Since the first pioneering report of hyperpolarized [1-¹³C]pyruvate magnetic resonance imaging (MRI) of the Warburg effect in prostate cancer patients, clinical dissemination of the technique has been rapid; close to 10 sites worldwide now possess a polarizer fit for the clinic, and more than 30 clinical trials, predominantly for oncological applications, are already registered on the US and European clinical trials databases. Hyperpolarized ¹³C probes to study pathophysiological processes beyond the Warburg effect, including tricarboxylic acid cycle metabolism, intra-cellular pH and cellular necrosis have also been demonstrated in the preclinical arena and are pending clinical translation, and the simultaneous injection of multiple co-polarized agents is opening the door to high-sensitivity, multi-functional molecular MRI with a single dose. Here, we review the biomedical applications to date of the two polarization methods that have been used for in vivo hyperpolarized ¹³C molecular MRI; namely, dissolution dynamic nuclear polarization and parahydrogen-induced polarization. The basic concept of hyperpolarization and the fundamental theory underpinning these two key ¹³C hyperpolarization methods, along with recent technological advances that have facilitated biomedical realization, are also covered.

T₂-fluid-attenuated inversion recovery images (FLAIR) mismatch sign is now known to be a specific yet insensitive image feature for IDH-mutant, 1p19q non-codeleted astrocytoma. The current study revealed that lesion presenting T₂-FLAIR mismatch exhibited extremely long T₁- and T₂-relaxation time while T₂-FLAIR matched lesions showed low to moderate values. On the other hand, IDH-wildtype tumors presented noticeably short T₁- and T₂-relaxation time. These different relaxation time characteristics seemed to render T₂-FLAIR mismatch sign of becoming such a unique and specific image feature for IDH-mutant, 1p19q non-codeleted astrocytoma.

Purpose: Increased use of deep convolutional neural networks (DCNNs) in medical imaging diagnosis requires determinate evaluation of diagnostic performance. We performed the fundamental investigation of diagnostic performance of DCNNs using the detection task of brain metastasis.

Methods: We retrospectively investigated AlexNet and GoogLeNet using 3117 positive and 37961 negative MRI images with and without metastasis regarding (1) diagnostic biases, (2) the optimal K number of K-fold cross validations (K-CVs), (3) the optimal positive versus negative image ratio, (4) the accuracy improvement curves, (5) the accuracy range prediction by the bootstrap method, and (6) metastatic lesion detection by regions with CNNs (R-CNNs).

Results: Respectively, AlexNet and GoogLeNet had (1) 50 ± 4.6% and 50 ± 4.9% of the maximal mean ± 95% confidence intervals (95% CIs) measured with equal-sized negative versus negative image datasets and positive versus positive image datasets, (2) no less than 10 and 4 of K number in K-CVs fell within the respective maximum biases of 4.6% or 4.9%, (3) 74% of the highest accuracy with equal positive versus negative image ratio dataset and 91% of that with four times of negative-to-positive image ratio dataset, (4) the accuracy improvement curves increasing from 69% to 74% and 73% to 88% as positive versus negative pairs of the training images increased from 500 to 2495, (5) at least nine and six out of 10-CV result sets essential to predict the accuracy ranges by the bootstrap method, and (6) 50% and 45% of metastatic lesion detection accuracies by R-CNNs.

Conclusions: Our research presented methodological fundamentals to evaluate diagnostic features in the visual recognition of DCNNs. Our series will help to conduct the accuracy investigation of computer diagnosis in medical imaging.

Purpose: This study aimed to evaluate the effect of chemical exchange saturation transfer (CEST) on the ischemic regions in hypoxic-ischemic encephalopathy (HIE) in comparison with diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) using a 7T-MRI.

Methods: We used neonatal rats (n = 8), aged 8 days, to clarify the progression of HIE. The rat model of HIE was developed by ligating and severing the left common carotid artery, followed by 45 minutes of recovery, and 60 minutes of hypoxia (8% O₂/92% N₂; 34°C). At 0–2 and 24 hours after the onset of HIE, CEST imaging, DWI, and MRS were performed with a 7T-MRI. The magnetization transfer ratio (MTR) asymmetry curves and four MTR asymmetry maps at 0.5, 1.0, 2.0, and 3.5 ppm were calculated using the CEST images. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps were calculated by DWI, and brain metabolites were assessed by MRS.

