Effect of Deep Learning Reconstruction on Respiratory-triggered T2-weighted MR Imaging of the Liver: A Comparison between the Single-shot Fast Spin-echo and Fast Spin-echo Sequences.

PURPOSE: To compare the effects of deep learning reconstruction (DLR) on respiratory-triggered T2-weighted MRI of the liver between single-shot fast spin-echo (SSFSE) and fast spin-echo (FSE) sequences. METHODS: Respiratory-triggered fat-suppressed liver T2-weighted MRI was obtained with the FSE and SSFSE sequences at the same spatial resolution in 55 patients. Conventional reconstruction (CR) and DLR were applied to each sequence, and the SNR and liver-to-lesion contrast were measured on FSE-CR, FSE-DLR, SSFSE-CR, and SSFSE-DLR images. Image quality was independently assessed by three radiologists. The results of the qualitative and quantitative analyses were compared among the four types of images using repeated-measures analysis of variance or Friedman's test for normally and non-normally distributed data, respectively, and a visual grading characteristics (VGC) analysis was performed to evaluate the image quality improvement by DLR on the FSE and SSFSE sequences. RESULTS: The liver SNR was lowest on SSFSE-CR and highest on FSE-DLR and SSFSE-DLR (P < 0.01). The liver-to-lesion contrast did not differ significantly among the four types of images. Qualitatively, noise scores were worst on SSFSE-CR but best on SSFSE-DLR because DLR significantly reduced noise (P < 0.01). In contrast, artifact scores were worst both on FSE-CR and FSE-DLR (P < 0.01) because DLR did not reduce the artifacts. Lesion conspicuity was significantly improved by DLR compared with CR in the SSFSE (P < 0.01) but not in FSE sequences for all readers. Overall image quality was significantly improved by DLR compared with CR for all readers in the SSFSE (P < 0.01) but only one reader in the FSE (P < 0.01). The mean area under the VGC curve values for the FSE-DLR and SSFSE-DLR sequences were 0.65 and 0.94, respectively. CONCLUSION: In liver T2-weighted MRI, DLR produced more marked improvements in image quality in SSFSE than in FSE.

High-resolution Diffusion-weighted Imaging of the Prostate Using Multiplexed Sensitivity-encoding: Comparison with the Conventional and Reduced Field-of-view Techniques.

PURPOSE: To compare objective and subjective image quality, lesion conspicuity, and apparent diffusion coefficient (ADC) of high-resolution multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) with conventional DWI (c-DWI) and reduced FOV DWI (rFOV-DWI) in prostate MRI. METHODS: Forty-seven patients who underwent prostate MRI, including c-DWI, rFOV-DWI, and MUSE-DWI, were retrospectively evaluated. SNR and ADC of normal prostate tissue and contrast-to-noise ratio (CNR) and ADC of prostate cancer (PCa) were measured and compared between the three sequences. Image quality and lesion conspicuity were independently graded by two radiologists using a 5-point scale and compared between the three sequences. RESULTS: The SNR of normal prostate tissue was significantly higher with rFOV-DWI than with the other two DWI techniques (P ≤ 0.01). The CNR of the PCa was significantly higher with rFOV-DWI than with MUSE-DWI (P < 0.05). The ADC of normal prostate tissue measured by rFOV-DWI was lower than that measured by MUSE-DWI and c-DWI (P < 0.01), while there was no difference in the ADC of cancers. In the qualitative analysis, MUSE-DWI showed significantly higher scores than rFOV-DWI and c-DWI for visibility of anatomy and overall image quality in both readers, and significantly higher scores for distortion in one of the two readers (P < 0.001). There was no difference in lesion conspicuity between the three sequences. CONCLUSION: High-resolution MUSE-DWI showed higher image quality and reduced distortion compared to c-DWI, while maintaining a wide FOV and similar ADC quantification, although no difference in lesion conspicuity was observed.

Evaluating the Safety of Simultaneous Intracranial Electroencephalography and Functional Magnetic Resonance Imaging Acquisition Using a 3 Tesla Magnetic Resonance Imaging Scanner.

