@article{zaitsev_motion_2015,
	title = {Motion {Artefacts} in {MRI}: a {Complex} {Problem} with {Many} {Partial} {Solutions}},
	volume = {42},
	issn = {1053-1807},
	shorttitle = {Motion {Artefacts} in {MRI}},
	url_ = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4517972/},
	doi = {10.1002/jmri.24850},
	abstract = {Subject motion during magnetic resonance imaging (MRI) has been problematic since its introduction as a clinical imaging modality. While sensitivity to particle motion or blood flow can be used to provide useful image contrast, bulk motion presents a considerable problem in the majority of clinical applications. It is one of the most frequent sources of artefacts. Over 30 years of research have produced numerous methods to mitigate or correct for motion artefacts, but no single method can be applied in all imaging situations. Instead, a ‘toolbox’ of methods exists, where each tool is suitable for some tasks, but not for others. This article reviews the origins of motion artefacts and presents current mitigation and correction methods. In some imaging situations, the currently available motion correction tools are highly effective; in other cases, appropriate tools still need to be developed. It seems likely that this multifaceted approach will be what eventually solves the motion sensitivity problem in MRI, rather than a single solution that is effective in all situations. This review places a strong emphasis on explaining the physics behind the occurrence of such artefacts, with the aim of aiding artefact detection and mitigation in particular clinical situations.},
	number = {4},
	url_date = {2020-05-29},
	journal = {Journal of magnetic resonance imaging : JMRI},
	author = {Zaitsev, Maxim and Maclaren, Julian. and Herbst, Michael},
	month = oct,
	year = {2015},
	pmid = {25630632},
	pmcid = {PMC4517972},
	keywords = {Glioma, anonymization, brain extraction, Brain tumor segmentation, BRATS, medical imaging, MRI data preprocessing},
	pages = {887--901},
	file_ = {PubMed Central Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\LAB2JXAX\\Zaitsev et al. - 2015 - Motion Artefacts in MRI a Complex Problem with Ma.pdf:application/pdf}
}

@article{godenschweger_motion_2016,
	title = {Motion correction in {MRI} of the brain},
	volume = {61},
	issn = {1361-6560},
	doi = {10.1088/0031-9155/61/5/R32},
	abstract = {Subject motion in MRI is a relevant problem in the daily clinical routine as well as in scientific studies. Since the beginning of clinical use of MRI, many research groups have developed methods to suppress or correct motion artefacts. This review focuses on rigid body motion correction of head and brain MRI and its application in diagnosis and research. It explains the sources and types of motion and related artefacts, classifies and describes existing techniques for motion detection, compensation and correction and lists established and experimental approaches. Retrospective motion correction modifies the MR image data during the reconstruction, while prospective motion correction performs an adaptive update of the data acquisition. Differences, benefits and drawbacks of different motion correction methods are discussed.},
	language = {eng},
	number = {5},
	journal = {Physics in Medicine and Biology},
	author = {Godenschweger, F. and Kägebein, U. and Stucht, D. and Yarach, U. and Sciarra, A. and Yakupov, R. and Lüsebrink, F. and Schulze, P. and Speck, O.},
	month = mar,
	year = {2016},
	pmid = {26864183},
	pmcid = {PMC4930872},
	keywords = {Magnetic Resonance Imaging, Humans, Brain, Artifacts, Image Processing, Computer-Assisted, Signal-To-Noise Ratio, Head Movements},
	pages = {R32--56},
	file_ = {Accepted Version:C\:\\Users\\212693886\\Box\\zotero\\storage\\NPIL3XN6\\Godenschweger et al. - 2016 - Motion correction in MRI of the brain.pdf:application/pdf}
}

@article{ma_fast_2018,
	title = {Fast {3D} magnetic resonance fingerprinting for a whole-brain coverage},
	volume = {79},
	issn = {1522-2594},
	doi = {10.1002/mrm.26886},
	abstract = {PURPOSE: The purpose of this study was to accelerate the acquisition and reconstruction time of 3D magnetic resonance fingerprinting scans.
METHODS: A 3D magnetic resonance fingerprinting scan was accelerated by using a single-shot spiral trajectory with an undersampling factor of 48 in the x-y plane, and an interleaved sampling pattern with an undersampling factor of 3 through plane. Further acceleration came from reducing the waiting time between neighboring partitions. The reconstruction time was accelerated by applying singular value decomposition compression in k-space. Finally, a 3D premeasured B1 map was used to correct for the B1 inhomogeneity.
RESULTS: The T1 and T2 values of the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI phantom showed a good agreement with the standard values, with an average concordance correlation coefficient of 0.99, and coefficient of variation of 7\% in the repeatability scans. The results from in vivo scans also showed high image quality in both transverse and coronal views.
CONCLUSIONS: This study applied a fast acquisition scheme for a fully quantitative 3D magnetic resonance fingerprinting scan with a total acceleration factor of 144 as compared with the Nyquist rate, such that 3D T1 , T2 , and proton density maps can be acquired with whole-brain coverage at clinical resolution in less than 5 min. Magn Reson Med 79:2190-2197, 2018. © 2017 International Society for Magnetic Resonance in Medicine.},
	language = {eng},
	number = {4},
	journal = {Magnetic Resonance in Medicine},
	author = {Ma, Dan and Jiang, Yun and Chen, Yong and McGivney, Debra and Mehta, Bhairav and Gulani, Vikas and Griswold, Mark},
	year = {2018},
	pmid = {28833436},
	pmcid = {PMC5868964},
	keywords = {Magnetic Resonance Imaging, Humans, Algorithms, Reproducibility of Results, Brain, Imaging, Three-Dimensional, Image Processing, Computer-Assisted, MR fingerprinting, Phantoms, Imaging, Data Compression, 3D, Healthy Volunteers, T1; T2; relaxometry},
	pages = {2190--2197},
	file_ = {Accepted Version:C\:\\Users\\212693886\\Box\\zotero\\storage\\Y3U954LG\\Ma et al. - 2018 - Fast 3D magnetic resonance fingerprinting for a wh.pdf:application/pdf}
}

