Estimating motion from MRI data

Ozturk, Cengizhan; Derbyshire, J. Andrew; McVeigh, Elliot R.

doi:10.1109/jproc.2003.817872

Cited by 46 publications

(33 citation statements)

References 135 publications

(134 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A common approach [1,[9][10][11] for the MRI-based measurement of soft tissue deformation is to employ SPatial Modulation of the Magnetization (SPAMM) tagged MRI [12,13]. In SPAMM tagged MRI the tissue is temporarily magnetically tagged using a periodic signal modulation and tracking of the tag pattern allows measurement of deformation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Validation of continuously tagged MRI for the measurement of dynamic 3D skeletal muscle tissue deformation

Moerman¹,

Sprengers²,

Simms³

et al. 2012

Medical Physics

View full text Add to dashboard Cite

Purpose: Typically SPAtial Modulation of the Magnetization (SPAMM) tagged MRI requires many repeated motion cycles limiting the applicability to highly repeatable tissue motions only. This paper describes the validation of a novel SPAMM tagged MRI and post-processing frame work for the measurement of complex and dynamic 3D soft tissue deformation following just 3 motion cycles. Techniques are applied to indentation induced deformation measurement of the upper arm and a silicone gel phantom.Methods: A SPAMM tagged MRI methodology is presented allowing continuous (3.3-3.6 Hz) sampling of 3D dynamic soft tissue deformation using non-segmented 3D acquisitions. The 3D deformation is reconstructed by the combination of 3 mutually orthogonal tagging directions, thus requiring only 3 repeated motion cycles. In addition a fully automatic post-processing framework is presented employing Gabor scale-space and filter-bank analysis for tag extrema segmentation and triangulated surface fitting aided by Gabor filter bank derived surface normals. Deformation is derived following tracking of tag surface triplet triangle intersections. The dynamic deformation measurements were validated using indentation tests (~20 mm deep at 12 mm/s) on a silicone gel soft tissue phantom containing contrasting markers which provide a reference measure of deformation. In addition, the techniques were evaluated in-vivo for dynamic skeletal muscle tissue deformation measurement during indentation of the biceps region of the upper arm in a volunteer. Results:For the phantom and volunteer tag point location precision were 44 µm and 92 µm respectively resulting in individual displacements precisions of 61 µm and 91 µm respectively. For both the phantom and volunteer data cumulative displacement measurement accuracy could be evaluated and the difference between initial and final locations showed a mean and standard deviation of 0.44 mm and 0.59 mm for the phantom and 0.40 mm and 0.73 mm for the human data. Finally accuracy of (cumulative) displacement was evaluated using marker tracking in the silicone gel phantom. Differences between true and predicted marker locations showed a mean of 0.35 mm and a standard deviation of 0.63 mm. Conclusions:A novel SPAMM tagged MRI and fully automatic post-processing framework for the measurement of complex 3D dynamic soft tissue deformation following just 3 repeated motion cycles was presented. The techniques demonstrate dynamic measurement of complex 3D soft tissue deformation at sub-voxel accuracy and precision and were validated for 3.3-3.6Hz sampling of deformation speeds up to 12 mm/s.

show abstract

Section: Introductionmentioning

confidence: 99%

“…A wide array of advanced post-processing methods have been proposed for SPAMM tagged MRI (for a detailed discussion see dedicated literature [9,18] and [19]); for instance using deformable models (see review article [20]), spline models (e.g. [21][22][23][24]) and energy based methods such as non-rigid image registration (e.g.…”

Section: Introductionmentioning

confidence: 99%

Validation of continuously tagged MRI for the measurement of dynamic 3D skeletal muscle tissue deformation

Moerman¹,

Sprengers²,

Simms³

et al. 2012

Medical Physics

View full text Add to dashboard Cite

show abstract

“…The first one is to use dedicated imaging modalities that directly measure motion information. Echocardiographic Doppler Tissue Imaging [1], Magnetic Resonance (MR) tagging [2] and several other phase encoding MR sequences [3] have been developed to directly measure displacement or velocity fields for cardiac motion analysis [4], [5], [6]. However, those modalities are not widely used in clinics and give access to limited components of these fields (often one or two dimensional).…”

mentioning

confidence: 99%

Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images

Prakosa¹,

Sermesant²,

Delingette³

et al. 2013

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Abstract-We propose a new approach for the generation of synthetic but visually realistic time series of cardiac images based on an electromechanical model of the heart and real clinical 4D image sequences. This is achieved by combining three steps. The first step is the simulation of a cardiac motion using an electromechanical model of the heart and the segmentation of the end diastolic image of a cardiac sequence. We use biophysical parameters related to the desired condition of the simulated subject. The second step extracts the cardiac motion from the real sequence using non-rigid image registration. Finally, a synthetic time series of cardiac images corresponding to the simulated motion is generated in the third step by combining the motion estimated by image registration and the simulated one. With this approach, image processing algorithms can be evaluated as we know the ground-truth motion underlying the image sequence. Moreover, databases of visually realistic images of controls and patients can be generated for which the underlying cardiac motion and some biophysical parameters are known.Such databases can open new avenues for machine learning approaches.

show abstract

“…It provides excellent contrast between soft tissues, and images can be acquired at positions and orientations freely defined by the practitioner. From a temporal sequence of MR images, boundaries and edges of tissues can be tracked by image processing techniques [10].…”

Section: Introductionmentioning

confidence: 99%

“…The former allows to derive a motion model of the underlying tissue by tracking a temporal sequence of images which have been previously marked by a pattern of dark lines (called tags). This pattern is achieved by modulation of the image intensity with a magnetic presaturation pulse [10]. The deformation field of the crossing line points can be calculated just following the temporal trajectories.…”

Section: Introductionmentioning

confidence: 99%

Strain Rate Tensor Estimation in Cine Cardiac MRI Based on Elastic Image Registration

Vegas-Sánchez-Ferrero¹,

Tristán‐Vega²,

Cordero‐Grande³

et al. 2009

Tensors in Image Processing and Computer Vision

View full text Add to dashboard Cite

In this paper we propose an alternative method to estimate and visualize the Strain Rate Tensor (ST) in Magnetic Resonance Images (MRI) when Phase Contrast MRI (PCMRI) and Tagged MRI (TMRI) are not available. This alternative is based on image processing techniques. Concretely, an elastic image registration algorithm is used to estimate the movement of the myocardium at each point. Our experiments with real data prove that the registration algorithm provides a useful deformation field to estimate the ST fields. A classification between regional normal and dysfunctional contraction patterns, as compared with professional diagnosis, points out that the parameters extracted from the estimated ST can represent these patterns.

show abstract

Estimating motion from MRI data

Cited by 46 publications

References 135 publications

Validation of continuously tagged MRI for the measurement of dynamic 3D skeletal muscle tissue deformation

Validation of continuously tagged MRI for the measurement of dynamic 3D skeletal muscle tissue deformation

Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images

Strain Rate Tensor Estimation in Cine Cardiac MRI Based on Elastic Image Registration

Contact Info

Product

Resources

About