scholarly journals Motion correction of free-breathing magnetic resonance renography using model-driven registration

Author(s):  
Dimitra Flouri ◽  
Daniel Lesnic ◽  
Constantina Chrysochou ◽  
Jehill Parikh ◽  
Peter Thelwall ◽  
...  

Abstract Introduction Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR). Materials and methods MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data). Results DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HDunregistered = 9.98 ± 9.76, HDregistered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses. Discussion MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods.

Author(s):  
Ning Jin ◽  
Juliana Serafim da Silveira ◽  
Marie-Pierre Jolly ◽  
David N. Firmin ◽  
George Mathew ◽  
...  

2014 ◽  
Vol 16 (S1) ◽  
Author(s):  
Laurent Bonnemains ◽  
Freddy Odille ◽  
Aboubaker Cherifi ◽  
Pierre-Yves Marie ◽  
Cedric Pasquier ◽  
...  

2014 ◽  
Vol 42 (2) ◽  
pp. 407-420 ◽  
Author(s):  
Joseph Y. Cheng ◽  
Tao Zhang ◽  
Nichanan Ruangwattanapaisarn ◽  
Marcus T. Alley ◽  
Martin Uecker ◽  
...  

2021 ◽  
Vol 17 (4) ◽  
pp. e1008806 ◽  
Author(s):  
Changjia Cai ◽  
Johannes Friedrich ◽  
Amrita Singh ◽  
M. Hossein Eybposh ◽  
Eftychios A. Pnevmatikakis ◽  
...  

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy’s performance in spike extraction and scalability are state-of-the-art.


2015 ◽  
Vol 2 (2) ◽  
pp. 65-71
Author(s):  
Xiaolin Zheng ◽  
Lihua Xiao ◽  
Xianmiao Fan ◽  
Ning Huang ◽  
Zihua Su ◽  
...  

1998 ◽  
Vol 16 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Alistair M. Howseman ◽  
David A. Porter ◽  
Chloe Hutton ◽  
Oliver Josephs ◽  
Robert Turner

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