Background: The unsurpassed sensitivity of intracranial electroencephalography (icEEG) and the growing interest in understanding human brain networks and ongoing activities in health and disease have make the simultaneous icEEG and functional magnetic resonance imaging acquisition (icEEG-fMRI) an attractive investigation tool. However, safety remains a crucial consideration, particularly due to the impact of the specific characteristics of icEEG and MRI technologies that were safe when used separately but may risk health when combined. Using a clinical 3-T scanner with body transmit and head-receive coils, we assessed the safety and feasibility of our icEEG-fMRI protocol. Methods: Using platinum and platinum-iridium grid and depth electrodes implanted in a custom-made acrylic-gel phantom, we assessed safety by focusing on three factors. First, we measured radio frequency (RF)-induced heating of the electrodes during fast spin echo (FSE, as a control) and the three sequences in our icEEG-fMRI protocol. Heating was evaluated with electrodes placed orthogonal or parallel to the static magnetic field. Using the configuration with the greatest heating observed, we then measured the total heating induced in our protocol, which is a continuous 70-min icEEG-fMRI session comprising localizer, echo-planar imaging (EPI), and magnetization-prepared rapid gradient-echo sequences. Second, we measured the gradient switching-induced voltage using configurations mimicking electrode implantation in the frontal and temporal lobes. Third, we assessed the gradient switching-induced electrode movement by direct visual detection and image analyses. Results: On average, RF-induced local heating on the icEEG electrode contacts tested were greater in the orthogonal than parallel configuration, with a maximum increase of 0.2°C during EPI and 1.9°C during FSE. The total local heating was below the 1°C safety limit across all contacts tested during the 70-min icEEG-fMRI session. The induced voltage was within the 100-mV safety limit regardless of the configuration. No gradient switching-induced electrode displacement was observed. Conclusion: We provide evidence that the additional health risks associated with heating, neuronal stimulation, or device movement are low when acquiring fMRI at 3 T in the presence of clinical icEEG electrodes under the conditions reported in this study. High specific absorption ratio sequences such as FSE should be avoided to prevent potential inadvertent tissue heating.

A novel algorithm for comprehensive quality assessment of clinical magnetic resonance images based on natural scene statistics in spatial domain.

BACKGROUND: A magnetic resonance imaging (MRI)-specific objective image quality assessment (IQA) algorithm, the quality evaluation using multidirectional filters for MRI (QEMDIM), was previously reported. QEMDIM requires a set of reference images to calculate the quality score (SQ) for an assessed image. SQ may be affected by the quality of the reference set owing to the calculation procedure. PURPOSE: To propose a modified version of the IQA algorithm and compare the IQA performance of the original and modified algorithms. ASSESSMENT: Brain axial T1- and T2-weighted spin-echo images of varying quality levels (noise and blurring) were acquired from seven healthy men. Subjective IQA (paired comparisons) was conducted on the images, and subjective quality scores were obtained. With reference sets of various quality levels, QEMDIM and modified IQA were applied to the same images that underwent the subjective IQA. The correlation of each SQ and modified score (Smod) with the subjective scores was evaluated for content-related subsets of assessed images and for each reference set. The effect of the reference-set quality on the distribution of the correlation coefficients (CCs) was statistically evaluated for SQ and Smod using a one-way analysis of variance test with a significance level of 0.05. We also evaluated the variation in Smod for images with almost the same qualities using the standard deviation (SD). RESULTS: The CCs of SQ varied significantly with the quality of the reference set, whereas that of Smod did not. The SD of Smod for almost-same-quality images was less than that corresponding to the confidence interval of the subjective scores. CONCLUSION: Our modified algorithm was superior to QEMDIM in terms of IQA performance in clinical practice, especially in terms of accuracy, robustness, and reproducibility.

Impact of adaptive image receive coil technology for liver MR imaging at 3.0 Tesla: Intraindividual comparison with use of conventional coil.

PURPOSE: A newly developed Adaptive Image Receive (AIR) coil is designed to be more flexible to conform to the human body habitus, and may improve image quality by reducing the distance between the coil element and the imaging subject. This study evaluated the AIR coil's usefulness for liver MR imaging at 3.0 T in comparison with that of a conventional coil retrospectively. METHOD: The study population comprised 50 consecutive patients, who underwent follow-up liver MR examinations with a 3.0-T MR system using both an AIR coil and a conventional coil to evaluate hepatocellular carcinoma. Three-dimensional fat-suppressed T1-weighted gradient-echo images before and after injection of an MRI contrast agent, T2-weighted single-shot fast spin-echo (SSFSE) images, and diffusion-weighted (DW) images obtained with the AIR coil were compared with corresponding images obtained with the conventional coil. One radiologist measured signal-to-noise ratios (SNRs), while two other radiologists used a 3-point scale to independently assess subjective image noise, artifacts, signal uniformity, and overall image quality. RESULTS: SNRs for the AIR coil were significantly higher than those for the conventional coil (P <.05). Subjective image noise for the AIR coil on pre- and postcontrast T1-weighted and DW images was lower than for the conventional coil for both readers (P <.05). Overall image quality on pre- and postcontrast T1-weighted and DW images for the AIR coil was better than that for the conventional coil for at least one reader (P <.05). CONCLUSIONS: In comparison with the conventional coil, AIR coil improved SNR and image quality of liver MR imaging.

Impact of Deep Learning Reconstruction Combined With a Sharpening Filter on Single-Shot Fast Spin-Echo T2-Weighted Magnetic Resonance Imaging of the Uterus.

