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Rotkopf LT, Zhang KS, Tavakoli AA, Bonekamp D, Ziener CH, Schlemmer HP. Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning. ROFO-FORTSCHR RONTG 2022; 194:975-982. [PMID: 35211930 DOI: 10.1055/a-1762-5854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND Perfusion MRI is a well-established imaging modality with a multitude of applications in oncological and cardiovascular imaging. Clinically used processing methods, while stable and robust, have remained largely unchanged in recent years. Despite promising results from novel methods, their relatively minimal improvement compared to established methods did not generally warrant significant changes to clinical perfusion processing. RESULTS AND CONCLUSION Machine learning in general and deep learning in particular, which are currently revolutionizing computer-aided diagnosis, may carry the potential to change this situation and truly capture the potential of perfusion imaging. Recent advances in the training of recurrent neural networks make it possible to predict and classify time series data with high accuracy. Combining physics-based tissue models and deep learning, using either physics-informed neural networks or universal differential equations, simplifies the training process and increases the interpretability of the resulting models. Due to their versatility, these methods will potentially be useful in bridging the gap between microvascular architecture and perfusion parameters, akin to MR fingerprinting in structural MR imaging. Still, further research is urgently needed before these methods may be used in clinical practice. KEY POINTS · Machine learning offers promising methods for processing of perfusion data.. · Recurrent neural networks can classify time series with high accuracy.. · Data augmentation is essentially especially when using small datasets.. CITATION FORMAT · Rotkopf LT, Zhang KS, Tavakoli AA et al. Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1762-5854.
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Affiliation(s)
- Lukas T Rotkopf
- Department of Radiology, German Cancer Research Centre, Heidelberg, Germany
| | - Kevin Sun Zhang
- Department of Radiology, German Cancer Research Centre, Heidelberg, Germany
| | | | - David Bonekamp
- Department of Radiology, German Cancer Research Centre, Heidelberg, Germany
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Abdulkareem M, Brahier MS, Zou F, Taylor A, Thomaides A, Bergquist PJ, Srichai MB, Lee AM, Vargas JD, Petersen SE. Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment. Front Cardiovasc Med 2022; 9:822269. [PMID: 35155637 PMCID: PMC8831539 DOI: 10.3389/fcvm.2022.822269] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 01/04/2022] [Indexed: 12/28/2022] Open
Abstract
Objectives Cardiac computed tomography (CCT) is a common pre-operative imaging modality to evaluate pulmonary vein anatomy and left atrial appendage thrombus in patients undergoing catheter ablation (CA) for atrial fibrillation (AF). These images also allow for full volumetric left atrium (LA) measurement for recurrence risk stratification, as larger LA volume (LAV) is associated with higher recurrence rates. Our objective is to apply deep learning (DL) techniques to fully automate the computation of LAV and assess the quality of the computed LAV values. Methods Using a dataset of 85,477 CCT images from 337 patients, we proposed a framework that consists of several processes that perform a combination of tasks including the selection of images with LA from all other images using a ResNet50 classification model, the segmentation of images with LA using a UNet image segmentation model, the assessment of the quality of the image segmentation task, the estimation of LAV, and quality control (QC) assessment. Results Overall, the proposed LAV estimation framework achieved accuracies of 98% (precision, recall, and F1 score metrics) in the image classification task, 88.5% (mean dice score) in the image segmentation task, 82% (mean dice score) in the segmentation quality prediction task, and R2 (the coefficient of determination) value of 0.968 in the volume estimation task. It correctly identified 9 out of 10 poor LAV estimations from a total of 337 patients as poor-quality estimates. Conclusions We proposed a generalizable framework that consists of DL models and computational methods for LAV estimation. The framework provides an efficient and robust strategy for QC assessment of the accuracy for DL-based image segmentation and volume estimation tasks, allowing high-throughput extraction of reproducible LAV measurements to be possible.
