A simple and fast adaptive nonlocal multispectral filtering algorithm for efficient noise reduction in magnetic resonance imaging

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging

PURPOSE

To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high-resolution breath-hold fat-suppressed T1-weighted imaging (HR-BH-FS-T1WI) in the hepatobiliary phase (HBP) of Gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI).

MATERIALS AND METHODS

This retrospective evaluated 42 patients with 98 liver tumors who underwent Gd-EOB-MRI between March 2023 and May 2023 using three techniques based on HBP imaging: isovoxel HR-BH-FS-T1WI reconstructed (1) with DLR (BH-DLR +) and (2) without DLR (BH-DLR -) and (3) HR-FS-T1WI scanned with a free-breathing technique using a navigator-echo-triggered technique and DLR (Navi-DLR +). The three techniques were qualitatively and quantitatively compared by the Friedman test and the Bonferroni post-hoc test. Tumor detectability was compared using the McNemar test.

RESULTS

BH-DLR + (3.85, average score of two radiologists) showed significantly better qualitative scores for image noise than BH-DLR - (2.84) and Navi-DLR + (3.37) (p < 0.0167), and Navi-DLR + showed significantly better scores than BH-DLR - (p < 0.0167). BH-DLR + (3.77) and BH-DLR - (3.77) showed significantly better qualitative scores for respiratory motion artifact than Navi-DLR + (2.75) (p < 0.0167), but there was no significant difference in scores between BH-DLR + and BH-DLR - (p > 0.0167). BH-DLR + (0.32) and Navi-DLR + (0.33) showed significantly higher lesion-to-nonlesion CR than BH-DLR - (0.29) (p < 0.0167), but there was no significant difference in lesion-to-nonlesion CR between BH-DLR + and Navi-DLR + (p > 0.0167). BH-DLR + (89.8%) showed significantly better tumor detectability than BH-DLR - (76.0%) and Navi-DLR + (77.6%) (p < 0.05).

CONCLUSION

The use of DLR for isovoxel HR-BH-FS-T1WI was effective in improving image quality and tumor detectability.

Deep learning-based magnetic resonance imaging reconstruction for improving the image quality of reduced-field-of-view diffusion-weighted imaging of the pancreas

BACKGROUND

It has been reported that deep learning-based reconstruction (DLR) can reduce image noise and artifacts, thereby improving the signal-to-noise ratio and image sharpness. However, no previous studies have evaluated the efficacy of DLR in improving image quality in reduced-field-of-view (reduced-FOV) diffusion-weighted imaging (DWI) [field-of-view optimized and constrained undistorted single-shot (FOCUS)] of the pancreas. We hypothesized that a combination of these techniques would improve DWI image quality without prolonging the scan time but would influence the apparent diffusion coefficient calculation.

AIM

To evaluate the efficacy of DLR for image quality improvement of FOCUS of the pancreas.

METHODS

This was a retrospective study evaluated 37 patients with pancreatic cystic lesions who underwent magnetic resonance imaging between August 2021 and October 2021. We evaluated three types of FOCUS examinations: FOCUS with DLR (FOCUS-DLR+), FOCUS without DLR (FOCUS-DLR-), and conventional FOCUS (FOCUS-conv). The three types of FOCUS and their apparent diffusion coefficient (ADC) maps were compared qualitatively and quantitatively.

RESULTS

FOCUS-DLR+ (3.62, average score of two radiologists) showed significantly better qualitative scores for image noise than FOCUS-DLR- (2.62) and FOCUS-conv (2.88) (P < 0.05). Furthermore, FOCUS-DLR+ showed the highest contrast ratio (CR) between the pancreatic parenchyma and adjacent fat tissue for b-values of 0 and 600 s/mm2 (0.72 ± 0.08 and 0.68 ± 0.08) and FOCUS-DLR- showed the highest CR between cystic lesions and the pancreatic parenchyma for the b-values of 0 and 600 s/mm2 (0.62 ± 0.21 and 0.62 ± 0.21) (P < 0.05), respectively. FOCUS-DLR+ provided significantly higher ADCs of the pancreas and lesion (1.44 ± 0.24 and 3.00 ± 0.66) compared to FOCUS-DLR- (1.39 ± 0.22 and 2.86 ± 0.61) and significantly lower ADCs compared to FOCUS-conv (1.84 ± 0.45 and 3.32 ± 0.70) (P < 0.05), respectively.

