent use of benchmark datasets across different works and use of private datasets for testing and training of newly proposed MAD techniques and (3) a lack of sufficiently large datasets for MRI MAD. To address these challenges, we demonstrate how MAs can be identified by formulating the problem as an unsupervised Anomaly Detection (AD) task. We compare the performance of three State-of-the-Art AD algorithms DeepSVDD, Interpolated Gaussian Descriptor and FewSOME on two open-source Brain MRI datasets on the task of MAD and MA severity classification, with FewSOME achieving a MAD AUC >90% on both datasets and a Spearman Rank Correlation Coefficient of 0.8 on the task of MA severity classification. These models are trained in the few shot setting, meaning large Brain MRI datasets are not required to build robust MAD algorithms. This work also sets a standard protocol for testing MAD algorithms on open-source benchmark datasets. In addition to addressing these challenges, we demonstrate how our proposed 'anomaly-aware' scoring function improves FewSOME's MAD performance in the setting where one and two shots of the anomalous class are available for training. Code available at https://github.com/niamhbelton/Unsupervised-Brain-MRI-Motion-Artefact-Detection/.
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Affiliation(s)
- Niamh Belton
- Science Foundation Ireland Centre for Research Training in Machine Learning, Ireland; School of Medicine, University College Dublin, Ireland.
| | - Misgina Tsighe Hagos
- Science Foundation Ireland Centre for Research Training in Machine Learning, Ireland; School of Computer Science, University College Dublin, Ireland
| | - Aonghus Lawlor
- School of Computer Science, University College Dublin, Ireland; Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Kathleen M Curran
- Science Foundation Ireland Centre for Research Training in Machine Learning, Ireland; School of Medicine, University College Dublin, Ireland
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Benedikt S, Zelger P, Horling L, Stock K, Pallua J, Schirmer M, Degenhart G, Ruzicka A, Arora R. Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid. Diagnostics (Basel) 2024; 14:568. [PMID: 38473040 DOI: 10.3390/diagnostics14050568] [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: 01/05/2024] [Revised: 02/20/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
In vivo high-resolution peripheral quantitative computed tomography (HR-pQCT) studies on bone characteristics are limited, partly due to the lack of standardized and objective techniques to describe motion artifacts responsible for lower-quality images. This study investigates the ability of such deep-learning techniques to assess image quality in HR-pQCT datasets of human scaphoids. In total, 1451 stacks of 482 scaphoid images from 53 patients, each with up to six follow-ups within one year, and each with one non-displaced fractured and one contralateral intact scaphoid, were independently graded by three observers using a visual grading scale for motion artifacts. A 3D-CNN was used to assess image quality. The accuracy of the 3D-CNN to assess the image quality compared to the mean results of three skilled operators was between 92% and 96%. The 3D-CNN classifier reached an ROC-AUC score of 0.94. The average assessment time for one scaphoid was 2.5 s. This study demonstrates that a deep-learning approach for rating radiological image quality provides objective assessments of motion grading for the scaphoid with a high accuracy and a short assessment time. In the future, such a 3D-CNN approach can be used as a resource-saving and cost-effective tool to classify the image quality of HR-pQCT datasets in a reliable, reproducible and objective way.
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Affiliation(s)
- Stefan Benedikt
- Department of Orthopedics and Traumatology, University Hospital Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Philipp Zelger
- Department of Otorhinolaryngology, Hearing, Speech & Voice Disorders, University Hospital Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Lukas Horling
- Department of Orthopedics and Traumatology, University Hospital Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Kerstin Stock
- Department of Orthopedics and Traumatology, University Hospital Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Johannes Pallua
- Department of Orthopedics and Traumatology, University Hospital Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Michael Schirmer
- Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
- Office Dr. Schirmer, 6060 Hall, Austria
| | - Gerald Degenhart
- Department of Radiology, University Hospital Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Alexander Ruzicka
- Department of Orthopedics and Traumatology, University Hospital Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
| | - Rohit Arora
- Department of Orthopedics and Traumatology, University Hospital Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
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Nabavi S, Simchi H, Moghaddam ME, Abin AA, Frangi AF. A generalised deep meta-learning model for automated quality control of cardiovascular magnetic resonance images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107770. [PMID: 37714020 DOI: 10.1016/j.cmpb.2023.107770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 08/01/2023] [Accepted: 08/17/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND AND OBJECTIVES Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treatment. A large number of medical images, the variety of imaging artefacts, and the workload of imaging centres are amongst the factors that reveal the necessity of automatic image quality assessment (IQA). However, automated IQA requires access to bulk annotated datasets for training deep learning (DL) models. Labelling medical images is a tedious, costly and time-consuming process, which creates a fundamental challenge in proposing DL-based methods for medical applications. This study aims to present a new method for CMR IQA when there is limited access to annotated datasets. METHODS The proposed generalised deep meta-learning model can evaluate the quality by learning tasks in the prior stage and then fine-tuning the resulting model on a small labelled dataset of the desired tasks. This model was evaluated on the data of over 6,000 subjects from the UK Biobank for five defined tasks, including detecting respiratory motion, cardiac motion, Aliasing and Gibbs ringing artefacts and images without artefacts. RESULTS The results of extensive experiments show the superiority of the proposed model. Besides, comparing the model's accuracy with the domain adaptation model indicates a significant difference by using only 64 annotated images related to the desired tasks. CONCLUSION The proposed model can identify unknown artefacts in images with acceptable accuracy, which makes it suitable for medical applications and quality assessment of large cohorts. CODE AVAILABILITY: https://github.com/HosseinSimchi/META-IQA-CMRImages.