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Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- *Correspondence: Musa Abdulkareem
| | - Mark S. Brahier
- Georgetown University School of Medicine, Washington, DC, United States
| | - Fengwei Zou
- Montefiore Medical Centre, Bronx, NY, United States
| | | | | | | | | | - Aaron M. Lee
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Jose D. Vargas
- Veterans Affairs Medical Center, Washington, DC, United States
- Georgetown University, Washington, DC, United States
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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Tripathi S, Sharma N. Computer-aided automatic approach for denoising of magnetic resonance images. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2021.1944914] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Sumit Tripathi
- School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Neeraj Sharma
- School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
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Zakeri A, Hokmabadi A, Ravikumar N, Frangi AF, Gooya A. A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment. Med Image Anal 2021; 75:102276. [PMID: 34753021 DOI: 10.1016/j.media.2021.102276] [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: 02/04/2021] [Revised: 10/10/2021] [Accepted: 10/15/2021] [Indexed: 11/16/2022]
Abstract
Automatic shape anomaly detection in large-scale imaging data can be useful for screening suboptimal segmentations and pathologies altering the cardiac morphology without intensive manual labour. We propose a deep probabilistic model for local anomaly detection in sequences of heart shapes, modelled as point sets, in a cardiac cycle. A deep recurrent encoder-decoder network captures the spatio-temporal dependencies to predict the next shape in the cycle and thus derive the outlier points that are attributed to excessive deviations from the network prediction. A predictive mixture distribution models the inlier and outlier classes via Gaussian and uniform distributions, respectively. A Gibbs sampling Expectation-Maximisation (EM) algorithm computes soft anomaly scores of the points via the posterior probabilities of each class in the E-step and estimates the parameters of the network and the predictive distribution in the M-step. We demonstrate the versatility of the method using two shape datasets derived from: (i) one million biventricular CMR images from 20,000 participants in the UK Biobank (UKB), and (ii) routine diagnostic imaging from Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Image (M&Ms). Experiments show that the detected shape anomalies in the UKB dataset are mostly associated with poor segmentation quality, and the predicted shape sequences show significant improvement over the input sequences. Furthermore, evaluations on U-Net based shapes from the M&Ms dataset reveals that the anomalies are attributable to the underlying pathologies that affect the ventricles. The proposed model can therefore be used as an effective mechanism to sift shape anomalies in large-scale cardiac imaging pipelines for further analysis.
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Affiliation(s)
- Arezoo Zakeri
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
| | - Alireza Hokmabadi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Alejandro F Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Ali Gooya
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
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A novel method for removing Rician noise from MRI based on variational mode decomposition. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102737] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Kuczera S, Alipoor M, Langkilde F, Maier SE. Optimized bias and signal inference in diffusion-weighted image analysis (OBSIDIAN). Magn Reson Med 2021; 86:2716-2732. [PMID: 34278590 DOI: 10.1002/mrm.28773] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 01/29/2021] [Accepted: 02/24/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE Correction of Rician signal bias in magnitude MR images. METHODS A model-based, iterative fitting procedure is used to simultaneously estimate true signal and underlying Gaussian noise with standard deviation σ g on a pixel-by-pixel basis in magnitude MR images. A precomputed function that relates absolute residuals between measured signals and model fit to σ g is used to iteratively estimate σ g . The feasibility of the method is evaluated and compared to maximum likelihood estimation (MLE) for diffusion signal decay simulations and diffusion-weighted images of the prostate considering 21 linearly spaced b-values from 0 to 3000 s/mm2 . A multidirectional analysis was performed with publically available brain data. RESULTS Model simulations show that the Rician bias correction algorithm is fast, with an accuracy and precision that is on par to model-based MLE and direct fitting in the case of pure Gaussian noise. Increased accuracy in parameter prediction in a low signal-to-noise ratio (SNR) scenario is ideally achieved by using a composite of multiple signal decays from neighboring voxels as input for the algorithm. For patient data, good agreement with high SNR reference data of diffusion in prostate is achieved. CONCLUSIONS OBSIDIAN is a novel, alternative, simple to implement approach for rapid Rician bias correction applicable in any case where differences between true signal decay and underlying model function can be considered negligible in comparison to noise. The proposed composite fitting approach permits accurate parameter estimation even in typical clinical scenarios with low SNR, which significantly simplifies comparison of complex diffusion parameters among studies.
