Purpose: Moyamoya disease (MMD) is a cerebrovascular disease associated with steno-occlusive changes in the arteries of the circle of Willis and with hemodynamic impairment. Previous studies have reported that parenchymal extracellular free water levels may be increased and the number of neurites may be decreased in patients with MMD. The aim of the present study was to investigate the postoperative changes in parenchymal free water and neurites and their relationship with cognitive improvement.

Methods: Multi-shell diffusion MRI (neurite orientation dispersion and density imaging and free water imaging using a bi-tensor model) was performed in 15 hemispheres of 13 adult patients with MMD (11 female, mean age 37.9 years) who had undergone revascularization surgery as well as age- and sex-matched normal controls. Parameter maps of free water and free-water-eliminated neurites were created, and the regional parameter values were compared among controls, patients before surgery, and patients after surgery.

Results: The anterior and middle cerebral artery territories of patients showed higher preoperative free water levels (P ≤ 0.007) and lower postoperative free water levels (P ≤ 0.001) than those of normal controls. The change in the dispersion of the white matter in the anterior cerebral artery territory correlated with cognitive improvement (r = –0.75; P = 0.004).

Conclusion: Our study suggests that increased parenchymal free water levels decreased after surgery and that postoperative changes in neurite parameters are related to postoperative cognitive improvement in adult patients with MMD. Diffusion analytical methods separately calculating free water and neurites may be useful for unraveling the pathophysiology of chronic ischemia and the postoperative changes that occur after revascularization surgery in this disease population.

Purpose: This study aimed to facilitate research progress in MR elastography (MRE) by providing a versatile and convenient application for MRE reconstruction, namely the MRE research tool (MRE-rTool). It can be used for a series of MRE image analyses, including phase unwrapping, arbitrary bandpass and directional filtering, noise assessment of the wave propagation image (motion SNR), and reconstruction of the elastogram in both 2D and 3D MRE acquisitions. To reinforce the versatility of MRE-rTool, the conventional method of motion SNR was modified into a new method that reflects the effects of image filtering.

Methods: MRE tests of the phantom and liver were performed using different estimation algorithms for stiffness value (algebraic inversion of the differential equation [AIDE], local frequency estimation [LFE] in MRE-rTool, and multimodel direct inversion [MMDI] in clinical reconstruction) and acquiring dimensions (2D and 3D acquisitions). This study also tested the accuracy of masking low SNR regions using modified and conventional motion SNR under various mechanical vibration powers.

Results: The stiffness values estimated using AIDE/LFE in MRE-rTool were comparable to that of MMDI (phantom, 3.71 ± 0.74, 3.60 ± 0.32, and 3.60 ± 0.54 kPa in AIDE, LFE, and MMDI; liver, 2.26 ± 0.31, 2.74 ± 0.16, and 2.21 ± 0.26 kPa in AIDE, LFE, and MMDI). The stiffness value in 3D acquisition was independent of the direction of the motion-encoding gradient and was more accurate than that of 2D acquisition. The masking of low SNR regions using the modified motion SNR worked better than that in the conventional motion SNR for each vibration power, especially when using a directional filter.

Conclusion: The performance of MRE-rTool on test data reached the level required in clinical MRE studies. MRE-rTool has the potential to facilitate MRE research, contribute to the future development of MRE, and has been freely released online.

Purpose: Although diffusion-weighted imaging (DWI) with ultra-high b-values is reported to be advantageous in the detection of some tumors, its applicability is not yet known in biliary malignancy. Therefore, this study aimed to evaluate the impact of measured b = 1400 s/mm² (M1400) and calculated b = 1400 s/mm² (C1400) DWI on image quality and quality of lesion discernibility using a modern 3T MR system compared to conventional b = 800 s/mm² DWI (M800).

Methods: We evaluated 56 patients who had pathologically proven biliary malignancy. All the patients underwent preoperative or baseline 3T MRI using DWI (b = 50, 400, 800, and 1400 s/mm²). The calculated DWI was obtained using a conventional DWI set (b = 50, 400, and 800). The tumor-to-bile contrast ratio (CR) and tumor SNR were compared between the different DWI images. Likert scores were given on a 5-point scale to assess the overall image quality, overall artifacts, ghost artifacts, misregistration artifacts, margin sharpness, and lesion discernibility. Repeated-measures analysis of variance with post hoc analyses was used for statistical evaluations.