@article{gomez_rapid_2020,
	title = {Rapid three-dimensional multiparametric {MRI} with quantitative transient-state imaging},
	volume = {10},
	copyright = {2020 The Author(s)},
	issn = {2045-2322},
	url_ = {https://www.nature.com/articles/s41598-020-70789-2},
	doi = {10.1038/s41598-020-70789-2},
	abstract = {Novel methods for quantitative, transient-state multiparametric imaging are increasingly being demonstrated for assessment of disease and treatment efficacy. Here, we build on these by assessing the most common Non-Cartesian readout trajectories (2D/3D radials and spirals), demonstrating efficient anti-aliasing with a k-space view-sharing technique, and proposing novel methods for parameter inference with neural networks that incorporate the estimation of proton density. Our results show good agreement with gold standard and phantom references for all readout trajectories at 1.5 T and 3 T. Parameters inferred with the neural network were within 6.58\% difference from the parameters inferred with a high-resolution dictionary. Concordance correlation coefficients were above 0.92 and the normalized root mean squared error ranged between 4.2 and 12.7\% with respect to gold-standard phantom references for T1 and T2. In vivo acquisitions demonstrate sub-millimetric isotropic resolution in under five minutes with reconstruction and inference times {\textless} 7 min. Our 3D quantitative transient-state imaging approach could enable high-resolution multiparametric tissue quantification within clinically acceptable acquisition and reconstruction times.},
	language = {en},
	number = {1},
	url_date = {2020-09-10},
	journal = {Scientific Reports},
	author = {Gómez, Pedro A. and Cencini, Matteo and Golbabaee, Mohammad and Schulte, Rolf F. and Pirkl, Carolin and Horvath, Izabela and Fallo, Giada and Peretti, Luca and Tosetti, Michela and Menze, Bjoern H. and Buonincontri, Guido},
	month = aug,
	year = {2020},
	note_ = {Number: 1
Publisher: Nature Publishing Group},
	pages = {13769},
	file_ = {Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\ELQQQADC\\Gómez et al. - 2020 - Rapid three-dimensional multiparametric MRI with q.pdf:application/pdf;Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\9UGK5G6T\\s41598-020-70789-2.html:text/html}
}
@article{buonincontri_three_2021,
	title = {Three dimensional {MRF} obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.{5T} and {3T}},
	volume = {226},
	issn = {1053-8119},
	url_ = {http://www.sciencedirect.com/science/article/pii/S1053811920310582},
	doi = {10.1016/j.neuroimage.2020.117573},
	abstract = {Magnetic resonance fingerprinting (MRF) is highly promising as a quantitative MRI technique due to its accuracy, robustness, and efficiency. Previous studies have found high repeatability and reproducibility of 2D MRF acquisitions in the brain. Here, we have extended our investigations to 3D MRF acquisitions covering the whole brain using spiral projection k-space trajectories. Our travelling head study acquired test/retest data from the brains of 12 healthy volunteers and 8 MRI systems (3 systems at 3 T and 5 at 1.5 T, all from a single vendor), using a study design not requiring all subjects to be scanned at all sites. The pulse sequence and reconstruction algorithm were the same for all acquisitions. After registration of the MRF-derived PD T1 and T2 maps to an anatomical atlas, coefficients of variation (CVs) were computed to assess test/retest repeatability and inter-site reproducibility in each voxel, while a General Linear Model (GLM) was used to determine the voxel-wise variability between all confounders, which included test/retest, subject, field strength and site. Our analysis demonstrated a high repeatability (CVs 0.7–1.3\% for T1, 2.0–7.8\% for T2, 1.4–2.5\% for normalized PD) and reproducibility (CVs of 2.0–5.8\% for T1, 7.4–10.2\% for T2, 5.2–9.2\% for normalized PD) in gray and white matter. Both repeatability and reproducibility improved when compared to similar experiments using 2D acquisitions. Three-dimensional MRF obtains highly repeatable and reproducible estimations of T1 and T2, supporting the translation of MRF-based fast quantitative imaging into clinical applications.},
	language = {en},
	url_date = {2021-01-19},
	journal = {NeuroImage},
	author = {Buonincontri, Guido and Kurzawski, Jan W. and Kaggie, Joshua D and Matys, Tomasz and Gallagher, Ferdia A and Cencini, Matteo and Donatelli, Graziella and Cecchi, Paolo and Cosottini, Mirco and Martini, Nicola and Frijia, Francesca and Montanaro, Domenico and Gómez, Pedro A. and Schulte, Rolf F and Retico, Alessandra and Tosetti, Michela},
	month = feb,
	year = {2021},
	keywords = {Brain, MR fingerprinting, 3D, MRI, Quantitation, Relaxometry, Three dimensional},
	pages = {117573},
	file_ = {ScienceDirect Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\6IDDQSIG\\Buonincontri et al. - 2021 - Three dimensional MRF obtains highly repeatable an.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\ZGHUHSBC\\S1053811920310582.html:text/html}
}