Results: In the ischemic regions of neonatal rats, FA was significantly increased at 0–2 hours and decreased at 24 hours after the onset of HIE. ADC in the ipsilateral side was significantly lower than that of contralateral side. All rats with HIE showed hypointense areas on MTR asymmetry maps (2.0 and 3.5 ppm), that did not correspond with the hyperintense areas on DWI. In addition, a significant increase in lactate levels was observed at 0–2 and 24 hours after the onset of HIE.

Conclusion: CEST MTR maps did not correspond with the hyperintense areas on DWI at 0–2 and 24 hours after the onset of HIE. The change of multi offset CEST signal may be primarily related to the brain metabolites and pH alterations, such as that caused by lactate, after the onset of HIE.

Purpose: Idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer’s disease (AD) are geriatric diseases and common causes of dementia. Recently, many studies on the segmentation, disease detection, or classification of MRI using deep learning have been conducted. The aim of this study was to differentiate iNPH and AD using a residual extraction approach in the deep learning method.

Methods: Twenty-three patients with iNPH, 23 patients with AD and 23 healthy controls were included in this study. All patients and volunteers underwent brain MRI with a 3T unit, and we used only whole-brain three-dimensional (3D) T₁-weighted images. We designed a fully automated, end-to-end 3D deep learning classifier to differentiate iNPH, AD and control. We evaluated the performance of our model using a leave-one-out cross-validation test. We also evaluated the validity of the result by visualizing important areas in the process of differentiating AD and iNPH on the original input image using the Gradient-weighted Class Activation Mapping (Grad-CAM) technique.

Results: Twenty-one out of 23 iNPH cases, 19 out of 23 AD cases and 22 out of 23 controls were correctly diagnosed. The accuracy was 0.90. In the Grad-CAM heat map, brain parenchyma surrounding the lateral ventricle was highlighted in about half of the iNPH cases that were successfully diagnosed. The medial temporal lobe or inferior horn of the lateral ventricle was highlighted in many successfully diagnosed cases of AD. About half of the successful cases showed nonspecific heat maps.

Conclusion: Residual extraction approach in a deep learning method achieved a high accuracy for the differential diagnosis of iNPH, AD, and healthy controls trained with a small number of cases.

Purpose: Diffusion tensor imaging (DTI) adds functional information to morphological magnetic resonance neurography (MRN) in the assessment of the brachial nerve plexus. To determine the most appropriate pulse sequence in scan times suited for diagnostic imaging in clinical routine, we compared image quality between simultaneous multi-slice readout-segmented (rs-DTI) and conventional single-shot (ss-DTI) echo-planar imaging techniques.

Methods: Institutional Review Board (IRB) approved study including 10 healthy volunteers. The supraclavicular brachial plexus, covering the nerve roots and trunks from C5 to C7, was imaged on both sides with rs-DTI and ss-DTI. Both sequences were acquired in scan times <7 min with b-values of 900 s/mm² and with isotropic spatial resolution.

Results: In rs-DTI image, the overall quality was significantly better and distortion artifacts were significantly lower (P = 0.001–0.002 and P = 0.001–0.002, respectively) for both readers. In ss-DTI, a trend toward lower degree of ghosting and motion artifacts was elicited (reader 1, P = 0.121; reader 2, P = 0.264). No significant differences between the two DTI techniques were found for signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR) and fractional anisotropy (FA) (P ≥ 0.475, P ≥ 0.624, and P ≥ 0.169, respectively). Interreader agreement for all examined parameters and all sequences ranged from intraclass correlation coefficient (ICC) 0.064 to 0.905 and Kappa 0.40 to 0.851.

Conclusion: Incomparable acquisition times rs-DTI showed higher image quality and less distortion artifacts than ss-DTI. The trend toward a higher degree of ghosting and motion artifacts in rs-DTI did not deteriorate image quality to a significant degree. Thus, rs-DTI should be considered for functional MRN of the brachial plexus.

Purpose: 2D cine phase contrast (PC)-MRI is a standard velocimetry for the superior mesenteric artery (SMA); however, the optimal localization of the measurement plane has never been fully discussed previously. The purpose of this Institutional Review Board approved prospective and single arm study is to test whether flow velocimetry of the SMA with combined use of 2D cine PC-MRI and meal challenge is dependent on the localizations of the measurement planes and to seek optimal section for velocimetry.

Methods: Seven healthy volunteers underwent cardiac phase resolved ECG gated 2D cine PC-MRI pre- and 30 min post-meal challenge at three measurement planes: proximal, curved mid section and distal straight section of the SMA at 3T. 4D Flow using 3D cine PC-MRI with vastly undersampled isotropic projection imaging (PC VIPR) was also performed right after 2D cine PC-MRI to delineate the flow dynamics within the SMA using streamline analysis. Two radiologists measured flow velocities, and rated the appearances of the abnormal flow in the SMA on streamlines derived from the 4D Flow and the computational fluid dynamics (CFD).