Results: The CR of the tumor-to-bile was significantly higher in both M1400 and C1400 than in M800 (P^a < 0.01). SNRs were significantly higher in M800, followed by C1400 and M1400 (P^a < 0.01). Lesion discernibility was significantly improved for M1400, followed by C1400 and M800 for both readers (P^a < 0.01).

Conclusion: Using a 3T MRI, both measured and calculated DWI with an ultra-high b-value offer superior lesion discernibility for biliary malignancy compared to the conventional DWI.

Purpose: We aimed to investigate the diagnostic feasibility of an adjusted diffusion-weighted imaging (DWI) lexicon using multiple b values to assess breast lesions according to DWI-based breast imaging reporting and data system (BI-RADS).

Methods: This Institutional Review Board (IRB)-approved prospective study included 127 patients with suspected breast cancer. Breast MRI was performed using a 3T scanner. Breast DW images were acquired using five b-values of 0, 200, 800, 1000, and 1500 s/mm² (5b-value DWI) on 3T MRI. Two readers independently assessed lesion characteristics and normal breast tissue using DWI alone (5b-value DWI and 2b-value DWI with b = 0 and 800 s/mm²) according to DWI-based BI-RADS and in combination with the standard dynamic contrast-enhanced images (combined MRI). Interobserver and intermethod agreements were assessed using kappa statistics. The specificity and sensitivity of lesion classification were evaluated.

Results: Ninety-five breast lesions (39 malignant and 56 benign) were evaluated. Interobserver agreement for lesion assessment on 5b-value DWI was very good (k ≥ 0.82) for DWI-based BI-RADS categories, lesion type, and mass characteristics; good (k = 0.75) in breast composition; and moderate (k ≥ 0.44) in background parenchymal signal (BPS) and non-mass distribution. Intermethod agreement between assessments performed using either 5b-value DWI or combined MRI was good-to-moderate (k = 0.52–0.67) for lesion type; moderate (k = 0.49–0.59) for DWI-based BI-RADS category and mass characteristics; and fair (k = 0.25–0.40) for mass shape, BPS, and breast composition. The sensitivity and positive predictive values (PPVs) for 5b-value DWI were 79.5%, 84.6% and 60.8%, 61.1% for each reader, respectively; 74.4%, 74.4% and 63.0%, 61.7% for 2b-value DWI; and 97.4%, 97.4% and 73.1%, 76.0% for combined MRI. The specificity and negative predictive values (NPVs) were 64.3%, 62.5% and 81.8%, 85.4% for 5b-value DWI; 69.6%, 67.9% and 79.6%, 79.2% for 2b-value DWI; and 75.0%, 78.6% and 97.7%, 97.8% for combined MRI.

Conclusion: Good observer agreement was observed in the 5b-value DWI. The 5b-value DWI based on multiple b-values might have the potential to complement the 2b-value DWI; however, their diagnostic performance tended to be inferior to that of combined MRI for the characterization of breast tumors.

Purpose: To investigate whether deep cervical lymph node (DCLN) ligation alters intracranial cerebrospinal fluid (CSF) tracer dynamics and outflow using a rat model with intrathecal dynamic contrast-enhanced (DCE) MRI.

Methods: Six bilateral DCLN-ligated and six sham-operated rats were subjected to DCE MRI with Gd-BTDO3A, and dynamic T1-weighted images were acquired. ROIs were collected from the CSF at the C1 level (CSF_C1), CSF between the olfactory bulbs (CSF_OB), CSF at the pituitary recess (CSF_PitR), and CSF at the pineal recess (CSF_PinR), upper nasal turbinate (UNT), olfactory bulbs, cerebrum, and the jugular region. Time-intensity curves were evaluated, and the maximum slope, peak timing, peak signal ratio, and elimination half-life for the four CSF ROIs and UNT were calculated and compared.