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Affiliation(s)
- Stefan Kuczera
- Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,MedTech West, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mohammad Alipoor
- Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - Fredrik Langkilde
- Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - Stephan E Maier
- Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Department of Radiology, Brigham Women's Hospital, Harvard Medical School, Boston, MA, USA
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Tripathi S, Sharma N. Denoising of magnetic resonance images using discriminative learning-based deep convolutional neural network. Technol Health Care 2021; 30:145-160. [PMID: 34024795 DOI: 10.3233/thc-212882] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The noise in magnetic resonance (MR) images causes severe issues for medical diagnosis purposes. OBJECTIVE In this paper, we propose a discriminative learning based convolutional neural network denoiser to denoise the MR image data contaminated with noise. METHODS The proposed method incorporates the use of depthwise separable convolution along with local response normalization with modified hyperparameters and internal skip connections to denoise the contaminated MR images. Moreover, the addition of parametric RELU instead of normal conventional RELU in our proposed architecture gives more stable and fine results. The denoised images were further segmented to test the appropriateness of the results. The network is trained on one dataset and tested on other dataset produces remarkably good results. RESULTS Our proposed network was used to denoise the images of different noise levels, and it yields better performance as compared with various networks. The SSIM and PSNR showed an average improvement of (7.2 ± 0.002) % and (8.5 ± 0.25) % respectively when tested on different datasets without retaining the network. An improvement of 5% and 6% was achieved in the values of mean intersection over union (mIoU) and BF score when the denoised images were segmented for testing the relevancy in biomedical imaging applications. The statistical test suggests that the obtained results are statistically significant as p< 0.05. CONCLUSION The denoised images obtained are more clinically suitable for medical image diagnosis purposes, as depicted by the evaluation parameters. Further, external clinical validation was performed by an experienced radiologist for testing the validation of the resulting images.
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Affiliation(s)
- Sumit Tripathi
- School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Neeraj Sharma
- Indian Institute of Technology (Banaras Hindu University), Varanasi, India
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Afzali M, Pieciak T, Newman S, Garyfallidis E, Özarslan E, Cheng H, Jones DK. The sensitivity of diffusion MRI to microstructural properties and experimental factors. J Neurosci Methods 2021; 347:108951. [PMID: 33017644 PMCID: PMC7762827 DOI: 10.1016/j.jneumeth.2020.108951] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 08/27/2020] [Accepted: 09/15/2020] [Indexed: 12/13/2022]
Abstract
Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.
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Affiliation(s)
- Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.
| | - Sharlene Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA.
| | - Eleftherios Garyfallidis
- Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA.
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
| | - Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA.
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
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Sudeep P, Palanisamy P, Kesavadas C, Rajan J. An improved nonlocal maximum likelihood estimation method for denoising magnetic resonance images with spatially varying noise levels. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2018.02.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Voldsbekk I, Maximov II, Zak N, Roelfs D, Geier O, Due-Tønnessen P, Elvsåshagen T, Strømstad M, Bjørnerud A, Groote I. Evidence for wakefulness-related changes to extracellular space in human brain white matter from diffusion-weighted MRI. Neuroimage 2020; 212:116682. [DOI: 10.1016/j.neuroimage.2020.116682] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/29/2020] [Accepted: 02/24/2020] [Indexed: 12/19/2022] Open
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Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank. Sci Rep 2020; 10:2408. [PMID: 32051456 PMCID: PMC7015892 DOI: 10.1038/s41598-020-58212-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 12/20/2019] [Indexed: 12/02/2022] Open
Abstract
In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics (heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factors including acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage (i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of the stacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slice motion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastolic cardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involved in UKBB CMR acquisition and for the ones who use the dataset for research purposes.
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Maximov II, Alnæs D, Westlye LT. Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank. Hum Brain Mapp 2019; 40:4146-4162. [PMID: 31173439 PMCID: PMC6865652 DOI: 10.1002/hbm.24691] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/14/2019] [Accepted: 05/27/2019] [Indexed: 12/30/2022] Open
Abstract
Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates cross-study harmonisation and reduces pipeline-related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.
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Affiliation(s)
- Ivan I. Maximov
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dag Alnæs
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
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Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, de Marvao A, O'Regan DP, Cook S, Glocker B, Matthews PM, Rueckert D. Learning-Based Quality Control for Cardiac MR Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1127-1138. [PMID: 30403623 DOI: 10.1109/tmi.2018.2878509] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this procedure is strongly operator-dependent, cumbersome, and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation; 2) inter-slice motion detection; 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method-integrating both regression and structured classification models-to extract landmarks and probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank and on 100 cases from the UK Digital Heart Project and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g., on UK Biobank, sensitivity and specificity, respectively, 88% and 99% for heart coverage estimation and 85% and 95% for motion detection), allowing their exclusion from the analyzed dataset or the triggering of a new acquisition.
