NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions

Jeurissen B, Tournier J-D, Dhollander T, Connelly A, Sijbers J (2014) Multitissue constrained spherical deconvolution for improved analysis of multi-shell diffusion mri data. Neuroimage 103:411–426

Article  PubMed  Google Scholar 

Schmahmann JD, Pandya DN, Wang R, Dai G, D’Arceuil HE, De Crespigny AJ, Wedeen VJ (2007) Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 130(3):630–653

Article  PubMed  Google Scholar 

Scherrer B, Gholipour A, Warfield SK (2011) Super-resolution in diffusion-weighted imaging. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011: 14th International Conference, Toronto, Canada, September 18–22, 2011, Proceedings, Part II 14. Springer, Berlin Heidelberg, pp 124–132. https://doi.org/10.1007/978-3-642-23629-7_16

Scherrer B, Gholipour A, Warfield SK (2012) Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions. Med Image Anal 16(7):1465–1476

Article  PubMed  PubMed Central  Google Scholar 

Alexander DC, Zikic D, Zhang J, Zhang H, Criminisi A (2014) Image quality transfer via random forest regression: applications in diffusion MRI. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14–18, 2014, Proceedings, Part III 17. Springer International Publishing, pp 225–232. https://doi.org/10.1007/978-3-319-10443-0_29

Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, Wu-Minn HCP Consortium (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62–79

Article  PubMed  Google Scholar 

Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, Christiaens D, Jeurissen B, Yeh CH, Connelly A (2019) MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202:116137

Dhollander T, Raffelt D, Connelly A (2016) Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. In: ISMRM workshop on breaking the barriers of diffusion MRI, vol 5, no. 5

Khan W, Egorova N, Khlif MS, Mito R, Dhollander T, Brodtmann A (2020) Three-tissue compositional analysis reveals in-vivo microstructural heterogeneity of white matter hyperintensities following stroke. Neuroimage 218:116869

Article  PubMed  Google Scholar 

Raffelt D, Dhollander T, Tournier JD, Tabbara R, Smith RE, Pierre E, Connelly A (2017) Bias field correction and intensity normalisation for quantitative analysis of apparent fibre density. In Proc Intl Soc Mag Reson Med 25:3541

Google Scholar 

Tournier JD, Calamante F, Gadian DG, Connelly A (2004) Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23(3):1176–1185

Article  PubMed  Google Scholar 

Tournier JD, Calamante F, Connelly A (2013) Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion weighted imaging. NMR Biomed 26(12):1775–1786

Article  PubMed  Google Scholar 

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: an imperative style, high performance deep learning library. Adv Neural Inf Process Syst 32. https://proceedings.neurips.cc/paper_files/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf

Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, pp 249–256. https://proceedings.mlr.press/v9/glorot10a.html

Zeng R, Lv J, Wang H, Zhou L, Barnett M, Calamante F, Wang C (2022) FOD-Net: a deep learning method for fiber orientation distribution angular super resolution. Medical Image Analysis 79:102431

Article  PubMed  Google Scholar 

Anderson AW (2005) Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magnet Reson Med: An Off J Int Soc Magn Reson Med 54(5):11941206

Article  Google Scholar 

Li Z, Fan Q, Bilgic B, Wang G, Wu W, Polimeni JR, Miller KL, Huang SY, Tian Q (2023) Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat). Med Image Anal 86:102744

Jha RR, Pathak SK, Nath V, Schneider W, Kumar BR, Bhavsar A, Nigam A (2022) VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI. Magn Reson Imaging 90:1–16

Article  PubMed  Google Scholar 

Hosseini SMH, Hassanpour M, Masoudnia S, Iraji S, Raminfard S, NazemZadeh M (2022) CTtrack: a CNN+ Transformer-based framework for fiber orientation estimation & tractography. Neuroscience Informatics 2(4):100099

Article  Google Scholar 

Jha RR, Kumar BR, Kathak S, Schneider W, Bhavsar A, Nigam A (2023) Undersampled single-shell to MSMT fODF reconstruction using CNN-based ODE solver. Comp Methods Programs Biomed 230:107339

Article  Google Scholar 

Karimi D, Gholipour A (2022) Diffusion tensor estimation with transformer neural networks. Artif Intell Med 130:102330

Article  PubMed  PubMed Central  Google Scholar 

Lucena O, Vos SB, Vakharia V, Duncan J, Ashkan K, Sparks R, Ourselin S (2021) Enhancing the estimation of fiber orientation distributions using convolutional neural networks. Comput Biol Med 135:104643

Article  PubMed  Google Scholar 

Basser PJ, Mattiello J, LeBihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson Serie B 103(3):247–254

Article  CAS  Google Scholar 

Basser PJ (2002) Relationships between diffusion tensor and q-space MRI. Magn Reson Med: Off J Int Soc Magn Reson Med 47(2):392–397

Article  Google Scholar 

Jbabdi S, Behrens TE, Smith SM (2010) Crossing fibres in tract-based spatial statistics. Neuroimage 49(1):249–256

Article  PubMed  Google Scholar 

Henderson F, Abdullah KG, Verma R, Brem S (2020) Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential. Neurosurg Focus 48(2):E6

Article  PubMed  PubMed Central  Google Scholar 

Schultz T, Westin CF, Kindlmann G (2010) Multi-diffusion-tensor fitting via spherical deconvolution: a unifying framework. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2010: 13th International Conference, Beijing, China, September 20–24, 2010, Proceedings, Part I 13. Springer, Berlin Heidelberg, pp 674–681. https://doi.org/10.1007/978-3-642-15705-9_82

Tuch DS (2004) Q-ball imaging. Magn Reson Med: Off Int Soc Magnet Reson Med 52(6):1358–1372

Article  Google Scholar 

Jeurissen B, Leemans A, Jones DK, Tournier JD, Sijbers J (2011) Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Hum Brain Mapp 32(3):461–479

Article  PubMed  Google Scholar 

Raffelt D, Tournier JD, Rose S, Ridgway GR, Henderson R, Crozier S, Salvado O, Connelly A (2012) Apparent fibre density: a novel measure for the analysis of diffusion weighted magnetic resonance images. Neuroimage 59(4):3976–3994

Christiaens D, Reisert M, Dhollander T, Sunaert S, Suetens P, Maes F (2015) Global tractography of multi-shell diffusion weighted imaging data using a multi-tissue model. Neuroimage 123:89–101

Article  PubMed  Google Scholar 

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