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Affiliation(s)
- Shahabedin Nabavi
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Hossein Simchi
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | | | - Ahmad Ali Abin
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Alejandro F Frangi
- Division of Informatics, Imaging and Data Sciences, Schools of Computer Science and Health Sciences, The University of Manchester, Manchester, UK; Medical Imaging Research Center (MIRC), Electrical Engineering and Cardiovascular Sciences Departments, KU Leuven, Leuven, Belgium; Alan Turing Institute, London, UK
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Tracey J, Moss L, Ashmore J. Application of synthetic data in the training of artificial intelligence for automated quality assurance in magnetic resonance imaging. Med Phys 2023; 50:5621-5629. [PMID: 36908158 DOI: 10.1002/mp.16361] [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: 06/14/2022] [Revised: 11/22/2022] [Accepted: 02/12/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND Magnetic resonance imaging scanner faults can be missed during routine quality assurance (QA) if they are subtle, intermittent, or the test being performed is insensitive to the type of fault. Coil element malfunction is a common fault within MRI scanners, which may go undetected for quite some time. Consequently, this may lead to poor image quality and the potential for misdiagnoses. PURPOSE Daily QA typically consists of an automated signal to noise ratio test and in some instances this test is insensitive to coil element malfunction. Instead of relying on daily QA testing, it was proposed to utilize patient images in conjunction with a trained neural network to detect coil element malfunction, even when it presents as a very subtle defect. The advantage to using patient images over phantom testing is real-time monitoring can be achieved. This allows clinical staff to focus more on patient throughput without being burdened by daily testing. METHODS A neural network was trained using simulated coil failure in 3958 abdominal or pelvic images from 497 patients. The accuracy of the trained network was then tested on an unseen dataset of 109 images from which 44 patients which had coil element malfunction present. Five MRI radiographers were shown 249 images with and without real coil malfunction to assess their accuracy compared to the neural network in identifying the scanner fault. RESULTS The neural network achieved an accuracy of 91.74% in identifying coil element malfunction in the unseen data. Radiographers tasked with identifying coil element malfunction had an average accuracy of 59.99%. In the same test case, the neural network outperformed all radiographers with an accuracy of 91.56%. CONCLUSION This work demonstrates that neural networks trained with artificial data can successfully identify MRI scanner coil element malfunction in clinical images. The method provided better accuracy than MRI radiographers (technologists) at identifying coil element malfunction and highlights the potential utility of AI methods as an alternative to support traditional QA. Further, our methodology of training neural networks with simulated data could potentially identify other faults, allowing centers to produce robust fault detection systems with minimal data.
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Affiliation(s)
- John Tracey
- Department of Medical Physics and Bioengineering, Raigmore Hospital, NHS Highland, Inverness, UK
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Laura Moss
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Department of Clinical Physics and Bioengineering, NHS Greater Glasgow and Clyde, Glasgow, UK
| | - Jonathan Ashmore
- Department of Medical Physics and Bioengineering, Raigmore Hospital, NHS Highland, Inverness, UK
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Manso Jimeno M, Vaughan JT, Geethanath S. Superconducting magnet designs and MRI accessibility: A review. NMR IN BIOMEDICINE 2023:e4921. [PMID: 36914280 DOI: 10.1002/nbm.4921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 02/13/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Presently, magnetic resonance imaging (MRI) magnets must deliver excellent magnetic field (B0 ) uniformity to achieve optimum image quality. Long magnets can satisfy the homogeneity requirements but require considerable superconducting material. These designs result in large, heavy, and costly systems that aggravate as field strength increases. Furthermore, the tight temperature tolerance of niobium titanium magnets adds instability to the system and requires operation at liquid helium temperature. These issues are crucial factors in the disparity of MR density and field strength use across the globe. Low-income settings show reduced access to MRI, especially to high field strengths. This article summarizes the proposed modifications to MRI superconducting magnet design and their impact on accessibility, including compact, reduced liquid helium, and specialty systems. Reducing the amount of superconductor inevitably entails shrinking the magnet size, resulting in higher field inhomogeneity. This work also reviews the state-of-the-art imaging and reconstruction methods to overcome this issue. Finally, we summarize the current and future challenges and opportunities in the design of accessible MRI.