@article{mehta_image_2018,
	title = {Image reconstruction algorithm for motion insensitive {MR} {Fingerprinting} ({MRF}): {MORF}},
	volume = {80},
	copyright = {© 2018 International Society for Magnetic Resonance in Medicine},
	issn = {1522-2594},
	shorttitle = {Image reconstruction algorithm for motion insensitive {MR} {Fingerprinting} ({MRF})},
	url_ = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27227},
	doi = {https://doi.org/10.1002/mrm.27227},
	abstract = {Purpose The purpose of this study is to increase the robustness of MR fingerprinting (MRF) toward subject motion. Methods A novel reconstruction algorithm, MOtion insensitive MRF (MORF), was developed, which uses an iterative reconstruction based retrospective motion correction approach. Each iteration loops through the following steps: pattern recognition, metric based identification of motion corrupted frames, registration based motion estimation, and motion compensated data consistency verification. The proposed algorithm was validated using in vivo 2D brain MRF data with retrospective in-plane motion introduced at different stages of the acquisition. The validation was performed using qualitative and quantitative comparisons between results from MORF, the iterative multi-scale (IMS) algorithm, and with the IMS results using data without motion for a ground truth comparison. Additionally, the MORF algorithm was evaluated in prospectively motion corrupted in vivo 2D brain MRF datasets. Results For datasets corrupted by in-plane motion both prospectively and retrospectively, MORF noticeably reduced motion artifacts compared with iterative multi-scale and closely resembled the results from data without motion, even when ∼54\% of data was motion corrupted during different parts of the acquisition. Conclusions MORF improves the insensitivity of MRF toward rigid-body motion occurring during any part of the MRF acquisition.},
	language = {en},
	number = {6},
	url_date = {2021-01-19},
	journal = {Magnetic Resonance in Medicine},
	author = {Mehta, Bhairav Bipin and Ma, Dan and Pierre, Eric Yann and Jiang, Yun and Coppo, Simone and Griswold, Mark Alan},
	year = {2018},
	note_ = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.27227},
	keywords = {quantitative imaging, MR fingerprinting, relaxation time, pattern recognition, motion compensation, motion estimation},
	pages = {2485--2500},
	file_ = {Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\4JZKPND6\\mrm.html:text/html}
}

@article{cruz_rigid_2019,
	title = {Rigid motion-corrected magnetic resonance fingerprinting},
	volume = {81},
	copyright = {© 2018 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine},
	issn = {1522-2594},
	url_ = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27448},
	doi = {https://doi.org/10.1002/mrm.27448},
	abstract = {Purpose Develop a method for rigid body motion-corrected magnetic resonance fingerprinting (MRF). Methods MRF has shown some robustness to abrupt motion toward the end of the acquisition. Here, we study the effects of different types of rigid body motion during the acquisition on MRF and propose a novel approach to correct for this motion. The proposed method (MC-MRF) follows 4 steps: (1) sliding window reconstruction is performed to produce high-quality auxiliary dynamic images; (2) rotation and translation motion is estimated from the dynamic images by image registration; (3) estimated motion is used to correct acquired k-space data with corresponding rotations and phase shifts; and (4) motion-corrected data are reconstructed with low-rank inversion. MC-MRF was validated in a standard T1/T2 phantom and 2D in vivo brain acquisitions in 7 healthy subjects. Additionally, the effect of through-plane motion in 2D MC-MRF was investigated. Results Simulation results show that motion in MRF can introduce artifacts in T1 and T2 maps, depending when it occurs. MC-MRF improved parametric map quality in all phantom and in vivo experiments with in-plane motion, comparable to the no-motion ground truth. Reduced parametric map quality, even after motion correction, was observed for acquisitions with through-plane motion, particularly for smaller structures in T2 maps. Conclusion Here, a novel method for motion correction in MRF (MC-MRF) is proposed, which improves parametric map quality and accuracy in comparison to no-motion correction approaches. Future work will include validation of 3D MC-MRF to enable also through-plane motion correction.},
	language = {en},
	number = {2},
	url_date = {2021-02-03},
	journal = {Magnetic Resonance in Medicine},
	author = {Cruz, Gastão and Jaubert, Olivier and Schneider, Torben and Botnar, Rene M. and Prieto, Claudia},
	year = {2019},
	note_ = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.27448},
	keywords = {MR fingerprinting, low rank, rigid motion correction},
	pages = {947--961},
	file_ = {Full Text:C\:\\Users\\212693886\\Box\\zotero\\storage\\DP9BSKDT\\Cruz et al. - 2019 - Rigid motion-corrected magnetic resonance fingerpr.pdf:application/pdf;Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\4N2LDGNL\\mrm.html:text/html}
}


@article{xu_rigid_2019,
	title = {Rigid motion correction for magnetic resonance fingerprinting with sliding-window reconstruction and image registration},
	volume = {57},
	issn = {0730-725X},
	url_ = {http://www.sciencedirect.com/science/article/pii/S0730725X18303904},
	doi = {10.1016/j.mri.2018.11.001},
	abstract = {Magnetic resonance fingerprinting (MRF) can be used to simultaneously obtain multiple parameter maps from a single pulse sequence. However, patient motion during MRF acquisition may result in blurring and artifacts in estimated parameter maps. In this work, a novel motion correction method was proposed to correct for rigid motion in MRF. The proposed method involved sliding-window reconstruction to obtain intermediate images followed by image registration to estimate rigid motion information between these images. Finally, the motion-corrupted k-space data were corrected with the estimated motion parameters and then reconstructed to obtain the parameter maps via the conventional MRF processing pipeline. The proposed method was evaluated using both simulations and in vivo MRF experiments with intently different types of motion. For motion-corrupted data, the proposed method yielded brain T1, T2 and proton density maps with obviously reduced blurring and artifacts and lower normalized root-mean-square error, compared to MRF without motion correction. In conclusion, motion-corrected MRF using the proposed method has the potential to produce accurate parameter maps in the presence of in-plane rigid motion.},
	language = {en},
	url_date = {2021-02-03},
	journal = {Magnetic Resonance Imaging},
	author = {Xu, Zhongbiao and Ye, Huihui and Lyu, Mengye and He, Hongjian and Zhong, Jianhui and Mei, Yingjie and Chen, Zhifeng and Wu, Ed X. and Chen, Wufan and Feng, Qianjin and Feng, Yanqiu},
	month = apr,
	year = {2019},
	keywords = {Magnetic resonance fingerprinting, Image registration, Motion correction, Sliding-window reconstruction},
	pages = {303--312},
	file_ = {ScienceDirect Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\KCIXKDS4\\S0730725X18303904.html:text/html}
}

