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SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency [article]

Noam Korngut, Elad Rotman, Onur Afacan, Sila Kurugol, Yael Zaffrani-Reznikov, Shira Nemirovsky-Rotman, Simon Warfield, Moti Freiman
2022 arXiv   pre-print
Intra-voxel incoherent motion (IVIM) analysis of fetal lungs Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging bio-markers that reflect, indirectly, diffusion and pseudo-diffusion  ...  Further, SUPER-IVIM-DC estimates of the pseudo-diffusion fraction parameter from limited DWI data of fetal lungs correlate better with gestational age compared to both to classical and DNN-based approaches  ...  In the past few years, state-of-the art deep-neural-networks (DNN)-based methods were introduced for IVIM parameter estimates. Bertleff et al.  ... 
arXiv:2206.03820v3 fatcat:lxxxd6is3fhftnpte7ehake3ze

A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging [article]

Davood Karimi, Lana Vasung, Camilo Jaimes, Fedel Machado-Rivas, Shadab Khan, Simon K. Warfield, Ali Gholipour
2020 arXiv   pre-print
In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel.  ...  Our method can be trained with either simulated or real diffusion-weighted imaging data.  ...  Several recent studies have attempted at estimating scalar diffusion parameters such as diffusion kurtosis measures and generalized fractional anisotropy using deep learning [8] - [11] .  ... 
arXiv:2006.11117v1 fatcat:soplx5sydnf3xlzwsxa6v7znca

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study

Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro (+7 others)
2021 PLoS ONE  
We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical  ...  A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed.  ...  fetal MRI data elaboration with semiautomatic segmentation; development of the classification system based on machine learning; development of the classification system based on deep learning; statistical  ... 
doi:10.1371/journal.pone.0259724 pmid:34752491 pmcid:PMC8577746 fatcat:jeidt5mt6rgcrhz7bs2notii6y

Through-plane super-resolution with autoencoders in diffusion magnetic resonance imaging of the developing human brain [article]

Hamza Kebiri, Erick J. Canales Rodríguez, Hélène Lajous, Priscille de Dumast, Gabriel Girard, Yasser Alemán-Gómez, Mériam Koob, András Jakab, Meritxell Bach Cuadra
2021 bioRxiv   pre-print
The evaluation was performed on both the original diffusion-weighted signal and on the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser.  ...  ABSTRACTFetal brain diffusion magnetic resonance images are often acquired with a lower through-plane than in-plane resolution.  ...  .: Helped in the preprocessing of the fetal data and acknowledged the manuscript. M.K.: Helped in the processing of the fetal data and acknowledged the manuscript.  ... 
doi:10.1101/2021.12.06.471406 fatcat:pqjretfyrnftjc7y42dll7h6lu

Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain

Hamza Kebiri, Erick J. Canales-Rodríguez, Hélène Lajous, Priscille de Dumast, Gabriel Girard, Yasser Alemán-Gómez, Mériam Koob, András Jakab, Meritxell Bach Cuadra
2022 Frontiers in Neurology  
Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution.  ...  The evaluation was performed on both the original diffusion-weighted signal and the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser.  ...  ACKNOWLEDGMENTS We thank Athena Taymourtash for the help in the acceleration of the technical analysis and Samuel Lamon (MD) for the help in the segmentation of the corpus callosum.  ... 
doi:10.3389/fneur.2022.827816 pmid:35585848 pmcid:PMC9109939 fatcat:whc4i5bqtrcg3elaeb67m4dxhi

Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI

Jacob Levman, Emi Takahashi
2016 Frontiers in Neuroanatomy  
This paper presents the results of a systematic review of the literature focusing on MVA applied to healthy subjects in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain.  ...  While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in brain MRI, the field is still young and significant research growth  ...  Their work was based on fetal T2 weighted structural MRI. Altaye et al. (2008) developed infant brain probability templates for MRI segmentation and normalization.  ... 
doi:10.3389/fnana.2015.00163 pmid:26834576 pmcid:PMC4720794 fatcat:wctsqbvl6baq5m7dm4snvry5lu

Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges

Hala Shaari, Jasmin Kevrić, Samed Jukić, Larisa Bešić, Dejan Jokić, Nuredin Ahmed, Vladimir Rajs
2021 Brain Sciences  
Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.  ...  This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning.  ...  (2020) [51] Posterior fossa (diffuse midline glioma, medulloblastoma, pilocytic astrocytoma, and ependymoma) Modified 2D ResNeXt-50-32x4d deep learning architecture T2-weighted MRIs  ... 
doi:10.3390/brainsci11060716 pmid:34071202 fatcat:usmduuhzyzcsrh7lgto3ejzbfu

A microstructure estimation Transformer inspired by sparse representation for diffusion MRI [article]

Tianshu Zheng, Cong Sun, Weihao Zheng, Wen Shi, Haotian Li, Yi Sun, Yi Zhang, Guangbin Wang, Chuyang Ye, Dan Wu
2022 arXiv   pre-print
Deep learning based approaches have been proposed to overcome these limitations.  ...  The mapping stage is essentially a decoder that computes the microstructural parameters from the output of the second stage, based on the weighted sum of normalized dictionary coefficients where the weights  ...  parameters in the fetal brain (Zheng et al., 2021) .  ... 
arXiv:2205.06450v1 fatcat:teqnpctrcrcv5ppbeecdlpbcdq