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Affiliation(s)
- Marina Manso Jimeno
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, New York, USA
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, New York, USA
| | - John Thomas Vaughan
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, New York, USA
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, New York, USA
| | - Sairam Geethanath
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, The Biomedical Engineering and Imaging Institute, New York, New York, USA
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Wu Y, Liu J, White GM, Deng J. Image-based motion artifact reduction on liver dynamic contrast enhanced MRI. Phys Med 2023; 105:102509. [PMID: 36565556 DOI: 10.1016/j.ejmp.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/13/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Liver MRI images often suffer from degraded quality due to ghosting or blurring artifacts caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove most motion artifacts. The stage-II network applied the generative adversarial network (GAN) and perceptual loss compensation to preserve image structural features. The stage-I network served as the generator of GAN and its pretrained parameters in stage-I were further updated via backpropagation during stage-II training. The stage-I network was trained using small image patches with simulated motion artifacts including image-space rotational and translational motion, and K-space based centric and interleaved linear motion, sinusoidal, and rotational motion to mimic liver motion patterns. The stage-II network training used full-size images with the same types of simulated motion. The liver DCE-MRI image volumes without obvious motion artifacts in 10 patients were used for the training process, of which 1020 images of 8 patients were used for training and 240 images of 2 patients for validation. Finally, the whole two-stage deep learning model was tested with simulated motion images (312 clean images from 5 test patients) and patient images with real motion artifacts (28 motion images from 12 patients). The resulted images after two-stage processing demonstrated reduced motion artifacts while preserved anatomic details without image blurriness, with SSIM of 0.935 ± 0.092, MSE of 60.7 ± 9.0 × 10-3, and PSNR of 32.054 ± 2.219.
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Affiliation(s)
- Yunan Wu
- Department of Electrical Computer Engineering, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA; Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA.
| | - Junchi Liu
- Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.
| | - Gregory M White
- Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA.
| | - Jie Deng
- Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA; Department of Radiation Oncology, UT Southwestern Medical Center, 2280 Inwood Rd, Dallas, TX 75235, USA.
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Walle M, Eggemann D, Atkins PR, Kendall JJ, Stock K, Müller R, Collins CJ. Motion grading of high-resolution quantitative computed tomography supported by deep convolutional neural networks. Bone 2023; 166:116607. [PMID: 36368464 DOI: 10.1016/j.bone.2022.116607] [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: 06/07/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022]
Abstract
Image quality degradation due to subject motion confounds the precision and reproducibility of measurements of bone density, morphology and mechanical properties from high-resolution peripheral quantitative computed tomography (HR-pQCT). Time-consuming operator-based scoring of motion artefacts remains the gold standard to determine the degree of acceptable motion. However, due to the subjectiveness of manual grading, HR-pQCT scans of poor quality, which cannot be used for analysis, may be accepted upon initial review, leaving patients with incomplete or inaccurate imaging results. Convolutional Neural Networks (CNNs) enable fast image analysis with relatively few pre-processing requirements in an operator-independent and fully automated way for image classification tasks. This study aimed to develop a CNN that can predict motion scores from HR-pQCT images, while also being aware of uncertain predictions that require manual confirmation. The CNN calculated motion scores within seconds and achieved a high F1-score (86.8 ± 2.8 %), with good precision (87.5 ± 2.7 %), recall (86.7 ± 2.9 %) and a substantial agreement with the ground truth measured by Cohen's kappa (κ = 68.6 ± 6.2 %); motion scores of the test dataset were predicted by the algorithm with comparable accuracy, precision, sensitivity and agreement as by the operators (p > 0.05). This post-processing approach may be used to assess the effect of motion scores on microstructural analysis and can be immediately implemented into clinical protocols, significantly reducing the time for quality assessment and control of HR-pQCT scans.
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Affiliation(s)
- Matthias Walle
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | | | - Penny R Atkins
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Jack J Kendall
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Kerstin Stock
- University Hospital for Orthopedics and Traumatology, Innsbruck, Austria
| | - Ralph Müller
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Caitlyn J Collins
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland; Virginia Tech, Department of Biomedical Engineering and Mechanics, Blacksburg, United States.
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Chen X, Lei Y, Su J, Yang H, Ni W, Yu J, Gu Y, Mao Y. A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges. Curr Neuropharmacol 2022; 20:1359-1382. [PMID: 34749621 PMCID: PMC9881077 DOI: 10.2174/1570159x19666211108141446] [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: 07/23/2021] [Revised: 09/07/2021] [Accepted: 10/10/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences. OBJECTIVE This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases. METHODS Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases. RESULTS For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion. CONCLUSION Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.