@article{kurzawski_retrospective_2020,
	title = {Retrospective rigid motion correction of three-dimensional magnetic resonance fingerprinting of the human brain},
	volume = {84},
	copyright = {© 2020 International Society for Magnetic Resonance in Medicine},
	issn = {1522-2594},
	url_ = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28301},
	doi = {https://doi.org/10.1002/mrm.28301},
	abstract = {Purpose To obtain three-dimensional (3D), quantitative and motion-robust imaging with magnetic resonance fingerprinting (MRF). Methods Our acquisition is based on a 3D spiral projection k-space scheme. We compared different orderings of trajectory interleaves in terms of rigid motion-correction robustness. In all tested orderings, we considered the whole dataset as a sum of 56 segments of 7-s duration, acquired sequentially with the same flip angle schedule. We performed a separate image reconstruction for each segment, producing whole-brain navigators that were aligned to the first segment using normalized correlation. The estimated rigid motion was used to correct the k-space data, and the aligned data were matched with the dictionary to obtain motion-corrected maps. Results A significant improvement on the motion-affected maps after motion correction is evident with the suppression of motion artifacts. Correlation with the motionless baseline improved by 20\% on average for both T1 and T2 estimations after motion correction. In addition, the average motion-induced quantification bias of 70 ms for T1 and 18 ms for T2 values was reduced to 12 ms and 6 ms, respectively, improving the reliability of quantitative estimations. Conclusion We established a method that allows correcting 3D rigid motion on a 7-s timescale during the reconstruction of MRF data using self-navigators, improving the image quality and the quantification robustness.},
	language = {en},
	number = {5},
	url_date = {2020-11-11},
	journal = {Magnetic Resonance in Medicine},
	author = {Kurzawski, Jan W. and Cencini, Matteo and Peretti, Luca and Gómez, Pedro A. and Schulte, Rolf F. and Donatelli, Graziella and Cosottini, Mirco and Cecchi, Paolo and Costagli, Mauro and Retico, Alessandra and Tosetti, Michela and Buonincontri, Guido},
	year = {2020},
	note_ = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28301},
	keywords = {MR fingerprinting, 3D motion correction},
	pages = {2606--2615},
	file_ = {Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\QT72TQ3A\\Kurzawski et al. - 2020 - Retrospective rigid motion correction of three-dim.pdf:application/pdf;Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\XJSD5W2T\\mrm.html:text/html;Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\JDD7TDQS\\mrm.html:text/html}
}


@article{usman_retrospective_2020,
	title = {Retrospective {Motion} {Correction} in {Multishot} {MRI} using {Generative} {Adversarial} {Network}},
	volume = {10},
	copyright = {2020 The Author(s)},
	issn = {2045-2322},
	url_ = {https://www.nature.com/articles/s41598-020-61705-9},
	doi = {10.1038/s41598-020-61705-9},
	abstract = {Multishot Magnetic Resonance Imaging (MRI) is a promising data acquisition technique that can produce a high-resolution image with relatively less data acquisition time than the standard spin echo. The downside of multishot MRI is that it is very sensitive to subject motion and even small levels of motion during the scan can produce artifacts in the final magnetic resonance (MR) image, which may result in a misdiagnosis. Numerous efforts have focused on addressing this issue; however, all of these proposals are limited in terms of how much motion they can correct and require excessive computational time. In this paper, we propose a novel generative adversarial network (GAN)-based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. First CG-SENSE reconstruction is employed to reconstruct an image from the motion-corrupted k-space data and then the GAN-based proposed framework is applied to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establish that the proposed method is robust and reduces several-fold the computational time reported by the current state-of-the-art technique.},
	language = {en},
	number = {1},
	url_date = {2021-02-04},
	journal = {Scientific Reports},
	author = {Usman, Muhammad and Latif, Siddique and Asim, Muhammad and Lee, Byoung-Dai and Qadir, Junaid},
	month = mar,
	year = {2020},
	note_ = {Number: 1
Publisher: Nature Publishing Group},
	pages = {4786},
	file_ = {Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\V82HJSHM\\Usman et al. - 2020 - Retrospective Motion Correction in Multishot MRI u.pdf:application/pdf;Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\U3HEAH4P\\s41598-020-61705-9.html:text/html}
}

@article{oksuz_brain_2021,
	title = {Brain {MRI} artefact detection and correction using convolutional neural networks},
	volume = {199},
	issn = {1872-7565},
	doi = {10.1016/j.cmpb.2020.105909},
	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.},
	language = {eng},
	journal = {Computer Methods and Programs in Biomedicine},
	author = {Oksuz, Ilkay},
	month = feb,
	year = {2021},
	pmid = {33373815},
	keywords = {Convolutional neural networks, Artefact detection, Brain MRI, Stroke segmentation},
	pages = {105909}
}

@article{gong_deep_2021,
	title = {Deep learning-based method for reducing residual motion effects in diffusion parameter estimation},
	volume = {85},
	copyright = {© 2020 International Society for Magnetic Resonance in Medicine},
	issn = {1522-2594},
	url_ = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28544},
	doi = {https://doi.org/10.1002/mrm.28544},
	abstract = {Purpose Conventional motion-correction techniques for diffusion MRI can introduce motion-level–dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects. Methods The data-rejection approach was adopted in which motion-corrupted data are discarded before model-fitting. A deep learning-based parameter estimation algorithm, using a hierarchical convolutional neural network (H-CNN), was combined with motion assessment and corrupted volume rejection. The method was designed to overcome the limitations of existing methods of this kind that produce parameter estimations whose quality depends strongly on a proportion of the data discarded. Evaluation experiments were conducted for the estimation of diffusion kurtosis and diffusion-tensor–derived measures at both the individual and group levels. The performance was compared with the robust approach of iteratively reweighted linear least squares (IRLLS) after motion correction with and without outlier replacement. Results Compared with IRLLS, the H-CNN-based technique is minimally sensitive to motion effects. It was tested at severe motion levels when 70\% to 90\% of the data are rejected and when random motion is present. The technique had a stable performance independent of the numbers and schemes of data rejection. A further test on a data set from children with attention-deficit hyperactivity disorder shows the technique can potentially ameliorate spurious group-level difference caused by head motion. Conclusion This method shows great potential for reducing residual motion effects in motion-corrupted diffusion-weighted–imaging data, bringing benefits that include reduced bias in derived metrics in individual scans and reduced motion-level–dependent bias in population studies employing diffusion MRI.},
	language = {en},
	number = {4},
	url_date = {2021-02-11},
	journal = {Magnetic Resonance in Medicine},
	author = {Gong, Ting and Tong, Qiqi and Li, Zhiwei and He, Hongjian and Zhang, Hui and Zhong, Jianhui},
	year = {2021},
	note_ = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28544},
	keywords = {neural network, diffusion tensor imaging, diffusion kurtosis imaging, head motion},
	pages = {2278--2293},
	file_ = {Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\CABDQHK4\\Gong et al. - 2021 - Deep learning-based method for reducing residual m.pdf:application/pdf;Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\9X3A43D5\\mrm.html:text/html}
}