CONCLUSION

This study evaluated the efficacy of DLR for image quality improvement in reduced-FOV DWI of the pancreas. DLR can significantly denoise images without prolonging the scan time or decreasing the spatial resolution. The denoising level of DWI can be controlled to make the images appear more natural to the human eye. However, this study revealed that DLR did not ameliorate pancreatic distortion. Additionally, physicians should pay attention to the interpretation of ADCs after DLR application because ADCs are significantly changed by DLR.

Principal component analysis denoising improves sensitivity of MR diffusion to detect white matter injury in neuroHIV

BACKGROUND AND PURPOSE

Diffusion-weighted imaging is able to capture important information about cerebral white matter (WM) structure. However, diffusion data can suffer from MRI and biological noise that degrades the quality of the images and makes finding important features difficult. We investigated how effectively local and nonlocal denoising increased the sensitivity to detect differences in cerebral WM in neuroHIV.

METHODS

We utilized principal component analysis (PCA) denoising to detect WM differences using fractional anisotropy. Local and nonlocal PCA denoising paradigms were implemented that varied in search area and number of components. We examined different-sized WM tracts that consistently show differences between people living with Human Immunodeficiency Virus (HIV) (PWH) and HIV-negative individuals (corpus callosum, forceps minor, and right uncinate fasciculus), and size-matched tracts not typically associated with HIV-related differences (spinothalamic, right medial lemniscus, and left occipitopontine). We first conducted a full sample comparison of WM differences between groups, and then randomly reduced the sample to the point where we still found differences in WM.

RESULTS

Nonlocal PCA denoising allowed us to detect differences after a sample reduction of 35% in the forceps minor, 17% in the right uncinate fasciculus, and 6% in the corpus callosum.

CONCLUSIONS

PCA denoising had a beneficial effect on detecting significant differences in PWH after sample size reduction. The smaller forceps minor tract and right uncinate fasciculus showed greater sensitivity to PCA denoising than the larger corpus callosum. These results show the importance of identifying the most effective PCA denoising strategy when investigating WM in PWH.

Myelin Water Fraction and Intra/Extracellular Water Geometric Mean T2 Normative Atlases for the Cervical Spinal Cord from 3T MRI

BACKGROUND AND PURPOSE

Acquiring and interpreting quantitative myelin-specific MRI data at an individual level is challenging because of technical difficulties and natural myelin variation in the population. To overcome these challenges, we used multiecho T₂ myelin water imaging (MWI) to create T₂ metric healthy population atlases that depict the mean and variation of myelin water fraction (MWF), and intra- and extracellular water mobility as described by geometric mean T₂ (IEGMT₂ ).

METHODS

Cervical cord MWI was performed at 3T on 20 healthy individuals (10M/10F, mean age: 36 years) and 3 relapsing remitting multiple sclerosis (RRMS) participants (1M/2F, age: 39/42/37 years). Anatomical data were collected for the purpose of image segmentation and registration. Atlases were created by coregistering and averaging T₂ metrics from all controls. Voxel-wise z-score maps from 3 RRMS participants were produced to demonstrate the preliminary utility of the MWF and IEGMT₂ atlases.

RESULTS

The average MWF atlas provides a representation of myelin in the spinal cord consistent with well-known spinal cord anatomical characteristics. The IEGMT₂ atlas also depicted structural variations in the spinal cord. Z-score analysis illustrated distinct abnormalities in MWF and IEGMT₂ in the 3 RRMS cases.

CONCLUSIONS

Our findings highlight the potential for using a quantitative T₂ relaxation metric atlas to visualize and detect pathology in spinal cord. Our MWF and IEGMT₂ atlases (URL: https://sourceforge.net/projects/mwi-spinal-cord-atlases/) can serve as normative references in the cervical spinal cord for other studies.