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Affiliation(s)
- Xi Chen
- School of Information Science and Technology, Fudan University, Shanghai, China; ,These authors contributed equally to this work
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,These authors contributed equally to this work
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Heng Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China; ,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,Address correspondence to these authors at the School of Information Science and Technology, Fudan University, Shanghai 200433, China; Tel: +86 021 65643202; Fax: +86 021 65643202; E-mail: Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China; Tel: +86 021 52889999; Fax: +86 021 62489191; E-mail:
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
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11
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From Accuracy to Reliability and Robustness in Cardiac Magnetic Resonance Image Segmentation: A Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083936] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR) image segmentation has achieved state-of-the-art performance. Despite achieving inter-observer variability in terms of different accuracy performance measures, visual inspections reveal errors in most segmentation results, indicating a lack of reliability and robustness of DL segmentation models, which can be critical if a model was to be deployed into clinical practice. In this work, we aim to bring attention to reliability and robustness, two unmet needs of cardiac image segmentation methods, which are hampering their translation into practice. To this end, we first study the performance accuracy evolution of CMR segmentation, illustrate the improvements brought by DL algorithms and highlight the symptoms of performance stagnation. Afterwards, we provide formal definitions of reliability and robustness. Based on the two definitions, we identify the factors that limit the reliability and robustness of state-of-the-art deep learning CMR segmentation techniques. Finally, we give an overview of the current set of works that focus on improving the reliability and robustness of CMR segmentation, and we categorize them into two families of methods: quality control methods and model improvement techniques. The first category corresponds to simpler strategies that only aim to flag situations where a model may be incurring poor reliability or robustness. The second one, instead, directly tackles the problem by bringing improvements into different aspects of the CMR segmentation model development process. We aim to bring the attention of more researchers towards these emerging trends regarding the development of reliable and robust CMR segmentation frameworks, which can guarantee the safe use of DL in clinical routines and studies.
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Manso Jimeno M, Ravi KS, Jin Z, Oyekunle D, Ogbole G, Geethanath S. ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning. Magn Reson Imaging 2022; 89:42-48. [PMID: 35176447 DOI: 10.1016/j.mri.2022.02.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 01/14/2023]
Abstract
Low-field MR scanners are more accessible in resource-constrained settings where skilled personnel are scarce. Images acquired in such scenarios are prone to artifacts such as wrap-around and Gibbs ringing. Such artifacts negatively affect the diagnostic quality and may be confused with pathology or reduce the region of interest visibility. As a first step solution, ArtifactID identifies wrap-around and Gibbs ringing in low-field brain MRI. We utilized two datasets: 179 T1-weighted pathological brain images from a 0.36 T scanner and 581 publicly available T1-weighted brain images. Individual binary classification models were trained to identify through-plane wrap-around, in-plane wrap-around, and Gibbs ringing. Visual explanations obtained via the GradCAM method helped develop trust in the wrap-around model. The mean precision and recall metrics across the four implemented models were 97.6% and 92.83% respectively. Agreement analysis of the models and the radiologists' labels returned Cohen's kappa values of 0.768 ± 0.062, 1.00 ± 0.000, 0.89 ± 0.085, and 0.878 ± 0.103 for the through-plane wrap-around, in-plane wrap-around, and Gibbs ringing models, respectively.
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Affiliation(s)
- Marina Manso Jimeno
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, NY 10027, USA; Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA
| | - Keerthi Sravan Ravi
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, NY 10027, USA; Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA
| | - Zhezhen Jin
- Mailman School of Public Health, Columbia University in the City of New York, New York, NY 10027, USA
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital, Ibadan 200285, Nigeria
| | - Godwin Ogbole
- Department of Radiology, University College Hospital, Ibadan 200285, Nigeria
| | - Sairam Geethanath
- Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA.
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Wu G, Ji H. Short-term memory neural network-based cognitive computing in sports training complexity pattern recognition. Soft comput 2022:1-16. [PMID: 35035279 PMCID: PMC8747855 DOI: 10.1007/s00500-021-06568-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/11/2021] [Indexed: 11/30/2022]
Abstract
With the development of Chinese sports, many sports training researchers try to use artificial intelligence technology to study the training methods and training elements of athletes. However, in reality, these methods are often based on different basic training principles, resulting in the reduction in the generalization ability of artificial intelligence networks. This paper studies the complexity of sports training principles by using an artificial intelligence network model. Based on the improved model of dropout optimization algorithm, this paper proposes an artificial intelligence sports training node prediction method based on the combination of dropout optimization algorithm and short-term memory neural network (LSTM), which avoids the establishment of complex sports training models. Based on artificial intelligence operation and maintenance records and sports training core capacity experimental data, the maximum node static estimation of artificial intelligence sports training is realized. The research shows that the node prediction model is established by using the method described in this paper. Through experimental comparison and analysis, the model has high prediction accuracy. Due to the state memory function of LSTM, it has advantages in the prediction of 2000 data on a long time scale. The mean absolute error percentage of the prediction results is less than 3.4%, and the maximum absolute error percentage is less than 5.2%. The artificial intelligence network model in this paper has good generalization ability. Compared with other models, the model proposed in this paper can get more accurate prediction results in sports training of different groups and effectively alleviate the problem of overfitting. Therefore, traditional stadiums and gymnasiums should actively introduce artificial intelligence technology with a more positive attitude, to realize the development and innovation in technology application, service innovation, management efficiency, and function integration.