@inproceedings{pawar_motion_2018,
	address = {Paris, France},
	title = {Motion {Correction} in {MRI} using {Deep} {Convolutional} {Neural} {Network}},
	language = {en},
	booktitle = {Proceedings of the 26th {Annual} {Meeting} of {International} {Society} for {Magnetic} {Resonance} in {Medicine} ({ISMRM})},
	author = {Pawar, Kamlesh and Chen, Zhaolin and Shah, N Jon and Egan, Gary F},
	year = {2018},
	pages = {3}
}

@article{miao_cnn_2016,
	title = {A {CNN} {Regression} {Approach} for {Real}-{Time} {2D}/{3D} {Registration}},
	volume = {35},
	issn = {1558-254X},
	doi = {10.1109/TMI.2016.2521800},
	abstract = {In this paper, we present a Convolutional Neural Network (CNN) regression approach to address the two major limitations of existing intensity-based 2-D/3-D registration technology: 1) slow computation and 2) small capture range. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the digitally reconstructed radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. An automatic feature extraction step is introduced to calculate 3-D pose-indexed features that are sensitive to the variables to be regressed while robust to other factors. The CNN regressors are then trained for local zones and applied in a hierarchical manner to break down the complex regression task into multiple simpler sub-tasks that can be learned separately. Weight sharing is furthermore employed in the CNN regression model to reduce the memory footprint. The proposed approach has been quantitatively evaluated on 3 potential clinical applications, demonstrating its significant advantage in providing highly accurate real-time 2-D/3-D registration with a significantly enlarged capture range when compared to intensity-based methods.},
	number = {5},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Miao, S. and Wang, Z. J. and Liao, R.},
	month = may,
	year = {2016},
	note_ = {Conference Name: IEEE Transactions on Medical Imaging},
	keywords = {Humans, Imaging, Three-Dimensional, Machine Learning, deep learning, medical image processing, neural nets, Biomedical imaging, Feature extraction, feature extraction, image reconstruction, regression analysis, Computed tomography, iterative methods, image registration, 2-D/3-D registration, 3D pose-indexed features, Arthroplasty, Replacement, Knee, Attenuation, automatic feature extraction step, CNN regression approach, CNN regressors, complex regression task, convolutional neural network, convolutional neural network regression approach, diagnostic radiography, digitally reconstructed radiograph, Echocardiography, Transesophageal, formation parameters, image guided intervention, intensity-based 2D-3D registration technology, intensity-based methods, iterative optimization, Knee Joint, memory footprint, multiple simpler subtasks, Neural Networks (Computer), optimisation, optimization-based methods, Radiography, real-time 2D-3D registration, Real-time systems, Regression Analysis, scalar-valued metric function, transformation parameters, X-ray images, X-ray imaging},
	pages = {1352--1363}
}


@article{hou_3-d_2018,
	title = {3-{D} {Reconstruction} in {Canonical} {Co}-{Ordinate} {Space} {From} {Arbitrarily} {Oriented} 2-{D} {Images}},
	volume = {37},
	issn = {1558-254X},
	doi = {10.1109/TMI.2018.2798801},
	abstract = {Limited capture range, and the requirement to provide high quality initialization for optimization-based 2-D/3-D image registration methods, can significantly degrade the performance of 3-D image reconstruction and motion compensation pipelines. Challenging clinical imaging scenarios, which contain significant subject motion, such as fetal in-utero imaging, complicate the 3-D image and volume reconstruction process. In this paper, we present a learning-based image registration method capable of predicting 3-D rigid transformations of arbitrarily oriented 2-D image slices, with respect to a learned canonical atlas co-ordinate system. Only image slice intensity information is used to perform registration and canonical alignment, no spatial transform initialization is required. To find image transformations, we utilize a convolutional neural network architecture to learn the regression function capable of mapping 2-D image slices to a 3-D canonical atlas space. We extensively evaluate the effectiveness of our approach quantitatively on simulated magnetic resonance imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline. Our learning based registration achieves an average spatial prediction error of 7 mm on simulated data and produces qualitatively improved reconstructions for heavily moving fetuses with gestational ages of approximately 20 weeks. Our model provides a general and computationally efficient solution to the 2-D/3-D registration initialization problem and is suitable for real-time scenarios.},
	number = {8},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Hou, B. and Khanal, B. and Alansary, A. and McDonagh, S. and Davidson, A. and Rutherford, M. and Hajnal, J. V. and Rueckert, D. and Glocker, B. and Kainz, B.},
	month = aug,
	year = {2018},
	note_ = {Conference Name: IEEE Transactions on Medical Imaging},
	keywords = {Magnetic Resonance Imaging, Humans, Female, Algorithms, Image Interpretation, Computer-Assisted, Brain, Imaging, Three-Dimensional, Machine Learning, Movement, machine learning, biomedical MRI, learning (artificial intelligence), Magnetic resonance imaging, medical image processing, Biomedical imaging, brain, magnetic resonance imaging, Image reconstruction, image reconstruction, regression analysis, motion compensation, 3D image reconstruction, 3D rigid transformations, canonical alignment, canonical coordinate space, clinical imaging scenarios, fetal brain imagery, fetal in-utero imaging, fetal MRI data, Fetus, high quality initialization, image registration, image slice intensity information, image transformations, learning-based image registration method, learning-based registration, Manuals, mapping 2D image slices, Motion compensation, motion compensation pipeline, obstetrics, optimization-based 2D image registration method, optimization-based 3D image registration method, Pregnancy, Robustness, significant subject motion, spatial transform initialization, synthetic motion, Three-dimensional displays, Two dimensional displays, volume reconstruction process},
	pages = {1737--1750},
	file_ = {IEEE Xplore Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\JA5ARHLY\\Hou et al. - 2018 - 3-D Reconstruction in Canonical Co-Ordinate Space .pdf:application/pdf}
}

