Normalized gradient fields for nonlinear motion correction of DCE-MRI time series

Hodneland, Erlend; Lundervold, Arvid; Rørvik, Jarle; Munthe-Kaas, Antonella Zanna

doi:10.1016/j.compmedimag.2013.12.007

Cited by 34 publications

(32 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In simulated results, matching using HOG descriptors performed well, but tests on real images lead to coarse and inaccurate depth maps. Using normalized gradients to compute global registration for medical images was proposed by Haber and Modersitzki [57] and extended Rühaak et al [58] and by Hodneland et al [59] among others. These works are intended to align medical images (e.g.…”

Section: Related Workmentioning

confidence: 99%

Generalized Assorted Camera Arrays: Robust Cross-Channel Registration and Applications

Holloway

Mitra

Koppal

et al. 2015

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-One popular technique for multi-modal imaging is Generalized Assorted Pixels (GAP), where an assorted pixel array on the image sensor allows for multi-modal capture. Unfortunately GAP is limited in its applicability because of the need for multi-modal filters that are amenable with semiconductor fabrication processes and results in a fixed multi-modal imaging configuration. In this paper, we advocate for Generalized Assorted Camera (GAC) arrays for multi-modal imaging-i.e., a camera array with filters of different characteristics placed in front of each camera aperture. GAC provides us with three distinct advantages over GAP: ease of implementation, flexible application dependent imaging since filters are external and can be changed and depth information that can be used for enabling novel applications (e.g. post-capture refocusing). The primary challenge in GAC arrays is that since the different modalities are obtained from different viewpoints, there is a need for accurate and efficient cross-channel registration. Traditional approaches such as SSD, SAD, and mutual information all result in multimodal registration errors. Here, we propose a robust crosschannel matching cost function, based on aligning normalized gradients, that allows us to compute cross-channel sub-pixel correspondences for scenes exhibiting non-trivial geometry. We highlight the promise of GAC arrays with our cross-channel normalized gradient cost for several applications such as low light imaging, post-capture refocusing, skin perfusion imaging using RGB+NIR and hyperspectral imaging.

show abstract

Section: Related Workmentioning

confidence: 99%

Generalized Assorted Camera Arrays: Robust Cross-Channel Registration and Applications

Holloway

Mitra

Koppal

et al. 2015

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…This phenomenon favours the use of a gradient dependent cost functional for registration. In recent work [14], [24] it was shown that normalized gradient fields (NGF) was a viable alternative to MI for the registration of DCE-MRI images. In this work, we therefore incorporate a version of NGF [21], [35] as data term for the registration task.…”

Section: ) Registrationmentioning

confidence: 99%

“…The spatial derivative of a function f was approximated either by central (D c ), forward (D + ) or backward (D − ) differences. The Euler-Lagrange equations to solve are given by (24). Note that b(u, φ) is not depending on u and φ through a differential operator, and is therefore applied on the right hand side.…”

Section: Numerical Implementationmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys

Hodneland

Hanson

Lundervold

et al. 2014

IEEE Trans. on Image Process.

Self Cite

View full text Add to dashboard Cite

Abstract-Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidneys requires proper motion correction and segmentation to enable an estimation of glomerular filtration rate through pharmacokinetic modeling. Traditionally, co-registration, segmentation, and pharmacokinetic modeling have been applied sequentially as separate processing steps. In this paper, a combined 4D model for simultaneous registration and segmentation of the whole kidney is presented. To demonstrate the model in numerical experiments, we used normalized gradients as data term in the registration and a Mahalanobis distance from the time courses of the segmented regions to a training set for supervised segmentation. By applying this framework to an input consisting of 4D image time series, we conduct simultaneous motion correction and two-region segmentation into kidney and background. The potential of the new approach is demonstrated on real DCE-MRI data from ten healthy volunteers.

show abstract

“…Over the years, several methods have been proposed to compensate for motion in DCE-MR series of the abdomen. Some approaches have used pairwise registration methods with mutual information (MI) as metric, where the individual images are registered one by one to a reference image (Rueckert et al [1999], Rohlfing et al [2003], Wells et al [1996], Hodneland et al [2014], Zöllner et al [2009]). These methods generally do not achieve the temporal smoothness needed for visual assessment, because of bias towards the choice of reference (Huizinga et al [2016], Metz et al [2011]).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of motion correction for clinical dynamic contrast enhanced MRI of the liver

et al. 2017

View full text Add to dashboard Cite

Abstract. Motion correction of 4D dynamic contrast enhanced MRI (DCE-MRI) series is required for diagnostic evaluation of liver lesions. The registration, however, is a challenging task, owing to rapid changes in image appearance. In this study, two different registration approaches are compared; a conventional pairwise method applying mutual information as metric and a groupwise method applying a principal component analysis based metric, introduced by Huizinga et al. [2016]. The pairwise method transforms the individual 3D images one by one to a reference image, whereas the groupwise registration method computes the metric on all the images simultaneously, exploiting the temporal information, and transforms all 3D images to a common space. The performance of the two registration methods was evaluated using 70 clinical 4D DCE-MRI series with the focus on the liver. The evaluation was based on the smoothness of the time intensity curves in lesions, lesion volume change after deformation and the smoothness of spatial deformation. Furthermore, the visual quality of subtraction images (pre-contrast image subtracted from the post contrast images) before and after registration was rated by two observers. Both registration methods improved the alignment of the DCE-MRI images in comparison to the non-corrected series. Furthermore, the groupwise method achieved better temporal alignment with smoother spatial deformations than the pairwise method. The quality of the subtraction images was graded satisfactory in 32% of the cases without registration and in 77% and 80% of the cases after pairwise and groupwise registration, respectively. In conclusion, the groupwise registration method outperforms the pairwise registration method and achieves clinically satisfying results. Registration leads to improved subtraction images.

show abstract

Normalized gradient fields for nonlinear motion correction of DCE-MRI time series

Cited by 34 publications

References 35 publications

Generalized Assorted Camera Arrays: Robust Cross-Channel Registration and Applications

Generalized Assorted Camera Arrays: Robust Cross-Channel Registration and Applications

Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys

Evaluation of motion correction for clinical dynamic contrast enhanced MRI of the liver

Contact Info

Product

Resources

About