Oscillating-gradient spin-echo sequences enable the measurement of diffusion weighting with a short diffusion time and can provide indications of internal structures. We report two cases of brain abscess in which the apparent diffusion coefficient (ADC) values appear higher at short diffusion times in comparison with those at long diffusion times. Diffusion time dependence of the ADC in brain abscesses suggests not only substrate viscosity but also restricted diffusion due to the structure within the lesions.

After introduction of the first human 7 tesla (7T) system in 1999, 7T MR systems have been employed as one of the most advanced platforms for human MR research for more than 20 years. Currently, two 7T MR models are approved for clinical use in the U.S.A. The approval facilitated introduction of the 7T system, summing up to around 100 worldwide. The approval in Japan is much awaited. As a clinical MR scanner, the 7T MR system is drawing attention in terms of safety.

Several large-sized studies on bioeffects have been reported for vertigo, dizziness, motion disturbances, nausea, and others. Such effects might also be found in MR workers and researchers. Frequency and severity of reported bioeffects will be presented and discussed, including their variances. The high resonance frequency and shorter RF wavelength of 7T increase the concern about the safety. Homogeneous RF pulse excitation is difficult even for the brain, and a multi-channel parallel transmit (pTx) system is considered mandatory. However, pTx may create a hot spot, which makes the estimation of specific absorption rate (SAR) to be difficult. The stronger magnetic field of 7T causes a large force of displacement and heating on metallic implants or devices, and the scan of patients with them should not be conducted at 7T. However, there are some opinions that such patients might be scanned even at 7T, if certain criteria are met. This article provides a brief review on the effect of the static magnetic field on humans (MR subjects, workers, and researchers) and neurons, in addition to scan sound, SAR, and metal implants and devices. Understanding and avoiding adverse effects will contribute to the reduction in safety risks and the prevention of incidents.

Schizophrenia is a common severe psychiatric disorder that affects approximately 1% of general population through the life course. Historically, in Kraepelin’s time, schizophrenia was a disease unit conceptualized as dementia praecox; however, since then, the disease concept has changed. Recent MRI studies had shown that the neuropathology of the brain in this disorder was characterized by mild progression before and after the onset of the disease, and that the brain alterations were relatively smaller than assumed. Although genetic factors contribute to the brain alterations in schizophrenia, which are thought to be trait differences, other changes include factors that are common in psychiatric diseases. Furthermore, it has been shown that the brain differences specific to schizophrenia were relatively small compared to other changes, such as those caused by brain development, aging, and gender. In addition, compared to the disease and participant factors, machine and imaging protocol differences could affect MRI signals, which should be addressed in multi-site studies. Recent advances in MRI modalities, such as multi-shell diffusion-weighted imaging, magnetic resonance spectroscopy, and multimodal brain imaging analysis, may be candidates to sharpen the characterization of schizophrenia-specific factors and provide new insights. The Brain/MINDS Beyond Human Brain MRI (BMB-HBM) project has been launched considering the differences and noises irrespective of the disease pathologies and includes the future perspectives of MRI studies for various psychiatric and neurological disorders. The sites use restricted MRI machines and harmonized multi-modal protocols, standardized image preprocessing, and traveling subject harmonization. Data sharing to the public will be planned in FY 2024. In the future, we believe that combining a high-quality human MRI dataset with genetic data, randomized controlled trials, and MRI for non-human primates and animal models will enable us to understand schizophrenia, elucidate its neural bases and therapeutic targets, and provide tools for clinical application at bedside.

This article presents an overview of deep learning (DL) and its applications to function approximation for MR in medicine. The aim of this article is to help readers develop various applications of DL. DL has made a large impact on the literature of many medical sciences, including MR. However, its technical details are not easily understandable for non-experts of machine learning (ML).

The first part of this article presents an overview of DL and its related technologies, such as artificial intelligence (AI) and ML. AI is explained as a function that can receive many inputs and produce many outputs. ML is a process of fitting the function to training data. DL is a kind of ML, which uses a composite of many functions to approximate the function of interest. This composite function is called a deep neural network (DNN), and the functions composited into a DNN are called layers. This first part also covers the underlying technologies required for DL, such as loss functions, optimization, initialization, linear layers, non-linearities, normalization, recurrent neural networks, regularization, data augmentation, residual connections, autoencoders, generative adversarial networks, model and data sizes, and complex-valued neural networks.

The second part of this article presents an overview of the applications of DL in MR and explains how functions represented as DNNs are applied to various applications, such as RF pulse, pulse sequence, reconstruction, motion correction, spectroscopy, parameter mapping, image synthesis, and segmentation.