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Pieciak T, Aja-Fernandez S, Vegas-Sanchez-Ferrero G. Non-Stationary Rician Noise Estimation in Parallel MRI Using a Single Image: A Variance-Stabilizing Approach. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017; 39:2015-2029. [PMID: 27845653 DOI: 10.1109/tpami.2016.2625789] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Parallel magnetic resonance imaging (pMRI) techniques have gained a great importance both in research and clinical communities recently since they considerably accelerate the image acquisition process. However, the image reconstruction algorithms needed to correct the subsampling artifacts affect the nature of noise, i.e., it becomes non-stationary. Some methods have been proposed in the literature dealing with the non-stationary noise in pMRI. However, their performance depends on information not usually available such as multiple acquisitions, receiver noise matrices, sensitivity coil profiles, reconstruction coefficients, or even biophysical models of the data. Besides, some methods show an undesirable granular pattern on the estimates as a side effect of local estimation. Finally, some methods make strong assumptions that just hold in the case of high signal-to-noise ratio (SNR), which limits their usability in real scenarios. We propose a new automatic noise estimation technique for non-stationary Rician noise that overcomes the aforementioned drawbacks. Its effectiveness is due to the derivation of a variance-stabilizing transformation designed to deal with any SNR. The method was compared to the main state-of-the-art methods in synthetic and real scenarios. Numerical results confirm the robustness of the method and its better performance for the whole range of SNRs.
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Senra Filho ACDS, Salmon CEG, Santos ACD, Murta Junior LO. Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter. ACTA ACUST UNITED AC 2017. [DOI: 10.1590/2446-4740.02017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Maximov II, Tonoyan AS, Pronin IN. Differentiation of glioma malignancy grade using diffusion MRI. Phys Med 2017; 40:24-32. [PMID: 28712716 DOI: 10.1016/j.ejmp.2017.07.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 06/26/2017] [Accepted: 07/04/2017] [Indexed: 12/31/2022] Open
Abstract
Modern diffusion MR protocols allow one to acquire the multi-shell diffusion data with high diffusion weightings in a clinically feasible time. In the present work we assessed three diffusion approaches based on diffusion and kurtosis tensor imaging (DTI, DKI), and neurite orientation dispersion and density imaging (NODDI) as possible biomarkers for human brain glioma grade differentiation based on the one diffusion protocol. We used three diffusion weightings (so called b-values) equal to 0, 1000, and 2500s/mm2 with 60 non-coplanar diffusion directions in the case of non-zero b-values. The patient groups of the glioma grades II, III, and IV consist of 8 subjects per group. We found that DKI, and NODDI scalar metrics can be effectively used as glioma grade biomarkers with a significant difference (p<0.05) for grading between low- and high-grade gliomas, in particular, for glioma II versus glioma III grades, and glioma III versus glioma IV grades. The use of mean/axial kurtosis and intra-axonal fraction/orientation dispersion index metrics allowed us to obtain the most feasible and reliable differentiation criteria. For example, in the case of glioma grades II, III, and IV the mean kurtosis is equal to 0.31, 0.51, and 0.90, and the orientation dispersion index is equal to 0.14, 0.30, and 0.59, respectively. The limitations and perspectives of the biophysical diffusion models based on intra-/extra-axonal compartmentalisation for glioma differentiation are discussed.
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Affiliation(s)
- Ivan I Maximov
- Experimental Physics III, TU Dortmund University, 44221, Germany.
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Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, Glocker B, de Marvao A, O’Regan D, Cook S, Rueckert D. Learning-Based Heart Coverage Estimation for Short-Axis Cine Cardiac MR Images. FUNCTIONAL IMAGING AND MODELLING OF THE HEART 2017. [DOI: 10.1007/978-3-319-59448-4_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Veraart J, Fieremans E, Novikov DS. Diffusion MRI noise mapping using random matrix theory. Magn Reson Med 2015; 76:1582-1593. [PMID: 26599599 DOI: 10.1002/mrm.26059] [Citation(s) in RCA: 421] [Impact Index Per Article: 46.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 10/06/2015] [Accepted: 10/26/2015] [Indexed: 01/12/2023]
Abstract
PURPOSE To estimate the spatially varying noise map using a redundant series of magnitude MR images. METHODS We exploit redundancy in non-Gaussian distributed multidirectional diffusion MRI data by identifying its noise-only principal components, based on the theory of noisy covariance matrices. The bulk of principal component analysis eigenvalues, arising due to noise, is described by the universal Marchenko-Pastur distribution, parameterized by the noise level. This allows us to estimate noise level in a local neighborhood based on the singular value decomposition of a matrix combining neighborhood voxels and diffusion directions. RESULTS We present a model-independent local noise mapping method capable of estimating the noise level down to about 1% error. In contrast to current state-of-the-art techniques, the resultant noise maps do not show artifactual anatomical features that often reflect physiological noise, the presence of sharp edges, or a lack of adequate a priori knowledge of the expected form of MR signal. CONCLUSIONS Simulations and experiments show that typical diffusion MRI data exhibit sufficient redundancy that enables accurate, precise, and robust estimation of the local noise level by interpreting the principal component analysis eigenspectrum in terms of the Marchenko-Pastur distribution. Magn Reson Med 76:1582-1593, 2016. © 2015 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA. .,Department of Physics, iMinds-Vision Lab, University of Antwerp, Antwerp, Belgium.
