Developments in MRI have made it possible to use diffusion-weighted MRI, perfusion MRI and proton MR spectroscopy (MRS) to study lesions in the brain. We evaluated whether these techniques provide useful, complementary information for grading gliomas, in comparison with conventional MRI. We studied 17 patients with histologically verified gliomas, adding multivoxel proton MRS, echoplanar diffusion and perfusion MRI the a routine MRI examination. The maximum relative cerebral blood volume (CBV), minimum apparent diffusion coefficient (ADC) and metabolic peak area ratios in proton MRS were calculated in solid parts of tumours on the same slice from each imaging data set. The mean minimum ADC of the 13 high-grade gliomas (0.92+/-0.27 x 10(-3) mm(2)/s) was lower than that of the four low-grade gliomas (1.28+/-0.15 x 10(-3) mm(2)/s) ( P<0.05). Means of maximum choline (Cho)/N-acetylaspartate (NAA), Cho/creatine (Cr), Cho/Cr in normal brain (Cr-n) and minimum NAA/Cr ratios were 5.90+/-2.62, 4.73+/-2.22, 2.66+/-0.68 and 0.40+/-0.06, respectively, in the high-grade gliomas, and 1.65+/-1.37, 1.84+/-1.20, 1.61+/-1.29 and 1.65+/-1.61, respectively, in the low-grade gliomas. Significant differences were found on spectroscopy between the high- and low-grade gliomas ( P<0.05). Mean maximum relative CBV in the high-grade gliomas (6.10+/-3.98) was higher than in the low-grade gliomas (1.74+/-0.57) ( P<0.05). Echoplanar diffusion, perfusion MRI and multivoxel proton MRS can offer diagnostic information, not available with conventional MRI, in the assessment of glioma grade.
The purpose of our study was to investigate whether quantitative diffusion-weighted images (DWI) were useful for monitoring the therapeutic response of primary bone tumors. We encountered 18 osteogenic and Ewing sarcomas. Magnetic resonance (MR) images were performed in all patients before and after therapy. We measured the apparent diffusion coefficient (ADC) values, contrast-to-noise ratio (CNR), and tumor volume of the bone tumors pre- and posttreatment. We determined change in ADC value, change in CNR on T2-weighted images (T2WI), change in CNR on gadopentetate dimeglumine (Gd)-T1-weighted images (Gd-T1WI), and change in tumor volume. The bone tumors were divided into two groups: group A was comprised of tumors with less than 90% necrosis after treatment and group B of tumors at least with 90%. Changes in ADC value, tumor volume, and CNR were compared between the groups. Change in the ADC value was statistically greater in group B than that in the group A (p = 0.003). There was no significant difference in the changes in CNR on T2WI (p = 0.683), in CNR on Gd-T1WI (p = 0.763), and tumor volume (p = 0.065). The ADC value on DWI is a promising tool for monitoring the therapeutic response of primary bone sarcomas.
Purpose:To test whether our proposed denoising approach with deep learning-based reconstruction (dDLR) can effectively denoise brain MR images.
Methods:In an initial experimental study, we obtained brain images from five volunteers and added different artificial noise levels. Denoising was applied to the modified images using a denoising convolutional neural network (DnCNN), a shrinkage convolutional neural network (SCNN), and dDLR. Using these brain MR images, we compared the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the three denoising methods. Two neuroradiologists assessed the image quality of the three types of images. In the clinical study, we evaluated the denoising effect of dDLR in brain images with different levels of actual noise such as thermal noise. Specifically, we obtained 2D-T 2 -weighted image, 2D-fluid-attenuated inversion recovery (FLAIR) and 3D-magnetization-prepared rapid acquisition with gradient echo (MPRAGE) from 15 healthy volunteers at two different settings for the number of image acquisitions (NAQ): NAQ2 and NAQ5. We reconstructed dDLR-processed NAQ2 from NAQ2, then compared with SSIM and PSNR. Two neuroradiologists separately assessed the image quality of NAQ5, NAQ2 and dDLR-NAQ2. Statistical analysis was performed in the experimental and clinical study. In the clinical study, the inter-observer agreement was also assessed.
Results:In the experimental study, PSNR and SSIM for dDLR were statistically higher than those of DnCNN and SCNN (P < 0.001). The image quality of dDLR was also superior to DnCNN and SCNN. In the clinical study, dDLR-NAQ2 was significantly better than NAQ2 images for SSIM and PSNR in all three sequences (P < 0.05), except for PSNR in FLAIR. For all qualitative items, dDLR-NAQ2 had equivalent or better image quality than NAQ5, and superior quality to that of NAQ2 (P < 0.05), for all criteria except artifact. The inter-observer agreement ranged from substantial to near perfect.Conclusion: dDLR reduces image noise while preserving image quality on brain MR images.
BACKGROUND AND PURPOSE:Although the prognostic value of perfusion MR imaging in various gliomas has been investigated, that in high-grade astrocytomas alone has not been fully evaluated. The purpose of this study was to evaluate retrospectively whether the tumor maximum relative cerebral blood volume (rCBV) on pretreatment perfusion MR imaging is of prognostic value in patients with high-grade astrocytoma.
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