Dataset Information

Improving Signal-to-Noise Ratio in Susceptibility Weighted Imaging: A Novel Multicomponent Non-Local Approach.

ABSTRACT: In susceptibility-weighted imaging (SWI), the high resolution required to obtain a proper contrast generation leads to a reduced signal-to-noise ratio (SNR). The application of a denoising filter to produce images with higher SNR and still preserve small structures from excessive blurring is therefore extremely desirable. However, as the distributions of magnitude and phase noise may introduce biases during image restoration, the application of a denoising filter is non-trivial. Taking advantage of the potential multispectral nature of MR images, a multicomponent approach using a Non-Local Means (MNLM) denoising filter may perform better than a component-by-component image restoration method. Here we present a new MNLM-based method (Multicomponent-Imaginary-Real-SWI, hereafter MIR-SWI) to produce SWI images with high SNR and improved conspicuity. Both qualitative and quantitative comparisons of MIR-SWI with the original SWI scheme and previously proposed SWI restoring pipelines showed that MIR-SWI fared consistently better than the other approaches. Noise removal with MIR-SWI also provided improvement in contrast-to-noise ratio (CNR) and vessel conspicuity at higher factors of phase mask multiplications than the one suggested in the literature for SWI vessel imaging. We conclude that a proper handling of noise in the complex MR dataset may lead to improved image quality for SWI data.

SUBMITTER: Borrelli P

PROVIDER: S-EPMC4452483 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Improving Signal-to-Noise Ratio in Susceptibility Weighted Imaging: A Novel Multicomponent Non-Local Approach.

Borrelli Pasquale P Palma Giuseppe G Tedeschi Enrico E Cocozza Sirio S Comerci Marco M Alfano Bruno B Haacke E Mark EM Salvatore Marco M

PloS one 20150601 6

In susceptibility-weighted imaging (SWI), the high resolution required to obtain a proper contrast generation leads to a reduced signal-to-noise ratio (SNR). The application of a denoising filter to produce images with higher SNR and still preserve small structures from excessive blurring is therefore extremely desirable. However, as the distributions of magnitude and phase noise may introduce biases during image restoration, the application of a denoising filter is non-trivial. Taking advantage ...[more]

PMID: 26030293

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Project description:BackgroundIn MRI, the signal-to-noise ratio (SNR) theoretically increases with B0 field strength. However, because of attenuation of the radiofrequency (RF) fields at 7T, it is not certain if this SNR gain can be realized for prostate imaging.Purpose/hypothesisTo investigate the SNR gain in prostate imaging at 7T as compared with 3T. It is expected that SNR will improve for prostate imaging at 7T compared with 3T.Study typeProspective.SubjectsFour healthy volunteers and one prostate cancer patient.Field strength/sequenceAll subjects were scanned at 3T and at 7T using optimal coil setups for both field strengths. For all volunteers, proton density-weighted images were acquired for SNR analysis and actual flip angle imaging (AFI) B 1 + | maps were acquired for correction of measured SNR values. In the patient, a T2 -weighted (T2 w) image was acquired at 3T and at 7T.AssessmentSNR was calculated in the prostate region for all volunteers. SNR was normalized for flip angle, receiver bandwidth, and voxel volume. SNR was also calculated for different sensitivity encoding (SENSE) acceleration factors.Statistical testingSNR values are represented as the arithmetic mean of SNR values in the prostate. Estimated SNR in the T2 w image is calculated as the arithmetic mean of the signal intensity (SI) divided by the standard deviation of the SI in a specified zone. Tumor-to-tissue contrast is calculated as (SItumor +SIzone )/( SItumor -SIzone ).ResultsAn increase in SNR ranging from 1.7-fold to 2.8-fold was measured in the prostate at 7T in comparison to 3T for four volunteers. At 7T, it is possible to achieve a 4-fold SENSE acceleration in the left-right direction with similar SNR to a nonaccelerated 3T image. T2 w imaging was done at 3T and 7T in one patient, where improved tumor-to-tissue contrast was demonstrated at 7T.Data conclusionSNR improves for prostate imaging at 7T as compared with 3T.Level of evidence2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1446-1455.

Project description:Several investigators have shown that noise equivalent count rate (NECR) is linearly proportional to the square of image signal-to-noise ratio (SNR) when PET images are reconstructed using filtered back-projection. However, to our knowledge, none have shown a similar relationship in fully 3D ordered-subset expectation maximization (OSEM) reconstruction. This paper has two aims. The first is to investigate the NECR-SNR relationship for 3D-OSEM reconstruction using phantom studies while the second aim is to evaluate the NECR-SNR relationship using patient data.An anthropomorphic phantom was scanned on a GE Discovery-STE (DSTE) PET∕CT scanner in 3D mode with an initial activity concentration of 66.34 kBq∕cc. PET data were acquired over the lower chest∕upper abdomen region in dynamic mode. The experiment was repeated with the same activity concentration on a GE Discovery-RX (DRX) scanner. Care was taken to place the phantom at identical positions in both scanners. PET data were then reconstructed using 3D Reprojection (3D-RP) and 3D-OSEM with different reconstruction parameters and the NECR and SNR for each frame∕image were calculated. SNR(2) was then plotted versus the NECR for each scanner, reconstruction method and parameters. In addition, 40 clinical PET∕CT studies from the two scanners (20 patients∕scanner) were evaluated retrospectively. The patient studies from each scanner were further divided into two subgroups of body mass indices (BMI). Each PET study was acquired in 3D mode and reconstructed using both 3D-OSEM and 3D-RP. The NECR and SNR of the bed position covering the patient liver were calculated for each patient and averaged for each subgroup. Comparisons of the NECR and SNR between scanner types and BMIs were performed using a t-test and a p value less than 0.05 was considered significant.Phantom results showed that SNR(2) versus NECR was linear for 3D-RP reconstruction across all activity concentration on both scanners, as expected. However, when 3D-OSEM was used, this relationship was nonlinear at activity concentrations beyond the peak NECR on both scanners. On the other hand, the plot of SNR(2) versus trues count rate was linear for 3D-OSEM across all activity concentrations on both scanners independent of reconstruction parameters used. In addition, for activity concentrations <30kBq∕cc, phantom results showed a higher SNR (by 12 ± 10%; p < 0.05) and NECR for the DRX scanner compared to DSTE for 3D-RP reconstruction. However, for 3D-OSEM reconstruction, these two scanners had similar SNRs (different by 2% ± 9%; p > 0.05), despite having different NECRs. Patient studies showed a statistically significant difference in NECR as well as the SNR for 3D-RP reconstruction between the two scanners. However, no statistically significant difference was found for 3D-OSEM. A statistically significant difference in both NECR and SNR were found between the different BMI subgroups for both 3D-RP and 3D-OSEM reconstructions.For the scanners and reconstruction algorithm used in this study, our results suggest that the image SNR cannot be predicted by the NEC when using 3D-OSEM reconstruction particularly for those clinical applications requiring high activity concentration. Instead, our results suggest that image SNR varies with activity concentration and is dominated by the 3D-OSEM reconstruction algorithm and its associated parameters, while not being affected by the scanner type for the range of activity concentrations usually found in the clinic.

Dataset Information

Improving Signal-to-Noise Ratio in Susceptibility Weighted Imaging: A Novel Multicomponent Non-Local Approach.

Publications

Improving Signal-to-Noise Ratio in Susceptibility Weighted Imaging: A Novel Multicomponent Non-Local Approach.

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