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Affiliation(s)
- Guang Wu
- College of Physical Education, Chongqing Technology and Business University, Chongqing, 400067 Nan’an China
| | - Hang Ji
- Shijiazhuang School of the Arts, Shijiazhuang, 050800 Hebei China
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Sagawa H, Itagaki K, Matsushita T, Miyati T. Evaluation of motion artifacts in brain magnetic resonance images using convolutional neural network-based prediction of full-reference image quality assessment metrics. J Med Imaging (Bellingham) 2022; 9:015502. [PMID: 35106324 PMCID: PMC8782596 DOI: 10.1117/1.jmi.9.1.015502] [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: 06/29/2021] [Accepted: 01/03/2022] [Indexed: 01/23/2023] Open
Abstract
Purpose: Motion artifacts in magnetic resonance (MR) images mostly undergo subjective evaluation, which is poorly reproducible, time consuming, and costly. Recently, full-reference image quality assessment (FR-IQA) metrics, such as structural similarity (SSIM), have been used, but they require a reference image and hence cannot be used to evaluate clinical images. We developed a convolutional neural network (CNN) model to quantify motion artifacts without using reference images. Approach: The brain MR images were obtained from an open dataset. The motion-corrupted images were generated retrospectively, and the peak signal-to-noise ratio, cross-correlation coefficient, and SSIM were calculated. The CNN was trained using these images and their FR-IQA metrics to predict the FR-IQA metrics without reference images. Receiver operating characteristic (ROC) curves were created for binary classification, with artifact scores < 4 indicating the need for rescanning. ROC curve analysis was performed on the binary classification of the real motion images. Results: The predicted FR-IQA metric having the highest correlation with the subjective evaluation was SSIM, which was able to classify images requiring rescanning with a sensitivity of 89.5%, specificity of 78.2%, and area under the ROC curve (AUC) of 0.930. The real motion artifacts were classified with the AUC of 0.928. Conclusions: Our CNN model predicts FR-IQA metrics with high accuracy, which enables quantitative assessment of motion artifacts in MR images without reference images. It enables classification of images requiring rescanning with a high AUC, which can improve the workflow of MR imaging examinations.
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Affiliation(s)
- Hajime Sagawa
- Kyoto University Hospital, Division of Clinical Radiology Service, Kyoto, Japan,Kanazawa University, Graduate School of Medical Sciences, Division of Health Sciences, Kanazawa, Japan,Address all correspondence to Hajime Sagawa,
| | - Koji Itagaki
- Kyoto University Hospital, Division of Clinical Radiology Service, Kyoto, Japan
| | - Tatsuhiko Matsushita
- Kyoto University Hospital, Division of Clinical Radiology Service, Kyoto, Japan,Kanazawa University, Pharmaceutical and Health Sciences, Institute of Medical, Faculty of Health Sciences, Kanazawa, Japan
| | - Tosiaki Miyati
- Kanazawa University, Graduate School of Medical Sciences, Division of Health Sciences, Kanazawa, Japan,Kanazawa University, Pharmaceutical and Health Sciences, Institute of Medical, Faculty of Health Sciences, Kanazawa, Japan
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Butskova A, Juhl R, Zukić D, Chaudhary A, Pohl KM, Zhao Q. Adversarial Bayesian Optimization for Quantifying Motion Artifact Within MRI. PREDICTIVE INTELLIGENCE IN MEDICINE. PRIME (WORKSHOP) 2021; 12928:83-92. [PMID: 35749100 PMCID: PMC9212065 DOI: 10.1007/978-3-030-87602-9_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Subject motion during an MRI sequence can cause ghosting effects or diffuse image noise in the phase-encoding direction and hence is likely to bias findings in neuroimaging studies. Detecting motion artifacts often relies on experts visually inspecting MRIs, which is subjective and expensive. To improve this detection, we develop a framework to automatically quantify the severity of motion artifact within a brain MRI. We formulate this task as a regression problem and train the regressor from a data set of MRIs with various amounts of motion artifacts. To resolve the issue of missing fine-grained ground-truth labels (level of artifacts), we propose Adversarial Bayesian Optimization (ABO) to infer the distribution of motion parameters (i.e., rotation and translation) underlying the acquired MRI data and then inject synthetic motion artifacts sampled from that estimated distribution into motion-free MRIs. After training the regressor on the synthetic data, we applied the model to quantify the motion level in 990 MRIs collected by the National Consortium on Alcohol and Neurodevelopment in Adolescence. Results show that the motion level derived by our approach is more reliable than the traditional metric based on Entropy Focus Criterion and manually defined binary labels.