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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19
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Lui D, Modhafar A, Haider MA, Wong A. Monte Carlo-based noise compensation in coil intensity corrected endorectal MRI. BMC Med Imaging 2015; 15:43. [PMID: 26459631 PMCID: PMC4601140 DOI: 10.1186/s12880-015-0081-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 09/15/2015] [Indexed: 11/10/2022] Open
Abstract
Background Prostate cancer is one of the most common forms of cancer found in males making early diagnosis important. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with the use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity inhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these inhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification and noise level variations. Methods In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity corrected endorectal MRI which allows for effective noise compensation and preservation of details within the prostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting non-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available. Results SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement of 11.7 and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared to existing approaches. Discussion Experimental results using both phantom and patient data showed that ACER provided strong performance in terms of SNR, CNR, edge preservation, subjective scoring when compared to a number of existing approaches. Conclusions A new noise compensation method was developed for the purpose of improving the quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise compensation performance can be achieved for the proposed approach, which is particularly important for processing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.
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Affiliation(s)
- Dorothy Lui
- Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada.
| | - Amen Modhafar
- Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada.
| | - Masoom A Haider
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada.
| | - Alexander Wong
- Department of Systems Design Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada.
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20
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Poot DHJ, Klein S. Detecting statistically significant differences in quantitative MRI experiments, applied to diffusion tensor imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1164-1176. [PMID: 25532168 DOI: 10.1109/tmi.2014.2380830] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this work we present a framework for reliably detecting significant differences in quantitative magnetic resonance imaging and evaluate it with diffusion tensor imaging (DTI) experiments. As part of this framework we propose a new spatially regularized maximum likelihood estimator that simultaneously estimates the quantitative parameters and the spatially-smoothly-varying noise level from the acquisitions. The noise level estimation method does not require repeated acquisitions. We show that the amount of regularization in this method can be set a priori to achieve a desired coefficient of variation of the estimated noise level. The noise level estimate allows the construction of a Cramér-Rao-lower-bound based test statistic that reliably assesses the significance of differences between voxels within a scan or across different scans. We show that the regularized noise level estimate improves upon existing methods and results in a substantially increased precision of the uncertainty estimates of the DTI parameters. It enables correct specification of the null distribution of the test statistic and with it the test statistic obtains the highest sensitivity and specificity. The source code of the estimation framework, test statistic and experiment scripts are made available to the community.
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21
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Glenn GR, Tabesh A, Jensen JH. A simple noise correction scheme for diffusional kurtosis imaging. Magn Reson Imaging 2015; 33:124-33. [PMID: 25172990 PMCID: PMC4268031 DOI: 10.1016/j.mri.2014.08.028] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 05/13/2014] [Accepted: 08/12/2014] [Indexed: 12/31/2022]
Abstract
PURPOSE Diffusional kurtosis imaging (DKI) is sensitive to the effects of signal noise due to strong diffusion weightings and higher order modeling of the diffusion weighted signal. A simple noise correction scheme is proposed to remove the majority of the noise bias in the estimated diffusional kurtosis. METHODS Weighted linear least squares (WLLS) fitting together with a voxel-wise, subtraction-based noise correction from multiple, independent acquisitions are employed to reduce noise bias in DKI data. The method is validated in phantom experiments and demonstrated for in vivo human brain for DKI-derived parameter estimates. RESULTS As long as the signal-to-noise ratio (SNR) for the most heavily diffusion weighted images is greater than 2.1, errors in phantom diffusional kurtosis estimates are found to be less than 5 percent with noise correction, but as high as 44 percent for uncorrected estimates. In human brain, noise correction is also shown to improve diffusional kurtosis estimates derived from measurements made with low SNR. CONCLUSION The proposed correction technique removes the majority of noise bias from diffusional kurtosis estimates in noisy phantom data and is applicable to DKI of human brain. Features of the method include computational simplicity and ease of integration into standard WLLS DKI post-processing algorithms.