Results: 2D cine PC-MRI measured increased temporally averaged flow velocity (mm/s) after the meal challenge only in the proximal (129.3 vs. 97.8, P = 0.0313) and distal section (166.9 vs. 96.2, P = 0.0313), not in the curved mid section (113.1 vs. 85.5, P = 0.0625). The average velocities were highest and their standard errors (8.5–26.5) were smallest at the distal straight section both before and after the meal challenge as compared with other sections. The streamline analysis depicted more frequent appearances of vertical or helical flow in the curved mid section both on 4D Flow and CFD (κ: 0.27–0.68).

Conclusion: SMA velocimetry with 2D cine PC-MRI was dependent on the localization of the measurement planes. Distal straight section, not in the curved mid section is recommended for MR velocimetry.

Purpose: It has been reported previously that intravenously administered gadolinium-based contrast agent (GBCA) leaks into the subarachnoid space around the cortical veins at 4 h after injection in all old people over 37 years, but not in younger people up to 37 years of age in 3D-real IR images. The purpose of this study was to investigate whether there was a strict threshold of 37 years of age for the leakage of the GBCA into the subarachnoid space.

Methods: The subjects included 190 patients, that were scanned for 3D-real IR images at 4 hours after intravenous injection of GBCA as a diagnostic test for endolymphatic hydrops. The patient’s age ranged from 14 to 81 years. Two experienced neuroradiologists evaluated the images to determine whether the GBCA leakage around the cortical veins was positive or negative. Any discrepancies between the two observers were discussed and a consensus was obtained.

A Mann–Whitney U test and receiver operating characteristic (ROC) curve analysis were used to compare the positive and the negative group and to set the age cut-off value for the prediction of GBCA leakage.

Results: The GBCA leakage around the cortical veins was negative in 35 patients and positive in 155 patients. The average age was 33 ± 11 years in the negative group, and 55 ± 12 years in the positive group (P < 0.01). In the ROC analysis for the age and leakage of the GBCA, an area under the curve was 0.905 and the cut-off age was 37.317 years (sensitivity of 0.942 and specificity of 0.771).

Conclusion: Intravenously administered GBCA leaks into the subarachnoid space around the cortical veins in most patients over 37 years of age. However, it should be noted that it can be found occasionally in patients under 37 years of age.

Purpose: The hybrid compressed sensing (hybrid-CS) technique can shorten the acquisition time compared with the sensitivity encoding (SENSE) technique in lumbar MRI. To evaluate the feasibility of a hybrid-CS technique in comparison with 3D isotropic T₂-weighted turbo spin-echo (3D volume isotropic turbo spin-echo acquisition [VISTA]) MRI of the lumbar spine.

Materials and Methods: The Institutional Review Board approved this study and informed consent was obtained from participants prior to study entry. Sixteen healthy volunteers underwent lumbar spine 3D VISTA with conventional parallel imaging for SENSE and hybrid-CS at 3T. We recorded the image acquisition times of SENSE and hybrid-CS. We compared the signal-to-noise ratio (SNR) in spine, cerebrospinal fluid (CSF), lumbar disc, epidural fat, and erector spinae muscle, and the contrast of spine, CSF, and disc, and performed qualitative image analysis assessment, between the two image sequences.

Results: The image acquisition time for hybrid-CS was 39.2% shorter than that of SENSE (218.4/358.8 s). The contrast of CSF and SNR of the spine was significantly higher with hybrid-CS than with SENSE (P < 0.05). The SNR of the disc and muscle was significantly higher with SENSE than with hybrid-CS (P < 0.05). There were no significant differences in the contrast of spine, disc, and fat, and SNR of CSF and fat between hybrid-CS and SENSE. There were no significant differences in the qualitative evaluation between hybrid-CS and SENSE.

Conclusion: Compared with SENSE, hybrid-CS for 3D VISTA can shorten image acquisition time without sacrificing image quality.

We currently obtain pre- and post-contrast enhanced whole brain 3D-real inversion recovery images for the evaluation of endolymphatic hydrops. We noticed that the space between the pial sheath surrounding the cortical veins and the cortical venous wall is enhanced and this enhancement seems to connect to the meningeal lymphatics along superior sagittal sinus. This new anatomical concept regarding the outflow from the glymphatic system might be important for the future research in neuroscience.