Purpose: In aortic stenosis (AS), the discrepancy between moderately accelerated flow and effective orifice area (EOA) continues to pose a challenge. We developed a method of measuring the vena contracta area as hemodynamic EOA using cardiac MRI focusing on AS patients with a moderately accelerated flow to solve the problem that AS severity can currently be determined only by echocardiography.

Methods: We investigated 40 patients with a peak transvalvular velocity > 3.0 m/s on transthoracic echocardiography (TTE). The patients were divided into highly accelerated and moderately accelerated AS groups according to whether or not the peak transvalvular velocity was ≥ 4.0 m/s. From the multislice 2D cine phase-contrast MRI data, the cross-sectional area of the vena contracta of the reconstructed streamline in the Valsalva sinus was defined as MRI-EOAs. Patient symptoms and echocardiography data, including EOA (defined as TTE-EOA), were derived from the continuity equation using TTE.

Results: All participants in the highly accelerated AS group (n = 19) showed a peak velocity ≥ 4.0 m/s in MRI. Eleven patients in the moderately accelerated AS group (n = 21) had a TTE-EOA < 1.00 cm². In the moderately accelerated AS group, MRI-EOAs demonstrated a strong correlation with TTE-EOAs (r = 0.76, P < 0.01). Meanwhile, in the highly accelerated AS group, MRI-EOAs demonstrated positivity but a moderate correlation with TTE-EOAs (r = 0.63, P = 0.004). MRI-EOAs were overestimated compared to TTE-EOAs. In terms of the moderately accelerated AS group, the best cut-off value for MRI-EOAs was < 1.23 cm², compatible with TTE-EOAs < 1.00 cm², with an excellent prediction of the New York Heart Association classification ≥ III (sensitivity 87.5%, specificity 76.9%).

Conclusion: MRI-EOAs may be an alternative to conventional echocardiography for patients with moderately accelerated AS, especially those with discordant echocardiographic parameters.

Purpose: Diffusion-weighted MRI (DWI) is an essential sequence for evaluating pediatric patients with moyamoya disease (MMD); however, acoustic noise associated with DWI may lead to motion artifact. Compared with conventional DWI (cDWI), quiet DWI (qDWI) is considered less noisy and able to keep children more relaxed and stable. This study aimed to evaluate the suitability of qDWI compared with cDWI for pediatric patients with MMD.

Methods: In this observational study, MR examinations of the brain were performed either with or without sedation in pediatric patients with MMD between September 2017 and August 2018. Three neuroradiologists independently evaluated the images for artifacts and restricted diffusion in the brain. The differences between qDWI and cDWI were compared statistically using a chi-square test.

Results: One-hundred and six MR scans of 56 patients with MMD (38 scans of 15 sedated patients: 6 boys and 9 girls; mean age, 5.2 years; range, 1–9 years; and 68 scans of 42 unsedated patients: 19 boys and 23 girls; mean age, 10.7 years; range, 7–16 years) were evaluated. MR examinations were performed either with or without sedation (except in one patient). In sedated patients, no artifact other than susceptibility was observed on qDWI, whereas four artifacts were observed on cDWI (P = .04). One patient awoke from sedation during cDWI scanning, while no patient awoke from sedation during qDWI acquisition. For unsedated patients, three scans showed artifacts on qDWI, whereas two scans showed artifacts on cDWI (P = .65). Regarding restricted diffusion, qDWI revealed three cases, while two cases were found on cDWI (P = .66).

Conclusion: qDWI induced fewer artifacts compared with cDWI in sedated patients, and similar frequencies of artifacts were induced by qDWI and by cDWI in unsedated patients. qDWI showed restricted diffusion comparable to cDWI.

Purpose: Mucinous adenocarcinoma (MA) is associated with worse clinicopathological characteristics and a poorer prognosis than non-MA. Moreover, MA is related to worse tumor regression grade and tumor downstaging than non-MA. This study investigated whether lesions in MA and non-MA can be quantitatively assessed by T2 mapping technique and compared with the diffusion-weighted imaging (DWI).

Methods: High-resolution MRI, DWI, and T2 mapping were performed on 81 patients diagnosed with rectal cancer via biopsy. Afterward, T2 and apparent diffusion coefficient (ADC) values were manually measured by a senior and a junior radiologist independently. By examining surgical specimens, the patients with MA and non-MA were identified. Inter-observer reproducibility was tested, and T2 and ADC values were compared using Mann–Whitney U test. Finally, receiver operating characteristic (ROC) curves were drawn to determine the cut-off value.