OBJECTIVE: This study aimed to evaluate the effects of deep learning (DL) reconstruction and a postprocessing sharpening filter on the image quality of single-shot fast spin-echo (SSFSE) T2-weighted imaging (T2WI) of the uterus. MATERIALS AND METHODS: Fifty consecutive patients who underwent pelvic magnetic resonance imaging were included. Parasagittal T2WI with a slice thickness of 4 mm was obtained with the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) and SSFSE sequences (mean scan time, 204 and 22 seconds, respectively). The following 3 types of SSFSE images were reconstructed, and the signal-to-noise ratio (SNR) and tissue contrast were assessed: conventional reconstruction (SSFSE-C), DL reconstruction (SSFSE-DL), and DL with a sharpening filter (SSFSE-DLF). Three radiologists independently assessed image quality, and area under the visual grading characteristics curve (AUCVGC) analysis was performed to compare the SSFSE and PROPELLER images. RESULTS: Compared with that of the PROPELLER images, the SNR of the SSFSE-C, SSFSE-DL, and SSFSE-DLF images was significantly lower (P < 0.05), significantly higher (P < 0.05), and equivalent, respectively. The SSFSE-DL images exhibited significantly lower contrast between the junctional zone and myometrium than those obtained with the other sequences (P < 0.05). In qualitative comparisons with the PROPELLER images, all 3 SSFSE sequences, SSFSE-DL, and SSFSE-DLF demonstrated significantly higher scores for artifacts, noise, and sharpness, respectively (P < 0.01). The overall image quality of SSFSE-C (mean AUCVGC, 0.03; P < 0.01) and SSFSE-DL (mean AUCVGC, 0.23; P < 0.01) was rated as significantly inferior, whereas that of SSFSE-DLF (mean AUCVGC, 0.69) was equivalent or significantly higher (P < 0.01). CONCLUSION: Using a combination of DL and a sharpening filter markedly increases the image quality of SSFSE of the uterus to the level of the PROPELLER sequence.

Evaluation of Image Quality in Three-dimensional Fat-suppressed T1-weighted Images with Fast Acquisition Mode for Upper Abdomen.

We compared the uniformity of fat-suppression and image quality using three-dimensional fat-suppressed T1-weighted gradient-echo sequences that are liver acquisition with volume acceleration (LAVA) and Turbo-LAVA at 3.0T-MRI. The subjects were seven patients with liver disease (mean age, 66.7±8.2 years). The axial slices of two LAVA sequences were used for the comparison of the uniformity of fat-suppression and image quality at a region-of-interest (ROI) of the liver dome, the porta, and the renal hilum. To yield a quantitative measurement of the uniformity of fat suppression, the percentage standard deviation (%SD) was calculated by comparing two sequences. For image signal to noise ratio (SNR), the contrast between the liver and fat (Cliver-fat), and the liver and muscle (Cliver-muscle), the other ROIs were placed in the superficial fat, liver, spleen, pancreas, and muscle. The %SD in Turbo-LAVA (28.1±16.8%) was lower than that in LAVA (41.5±13.4%). The SNRs in Turbo-LAVA (17.8±4.1 [liver], 12.5±3.0 [pancreas], 14.7±1.6 [spleen], 8.2±3.5 [fat]) were lower than those in LAVA (20.9±6.1 [liver], 16.8±4.1 [pancreas], 17.4±2.4 [spleen], 12.0±4.5 [fat]). While, the Cliver-fat in the Turbo-LAVA (0.72±0.06) was significantly higher than that in LAVA (0.59±0.07). Turbo-LAVA sequence offers superior and more homogenous fat-suppression in comparison to LAVA sequence.

Apparent Diffusion Coefficient Value Is Not Dependent on Magnetic Resonance Systems and Field Strength Under Fixed Imaging Parameters in Brain.

OBJECTIVE: The aim of the study was to investigate the causes of apparent diffusion coefficient (ADC) measurement errors and to determine the optimal scanning parameters that are independent of the field strength and vendors of the magnetic resonance (MR) system. MATERIALS AND METHODS: Brain MR images of 10 healthy volunteers were scanned using 6 MR scanners of different field strengths and vendors in 2 different institutions. Ethical review board approvals were obtained for this study, and all volunteers gave their informed consents. Coefficient of variation (CV) of ADC values were compared for their differences in various MR scanners and in the scanned subjects. RESULTS: The CV of ADC values for 6 different scanners of 6 brains was 3.32%. The CV for repeated measurements in 1 day (10 scans per day) and in 10 days (scan per day for 10 days) for 1 subject was 1.72% and 2.96%, respectively (n = 5, P < 0.001). The CV of measurements for 10 healthy subjects was 5.22%. The measurement errors of the ADC values for 6 different MR units in 1 subject were higher than the intrascanner variance for the same subject but were lower than the intersubject variance for the same scanner. CONCLUSIONS: The variance in the ADC values for different MR scanners is reasonably small if appropriate scanning parameters (repetition time, >3000 ms; echo time, minimum; and high enough signal-to-noise ratio of high-b diffusion-weighted image) are used.