[Effect of Imaging Parameters for High-resolution Diffusion-weighted Imaging on Signal-to-noise Ratio and ADC in Prostate Examination].

Readout-segmented echo-planar imaging (readout segmentation of long variable echo trains [RESOLVE]) can be set to higher resolution than single-shot echo planar imaging, but there is concern that the signal-to-noise ratio (SNR) is low. The purpose was to examine the effect of imaging parameters (repetition time: TR, number of excitations: NEX) for RESOLVE on SNR and apparent diffusion coefficient (ADC) value in 1.5 T MRI, assuming a prostate examination. We imaged eight healthy male volunteers at a b value of 800 s/mm2. SNR and ADC value were calculated by setting the ROI in the transition zone (TZ) and the peripheral zone (PZ) of the prostate. Then, 3 radiologists visually evaluated the graininess. In TZ, there was no significant difference in SNR with changing TR. In PZ, SNR increased with increasing TR. In PZ, median SNR was 8.1 [6.9-9.3] at TR=11000 ms and NEX=2. On the other hand, at TR=5000 ms and NEX=3,4, median SNRs were 8.5 [7.5-9.3] and 9.8 [8.8-11.2]. Moreover, NEX=5 with median SNR of 11.1 [10.7-11.7] was significant (p<0.008). Setting more NEX was more effective in increasing SNR. In addition, visual evaluation showed similar results. The ADC value in TZ was around 1404×10-6 mm2/s, and the ADC value in PZ was around 1469×10-6 mm2/s. There were no significant differences under each condition. In conclusion, NEX is more effective than TR for improving SNR both quantitatively and visually in PZ.

Simultaneous superresolution reconstruction and distortion correction for single-shot EPI DWI using deep learning.

PURPOSE: Single-shot (SS) EPI is widely used for clinical DWI. This study aims to develop an end-to-end deep learning-based method with a novel loss function in an improved network structure to simultaneously increase the resolution and correct distortions for SS-EPI DWI. THEORY AND METHODS: Point-spread-function (PSF)-encoded EPI can provide high-resolution, distortion-free DWI images. A distorted image from SS-EPI can be described as the convolution between a PSF function with a distortion-free image. The deconvolution process to recover the distortion-free image can be achieved with a convolution neural network, which also learns the mapping function between low-resolution SS-EPI and high-resolution reference PSF-EPI to achieve superresolution. To suppress the oversmoothing effect, we proposed a modified generative adversarial network structure, in which a dense net with gradient map guidance and a multilevel fusion block was used as the generator. A fractional anisotropy loss was proposed to utilize the diffusion anisotropy information among diffusion directions. In vivo brain DWI data were used to test the proposed method. RESULTS: The results show that distortion-corrected high-resolution DWI images with restored structural details can be obtained from low-resolution SS-EPI images by taking advantage of the high-resolution anatomical images. Additionally, the proposed network can improve the quantitative accuracy of diffusion metrics compared with previously reported networks. CONCLUSION: Using high-resolution, distortion-free EPI-DWI images as references, a deep learning-based method to simultaneously increase the perceived resolution and correct distortions for low-resolution SS-EPI was proposed. The results show that DWI image quality and diffusion metrics can be improved.

Distortion correction of single-shot EPI enabled by deep-learning.

PURPOSE: A distortion correction method for single-shot EPI was proposed. Point-spread-function encoded EPI (PSF-EPI) images were used as the references to correct traditional EPI images based on deep neural network. THEORY AND METHODS: The PSF-EPI method can obtain distortion-free echo planar images. In this study, a 2D U-net based network was trained to achieve the distortion correction of single-shot EPI (SS-EPI) images, using PSF-EPI images as targets in the training stage. Anatomical T2W-TSE images were also fed into the network to improve the quality of the results. The applications in diffusion-weighted images were used as examples in this work. The network was trained on data acquired on healthy volunteers and tested on data of both healthy volunteers and patients. The corrected EPI images from the proposed method were also compared with those from field-mapping and top-up based distortion correction methods. RESULTS: Experimental results showed that the proposed method can correct for EPI distortions better than both the field-mapping and top-up based methods, and the results were close to the distortion-free images from PSF-EPI. Additionally, inclusion of T2W-TSE images helped improve distortion correction of the SS-EPI images without contaminating the output noticeably. The experiments with patients and different MRI platforms demonstrated the generalization feasibility of the proposed method preliminarily. CONCLUSION: Through the correction of diffusion-weighted images, the proposed deep-learning based method was demonstrated to have the feasibility to correct for the distortion of EPI images.

