Role of free-breathing motion-corrected late gadolinium enhancement technique for image quality assessment and LGE quantification

2021 ◽  
Vol 135 ◽  
pp. 109510
Author(s):  
Yunfei Yu ◽  
Yinyin Chen ◽  
Shihai Zhao ◽  
Meiying Ge ◽  
Shan Yang ◽  
...  
2016 ◽  
Vol 46 (7) ◽  
pp. 983-990 ◽  
Author(s):  
Laura Olivieri ◽  
Russell Cross ◽  
Kendall J. O’Brien ◽  
Hui Xue ◽  
Peter Kellman ◽  
...  

2013 ◽  
Vol 6 (3) ◽  
pp. 423-432 ◽  
Author(s):  
Kayla M. Piehler ◽  
Timothy C. Wong ◽  
Kathy S. Puntil ◽  
Karolina M. Zareba ◽  
Kathie Lin ◽  
...  

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Giorgia Milotta ◽  
Camila Munoz ◽  
Karl P. Kunze ◽  
Radhouene Neji ◽  
Stefano Figliozzi ◽  
...  

Abstract Purpose To develop a free-breathing whole-heart isotropic-resolution 3D late gadolinium enhancement (LGE) sequence with Dixon-encoding, which provides co-registered 3D grey-blood phase-sensitive inversion-recovery (PSIR) and complementary 3D fat volumes in a single scan of < 7 min. Methods A free-breathing 3D PSIR LGE sequence with dual-echo Dixon readout with a variable density Cartesian trajectory with acceleration factor of 3 is proposed. Image navigators are acquired to correct both inversion recovery (IR)-prepared and reference volumes for 2D translational respiratory motion, enabling motion compensated PSIR reconstruction with 100% respiratory scan efficiency. An intermediate PSIR reconstruction is performed between the in-phase echoes to estimate the signal polarity which is subsequently applied to the IR-prepared water volume to generate a water grey-blood PSIR image. The IR-prepared water volume is obtained using a water/fat separation algorithm from the corresponding dual-echo readout. The complementary fat-volume is obtained after water/fat separation of the reference volume. Ten patients (6 with myocardial scar) were scanned with the proposed water/fat grey-blood 3D PSIR LGE sequence at 1.5 T and compared to breath-held grey-blood 2D LGE sequence in terms of contrast ratio (CR), contrast-to-noise ratio (CNR), scar depiction, scar transmurality, scar mass and image quality. Results Comparable CRs (p = 0.98, 0.40 and 0.83) and CNRs (p = 0.29, 0.40 and 0.26) for blood-myocardium, scar-myocardium and scar-blood respectively were obtained with the proposed free-breathing 3D water/fat LGE and 2D clinical LGE scan. Excellent agreement for scar detection, scar transmurality, scar mass (bias = 0.29%) and image quality scores (from 1: non-diagnostic to 4: excellent) of 3.8 ± 0.42 and 3.6 ± 0.69 (p > 0.99) were obtained with the 2D and 3D PSIR LGE approaches with comparable total acquisition time (p = 0.29). Similar agreement in intra and inter-observer variability were obtained for the 2D and 3D acquisition respectively. Conclusion The proposed approach enabled the acquisition of free-breathing motion-compensated isotropic-resolution 3D grey-blood PSIR LGE and fat volumes. The proposed approach showed good agreement with conventional 2D LGE in terms of CR, scar depiction and scan time, while enabling free-breathing acquisition, whole-heart coverage, reformatting in arbitrary views and visualization of both water and fat information.


2021 ◽  
Vol 2021 (29) ◽  
pp. 170-174
Author(s):  
Ji Jason ◽  
Dalin Tian ◽  
Ming Ronnier Luo

When evaluating the image quality, people mostly would like to concentrate on the color appearance of memory objects, representing the naturalness and reality of the image scene. Generally, an image with objects which have perfect memory colors reproduction will give natural and harmonious feelings. Many previous studies had proved the critical role of naturalness in image quality assessment, but it was still tough to scale the image naturalness precisely. In this study, natural images with blue sky, green grass, and skin colors were selected and partially rendered to develop the model of preference and naturalness of typical memory colors. A psychophysical experiment was conducted to collect the visual data of these images. Afterward, the psychophysical data were used to build the preference models and naturalness models, respectively. The models were then compared with previous studies. Results showed that the new models could accurately predict the preference and naturalness of target memory colors.


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