Super-resolution Reconstruction in Ultrahigh-field MRI

Overview

Journal Biophys Rep (N Y)

Specialty Biophysics

Date 2023 Apr 28

PMID 37114210

Authors

Macy Payne

Ivina Mali

Thomas Mueller

Mary Cain

Ronen Segev

Stefan H Bossmann

Affiliations

Soon will be listed here.

Abstract

Magnetic resonance imaging (MRI) is a highly significant imaging platform for a variety of medical and research applications. However, the low spatiotemporal resolution of conventional MRI limits its applicability toward rapid acquisition of ultrahigh-resolution scans. Current aims at high-resolution MRI focus on increasing the accuracy of tissue delineation, assessments of structural integrity, and early identification of malignancies. Unfortunately, high-resolution imaging often leads to decreased signal/noise (SNR) and contrast/noise (CNR) ratios and increased time cost, which are unfeasible in many clinical and academic settings, offsetting any potential benefits. In this study, we apply and assess the efficacy of super-resolution reconstruction (SRR) through iterative back-projection utilizing through-plane voxel offsets. SRR allows for high-resolution imaging in condensed time frames. Rat skulls and archerfish samples, typical models in academic settings, were used to demonstrate the impact of SRR on varying sample sizes and applicability for translational and comparative neuroscience. The SNR and CNR increased in samples that did not fully occupy the imaging probe and in instances where the low-resolution data were acquired in three dimensions, while the CNR was found to increase with both 3D and 2D low-resolution data reconstructions when compared with directly acquired high-resolution images. Limitations to the applied SRR algorithm were investigated to determine the maximum ratios between low-resolution inputs and high-resolution reconstructions and the overall cost effectivity of the strategy. Overall, the study revealed that SRR could be used to decrease image acquisition time, increase the CNR in nearly all instances, and increase the SNR in small samples.

References

Kangarlu A, Burgess R, Zhu H, Nakayama T, Hamlin R, Abduljalil A . Cognitive, cardiac, and physiological safety studies in ultra high field magnetic resonance imaging. Magn Reson Imaging. 1999; 17(10):1407-16. DOI: 10.1016/s0730-725x(99)00086-7. View

Calabrese E, Badea A, Watson C, Johnson G . A quantitative magnetic resonance histology atlas of postnatal rat brain development with regional estimates of growth and variability. Neuroimage. 2013; 71:196-206. PMC: 3639493. DOI: 10.1016/j.neuroimage.2013.01.017. View

Sergejeva M, Papp E, Bakker R, Gaudnek M, Okamura-Oho Y, Boline J . Anatomical landmarks for registration of experimental image data to volumetric rodent brain atlasing templates. J Neurosci Methods. 2014; 240:161-9. DOI: 10.1016/j.jneumeth.2014.11.005. View

Cozzi J, Fraichard A, Thiam K . Use of genetically modified rat models for translational medicine. Drug Discov Today. 2008; 13(11-12):488-94. DOI: 10.1016/j.drudis.2008.03.021. View

Kjonigsen L, Lillehaug S, Bjaalie J, Witter M, Leergaard T . Waxholm Space atlas of the rat brain hippocampal region: three-dimensional delineations based on magnetic resonance and diffusion tensor imaging. Neuroimage. 2015; 108:441-9. DOI: 10.1016/j.neuroimage.2014.12.080. View

Miloushev V, Deh K, Keshari K . "Free super-resolution MRI by BRICKD slices". J Magn Reson. 2022; 341:107246. PMC: 9531552. DOI: 10.1016/j.jmr.2022.107246. View

Greenspan H, Oz G, Kiryati N, Peled S . MRI inter-slice reconstruction using super-resolution. Magn Reson Imaging. 2002; 20(5):437-46. DOI: 10.1016/s0730-725x(02)00511-8. View

Homberg J, Wohr M, Alenina N . Comeback of the Rat in Biomedical Research. ACS Chem Neurosci. 2017; 8(5):900-903. DOI: 10.1021/acschemneuro.6b00415. View

Farsiu S, Robinson M, Elad M, Milanfar P . Fast and robust multiframe super resolution. IEEE Trans Image Process. 2004; 13(10):1327-44. DOI: 10.1109/tip.2004.834669. View

10.

Mufti N, Ebner M, Patel P, Aertsen M, Gaunt T, Humphries P . Super-resolution Reconstruction MRI Application in Fetal Neck Masses and Congenital High Airway Obstruction Syndrome. OTO Open. 2021; 5(4):2473974X211055372. PMC: 8549475. DOI: 10.1177/2473974X211055372. View

11.

Zhu X, Sun L, Chen Y, Ye Z, Shen Z, Li G . Combination of cascade chemical reactions with graphene-DNA interaction to develop new strategy for biosensor fabrication. Biosens Bioelectron. 2013; 47:32-7. DOI: 10.1016/j.bios.2013.02.039. View

12.

Karoubi N, Segev R, Wullimann M . The Brain of the Archerfish : A Nissl-Based Neuroanatomical Atlas and Catecholaminergic/Cholinergic Systems. Front Neuroanat. 2016; 10:106. PMC: 5104738. DOI: 10.3389/fnana.2016.00106. View

13.

Ben-Tov M, Ben-Shahar O, Segev R . What a predator can teach us about visual processing: a lesson from the archerfish. Curr Opin Neurobiol. 2018; 52:80-87. DOI: 10.1016/j.conb.2018.04.001. View

14.

Sarasaen C, Chatterjee S, Breitkopf M, Rose G, Nurnberger A, Speck O . Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge. Artif Intell Med. 2021; 121:102196. DOI: 10.1016/j.artmed.2021.102196. View

15.

Gholipour A, Estroff J, Warfield S . Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI. IEEE Trans Med Imaging. 2010; 29(10):1739-58. PMC: 3694441. DOI: 10.1109/TMI.2010.2051680. View

16.

Carmi E, Liu S, Liu S, Alon N, Fiat A, Fiat D . Resolution enhancement in MRI. Magn Reson Imaging. 2006; 24(2):133-54. DOI: 10.1016/j.mri.2005.09.011. View

17.

Bernstein M, Fain S, Riederer S . Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy. J Magn Reson Imaging. 2001; 14(3):270-80. DOI: 10.1002/jmri.1183. View

18.

Sadeghi-Tarakameh A, DelaBarre L, Lagore R, Torrado-Carvajal A, Wu X, Grant A . In vivo human head MRI at 10.5T: A radiofrequency safety study and preliminary imaging results. Magn Reson Med. 2019; 84(1):484-496. PMC: 7695227. DOI: 10.1002/mrm.28093. View

19.

Newport C, Wallis G, Siebeck U . Concept learning and the use of three common psychophysical paradigms in the archerfish (Toxotes chatareus). Front Neural Circuits. 2014; 8:39. PMC: 4006028. DOI: 10.3389/fncir.2014.00039. View