Optimum acquisition times of two spin echoes for MR image synthesis

Lee, James N.; Riederer, Stephen J.

doi:10.1002/mrm.1910030416

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…The temperature rise induced changes in intrinsic NMR sample parameters. T 1 , T 2 relaxation times and proton density (PD) were mapped according to the temperature from three images successively acquired using a single spin-echo sequence [8] [i.e., two images at different repetition times (TRs) for T 1 : TR 1 /TR 2 =1000/5000 ms at the fixed echo time (TE)=7.2 ms; and two images at different TEs for T 2 and PD: TE 1 /TE 2 =7.2/37.2 ms and at the fixed TR=5000 ms]. Shimming, tuning and flip angle calibration procedures were carried out at each temperature step in order to minimize estimation errors due to instrumental shifts due to temperature changes.…”

Section: Measurement Of Muscle Nuclear Magnetic Resonance (Nmr) Properties During Heatingmentioning

confidence: 99%

Mapping of muscle deformation during heating: in situ dynamic MRI and nonlinear registration

Bouhrara¹,

Lehallier²,

Clerjon³

et al. 2012

Magnetic Resonance Imaging

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We present developments in dynamic magnetic resonance imaging that allow internal structural muscle markers to be followed during heating. This monitoring is based on quantitative characterization of the experimental conditions and their temperature time course. A nonlinear image registration technique was optimized and applied to consecutively acquired images to measure the deformation fields in the muscle. A model coupling local deformation and temperature was obtained, which for the first time takes into account the variations of deformation and temperature in the sample. This modeling opens the way to a better understanding of the mechanisms responsible for mass loss and degradation of the textural properties of muscle during heating.

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Section: Measurement Of Muscle Nuclear Magnetic Resonance (Nmr) Properties During Heatingmentioning

confidence: 99%

Mapping of muscle deformation during heating: in situ dynamic MRI and nonlinear registration

Bouhrara¹,

Lehallier²,

Clerjon³

et al. 2012

Magnetic Resonance Imaging

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“…Most commonly, SNR [5], CNR [9], noise level [17], or contours of parameter sensitivity [ 131 are plotted using mathematical models and typical parameter values for normal tissues, e.g., white matter and gray matter. Calculations are performed for multiple combinations of timing intervals.…”

Section: B Review Of Previous Mri Optimization Problemsmentioning

confidence: 99%

Optimization of MRI protocols and pulse sequence parameters for eigenimage filtering

Soltanian-Zadeh

Saigal

Windham

et al. 1994

IEEE Trans. Med. Imaging

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The eigenimage filter generates a composite image in which a desired feature is segmented from interfering features. The signal-to-noise ratio (SNR) of the eigenimage equals its contrast-to-noise ratio (CNR) and is directly proportional to the dissimilarity between the desired and interfering features. Since image gray levels are analytical functions of magnetic resonance imaging (MRI) parameters, it is possible to maximize this dissimilarity by optimizing these parameters. For optimization, the authors consider four MRI pulse sequences: multiple spin-echo (MSE); spin-echo (SE); inversion recovery (IR); and gradient-echo (GE). The authors use the mathematical expressions for MRI signals along with intrinsic tissue parameters to express the objective function (normalized SNR of the eigenimage) in terms of MRI parameters. The objective function along with a set of diagnostic or instrumental constraints define a multidimensional nonlinear constrained optimization problem, which the authors solve by the fixed point approach. The optimization technique is demonstrated through its application to phantom and brain images. The authors show that the optimal pulse sequence parameters for a sequence of four MSE and one IR images almost doubles the smallest normalized SNR of the brain eigenimages, as compared to the conventional brain protocol.

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“…One algorithm uses an iteratively gencrated lookup table to map the quotient of two data points to T1, and then determines the pseudodensity using back-substitution. The other algorithms were previously discussed [18] [21]. Brosnan, et al [9] discuss an improvement over Wiener filtering, "measurement-dependent filtering", which uses multiple data sources to decrease the noise in a previously calculated T2 image while maintaining step edges.…”

Section: Introductionmentioning

confidence: 99%

“…Breger, et al [8] used an iterative X 2 minimization to produce pseudodensity and 7'1 from four 7'1 weighted images, and then used the same technique to calculate pseudodensity and T2 from four T2 weighted images. Lee and Riederer [18] used two or more images with constant TR and differing TE and directly calculated T2 and pseudodensity, synthesized data using those values, and then evaluated the noise in the produced synthetics. Graumann, Fischer and Oppelt [13] designed a specific pulse sequence in order to directly calculate T1 and Tz without the use of minimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Image restoration

et al. 1995

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Abstract. We introduce a novel technique for Magnetic Resonance Image (MRI) restoration, using a physical model (spin equation) and corresponding basis images. We determine the basis images (proton density and nuclear relaxation times) from the MRI data and use them to obtain excellent restorations.Magnetic Resonance Images depend nonlinearly on proton density, p, two nuclear relaxation times, T1 and Tz, and two control parameters, TE and TR. We model images a Markov random fields and introduce two maximum a posteriori (MAP) restorations; quadratic smoothing and a nonlinear technique. We also introduce a novel method of global optimization necessary for the nonlinear technique.

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Optimum acquisition times of two spin echoes for MR image synthesis

Cited by 7 publications

References 5 publications

Mapping of muscle deformation during heating: in situ dynamic MRI and nonlinear registration

Mapping of muscle deformation during heating: in situ dynamic MRI and nonlinear registration

Optimization of MRI protocols and pulse sequence parameters for eigenimage filtering

Magnetic Resonance Image restoration

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