The SIMRI project: a versatile and interactive MRI simulator

Benoit-Cattin, Hugues; Collewet, Guylaine; Belaroussi, Boubakeur; Saint‐Jalmes, Hervé; Odet, C.

doi:10.1016/j.jmr.2004.09.027

Cited by 154 publications

(125 citation statements)

References 28 publications

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“…This was applied in the context of MR brain images [10], [11], cardiac MR sequences [12], tridimensional echocardiographic (US) sequences [13], [14], [4], [15] and Computed Tomographic (CT) images [16], [17]. A platform was recently developed to facilitate the access to several medical image simulators [18].…”

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

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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.

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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

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show abstract

“…The virtual object is defined by set of physical parameters that is needed to calculate magnetization vector [95]. These values are spin density (ρ), spin-lattice relaxation time (T1) and spin-spin relaxation time (T2);…”

Section: Figure 4-2 Simri Overview [95]mentioning

confidence: 99%

“…The local magnetic field is defined as: Considering the effect of applying a RF excitation pulse, applying the gradients and spin magnetization relaxation, the magnetization vector evolution can be iteratively computed by [95]: Where θg is linked to the applied gradient ) (t G  by:…”

Section: Figure 4-2 Simri Overview [95]mentioning

confidence: 99%

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MRI-based communication for untethered intelligent medical microrobots

Sharafi

Olamaei

2015

J Micro-Bio Robot

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“…In [7], the additional * 2 T exponential weighting of signal has been applied in order to reflect dephasing from 0 B  inhomogeneities. However, this approach requires special treatment of the time scale in order to generate proper spin-echo signal formation by 180 0 refocusing pulses.…”

Section: Introductionmentioning

confidence: 99%