Results: Delayed tracer arrival in the rostral CSF space and the nasal cavity with tracer retention in the ventral CSF space were observed in the ligation group. The maximum slopes were smaller in the ligation group at UNT (sham: 0.075 ± 0.0061, ligation: 0.044 ± 0.0086/min, P = 0.011). A significant difference was not detected in peak timings. The peak signal ratio values were lower in the ligation group at UNT (sham: 2.12 ± 0.19, ligation: 1.72 ± 0.11, P = 0.011). The elimination half-life was delayed in the ligation group at CSF_C1 (sham: 30.5 ± 2.70, ligation: 44.4 ± 12.6 min, P = 0.043), CSF_OB (sham: 30.2 ± 2.67, ligation: 44.8 ± 7.47 min, P = 0.021), and CSF_PitR (sham: 30.2 ± 2.49, ligation: 41.3 ± 7.57 min, P = 0.021).

Conclusion: The DCLN ligation in rats blocked CSF outflow into the nasal cavity and caused CSF retention.

Purpose: Deep neural networks (DNNs) for MRI reconstruction often require large datasets for training. Still, in clinical settings, the domains of datasets are diverse, and how robust DNNs are to domain differences between training and testing datasets has been an open question. Here, we numerically and clinically evaluate the generalization of the reconstruction networks across various domains under clinically practical conditions and provide practical guidance on what points to consider when selecting models for clinical application.

Methods: We compare the reconstruction performance between four network models: U-Net, the deep cascade of convolutional neural networks (DC-CNNs), Hybrid Cascade, and variational network (VarNet). We used the public multicoil dataset fastMRI for training and testing and performed a single-domain test, where the domains of the dataset used for training and testing were the same, and cross-domain tests, where the source and target domains were different. We conducted a single-domain test (Experiment 1) and cross-domain tests (Experiments 2–4), focusing on six factors (the number of images, sampling pattern, acceleration factor, noise level, contrast, and anatomical structure) both numerically and clinically.

Results: U-Net had lower performance than the three model-based networks and was less robust to domain shifts between training and testing datasets. VarNet had the highest performance and robustness among the three model-based networks, followed by Hybrid Cascade and DC-CNN. Especially, VarNet showed high performance even with a limited number of training images (200 images/10 cases). U-Net was more robust to domain shifts concerning noise level than the other model-based networks. Hybrid Cascade showed slightly better performance and robustness than DC-CNN, except for robustness to noise-level domain shifts. The results of the clinical evaluations generally agreed with the results of the quantitative metrics.

Conclusion: In this study, we numerically and clinically evaluated the robustness of the publicly available networks using the multicoil data. Therefore, this study provided practical guidance for clinical applications.

Purpose: Glioblastoma patients develop recurrence in the opposite hemisphere far from the primary tumor site even after complete resection. This is one of the main reasons for short disease survival. Our aim in this study is to detect microstructural changes in the contralateral hemisphere of glioblastoma patients using different diffusion models with the fully automated tract-based spatial statistics (TBSS) method.

Methods: Fourteen right-sided and eleven left-sided glioblastoma patients without any treatment and eighteen age- and gender-matched controls were included in the study. Multi-shell diffusion weighted images were created with a 3T MRI device. After various preprocessing steps, images of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), axial kurtosis (AK), mean kurtosis (MK), radial kurtosis (RK), intracellular volume fraction (ICVF), orientation dispersion index (ODI), and isotropic water fraction (ISO) were obtained. TBSS was used to compare diffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging parameters of right- and left-sided glioblastoma patients with the control group for the contralateral hemisphere.

Results: Both right-sided and left-sided glioblastoma patients have shown an increase in MD and ODI in the contralateral hemisphere. While right-sided glioblastoma patients showed an increase in RD, AD, and ISO in a more limited area in the contralateral hemisphere, left-sided glioblastoma patients showed an increase in MK and AK. FA, ICVF, and RK did not show any difference in both groups.

Conclusion: There are microstructural changes in the contralateral hemisphere in glioblastoma patients, and these changes differ between right-sided and left-sided glioblastoma patients.