Distortion correction for high-resolution single-shot EPI DTI using a modified field-mapping method.

PURPOSE: The widely used single-shot EPI (SS-EPI) diffusion tensor imaging (DTI) suffers from strong image distortion due to B0 inhomogeneity, especially for high-resolution imaging. Traditional methods such as the field-mapping method and the top-up method have various deficiencies in high-resolution SS-EPI DTI distortion correction. This study aims to propose a robust distortion correction approach, which combines the advantages of traditional methods and overcomes their deficiencies, for high-resolution SS-EPI DTI. METHODS: The proposed correction method is based on the echo planar spectroscopic imaging field-mapping followed by an intensity correction procedure. To evaluate the efficacy of distortion correction, the proposed method was compared with the conventional field-mapping method and the top-up method, using a newly developed quantitative evaluation framework. The correction results were also compared with multi-shot EPI DTI to investigate whether the proposed method can provide high-resolution SS-EPI DTI with high geometric fidelity and high time efficiency. RESULTS: The results show that accurate field-mapping and intensity correction are critical to distortion correction in high-resolution SS-EPI DTI. The proposed method can provide more precise field maps and better correction results than the other two methods (p < 0.0001), and the corrected images show higher geometric fidelity than those from MS-EPI DTI. CONCLUSION: An effective method is proposed to reduce image distortion in high-resolution SS-EPI DTI. It is practical to achieve high-resolution DTI with high time efficiency and high structure accuracy using this method.

Phase-matched virtual coil reconstruction for highly accelerated diffusion echo-planar imaging.

PURPOSE: To propose a virtual coil (VC) acquisition/reconstruction framework to improve highly accelerated single-shot EPI (SS-EPI) and generalized slice dithered enhanced resolution (gSlider) acquisition in high-resolution diffusion imaging (DI). METHODS: For robust VC-GRAPPA reconstruction, a background phase correction scheme was developed to match the image phase of the reference data with the corrupted phase of the accelerated diffusion-weighted data, where the corrupted phase of the diffusion data varies from shot to shot. A Gy prewinding-blip was also added to the EPI acquisition, to create a shifted-ky sampling strategy that allows for better exploitation of VC concept in the reconstruction. To evaluate the performance of the proposed methods, 1.5 mm isotropic whole-brain SS-EPI and 860 µm isotropic whole-brain gSlider-EPI diffusion data were acquired at an acceleration of 8-9 fold. Conventional and VC-GRAPPA reconstructions were performed and compared, and corresponding g-factors were calculated. RESULTS: The proposed VC reconstruction substantially improves the image quality of both SS-EPI and gSlider-EPI, with reduced g-factor noise and reconstruction artifacts when compared to the conventional method. This has enabled high-quality low-noise diffusion imaging to be performed at 8-9 fold acceleration. CONCLUSIONS: The proposed VC acquisition/reconstruction framework improves the reconstruction of DI at high accelerations. The ability to now employ such high accelerations will allow DI with EPI at reduced distortion and faster scan time, which should be beneficial for many clinical and neuroscience applications.

Detection of cholesteatoma: High-resolution DWI using RS-EPI and parallel imaging at 3 tesla.