Purpose: Recently, the use of 3D real inversion recovery (3D-real IR) imaging has been proposed for the evaluation of endolymphatic hydrops (EH). This method shows similar contrast between the endolymphatic and perilymphatic spaces and surrounding bone compared with the hybrid of reversed image of positive endolymph signal and native image of perilymph signal multiplied with heavily T₂-weighted MR cisternography (HYDROPS-Mi2) image. We measured the volume of the endolymphatic space using 3D-real IR and HYDROPS-Mi2 images, and compared the measurements obtained with both techniques.

Methods: HYDROPS-Mi2 and 3D-real IR images were obtained for 30 ears from 15 patients with clinical suspicion of EH; imaging was performed 4 h after intravenous administration of a single dose of gadolinium-based contrast agent. We measured the volume of the endolymphatic space in the cochlea and vestibule by manually drawing the regions of interest. The correlation between endolymphatic volume determined from HYDROPS-Mi2 images and 3D-real IR images was calculated.

Results: There was a strong positive linear correlation between the cochlear and vestibular endolymphatic volume determined from HYDROPS-Mi2 and 3D-real IR images. The Spearman’s rank correlation coefficient (ρ) between the measurements obtained with both images was 0.805 (P < 0.001) for the cochlea and 0.826 (P < 0.001) for the vestibule.

Conclusion: The endolymphatic volume measured using 3D-real IR images strongly correlated with that measured using HYDROPS-Mi2 images. Thus, 3D-real IR imaging might be a suitable method for the measurement of endolymphatic volume.

Purpose: To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach.

Materials and Methods: A multi-channel convolutional neural network-based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T₁-weighted spoiled gradient echo sequence for the liver, which contained breath-hold failures occurring during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland–Altman plots.

Results: The proposed network was found to be significantly reducing the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images.

Conclusion: A deep learning-based method for removing motion artifacts in images obtained via DCE-MRI of the liver was demonstrated and validated.

Purpose: To develop a fast 3D MRI simulator for arbitrary k-space sampling using a graphical processing unit (GPU) and demonstrate its performance by comparing simulation and experimental results in a real MRI system.

Materials and Methods: A fast 3D MRI simulator using a GeForce GTX 1080 GPU (NVIDIA Corporation, Santa Clara, CA, USA) was developed using C++ and the CUDA 8.0 platform (NVIDIA Corporation). The unique advantage of this simulator was that it could use the same pulse sequence as used in the experiment. The performance of the MRI simulator was measured using two GTX 1080 GPUs and 3D Cones sequences. The MRI simulation results for 3D non-Cartesian sampling trajectories like 3D Cones sequences using a numerical 3D phantom were compared with the experimental results obtained with a real MRI system and a real 3D phantom.

Results: The performance of the MRI simulator was about 3800–4900 gigaflops for 128- to 4-shot 3D Cones sequences with 256³ voxels, which was about 60% of the performance of the previous MRI simulator optimized for Cartesian sampling calculated for a Cartesian sampling gradient-echo sequence with 256³ voxels. The effects of the static magnetic field inhomogeneity, radio-frequency field inhomogeneity, gradient field nonlinearity, and fast repetition times on the MR images were reproduced in the simulated images as observed in the experimental images.

Conclusion: The 3D MRI simulator developed for arbitrary k-space sampling optimized using GPUs is a powerful tool for the development and evaluation of advanced imaging sequences including both Cartesian and non-Cartesian k-space sampling.

In the 1980’s some of the earliest studies of arterial spin labeling (ASL) MRI have demonstrated its ability to generate MR angiography (MRA) images. Thanks to many technical improvements, ASL has been successfully moving its position from the realm of research into the clinical area, albeit more known as perfusion imaging than as MRA. For MRA imaging, other techniques such as time-of-flight, phase contrast MRA and contrast-enhanced (CE) MRA are more popular choices for clinical applications. In the last decade, however, ASL-MRA has been experiencing a remarkable revival, especially because of its non-invasive nature, i.e. the fact that it does not rely on the use of contrast agent. Very importantly, there are additional benefits of using ASL for MRA. For example, its higher flexibility to achieve both high spatial and temporal resolution than CE dynamic MRA, and the capability of vessel specific visualization, in which the vascular tree arising from a selected artery can be exclusively visualized. In this article, the implementation and recent developments of ASL-based MRA are discussed; not only focusing on the basic sequences based upon pulsed ASL or pseudo-continuous ASL, but also including more recent labeling approaches, such as vessel-selective labeling, velocity-selective ASL, vessel-encoded ASL and time-encoded ASL. Although these ASL techniques have been already utilized in perfusion imaging and their usefulness has been suggested by many studies, some additional considerations should be made when employing them for MRA, since there is something more than the difference of the spatial resolution of the readout sequence. Moreover, extensive discussion is included on what readout sequence to use, especially by highlighting how to achieve high spatial resolution while keeping scan-time reasonable such that the ASL-MRA sequence can easily be included into a clinical examination.