Applications of Deep Learning to Neuro-Imaging Techniques

Guangming Zhu, Bin Jiang, Liz Tong, Yuan Xie, Greg Zaharchuk, Max Wintermark
2019 Frontiers in Neurology  
Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis,  ...  This article will address this topic and will seek to present an overview of deep learning applied to neuroimaging techniques.  ...  For PET/MRI, attenuation coefficient (µ) has been estimated from segmentation-and atlas-based algorithms.  ... 
doi:10.3389/fneur.2019.00869 pmid:31474928 pmcid:PMC6702308 fatcat:yki64mb57jhafduasd3hohfkgi

A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling

Ahmed E. Fetit, Amir Alansary, Lucilio Cordero-Grande, John Cupitt, Alice B. Davidson, A. David Edwards, Joseph V. Hajnal, Emer J. Hughes, Konstantinos Kamnitsas, Vanessa Kyriakopoulou, Antonios Makropoulos, Prachi A. Patkee (+3 others)
2020 International Conference on Medical Imaging with Deep Learning  
We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI.  ...  The deep learning system was developed, refined, and validated on 249 3D T2-weighted scans obtained from the Developing Human Connectome Project's fetal cohort, acquired at 3T.  ...  The system is based on the deep learning family of algorithms (LeCun et al., 2015) , namely convolutional neural networks (CNNs), and was designed to work on volumetric, T2-weighted brain MRI scans.  ... 
dblp:conf/midl/FetitACCDEHHKKM20 fatcat:6jy65ebhzffphnn7ku74ptmggu

Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface [article]

Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loic Le Folgoc, John Cupitt, Antonios Makropoulos, Andreas Schuh, Daniel Rueckert, Amir Alansary
2020 arXiv   pre-print
In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface.  ...  Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes.  ...  Background The majority of work in deep learning for medical imaging typically focuses on the application of CNNs to Euclidean data, e.g. MRI and ultrasound images [11, 21] .  ... 
arXiv:2008.06098v2 fatcat:2hx4ngpvd5gxdkro5z3lotcr6a

Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect: INtegrated SPECTral Component Estimation and Mapping

Paddy J. Slator, Jana Hutter, Razvan V. Marinescu, Marco Palombo, Laurence H. Jackson, Alison Ho, Lucy C. Chappell, Mary Rutherford, Joseph V. Hajnal, Daniel C. Alexander
2021 Medical Image Analysis  
We introduce and demonstrate an unsupervised machine learning technique for spectroscopic analysis of quantitative MRI experiments.  ...  Our algorithm vastly reduces the data required to reliably estimate spectra, opening up the possibility of quantitative MRI spectroscopy in a wide range of new applications.  ...  Acknowledgments We thank all mothers, midwives, obstetricians, and radiographers who played a key role in obtaining the datasets.  ... 
doi:10.1016/ pmid:33934005 pmcid:PMC8543043 fatcat:p73hfipu7fapzevhenffekgoq4

Combined diffusion‐relaxometry microstructure imaging: Current status and future prospects

Paddy J. Slator, Marco Palombo, Karla L. Miller, Carl‐Fredrik Westin, Frederik Laun, Daeun Kim, Justin P. Haldar, Dan Benjamini, Gregory Lemberskiy, Joao P. Almeida Martins, Jana Hutter
2021 Magnetic Resonance in Medicine  
When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ .  ...  Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space.  ...  ACKNOWLEDGEMENTS The authors thank the ISMRM for supporting their combined diffusion-relaxometry microstructure imaging memberinitiated symposium at the ISMRM 2019 Annual Meeting & Exhibition.  ... 
doi:10.1002/mrm.28963 pmid:34411331 pmcid:PMC8568657 fatcat:b7rdybwwdbgppkdrbxqbiayjxq

2014 Index IEEE Transactions on Medical Imaging Vol. 33

2014 IEEE Transactions on Medical Imaging  
., +, TMI Feb. 2014 301-317 Uncertainty Estimation in Diffusion MRI Using the Nonlocal Bootstrap. Yap, P.  ...  N., +, TMI Feb. 2014 546-555 Uncertainty Estimation in Diffusion MRI Using the Nonlocal Bootstrap. Yap, P.  ...  MRI Upsampling Using Feature-Based Nonlocal Means Approach. Jafari-Khouzani, K., 1969 -1985 Numerical Surrogates for Human Observers in Myocardial Motion Evaluation From SPECT Images.  ... 
doi:10.1109/tmi.2014.2386278 fatcat:poarfhfto5bm5mhfl7ugwtw4xy

On the use of multicompartment models of diffusion and relaxation for placental imaging

Andrew Melbourne
2021 Placenta  
Multi-compartment models of diffusion and relaxation are ubiquitous in magnetic resonance research especially applied to neuroimaging applications.  ...  parameters being estimated.  ...  = exp (− − ) [1b] 125 Estimation of single parameters such as ADC, T2 and T2* require relatively little processing 126 power and the results are relatively free from bias in controlled MRI (keeping all  ... 
doi:10.1016/j.placenta.2021.07.302 pmid:34392172 fatcat:j46y2ciitna6hkek3ftgcsnoum
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