@inproceedings{hou_computing_2018,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Computing {CNN} {Loss} and {Gradients} for {Pose} {Estimation} with {Riemannian} {Geometry}},
	isbn = {978-3-030-00928-1},
	doi = {10.1007/978-3-030-00928-1_85},
	abstract = {Pose estimation, i.e. predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image analysis. Deep learning methods often parameterise poses with a representation that separates rotation and translation. As commonly available frameworks do not provide means to calculate loss on a manifold, regression is usually performed using the L2-norm independently on the rotation’s and the translation’s parameterisations. This is a metric for linear spaces that does not take into account the Lie group structure of SE(3).In this paper, we propose a general Riemannian formulation of the pose estimation problem, and train CNNs directly on SE(3) equipped with a left-invariant Riemannian metric. The loss between the ground truth and predicted pose (elements of the manifold) is calculated as the Riemannian geodesic distance, which couples together the translation and rotation components. Network weights are updated by back-propagating the gradient with respect to the predicted pose on the tangent space of the manifold SE(3). We thoroughly evaluate the effectiveness of our loss function by comparing its performance with popular and most commonly used existing methods, on tasks such as image-based localisation and intensity-based 2D/3D registration. We also show that hyper-parameters, used in our loss function to weight the contribution between rotations and translations, can be intrinsically calculated from the dataset to achieve greater performance margins.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2018},
	publisher = {Springer International Publishing},
	author = {Hou, Benjamin and Miolane, Nina and Khanal, Bishesh and Lee, Matthew C. H. and Alansary, Amir and McDonagh, Steven and Hajnal, Jo V. and Rueckert, Daniel and Glocker, Ben and Kainz, Bernhard},
	editor = {Frangi, Alejandro F. and Schnabel, Julia A. and Davatzikos, Christos and Alberola-López, Carlos and Fichtinger, Gabor},
	year = {2018},
	pages = {756--764},
	file_ = {Springer Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\IGEEWGDV\\Hou et al. - 2018 - Computing CNN Loss and Gradients for Pose Estimati.pdf:application/pdf}
}




@article{zhang_beyond_2017,
	title = {Beyond a {Gaussian} {Denoiser}: {Residual} {Learning} of {Deep} {CNN} for {Image} {Denoising}},
	volume = {26},
	issn = {1941-0042},
	shorttitle = {Beyond a {Gaussian} {Denoiser}},
	doi = {10.1109/TIP.2017.2662206},
	abstract = {The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.},
	number = {7},
	journal = {IEEE Transactions on Image Processing},
	author = {Zhang, K. and Zuo, W. and Chen, Y. and Meng, D. and Zhang, L.},
	month = jul,
	year = {2017},
	note_ = {Conference Name: IEEE Transactions on Image Processing},
	keywords = {convolutional neural networks, learning (artificial intelligence), Neural networks, Training, additive white Gaussian noise, AWGN, batch normalization, Computational modeling, deep CNN, discriminative denoising models, discriminative model learning, DnCNN model, feedforward denoising convolutional neural networks, feedforward neural nets, Gaussian denoiser, Gaussian processes, GPU computing, image denoising, Image denoising, JPEG image deblocking, learning algorithm, neural net architecture, Noise level, Noise reduction, regularization method, residual learning, residual learning strategy, single image super-resolution, training process, Transform coding, unknown noise level, very deep architecture},
	pages = {3142--3155},
	file_ = {IEEE Xplore Abstract Record:C\:\\Users\\212693886\\Box\\zotero\\storage\\XNKM73TZ\\7839189.html:text/html;Submitted Version:C\:\\Users\\212693886\\Box\\zotero\\storage\\7DIEQKA8\\Zhang et al. - 2017 - Beyond a Gaussian Denoiser Residual Learning of D.pdf:application/pdf}
}



@article{jin_deep_2017,
	title = {Deep {Convolutional} {Neural} {Network} for {Inverse} {Problems} in {Imaging}},
	volume = {26},
	issn = {1941-0042},
	doi = {10.1109/TIP.2017.2713099},
	abstract = {In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyperparameter selection. The starting point of this paper is the observation that unrolled iterative methods have the form of a CNN (filtering followed by pointwise nonlinearity) when the normal operator (H*H, where H* is the adjoint of the forward imaging operator, H) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 × 512 image on the GPU.},
	number = {9},
	journal = {IEEE Transactions on Image Processing},
	author = {Jin, K. H. and McCann, M. T. and Froustey, E. and Unser, M.},
	month = sep,
	year = {2017},
	note_ = {Conference Name: IEEE Transactions on Image Processing},
	keywords = {learning (artificial intelligence), medical image processing, Neural networks, magnetic resonance imaging, Image reconstruction, image reconstruction, feedforward neural nets, residual learning, reconstruction algorithms, adjoint operators, biomedical imaging, biomedical signal processing, CNN, computed tomography, Computed tomography, computerised tomography, Convolution, deep convolutional neural network, direct inversion, forward model, forward operators, GPU, hyperparameter selection, ill-posed inverse problems, image resolution, Image restoration, image structure, Inverse problems, iterative methods, Iterative methods, multiresolution decomposition, normal-convolutional inverse problems, parallel beam X-ray computed tomography, regularized iterative algorithms, synthetic phantoms, tomography, total variation-regularized iterative reconstruction},
	pages = {4509--4522},
	file_ = {IEEE Xplore Abstract Record:C\:\\Users\\212693886\\Box\\zotero\\storage\\JQZI2XPQ\\7949028.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\3HAMJTP8\\Jin et al. - 2017 - Deep Convolutional Neural Network for Inverse Prob.pdf:application/pdf}
}