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Affiliation(s)
| | - Rain Juhl
- Stanford University, Stanford, CA, USA
| | | | | | - Kilian M Pohl
- Stanford University, Stanford, CA, USA
- SRI International, Menlo Park, CA, USA
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Lyu Q, Shan H, Xie Y, Kwan AC, Otaki Y, Kuronuma K, Li D, Wang G. Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2170-2181. [PMID: 33856986 PMCID: PMC8376223 DOI: 10.1109/tmi.2021.3073381] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.
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Oksuz I. Brain MRI artefact detection and correction using convolutional neural networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105909. [PMID: 33373815 DOI: 10.1016/j.cmpb.2020.105909] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 12/13/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Brain MRI is one of the most commonly used diagnostic imaging tools to detect neurodegenerative disease. Diagnostic image quality is a key factor to enable robust image analysis algorithms developed for downstream tasks such as segmentation. In clinical practice, one of the main challenges is the presence of image artefacts, which can lead to low diagnostic image quality. METHODS In this paper, we propose using dense convolutional neural networks to detect and a residual U-net architecture to correct motion related brain MRI artefacts. We first generate synthetic artefacts using an MR physics based corruption strategy. Then, we use a detection strategy based on dense convolutional neural network to detect artefacts. The detected artefacts are corrected using a residual U-net network trained on corrupted data. RESULTS Our pipeline for detection and correction of artefacts is capable of reaching not only better quality image quality, but also better segmentation accuracy of stroke segmentation. The algorithm is validated using 28 cases brain MRI stroke segmentation dataset and showed an accuracy of 97.8% for detecting artefacts in our experiments. We also illustrated the improved image quality and segmentation accuracy with the proposed correction algorithm. CONCLUSIONS Ensuring high image quality and high segmentation quality jointly can improve the automatic image analysis pipelines and reduce the influence of low image quality on final prognosis. With this work, we illustrate a performance analysis on brain MRI stroke segmentation.
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Affiliation(s)
- Ilkay Oksuz
- Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey; School of Biomedical Engineering & Imaging Sciences, King's College London, U.K.
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Rücker N, Pflüger L, Maier A. Hardware Failure Prediction on Imbalanced Times Series Data : Generation of Artificial Data Using Gaussian Process and Applying LSTMFCN to Predict Broken Hardware. J Digit Imaging 2021; 34:182-189. [PMID: 33409816 PMCID: PMC7887121 DOI: 10.1007/s10278-020-00411-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 11/22/2020] [Accepted: 12/02/2020] [Indexed: 11/19/2022] Open
Abstract
Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan, several image features are captured which represent the used coil's condition. These image features recorded over time are used to train machine learning models for classification of coils into normal and broken coils for faster and easier maintenance. The state-of-the-art techniques for classification of time series involve different kinds of neural networks. We leveraged sequential data and trained three models, long short-term memory (LSTM), fully convolutional network (FCN), and the combination of those called LSTMFCN as reported by Karim et al. (IEEE access 6:1662-1669, 2017). We found LSTMFCN to combine the benefits of LSTM and FCN. Thus, we achieved the highest F1-score of 87.45% and the highest accuracy of 99.35% using LSTMFCN. Furthermore, we tackled the high data imbalance of only 2.1% data collected from broken coils by training a Gaussian process (GP) regressor and adding predicted sequences as artificial samples to our broken labelled data. Adding 40 synthetic samples increased the classification results of LSTMFCN to an F1-score of 92.30% and accuracy of 99.83%. Thus, MRI head/neck coils can be classified normal or broken by training a LSTMFCN on image features, successfully. Augmenting the data using GP-generated samples can improve the performance even further.
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Affiliation(s)
- Nadine Rücker
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany.
| | - Lea Pflüger
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany
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Oksuz I, Clough JR, Ruijsink B, Anton EP, Bustin A, Cruz G, Prieto C, King AP, Schnabel JA. Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:4001-4010. [PMID: 32746141 DOI: 10.1109/tmi.2020.3008930] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly neglected point in the medical image analysis community is the vast amount of clinical images that have severe image artefacts due to organ motion, movement of the patient and/or image acquisition related issues. In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity. The method is based on our recently developed joint artefact detection and reconstruction method, which reconstructs high quality MR images from k-space using a joint loss function and essentially converts the artefact correction task to an under-sampled image reconstruction task by enforcing a data consistency term. In this paper, we propose to use a segmentation network coupled with this in an end-to-end framework. Our training optimises three different tasks: 1) image artefact detection, 2) artefact correction and 3) image segmentation. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted cardiac MR k-space data and uncorrected reconstructed images. Using a test set of 500 2D+time cine MR acquisitions from the UK Biobank data set, we achieve demonstrably good image quality and high segmentation accuracy in the presence of synthetic motion artefacts. We showcase better performance compared to various image correction architectures.