Results: Of the 81 patients, 11 patients with MA were confirmed by pathology. The inter-observer reproducibility of T2 and ADC values showed an excellent intraclass correlation coefficient (ICC) of 0.993 and 0.913, respectively. MA had higher T2 (87.9 ± 5.11 ms) (P = 0.000) and ADC (2.03 × 10⁻³ mm²/s) (P = 0.000) values than non-MA (66.6 ± 6.86 ms and 1.17 × 10⁻³ mm²/s, respectively). The area under the ROC curves (AUC) of the T2 and ADC values were 0.999 (95% confidence interval [CI]: 0.953–1) and 0.979 (95% CI: 0.920–0.998), respectively. When the cutoff value in T2 mapping was 80 ms, the Youden index was the largest, sensitivity was 100%, and specificity was 97%.

Conclusion: As a stable quantitative sequence, T2 mapping of MRI is useful in differentiating MA from non-MA. Compared to ADC values, T2 values are also diagnostically effective and non-inferior to ADC values.

Purpose: To compare the diagnostic performance of dynamic contrast-enhanced-MR (DCE-MR) and delayed contrast-enhanced (CE)-MRI added to unenhanced MRI, including diffusion weighted image (DWI) for differentiating malignant adnexal tumors, conducting a retrospective blinded image interpretation study.

Methods: Data of 80 patients suspected of having adnexal tumors by ultrasonography between April 2008 and August 2018 were used for the study. All patients had undergone preoperative MRI and surgical resection at our institution. Four radiologists (two specialized in gynecological radiology and two non-specialized) were enrolled for blinded review of the MR images. A 3-point scale was used: 0 = benign, 1 = indeterminate, and 2 = malignant. Three imaging sets were reviewed: Set A, unenhanced MRI including DWI; Set B, Set A and delayed CE-T1WI; and Set C, Set A and DCE-MRI. Imaging criteria for benign and malignant tumors were given in earlier reports. The diagnostic performance of the three imaging sets of the four readers was calculated. Their areas under the curve (AUCs) were compared using the DeLong method.

Results: Accuracies of Set B were 81%–88%. Those of Set C were 81%–85%. The AUCs of Set B were 0.83 and 0.89. Those of Set C were 0.81–0.86. For two readers, Set A showed lower accuracy and AUC than Set B/Set C (less than 0.80), although those were equivalent in other readers. No significant difference in AUCs was found among the three sequence sets. Intrareader agreement was moderate to almost perfect in Sets A and B, and substantial to almost perfect in Set C.

Conclusion: DCE-MR showed no superiority for differentiating malignant adnexal tumors from benign tumors compared to delayed CE-T1WI with conventional MR and DWI.

Purpose: The staging of liver fibrosis is clinically important, and a less invasive method is preferred. Quantitative susceptibility mapping (QSM) has shown a great potential in estimating liver fibrosis in addition to R2* relaxometry. However, few studies have compared QSM analysis and liver fibrosis. We aimed to evaluate the feasibility of estimating liver fibrosis by using QSM and R2*-based histogram analyses by comparing it with ultrasound-based transient elastography and the stage of histologic fibrosis.

Methods: Fourteen patients with liver disease were enrolled. Data sets of multi-echo gradient echo sequence with breath-holding were acquired on a 3-Tesla scanner. QSM and R2* were reconstructed by water–fat separation method, and ROIs were analyzed for these images. Quantitative parameters with histogram features (mean, variance, skewness, kurtosis, and 1st, 10th, 50th, 90th, and 99th percentiles) were extracted. These data were compared with the elasticity measured by ultrasound transient elastography and histological stage of liver fibrosis (F0 to F4, based on the new Inuyama classification) determined by biopsy or hepatectomy. The correlation of histogram parameters with intrahepatic elasticity and histologically confirmed fibrosis stage was examined. Texture parameters were compared between subgroups divided according to fibrosis stage. Receiver operating characteristic (ROC) analysis was also performed. P < 0.05 indicated statistical significance.

Results: The six histogram parameters of both QSM and R2*were significantly correlated with intrahepatic elasticity. In particular, three parameters (variance, percentiles [90th and 99th]) of QSM showed high correlation (r = 0.818–0.844), whereas R2* parameters showed a moderate correlation with elasticity. Four parameters of QSM were significantly correlated with fibrosis stage (ρ = 0.637–0.723) and differentiated F2–4 from F0–1 fibrosis and F3–4 from F0–2 fibrosis with areas under the ROC curve of > 0.8, but those of R2* did not.