The purpose of this study is to evaluate the impact of RS-EPI-DWI in the detection of cholesteatoma and to compare with single-shot echo-planar DWI (SS-EPI-DWI). Diffusion-weighted and apparent diffusion-coefficient (ADC) images were obtained using RS-EPI and SS-EPI techniques in 30 patients. Presence of cholesteatoma (3 point scale), amount of artefacts (4 point scale), visibility (4 point scale), and ADC values of the lesions were assessed. The results of both techniques were compared with each other and gold-standard (GS) test results. Lesion visibility and presence of artefact scores of RS-EPI-DWI group were significantly different from those of the SS-EPI group. RS-EPI-DWI images had fewer artefacts and higher visibility scores. The sensitivity, specificity, negative/positive-predictive, and overall-agreement values of RS-EPI-DWI technique were 100%, 78%, 100%, 74%, and 87%; respectively. These values for SS-EPI-DWI technique were 91%, 60%, 88%, 67%, and 75%; respectively. Also, these values were higher on axial plane than coronal plane images for ADC measurements. Based on gold-standard test findings, agreement values were good (κ=0.74) for RS-EPI-DWI and moderate for SS-EP-DWI (κ=0.50) techniques (P<0.001 for both). The RS-EPI-DWI technique allows a higher spatial-resolution and this technique is less susceptible to artefacts when compared with SS-EPI technique.

Model-based iterative reconstruction for single-shot EPI at 7T.

PURPOSE: To describe a model-based reconstruction strategy for single-shot echo planar imaging (EPI) that intrinsically accounts for k-space nonuniformity, Nyquist ghosting, and geometric distortions during rather than before or after image reconstruction. METHODS: Ramp sampling and inhomogeneous B0 field-induced distortion cause the EPI samples to lie on a non-Cartesian grid, thus requiring the nonuniform fast Fourier transform. Additionally, a 2D Nyquist ghost phase correction without the need for extra navigator acquisition is included in the proposed reconstruction. Coil compression is also incorporated to reduce the computational load. The proposed method is applied to phantom and human brain MRI data. RESULTS: The results demonstrate that Nyquist ghosting and geometric distortions are reduced by the proposed reconstruction. The proposed 2D phase correction is superior to a conventional 1D correction. The reductions of both artifacts lead to improved temporal signal-to-noise ratio (tSNR). The virtual coil results suggest that the processing time can be reduced by up to 75%, with a mean tSNR loss of only 3.2% when using 8-virtual instead of 32-physical coils for twofold undersampled data. CONCLUSION: The proposed reconstruction improves the quality (ghosting, geometry, and tSNR) of EPI without requiring calibration data for Nyquist ghost correction. Magn Reson Med 78:2250-2264, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Diffusion-weighted imaging of the sellar region: a comparison study of BLADE and single-shot echo planar imaging sequences.

PURPOSE: The purpose of this study is to compare BLADE diffusion-weighted imaging (DWI) with single-shot echo planar imaging (EPI) DWI on the aspects of feasibility of imaging the sellar region and image quality. METHODS: A total of 3 healthy volunteers and 52 patients with suspected lesions in the sellar region were included in this prospective intra-individual study. All exams were performed at 3.0T with a BLADE DWI sequence and a standard single-shot EP-DWI sequence. Phantom measurements were performed to measure the objective signal-to-noise ratio (SNR). Two radiologists rated the image quality according to the visualisation of the internal carotid arteries, optic chiasm, pituitary stalk, pituitary gland and lesion, and the overall image quality. One radiologist measured lesion sizes for detecting their relationship with the image score. RESULTS: The SNR in BLADE DWI sequence showed no significant difference from the single-shot EPI sequence (P>0.05). All of the assessed regions received higher scores in BLADE DWI images than single-shot EP-DWI.

