Accuracy of Signal-to-noise Ratio Measurement Method for Magnetic Resonance Images

Ogura, Akio; Miyai, Akira; Maeda, Fumie; Fukutake, Hiroyuki; Kikumoto, Rikiya

doi:10.6009/jjrt.kj00003174111

Cited by 30 publications

(11 citation statements)

References 4 publications

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“…SNR is computed with (1) [17]:

SN R_{1} = \frac{S}{σ},

where σ is the 2D standard deviation (SD) of pixel intensity in the ROI.…”

Section: Methodsmentioning

confidence: 99%

“…SNR was then computed with (2) [17–21], where the factor

\sqrt{2}

is due to the property of the addition of the variances when two images are added or subtracted:

SN R_{2} = \sqrt{2} S / σ,

where σ is the 2D SD of pixel intensity in the ROI.…”

Section: Methodsmentioning

confidence: 99%

“…Since MR noise in the air follows Rayleigh distribution, the mean value of the signal in the second ROI ( μ air ) is equal to the SD of the noise, multiplied for the coefficient

\sqrt{π / 2}

. So, SNR was computed with (4) [17, 20]:

SN R_{4} = \sqrt{\frac{π}{2}} \cdot \frac{S}{μ_{air}} .

…”

Section: Methodsmentioning

confidence: 99%

“…Then, we considered a second ROI on the second image (B), located as the first ROI in the first image. The SD of the noise was evaluated in the same ROI position and computed as suggested by Ogura et al [17] with (5):

σ^{} = \sqrt{τ_{ROIA-ROIB}^{2} + τ_{ROIB-ROIA}^{2} + 2 \cdot ν_{ROIB-ROIA} \cdot ν_{ROIA-ROIB}},

where τ was the standard deviation and ν was the mean value of the pixel in an image obtained as the difference of image A minus image B (ROIA-ROIB) or vice versa.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application

Laganà

Rovaris

Ceccarelli

et al. 2010

Computational Intelligence and Neuroscience

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Background. Magnetic Resonance (MR) diffusion tensor imaging (DTI) is able to quantify in vivo tissue microstructure properties and to detect disease related pathology of the central nervous system. Nevertheless, DTI is limited by low spatial resolution associated with its low signal-to-noise-ratio (SNR). Aim. The aim is to select a DTI sequence for brain clinical studies, optimizing SNR and resolution. Methods and Results. We applied 6 methods for SNR computation in 26 DTI sequences with different parameters using 4 healthy volunteers (HV). We choosed two DTI sequences for their high SNR, they differed by voxel size and b-value. Subsequently, the two selected sequences were acquired from 30 multiple sclerosis (MS) patients with different disability and lesion load and 18 age matched HV. We observed high concordance between mean diffusivity (MD) and fractional anysotropy (FA), nonetheless the DTI sequence with smaller voxel size displayed a better correlation with disease progression, despite a slightly lower SNR. The reliability of corpus callosum (CC) fiber tracking with the chosen DTI sequences was also tested. Conclusions. The sensitivity of DTI-derived indices to MS-related tissue abnormalities indicates that the optimized sequence may be a powerful tool in studies aimed at monitoring the disease course and severity.

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“…SNR is computed with (1) [17]:

SN R_{1} = \frac{S}{σ},

where σ is the 2D standard deviation (SD) of pixel intensity in the ROI.…”

Section: Methodsmentioning

confidence: 99%

“…SNR was then computed with (2) [17–21], where the factor

\sqrt{2}

is due to the property of the addition of the variances when two images are added or subtracted:

SN R_{2} = \sqrt{2} S / σ,

where σ is the 2D SD of pixel intensity in the ROI.…”

Section: Methodsmentioning

confidence: 99%

“…Since MR noise in the air follows Rayleigh distribution, the mean value of the signal in the second ROI ( μ air ) is equal to the SD of the noise, multiplied for the coefficient

\sqrt{π / 2}

. So, SNR was computed with (4) [17, 20]:

SN R_{4} = \sqrt{\frac{π}{2}} \cdot \frac{S}{μ_{air}} .