Conclusion: QSM may serve as a promising surrogate indicator in detecting liver fibrosis.

Purpose: To compare the performance of a 12-channel flexible head coil (HFC12) with commercial 16-channel (HRC16) and 24-channel (HRC24) rigid coils.

Methods: The phantom study was performed on a 1.5 T MR scanner with HFC12, HRC16, and HRC24. The SNR and noise correlation matrix of T1WI, T2WI, and diffusion weighted imaging (DWI) were measured. The SNR profiles were created according to the SNR. In addition, 1/g-factors were calculated in different acceleration directions. In the in vivo study, T1WI, T2WI, and DWI were performed in one healthy volunteer with three different coils. The SNR and noise correlation matrix were measured.

Results: In the phantom study and in vivo study, the SNR of HFC12 in the transverse, sagittal, and coronal planes was the highest, followed by HRC24, and that of HRC16 was the lowest. The SNR profiles showed that the SNR at the edge of HFC12 was the highest. The mean value of the noise correlation matrix of HFC12 was the highest. The 1/g-factor results showed that HFC12 obtained the best acceleration ability in the head–foot acceleration direction when the reduction factor was set to two. The SNR of HFC12 in most cortices was significantly higher than that of HRC16 and HRC24, except in the occipital cortex. The SNR of HRC24 in the occipital cortex was higher than that of HFC12.

Conclusion: The SNR of HFC12 in T1WI, T2WI, and DWI was better than that of the HRC24 and HFC16. The SNR of HFC12 in the cortex was significantly higher than that of the commercial rigid head coil, except in the occipital cortex.

Purpose: To compare reliability and elucidate clinical application of magnetization-prepared rapid gradient-echo (MPRAGE) with 9-fold acceleration by using wave-controlled aliasing in parallel imaging (Wave-CAIPI 3 × 3) in comparison to conventional MPRAGE accelerated by using generalized autocalibrating partially parallel acquisition (GRAPPA) 2 × 1.

Methods: A total of 26 healthy volunteers and 33 patients were included in this study. Subjects were scanned with two MPRAGEs, GRAPPA 2 × 1 and Wave-CAIPI 3 × 3 acquired in 5 min 21 s and 1 min 42 s, respectively, on a 3T MR scanner. Healthy volunteers underwent additional two MPRAGEs (CAIPI 3 × 3 and GRAPPA 3 × 3). The image quality of the four MPRAGEs was visually evaluated with a 5-point scale in healthy volunteers, and the SNR of four MPRAGEs was also calculated by measuring the phantom 10 times with each MPRAGE. Based on the results of the visual evaluation, voxel-based morphometry (VBM) analyses, including subfield analysis, were performed only for GRAPPA 2 × 1 and Wave-CAIPI 3 × 3. Correlation of segmentation results between GRAPPA 2 × 1 and Wave-CAIPI 3 × 3 was assessed.

Results: In visual evaluations, scores for MPRAGE GRAPPA 2 × 1 (mean rank: 4.00) were significantly better than those for Wave-CAIPI 3 × 3 (mean rank: 3.00), CAIPI 3 × 3 (mean rank: 1.83), and GRAPPA 3 × 3 (mean rank: 1.17), and scores for Wave-CAIPI 3×3 were significantly better than those for CAIPI 3 × 3 and GRAPPA 3 × 3. Image noise was evident at the center for additional MPRAGE CAIPI 3 × 3 and GRAPPA 3 × 3. The correlation of segmentation results between GRAPPA 2 × 1 and Wave-CAIPI 3 × 3 was higher than 0.85 in all VOIs except globus pallidus. Subfield analysis of hippocampus also showed a high correlation between GRAPPA 2 × 1 and Wave-CAIPI 3 × 3.

Conclusion: MPRAGE Wave-CAIPI 3 × 3 shows relatively better contrast, despite of its short scan time of 1 min 42 s. The volumes derived from automated segmentation of MPRAGE Wave-CAIPI are considered to be reliable measures.

A 4D numerical phantom, which is defined in the 3D spatial axes and the resonance frequency axis, is indispensable for Bloch simulations of biological tissues with complex distribution of materials. In this study, a 4D numerical phantom was created using MR image datasets of a biological sample containing water and fat, and the Bloch simulations were performed using the 4D numerical phantom. As a result, 3D images of the sample containing water and fat were successfully reproduced, which demonstrated the usefulness of the concept of the 4D numerical phantom.