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Mohebbian M, Walia E, Habibullah M, Stapleton S, Wahid KA. Classifying MRI motion severity using a stacked ensemble approach. Magn Reson Imaging 2020; 75:107-115. [PMID: 33148512 DOI: 10.1016/j.mri.2020.10.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 10/13/2020] [Accepted: 10/14/2020] [Indexed: 10/23/2022]
Abstract
Motion artifacts are a common occurrence in Magnetic Resonance Imaging exam. Motion during acquisition has a profound impact on workflow efficiency, often requiring a repeat of sequences. Furthermore, motion artifacts may escape notice by technologists, only to be revealed at the time of reading by the radiologists, affecting their diagnostic quality. There is a paucity of clinical tools to identify and quantitatively assess the severity of motion artifacts in MRI. An image with subtle motion may still have diagnostic value, while severe motion may be uninterpretable by radiologists and requires the exam to be repeated. Therefore, a tool for the automatic identification of motion artifacts would aid in maintaining diagnostic quality, while potentially driving workflow efficiencies. Here we aim to quantify the severity of motion artifacts from MRI images using deep learning. Impact of subject movement parameters like displacement and rotation on image quality is also studied. A state-of-the-art, stacked ensemble model was developed to classify motion artifacts into five levels (no motion, slight, mild, moderate and severe) in brain scans. The stacked ensemble model is able to robustly predict rigid-body motion severity across different acquisition parameters, including T1-weighted and T2-weighted slices acquired in different anatomical planes. The ensemble model with XGBoost metalearner achieves 91.6% accuracy, 94.8% area under the curve, 90% Cohen's Kappa, and is observed to be more accurate and robust than the individual base learners.
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Affiliation(s)
- MohammadReza Mohebbian
- Department of Electrical and Computer Engineering, University of Saskatchewan S7N 5A9, Saskatoon, Saskatchewan, Canada.
| | - Ekta Walia
- Advanced Innovation, Enterprise Operational Informatics, Philips HealthCare, 281 Hillmount Road, L6C2S3, Markham, Ontario, Canada
| | - Mohammad Habibullah
- Department of Electrical and Computer Engineering, University of Saskatchewan S7N 5A9, Saskatoon, Saskatchewan, Canada
| | - Shawn Stapleton
- Advanced Innovation, Enterprise Operational Informatics, Philips HealthCare, North America
| | - Khan A Wahid
- Department of Electrical and Computer Engineering, University of Saskatchewan S7N 5A9, Saskatoon, Saskatchewan, Canada
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Lin A, Kolossváry M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence: improving the efficiency of cardiovascular imaging. Expert Rev Med Devices 2020; 17:565-577. [PMID: 32510252 PMCID: PMC7382901 DOI: 10.1080/17434440.2020.1777855] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 06/01/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or phenotypes, or guide treatment strategies. Noninvasive imaging remains a cornerstone for the diagnosis, risk stratification, and management of patients with cardiovascular disease. AI can facilitate every stage of the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways. AREAS COVERED In this paper, we review state-of-the-art AI techniques and their current applications in cardiac imaging, and discuss the future role of AI as a precision medicine tool. EXPERT OPINION Cardiovascular medicine is primed for scalable AI applications which can interpret vast amounts of clinical and imaging data in greater depth than ever before. AI-augmented medical systems have the potential to improve workflow and provide reproducible and objective quantitative results which can inform clinical decisions. In the foreseeable future, AI may work in the background of cardiac image analysis software and routine clinical reporting, automatically collecting data and enabling real-time diagnosis and risk stratification.
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Affiliation(s)
- Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Pál Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Piotr J Slomka
- Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
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Liu J, Kocak M, Supanich M, Deng J. Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB). Magn Reson Imaging 2020; 71:69-79. [PMID: 32428549 DOI: 10.1016/j.mri.2020.05.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 04/17/2020] [Accepted: 05/11/2020] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI. METHODS We developed a deep residual network with densely connected multi-resolution blocks (DRN-DCMB) model to reduce the motion artifacts in T1 weighted (T1W) spin echo images acquired on different imaging planes before and after contrast injection. The DRN-DCMB network consisted of multiple multi-resolution blocks connected with dense connections in a feedforward manner. A single residual unit was used to connect the input and output of the entire network with one shortcut connection to predict a residual image (i.e. artifact image). The model was trained with five motion-free T1W image stacks (pre-contrast axial and sagittal, and post-contrast axial, coronal, and sagittal images) with simulated motion artifacts. RESULTS In other 86 testing image stacks with simulated artifacts, our DRN-DCMB model outperformed other state-of-the-art deep learning models with significantly higher structural similarity index (SSIM) and improvement in signal-to-noise ratio (ISNR). The DRN-DCMB model was also applied to 121 testing image stacks appeared with various degrees of real motion artifacts. The acquired images and processed images by the DRN-DCMB model were randomly mixed, and image quality was blindly evaluated by a neuroradiologist. The DRN-DCMB model significantly improved the overall image quality, reduced the severity of the motion artifacts, and improved the image sharpness, while kept the image contrast. CONCLUSION Our DRN-DCMB model provided an effective method for reducing motion artifacts and improving the overall clinical image quality of brain MRI.