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Affiliation(s)
- G Russell Glenn
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neurosciences, Medical University of South Carolina, Charleston, SC, USA.
| | - Ali Tabesh
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
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22
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Denoising of 3D magnetic resonance images by using higher-order singular value decomposition. Med Image Anal 2015; 19:75-86. [DOI: 10.1016/j.media.2014.08.004] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2014] [Revised: 07/09/2014] [Accepted: 08/30/2014] [Indexed: 11/23/2022]
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Aja-Fernández S, Pieciak T, Vegas-Sánchez-Ferrero G. Spatially variant noise estimation in MRI: a homomorphic approach. Med Image Anal 2014; 20:184-97. [PMID: 25499191 DOI: 10.1016/j.media.2014.11.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Revised: 11/11/2014] [Accepted: 11/15/2014] [Indexed: 11/25/2022]
Abstract
The reliable estimation of noise characteristics in MRI is a task of great importance due to the influence of noise features in extensively used post-processing algorithms. Many methods have been proposed in the literature to retrieve noise features from the magnitude signal. However, most of them assume a stationary noise model, i.e., the features of noise do not vary with the position inside the image. This assumption does not hold when modern scanning techniques are considered, e.g., in the case of parallel reconstruction and intensity correction. Therefore, new noise estimators must be found to cope with non-stationary noise. Some methods have been recently proposed in the literature. However, they require multiple acquisitions or extra information which is usually not available (biophysical models, sensitivity of coils). In this work we overcome this drawback by proposing a new method that can accurately estimate the non-stationary parameters of noise from just a single magnitude image. In the derivation, we considered the noise to follow a non-stationary Rician distribution, since it is the most common model in real acquisitions (e.g., SENSE reconstruction), though it can be easily generalized to other models. The proposed approach makes use of a homomorphic separation of the spatially variant noise in two terms: a stationary noise term and one low frequency signal that correspond to the x-dependent variance of noise. The non-stationary variance of noise is then estimated by a low pass filtering with a Rician bias correction. Results in real and synthetic experiments evidence the better performance and the lowest error variance of the proposed methodology when compared to the state-of-the-art methods.
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Affiliation(s)
| | - Tomasz Pieciak
- AGH University of Science and Technology, Krakow, Poland.
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Collier Q, Veraart J, Jeurissen B, den Dekker AJ, Sijbers J. Iterative reweighted linear least squares for accurate, fast, and robust estimation of diffusion magnetic resonance parameters. Magn Reson Med 2014; 73:2174-84. [PMID: 24986440 DOI: 10.1002/mrm.25351] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 05/20/2014] [Accepted: 06/13/2014] [Indexed: 12/20/2022]
Abstract
PURPOSE Diffusion-weighted magnetic resonance imaging suffers from physiological noise, such as artifacts caused by motion or system instabilities. Therefore, there is a need for robust diffusion parameter estimation techniques. In the past, several techniques have been proposed, including RESTORE and iRESTORE (Chang et al. Magn Reson Med 2005; 53:1088-1095; Chang et al. Magn Reson Med 2012; 68:1654-1663). However, these techniques are based on nonlinear estimators and are consequently computationally intensive. METHOD In this work, we present a new, robust, iteratively reweighted linear least squares (IRLLS) estimator. IRLLS performs a voxel-wise identification of outliers in diffusion-weighted magnetic resonance images, where it exploits the natural skewness of the data distribution to become more sensitive to both signal hyperintensities and signal dropouts. RESULTS Both simulations and real data experiments were conducted to compare IRLLS with other state-of-the-art techniques. While IRLLS showed no significant loss in accuracy or precision, it proved to be substantially faster than both RESTORE and iRESTORE. In addition, IRLLS proved to be even more robust when considering the overestimation of the noise level or when the signal-to-noise ratio is low. CONCLUSION The substantially shortened calculation time in combination with the increased robustness and accuracy, make IRLLS a practical and reliable alternative to current state-of-the-art techniques for the robust estimation of diffusion-weighted magnetic resonance parameters.
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Affiliation(s)
- Quinten Collier
- iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jelle Veraart
- iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Arnold J den Dekker
- iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.,Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
| | - Jan Sijbers
- iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
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Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters. Magn Reson Imaging 2014; 32:702-20. [DOI: 10.1016/j.mri.2014.03.004] [Citation(s) in RCA: 95] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2013] [Revised: 01/13/2014] [Accepted: 03/07/2014] [Indexed: 11/24/2022]
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26
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André ED, Grinberg F, Farrher E, Maximov II, Shah NJ, Meyer C, Jaspar M, Muto V, Phillips C, Balteau E. Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging. PLoS One 2014; 9:e94531. [PMID: 24722363 PMCID: PMC3983191 DOI: 10.1371/journal.pone.0094531] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Accepted: 03/18/2014] [Indexed: 11/18/2022] Open
Abstract
Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.