@article{liu_motion_2020-1,
	title = {Motion artifacts reduction in brain {MRI} by means of a deep residual network with densely connected multi-resolution blocks ({DRN}-{DCMB})},
	volume = {71},
	issn = {0730-725X},
	url_ = {https://www.sciencedirect.com/science/article/pii/S0730725X20300175},
	doi = {10.1016/j.mri.2020.05.002},
	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.},
	language = {en},
	url_date = {2021-02-11},
	journal = {Magnetic Resonance Imaging},
	author = {Liu, Junchi and Kocak, Mehmet and Supanich, Mark and Deng, Jie},
	month = sep,
	year = {2020},
	keywords = {MRI, Deep leaning, Dense connection, Motion artifact, Multi-resolution block, Residual learning},
	pages = {69--79},
	file_ = {ScienceDirect Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\4IBWXBF7\\S0730725X20300175.html:text/html}
}


@article{tamada_motion_2019,
	title = {Motion {Artifact} {Reduction} {Using} a {Convolutional} {Neural} {Network} for {Dynamic} {Contrast} {Enhanced} {MR} {Imaging} of the {Liver}},
	volume = {19},
	issn = {1347-3182},
	url_ = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067907/},
	doi = {10.2463/mrms.mp.2018-0156},
	abstract = {Purpose:
To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach.

Materials and Methods:
A multi-channel convolutional neural network-based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T1-weighted spoiled gradient echo sequence for the liver, which contained breath-hold failures occurring during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland–Altman plots.

Results:
The proposed network was found to be significantly reducing the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images.

Conclusion:
A deep learning-based method for removing motion artifacts in images obtained via DCE-MRI of the liver was demonstrated and validated.},
	number = {1},
	url_date = {2020-04-01},
	journal = {Magnetic Resonance in Medical Sciences},
	author = {Tamada, Daiki and Kromrey, Marie-Luise and Ichikawa, Shintaro and Onishi, Hiroshi and Motosugi, Utaroh},
	month = apr,
	year = {2019},
	pmid = {31061259},
	pmcid = {PMC7067907},
	pages = {64--76},
	file_ = {PubMed Central Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\LY2DWZS6\\Tamada et al. - 2019 - Motion Artifact Reduction Using a Convolutional Ne.pdf:application/pdf}
}

@inproceedings{ulas_direct_2018,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Direct {Estimation} of {Pharmacokinetic} {Parameters} from {DCE}-{MRI} {Using} {Deep} {CNN} with {Forward} {Physical} {Model} {Loss}},
	isbn = {978-3-030-00928-1},
	doi = {10.1007/978-3-030-00928-1_5},
	abstract = {Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters in body tissues, in which series of T1T1T\_1-weighted images are collected following the administration of a paramagnetic contrast agent. Unfortunately, in many applications, conventional clinical DCE-MRI suffers from low spatiotemporal resolution and insufficient volume coverage. In this paper, we propose a novel deep learning based approach to directly estimate the PK parameters from undersampled DCE-MRI data. Specifically, we design a custom loss function where we incorporate a forward physical model that relates the PK parameters to corrupted image-time series obtained due to subsampling in k-space. This allows the network to directly exploit the knowledge of true contrast agent kinetics in the training phase, and hence provide more accurate restoration of PK parameters. Experiments on clinical brain DCE datasets demonstrate the efficacy of our approach in terms of fidelity of PK parameter reconstruction and significantly faster parameter inference compared to a model-based iterative reconstruction method.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2018},
	publisher = {Springer International Publishing},
	author = {Ulas, Cagdas and Tetteh, Giles and Thrippleton, Michael J. and Armitage, Paul A. and Makin, Stephen D. and Wardlaw, Joanna M. and Davies, Mike E. and Menze, Bjoern H.},
	editor = {Frangi, Alejandro F. and Schnabel, Julia A. and Davatzikos, Christos and Alberola-López, Carlos and Fichtinger, Gabor},
	year = {2018},
	pages = {39--47},
	file_ = {Submitted Version:C\:\\Users\\212693886\\Box\\zotero\\storage\\HIPHGY4M\\Ulas et al. - 2018 - Direct Estimation of Pharmacokinetic Parameters fr.pdf:application/pdf}
}



@article{kamnitsas_efficient_2017,
	title = {Efficient multi-scale {3D} {CNN} with fully connected {CRF} for accurate brain lesion segmentation},
	volume = {36},
	issn = {1361-8415},
	url_ = {http://www.sciencedirect.com/science/article/pii/S1361841516301839},
	doi = {10.1016/j.media.2016.10.004},
	abstract = {We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network’s soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.},
	language = {en},
	url_date = {2021-01-22},
	journal = {Medical Image Analysis},
	author = {Kamnitsas, Konstantinos and Ledig, Christian and Newcombe, Virginia F. J. and Simpson, Joanna P. and Kane, Andrew D. and Menon, David K. and Rueckert, Daniel and Glocker, Ben},
	month = feb,
	year = {2017},
	keywords = {Deep learning, 3D convolutional neural network, Brain lesions, Fully connected CRF, Segmentation},
	pages = {61--78},
	file_ = {ScienceDirect Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\TTLADUN9\\Kamnitsas et al. - 2017 - Efficient multi-scale 3D CNN with fully connected .pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\YM65VT87\\S1361841516301839.html:text/html}
}