…”

Section: Methodsmentioning

confidence: 99%

σ^{} = \sqrt{τ_{ROIA-ROIB}^{2} + τ_{ROIB-ROIA}^{2} + 2 \cdot ν_{ROIB-ROIA} \cdot ν_{ROIA-ROIB}},

where τ was the standard deviation and ν was the mean value of the pixel in an image obtained as the difference of image A minus image B (ROIA-ROIB) or vice versa.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application

Laganà

Rovaris

Ceccarelli

et al. 2010

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…6 Since the noise varies spatially and does not follow a normal distribution function, SNR values obtained using the single image method are inaccurate as has been shown previously. [8][9][10][11] The aim of our current study is to validate a method of SNR evaluation for protocols involving PPI methods in MRI that is accurate and relatively easy for physicists to implement in the field, primarily for protocol evaluation but also for coil quality control. 8,12 However, the current study extends the generality of the method by performing SNR measurements on data acquired using four different types of pulse sequences and two different methods of parallel image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Measuring signal‐to‐noise ratio in partially parallel imaging MRI

2011

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Purpose: To assess five different methods of signal-to-noise ratio (SNR) measurement for partially parallel imaging (PPI) acquisitions. Methods: Measurements were performed on a spherical phantom and three volunteers using a multichannel head coil a clinical 3T MRI system to produce echo planar, fast spin echo, gradient echo, and balanced steady state free precession image acquisitions. Two different PPI acquisitions, generalized autocalibrating partially parallel acquisition algorithm and modified sensitivity encoding with acceleration factors (R) of 2-4, were evaluated and compared to nonaccelerated acquisitions. Five standard SNR measurement techniques were investigated and Bland-Altman analysis was used to determine agreement between the various SNR methods. The estimated g-factor values, associated with each method of SNR calculation and PPI reconstruction method, were also subjected to assessments that considered the effects on SNR due to reconstruction method, phase encoding direction, and R-value. Results: Only two SNR measurement methods produced g-factors in agreement with theoretical expectations (g ! 1). Bland-Altman tests demonstrated that these two methods also gave the most similar results relative to the other three measurements. R-value was the only factor of the three we considered that showed significant influence on SNR changes. Conclusions: Non-signal methods used in SNR evaluation do not produce results consistent with expectations in the investigated PPI protocols. Two of the methods studied provided the most accurate and useful results. Of these two methods, it is recommended, when evaluating PPI protocols, the image subtraction method be used for SNR calculations due to its relative accuracy and ease of implementation.

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Design of microstrip‐based surface coils for low‐field small‐bore MR applications

Seo

2012

Concepts Magn Reson

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Microstrip transmission line concept, which has been used to construct microwave integrated circuits or high frequency (!300 MHz) antennas, is introduced to surface receive coils for use in a small volume coil at 1T and the feasibility of the microstrip-based coils is investigated. Both single-turn and two-turn microstrip receive coils are fabricated. The size of the square shaped coil loop is 2.5 3 2.5 cm 2 . A lengthening inductor (toroid) is used to replace a long coaxial cable (5217 cm) needed for low-field small-bore MR applications. The lengthening inductor, matching, and tuning capacitors are located outside the magnet. One pulse and a gradient reversed form of FISP sequences are used to acquire NMR signals and MR images of gel phantoms, respectively. MR images acquired by the two-turn coil have higher SNR and more uniform signal intensity than those obtained by the single-turn coil and the volume coil. A novel, easy, and efficient method to design small microstrip-based RF surface receive coils is developed and tested for low-field small-bore MR applications.

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Accuracy of Signal-to-noise Ratio Measurement Method for Magnetic Resonance Images

Cited by 30 publications

References 4 publications

DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application

DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application

Measuring signal‐to‐noise ratio in partially parallel imaging MRI

Design of microstrip‐based surface coils for low‐field small‐bore MR applications

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