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Affiliation(s)
- Elodie D. André
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Farida Grinberg
- Institute of Neuroscience and Medicine - 4, Juelich, Germany
- Department of Neurology, Faculty of Medicine, Jülich Aachen Research Alliance, RWTH Aachen University, Aachen, Germany
- * E-mail:
| | | | - Ivan I. Maximov
- Institute of Neuroscience and Medicine - 4, Juelich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine - 4, Juelich, Germany
- Department of Neurology, Faculty of Medicine, Jülich Aachen Research Alliance, RWTH Aachen University, Aachen, Germany
| | | | - Mathieu Jaspar
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Vincenzo Muto
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Christophe Phillips
- Cyclotron Research Centre, University of Liège, Liège, Belgium
- Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
| | - Evelyne Balteau
- Cyclotron Research Centre, University of Liège, Liège, Belgium
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Tax CM, Otte WM, Viergever MA, Dijkhuizen RM, Leemans A. REKINDLE: Robust extraction of kurtosis INDices with linear estimation. Magn Reson Med 2014; 73:794-808. [PMID: 24687400 DOI: 10.1002/mrm.25165] [Citation(s) in RCA: 118] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Revised: 01/10/2014] [Accepted: 01/14/2014] [Indexed: 01/02/2023]
Affiliation(s)
- Chantal M.W. Tax
- Image Sciences Institute, University Medical Center Utrecht; Utrecht The Netherlands
| | - Willem M. Otte
- Image Sciences Institute, University Medical Center Utrecht; Utrecht The Netherlands
- Department of Pediatric Neurology; University Medical Center Utrecht; Utrecht The Netherlands
| | - Max A. Viergever
- Image Sciences Institute, University Medical Center Utrecht; Utrecht The Netherlands
| | - Rick M. Dijkhuizen
- Image Sciences Institute, University Medical Center Utrecht; Utrecht The Netherlands
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht; Utrecht The Netherlands
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28
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Lam F, Babacan SD, Haldar JP, Weiner MW, Schuff N, Liang ZP. Denoising diffusion-weighted magnitude MR images using rank and edge constraints. Magn Reson Med 2014; 71:1272-84. [PMID: 23568755 PMCID: PMC3796128 DOI: 10.1002/mrm.24728] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2012] [Accepted: 02/15/2013] [Indexed: 01/06/2023]
Abstract
PURPOSE To improve signal-to-noise ratio for diffusion-weighted magnetic resonance images. METHODS A new method is proposed for denoising diffusion-weighted magnitude images. The proposed method formulates the denoising problem as an maximum a posteriori} estimation problem based on Rician/noncentral χ likelihood models, incorporating an edge prior and a low-rank model. The resulting optimization problem is solved efficiently using a half-quadratic method with an alternating minimization scheme. RESULTS The performance of the proposed method has been validated using simulated and experimental data. Diffusion-weighted images and noisy data were simulated based on the diffusion tensor imaging model and Rician/noncentral χ distributions. The simulation study (with known gold standard) shows substantial improvements in single-to-noise ratio and diffusion tensor estimation after denoising. In vivo diffusion imaging data at different b-values were acquired. Based on the experimental data, qualitative improvement in image quality and quantitative improvement in diffusion tensor estimation were demonstrated. Additionally, the proposed method is shown to outperform one of the state-of-the-art nonlocal means-based denoising algorithms, both qualitatively and quantitatively. CONCLUSION The single-to-noise ratio of diffusion-weighted images can be effectively improved with rank and edge constraints, resulting in an improvement in diffusion parameter estimation accuracy.