@article{kim_improving_2017,
	title = {Improving {Arterial} {Spin} {Labeling} by {Using} {Deep} {Learning}},
	volume = {287},
	issn = {0033-8419},
	url_ = {https://pubs.rsna.org/doi/10.1148/radiol.2017171154},
	doi = {10.1148/radiol.2017171154},
	abstract = {PurposeTo develop a deep learning algorithm that generates arterial spin labeling (ASL) perfusion images with higher accuracy and robustness by using a smaller number of subtraction images.Materials and MethodsFor ASL image generation from pair-wise subtraction, we used a convolutional neural network (CNN) as a deep learning algorithm. The ground truth perfusion images were generated by averaging six or seven pairwise subtraction images acquired with (a) conventional pseudocontinuous arterial spin labeling from seven healthy subjects or (b) Hadamard-encoded pseudocontinuous ASL from 114 patients with various diseases. CNNs were trained to generate perfusion images from a smaller number (two or three) of subtraction images and evaluated by means of cross-validation. CNNs from the patient data sets were also tested on 26 separate stroke data sets. CNNs were compared with the conventional averaging method in terms of mean square error and radiologic score by using a paired t test and/or Wilcoxon signed-rank test.ResultsMean square errors were approximately 40\% lower than those of the conventional averaging method for the cross-validation with the healthy subjects and patients and the separate test with the patients who had experienced a stroke (P {\textless} .001). Region-of-interest analysis in stroke regions showed that cerebral blood flow maps from CNN (mean ± standard deviation, 19.7 mL per 100 g/min ± 9.7) had smaller mean square errors than those determined with the conventional averaging method (43.2 ± 29.8) (P {\textless} .001). Radiologic scoring demonstrated that CNNs suppressed noise and motion and/or segmentation artifacts better than the conventional averaging method did (P {\textless} .001).ConclusionCNNs provided superior perfusion image quality and more accurate perfusion measurement compared with those of the conventional averaging method for generation of ASL images from pair-wise subtraction images.© RSNA, 2017},
	number = {2},
	url_date = {2021-01-22},
	journal = {Radiology},
	author = {Kim, Ki Hwan and Choi, Seung Hong and Park, Sung-Hong},
	month = dec,
	year = {2017},
	note_ = {Publisher: Radiological Society of North America},
	pages = {658--666},
	file_ = {Full Text PDF:C\:\\Users\\212693886\\Box\\zotero\\storage\\YIYJ75KY\\Kim et al. - 2017 - Improving Arterial Spin Labeling by Using Deep Lea.pdf:application/pdf;Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\WZJV8ULH\\radiol.html:text/html}
}


@article{yaman_self-supervised_2020,
	title = {Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data},
	volume = {84},
	copyright = {© 2020 International Society for Magnetic Resonance in Medicine},
	issn = {1522-2594},
	url_ = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28378},
	doi = {https://doi.org/10.1002/mrm.28378},
	abstract = {Purpose To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully sampled data sets. Methods Self-supervised learning via data undersampling (SSDU) for physics-guided deep learning reconstruction partitions available measurements into two disjoint sets, one of which is used in the data consistency (DC) units in the unrolled network and the other is used to define the loss for training. The proposed training without fully sampled data is compared with fully supervised training with ground-truth data, as well as conventional compressed-sensing and parallel imaging methods using the publicly available fastMRI knee database. The same physics-guided neural network is used for both proposed SSDU and supervised training. The SSDU training is also applied to prospectively two-fold accelerated high-resolution brain data sets at different acceleration rates, and compared with parallel imaging. Results Results on five different knee sequences at an acceleration rate of 4 shows that the proposed self-supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed-sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study. The results on prospectively subsampled brain data sets, in which supervised learning cannot be used due to lack of ground-truth reference, show that the proposed self-supervised approach successfully performs reconstruction at high acceleration rates (4, 6, and 8). Image readings indicate improved visual reconstruction quality with the proposed approach compared with parallel imaging at acquisition acceleration. Conclusion The proposed SSDU approach allows training of physics-guided deep learning MRI reconstruction without fully sampled data, while achieving comparable results with supervised deep learning MRI trained on fully sampled data.},
	language = {en},
	number = {6},
	url_date = {2021-02-12},
	journal = {Magnetic Resonance in Medicine},
	author = {Yaman, Burhaneddin and Hosseini, Seyed Amir Hossein and Moeller, Steen and Ellermann, Jutta and Uğurbil, Kâmil and Akçakaya, Mehmet},
	year = {2020},
	note_ = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28378},
	keywords = {convolutional neural networks, deep learning, accelerated imaging, image reconstruction, parallel imaging, self-supervised learning},
	pages = {3172--3191},
	file_ = {Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\8FTPR6EK\\mrm.html:text/html;Submitted Version:C\:\\Users\\212693886\\Box\\zotero\\storage\\H5HBQVKT\\Yaman et al. - 2020 - Self-supervised learning of physics-guided reconst.pdf:application/pdf}
}




@article{liu_motion_2020,
	title = {Motion artifacts reduction in brain {MRI} by means of a deep residual network with densely connected multi-resolution blocks ({DRN}-{DCMB})},
	volume = {71},
	issn = {0730-725X},
	url_ = {https://www.sciencedirect.com/science/article/pii/S0730725X20300175},
	doi = {10.1016/j.mri.2020.05.002},
	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.},
	language = {en},
	url_date = {2021-02-11},
	journal = {Magnetic Resonance Imaging},
	author = {Liu, Junchi and Kocak, Mehmet and Supanich, Mark and Deng, Jie},
	month = sep,
	year = {2020},
	keywords = {MRI, Deep leaning, Dense connection, Motion artifact, Multi-resolution block, Residual learning},
	pages = {69--79},
	file_ = {ScienceDirect Snapshot:C\:\\Users\\212693886\\Box\\zotero\\storage\\TAJL8DPT\\S0730725X20300175.html:text/html}
}