Detection of Hepatic Lesion: Comparison of Free-Breathing and Respiratory-Triggered Diffusion-Weighted MR imaging on 1.5-T MR system

PURPOSE: To compare free-breathing and respiratory-triggered diffusion-weighted imaging on 1.5-T MR system in the detection of hepatic lesions. MATERIALS AND METHODS: This single-institution study was approved by our institutional review board. Forty-seven patients (mean 57.9 year; M:F = 25:22) underwent hepatic MR imaging on 1.5-T MR system using both free-breathing and respiratory-triggered diffusion-weighted imaging (DWI) at a single examination. Two radiologists retrospectively reviewed respiratory-triggered and free-breathing sets (B50, B400, B800 diffusion weighted images and ADC map) in random order with a time interval of 2 weeks. Liver SNR and lesion-to-liver CNR of DWI were calculated measuring ROI. RESULTS: Total of 62 lesions (53 benign, 9 malignant) that included 32 cysts, 13 hemangiomas, 7 hepatocellular carcinomas (HCCs), 5 eosinophilic infiltration, 2 metastases, 1 eosinophilic abscess, focal nodular hyperplasia, and pseudolipoma of Glisson's capsule were reviewed by two reviewers. Though not reaching statistical significance, the overall lesion sensitivities were increased in respiratory-triggered DWI [reviewer1: reviewer2, 47/62(75.81%):45/62(72.58%)] than free-breathing DWI [44/62(70.97%):41/62(66.13%)]. Especially for smaller than 1 cm hepatic lesions, sensitivity of respiratory-triggered DWI [24/30(80%):21/30(70%)] was superior to free-breathing DWI [17/30(56.7%):15/30(50%)]. The diagnostic accuracy measuring the area under the ROC curve (Az value) of free-breathing and respiratory-triggered DWI was not statistically different. Liver SNR and lesion-to-liver CNR of respiratorytriggered DWI (87.6+/-41.4, 41.2+/-62.5) were higher than free-breathing DWI (38.8+/-13.6, 24.8+/-36.8) (p value <0.001, respectively). CONCLUSION: Respiratory-triggered diffusion-weighted MR imaging seemed to be better than free-breathing diffusion-weighted MR imaging on 1.5-T MR system for the detection of smaller than 1 cm lesions by providing high SNR and CNR.

Detection of Hepatic Lesion: Comparison of Free-Breathing and Respiratory-Triggered Diffusion-Weighted MR imaging on 1.5-T MR system

PURPOSE: To compare free-breathing and respiratory-triggered diffusion-weighted imaging on 1.5-T MR system in the detection of hepatic lesions. MATERIALS AND METHODS: This single-institution study was approved by our institutional review board. Forty-seven patients (mean 57.9 year; M:F = 25:22) underwent hepatic MR imaging on 1.5-T MR system using both free-breathing and respiratory-triggered diffusion-weighted imaging (DWI) at a single examination. Two radiologists retrospectively reviewed respiratory-triggered and free-breathing sets (B50, B400, B800 diffusion weighted images and ADC map) in random order with a time interval of 2 weeks. Liver SNR and lesion-to-liver CNR of DWI were calculated measuring ROI. RESULTS: Total of 62 lesions (53 benign, 9 malignant) that included 32 cysts, 13 hemangiomas, 7 hepatocellular carcinomas (HCCs), 5 eosinophilic infiltration, 2 metastases, 1 eosinophilic abscess, focal nodular hyperplasia, and pseudolipoma of Glisson's capsule were reviewed by two reviewers. Though not reaching statistical significance, the overall lesion sensitivities were increased in respiratory-triggered DWI [reviewer1: reviewer2, 47/62(75.81%):45/62(72.58%)] than free-breathing DWI [44/62(70.97%):41/62(66.13%)]. Especially for smaller than 1 cm hepatic lesions, sensitivity of respiratory-triggered DWI [24/30(80%):21/30(70%)] was superior to free-breathing DWI [17/30(56.7%):15/30(50%)]. The diagnostic accuracy measuring the area under the ROC curve (Az value) of free-breathing and respiratory-triggered DWI was not statistically different. Liver SNR and lesion-to-liver CNR of respiratorytriggered DWI (87.6+/-41.4, 41.2+/-62.5) were higher than free-breathing DWI (38.8+/-13.6, 24.8+/-36.8) (p value <0.001, respectively). CONCLUSION: Respiratory-triggered diffusion-weighted MR imaging seemed to be better than free-breathing diffusion-weighted MR imaging on 1.5-T MR system for the detection of smaller than 1 cm lesions by providing high SNR and CNR.