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Affiliation(s)
- Fan Lam
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - S. Derin Babacan
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, California
| | - Michael W. Weiner
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Norbert Schuff
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Zhi-Pei Liang
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois
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Rosenberg J, Maximov II, Reske M, Grinberg F, Shah NJ. "Early to bed, early to rise": diffusion tensor imaging identifies chronotype-specificity. Neuroimage 2013; 84:428-34. [PMID: 24001455 DOI: 10.1016/j.neuroimage.2013.07.086] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Revised: 07/16/2013] [Accepted: 07/26/2013] [Indexed: 01/08/2023] Open
Abstract
Sleep and wakefulness are crucial prerequisites for cognitive efficiency, the disturbances of which severely impact performance and mood as present e.g. after time zone traveling, in shift workers or patients with sleep or affective disorders. Based on their individual disposition to sleep and wakefulness, humans can be categorized as early (EC), late (LC) or intermediate (IC) chronotypes. While ECs tend to wake up early in the morning and find it difficult to remain awake beyond their usual bedtime, LCs go to bed late and have difficulties getting up. Beyond sleep/wake timings, chronotypes show distinct patterns of cognitive performance, gene expression, endocrinology and lifestyle. However, little is known about brain structural characteristics potentially underlying differences. Specifically, white matter (WM) integrity is crucial for intact brain function and has been related to various lifestyle habits, suggesting differences between chronotypes. Hence, the present study draws on Diffusion Tensor Imaging as a powerful tool to non-invasively probe WM architecture in 16 ECs, 23 LCs and 20 ICs. Track-based spatial statistics highlight that LCs were characterized by WM differences in the frontal and temporal lobes, cingulate gyrus and corpus callosum. Results are discussed in terms of findings reporting late chronotypes to exhibit a chronic form of jet lag accompanied with sleep disturbances, vulnerability to depression and higher consumption of nicotine and alcohol. This study has far-reaching implications for health and the economy. Ideally, work schedules should fit in with chronotype-specificity whenever possible.
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Affiliation(s)
- Jessica Rosenberg
- Institute of Neuroscience and Medicine-4, Medical Imaging Physics, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Department of Neurology, Faculty of Medicine, RWTH Aachen, JARA, 52074 Aachen, Germany.
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Feng Y, He T, Feng M, Carpenter JP, Greiser A, Xin X, Chen W, Pennell DJ, Yang GZ, Firmin DN. Improved pixel-by-pixel MRI R2* relaxometry by nonlocal means. Magn Reson Med 2013; 72:260-8. [DOI: 10.1002/mrm.24914] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2013] [Revised: 06/24/2013] [Accepted: 07/16/2013] [Indexed: 12/31/2022]
Affiliation(s)
- Yanqiu Feng
- School of Biomedical Engineering; Southern Medical University; Guangzhou China
| | - Taigang He
- Cardiovascular Sciences Research Centre; St George's University of London; London United Kingdom
- Cardiovascular Magnetic Resonance Unit; Royal Brompton Hospital; London United Kingdom
| | - Meiyan Feng
- School of Biomedical Engineering; Southern Medical University; Guangzhou China
| | - John-Paul Carpenter
- Cardiovascular Magnetic Resonance Unit; Royal Brompton Hospital; London United Kingdom
- National Heart and Lung Institute; Imperial College; London United Kingdom
| | | | - Xuegang Xin
- School of Biomedical Engineering; Southern Medical University; Guangzhou China
| | - Wufan Chen
- School of Biomedical Engineering; Southern Medical University; Guangzhou China
| | - Dudley J. Pennell
- Cardiovascular Magnetic Resonance Unit; Royal Brompton Hospital; London United Kingdom
- National Heart and Lung Institute; Imperial College; London United Kingdom
| | | | - David N. Firmin
- Cardiovascular Magnetic Resonance Unit; Royal Brompton Hospital; London United Kingdom
- National Heart and Lung Institute; Imperial College; London United Kingdom
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Veraart J, Rajan J, Peeters RR, Leemans A, Sunaert S, Sijbers J. Comprehensive framework for accurate diffusion MRI parameter estimation. Magn Reson Med 2012; 70:972-84. [PMID: 23132517 DOI: 10.1002/mrm.24529] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2012] [Revised: 09/21/2012] [Accepted: 09/24/2012] [Indexed: 11/12/2022]
Abstract
During the last decade, many approaches have been proposed for improving the estimation of diffusion measures. These techniques have already shown an increase in accuracy based on theoretical considerations, such as incorporating prior knowledge of the data distribution. The increased accuracy of diffusion metric estimators is typically observed in well-defined simulations, where the assumptions regarding properties of the data distribution are known to be valid. In practice, however, correcting for subject motion and geometric eddy current deformations alters the data distribution tremendously such that it can no longer be expressed in a closed form. The image processing steps that precede the model fitting will render several assumptions on the data distribution invalid, potentially nullifying the benefit of applying more advanced diffusion estimators. In this work, we present a generic diffusion model fitting framework that considers some statistics of diffusion MRI data. A central role in the framework is played by the conditional least squares estimator. We demonstrate that the accuracy of that particular estimator can generally be preserved, regardless the applied preprocessing steps, if the noise parameter is known a priori. To fulfill that condition, we also propose an approach for the estimation of spatially varying noise levels.
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Affiliation(s)
- Jelle Veraart
- IBBT Vision Laboratory, Department of Physics, University of Antwerp, Antwerp, Belgium
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