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Affiliation(s)
- Junchi Liu
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, 10 W 35th St, Chicago, IL 60616, USA
| | - Mehmet Kocak
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, 1653 W. Congress Pkwy, Chicago, IL 60612, USA
| | - Mark Supanich
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, 1653 W. Congress Pkwy, Chicago, IL 60612, USA
| | - Jie Deng
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, 1653 W. Congress Pkwy, Chicago, IL 60612, USA.
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Ruijsink B, Puyol-Antón E, Oksuz I, Sinclair M, Bai W, Schnabel JA, Razavi R, King AP. Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. JACC Cardiovasc Imaging 2019; 13:684-695. [PMID: 31326477 PMCID: PMC7060799 DOI: 10.1016/j.jcmg.2019.05.030] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/26/2019] [Accepted: 05/16/2019] [Indexed: 12/13/2022]
Abstract
Objectives This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output. Background Analysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice. Methods The framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps’ ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank. Results Automated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ≥ 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects. Conclusions The study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.
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Affiliation(s)
- Bram Ruijsink
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Adult and Paediatric Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, London, United Kingdom.
| | - Esther Puyol-Antón
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ilkay Oksuz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Matthew Sinclair
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Wenjia Bai
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Department of Medicine, Imperial College London, London, United Kingdom
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Adult and Paediatric Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, London, United Kingdom
| | - Andrew P King
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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24
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Oksuz I, Ruijsink B, Puyol-Antón E, Clough JR, Cruz G, Bustin A, Prieto C, Botnar R, Rueckert D, Schnabel JA, King AP. Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning. Med Image Anal 2019; 55:136-147. [PMID: 31055126 PMCID: PMC6688894 DOI: 10.1016/j.media.2019.04.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 02/13/2019] [Accepted: 04/17/2019] [Indexed: 11/17/2022]
Abstract
Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.
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Affiliation(s)
- Ilkay Oksuz
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK.
| | - Bram Ruijsink
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK; Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Esther Puyol-Antón
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - James R Clough
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Gastao Cruz
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Aurelien Bustin
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Rene Botnar
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College, London, UK
| | - Julia A Schnabel
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
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25
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Sreekumari A, Shanbhag D, Yeo D, Foo T, Pilitsis J, Polzin J, Patil U, Coblentz A, Kapadia A, Khinda J, Boutet A, Port J, Hancu I. A Deep Learning-Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations. AJNR Am J Neuroradiol 2019; 40:217-223. [PMID: 30606726 DOI: 10.3174/ajnr.a5926] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 11/02/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE MR imaging rescans and recalls can create large hospital revenue loss. The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series. MATERIALS AND METHODS A deep learning-based approach was developed, outputting a probability for a series to be clinically useful. Comparison of this per-series probability with a threshold, which can depend on scan indication and reading radiologist, determines whether a series needs to be rescanned. The deep learning classification performance was compared with that of 4 technologists and 5 radiologists in 49 test series with low and moderate motion artifacts. These series were assumed to be scanned for 2 scan indications: screening for multiple sclerosis and stroke. RESULTS The image-quality rating was found to be scan indication- and reading radiologist-dependent. Of the 49 test datasets, technologists created a mean ratio of rescans/recalls of (4.7 ± 5.1)/(9.5 ± 6.8) for MS and (8.6 ± 7.7)/(1.6 ± 1.9) for stroke. With thresholds adapted for scan indication and reading radiologist, deep learning created a rescan/recall ratio of (7.3 ± 2.2)/(3.2 ± 2.5) for MS, and (3.6 ± 1.5)/(2.8 ± 1.6) for stroke. Due to the large variability in the technologists' assessments, it was only the decrease in the recall rate for MS, for which the deep learning algorithm was trained, that was statistically significant (P = .03). CONCLUSIONS Fast, automated deep learning-based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year.
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Affiliation(s)
- A Sreekumari
- From the GE Global Research Center (A.S., D.S., U.P.), Bangalore, India
| | - D Shanbhag
- From the GE Global Research Center (A.S., D.S., U.P.), Bangalore, India
| | - D Yeo
- GE Global Research Center (D.Y., T.F., I.H.), Niskayuna, New York
| | - T Foo
- GE Global Research Center (D.Y., T.F., I.H.), Niskayuna, New York
| | - J Pilitsis
- Albany Medical College (J.Pilitsis), Albany, New York
| | - J Polzin
- GE Healthcare (J.Polzin), Milwaukee, Wisconsin
| | - U Patil
- From the GE Global Research Center (A.S., D.S., U.P.), Bangalore, India
| | - A Coblentz
- University Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada
| | - A Kapadia
- University Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada
| | - J Khinda
- University Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada
| | - A Boutet
- University Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada
| | - J Port
- Mayo Clinic (J.Port), Rochester, Minnesota
| | - I Hancu
- GE Global Research Center (D.Y., T.F., I.H.), Niskayuna, New York
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26
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Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION – MICCAI 2018 2018. [DOI: 10.1007/978-3-030-00928-1_29] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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