Purpose: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in different body fields. However, there have been no reports regarding the utility of DLR for image quality and T-factor assessment improvements on T2-weighted imaging (T2WI), short inversion time (TI) inversion recovery (STIR) imaging, and unenhanced- and contrast-enhanced (CE) 3D fast spoiled gradient echo (GRE) imaging with and without CS in comparison with thin-section multidetector-row CT (MDCT) for non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine the utility of DLR for improving image quality and the appropriate sequence for T-category assessment for NSCLC patients.

Methods: As subjects for this study, 213 pathologically diagnosed NSCLC patients who underwent thin-section MDCT and MR imaging as well as T-factor diagnosis were retrospectively enrolled. SNR of each tumor was calculated and compared by paired t-test for each sequence with and without DLR. T-factor for each patient was assessed with thin-section MDCT and all MR sequences, and the accuracy for T-factor diagnosis was compared among all sequences and thin-section CT by means of McNemar’s test.

Results: SNRs of T2WI, STIR imaging, unenhanced thin-section Quick 3D imaging, and CE-thin-section Quick 3D imaging with DLR were significantly higher than SNRs of those without DLR (P < 0.05). Diagnostic accuracy of STIR imaging and CE-thick- or thin-section Quick 3D imaging was significantly higher than that of thin-section CT, T2WI, and unenhanced thick- or thin-section Quick 3D imaging (P < 0.05).

Conclusion: DLR is thus considered useful for image quality improvement on MR imaging. STIR imaging and CE-Quick 3D imaging with or without CS were validated as appropriate MR sequences for T-factor evaluation in NSCLC patients.

The usefulness of a highly targeted pencil beam (PB) label was compared with the commonly used slab label for direct visualization of pancreaticobiliary reflux using the time-spatial labeling inversion pulse (time-SLIP) technique. Signal profiles of flow phantom images obtained with a 1.5T MRI were analyzed. Both labels had similar labeling capabilities, but the edge characteristics of the PB label were blunt. Next, sixty-eight patients were classified into two groups according to the angle of the pancreaticobiliary ducts, and the displacement of the pancreatic ducts in respiratory fluctuation was measured. The results were approximately 7 mm in both groups. The blunt edge characteristics of the PB label suggest that it is robust to respiratory fluctuations. The overall labeling ability of the PB is comparable to that of the slab. In the larger angle of pancreaticobiliary ducts, the PB label may be able to label the pancreatic duct more selectively.

Synthetic MRI is being increasingly used for the quantification of brain longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) values. However, the effect of fast imaging protocols on these quantitative values has not been fully estimated. The purpose of this study was to investigate the effect of fast scan parameters on T1, T2, and PD measured with a multi-dynamic multi-echo (MDME) sequence of normal brain at 3.0T. Thirty-four volunteers were scanned using 3 MDME sequences with different scan times (named Fast, 2 min, 29 sec; Routine, 4 min, 07 sec; and Research, 7 min, 46 sec, respectively). The measured T1, T2, and PD in 18 volumes of interest (VOI) of brain were compared between the 3 sequences using rank sum test, t test, coefficients of variation (CVs) analysis, correlation analysis, and Bland-Altman analysis. We found that even though T1, T2, and PD were significantly different between the 3 sequences in most of the brain regions, the intersequence CVs were relatively low and linear correlation were high. Bland-Altman plots showed that most of the values fall within the 95% prediction limits. We concluded that fast imaging protocols of MDME sequence used in our study can potentially be used for quantitative evaluation of brain tissues. Since changing scan parameters can affect the measured T1, T2, and PD values, it is necessary to use consistent scan parameter for comparing or following up cases quantitatively.

Recent high-performance gradient coils are fabricated mainly at the expense of spatial linearity. In this study, we measured the spatial nonlinearity of the magnetic field generated by the gradient coils of two MRI systems with high-performance gradient coils. The nonlinearity of the gradient fields was measured using 3D gradient echo sequences and a spherical phantom with a built-in lattice structure. The spatial variation of the gradient field was approximated to the 3rd order polynomials. The coefficients of the polynomials were calculated using the steepest descent method. The geometric distortion of the acquired 3D MR images was corrected using the polynomials and compared with the 3D images corrected using the harmonic functions provided by the MRI venders. As a result, it was found that the nonlinearity correction formulae provided by the vendors were insufficient and needed to be verified or corrected using a geometric phantom such as used in this study.