Purpose: Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) are representative disorders of dementia of the elderly and the neuroimaging has contributed to early diagnosis by estimation of alterations of brain volume, blood flow and metabolism. A brain network analysis by MR imaging (MR connectome) is a recently developed technique and can estimate the dysfunction of the brain network in AD and DLB. A graph theory which is a major technique of network analysis is useful for a group study to extract the feature of disorders, but is not necessarily suitable for the disorder differentiation at the individual level. In this investigation, we propose a deep learning technique as an alternative method of the graph analysis for recognition and classification of AD and DLB at the individual subject level.

Materials and Methods: Forty-eight brain structural connectivity data of 18 AD, 8 DLB and 22 healthy controls were applied to the machine learning consisting of a six-layer convolution neural network (CNN) model. Estimation of the deep learning model to classify AD, DLB and non-AD/DLB was performed using the 4-fold cross-validation method.

Results: The accuracy, average precision and recall of our CNN model were 0.73, 0.78 and 0.73, and the specificity precision and recall were 0.68 and 0.79 in AD, 0.94 and 0.65 in DLB and 0.73 and 0.75 in non-AD/DLB. The triangular probability map of the MR connectome revealed the probability of AD, DLB and non-AD/DLB in each subject.

Conclusion: Our preliminary investigation revealed the adaptation of deep learning to the MR connectome and proposed its utility in the differentiation of dementia disorders at the individual subject level.

The central nervous system (CNS) was previously thought to be the only organ system lacking lymphatic vessels to remove waste products from the interstitial space. Recently, based on the results from animal experiments, the glymphatic system was hypothesized. In this hypothesis, cerebrospinal fluid (CSF) enters the periarterial spaces, enters the interstitial space of the brain parenchyma via aquaporin-4 (AQP4) channels in the astrocyte end feet, and then exits through the perivenous space, thereby clearing waste products. From the perivenous space, the interstitial fluid drains into the subarachnoid space and meningeal lymphatics of the parasagittal dura. It has been reported that the glymphatic system is particularly active during sleep. Impairment of glymphatic system function might be a cause of various neurodegenerative diseases such as Alzheimer’s disease, normal pressure hydrocephalus, glaucoma, and others. Meningeal lymphatics regulate immunity in the CNS. Many researchers have attempted to visualize the function and structure of the glymphatic system and meningeal lymphatics in vivo using MR imaging. In this review, we aim to summarize these in vivo MR imaging studies and discuss the significance, current limitations, and future directions. We also discuss the significance of the perivenous cyst formation along the superior sagittal sinus, which is recently discovered in the downstream of the glymphatic system.

Purpose: The purpose of our study was to investigate the effect of different slice thicknesses and/or interslice gaps on longitudinal and transverse relaxation times (T₁ and T₂) measured by a multi-dynamic, multi-echo (MDME) sequence.

Materials and Methods: This retrospective study included nine healthy subjects who underwent MDME sequence (at 3T) with four different combinations of slice thicknesses and/or interslice gaps: slice thickness of 4 mm and interslice gap of 0 mm (TH4/G0), TH4/G1, TH5/G0, and TH5/G1. T₁ and T₂ were measured in various brain regions by a qualified neuroradiologist with 8 years of clinical experience: the frontal white matter (WM), occipital WM, genu, splenium, frontal cortex, thalamus, putamen, caudate head, and cerebrospinal fluid (CSF). The paired samples t-test was used to investigate the effect of different slice thicknesses and interslice gaps (TH4/G0 versus TH4/G1 and TH5/G0 versus TH5/G1). P < 0.013 was considered statistically significant.

Results: T₂ in all brain regions and T₁ in the frontal WM, putamen, and CSF did not significantly change for different slice thicknesses and/or gaps (Ps > 0.013). In addition, T₁ in all brain regions of interest did not significantly change between TH4/G0, TH4/G1, TH5/G0 and TH5/G1. However, T₁ in some of the brain regions was higher with TH4/G0 than with TH5/G0 (occipital WM, frontal cortex, and caudate head) and with TH4/G1 than with TH5/G1 (occipital WM, genu, splenium and thalamus, all Ps < 0.013).

Conclusion: T₂ estimated using the MDME sequence was stable regardless of slice thickness or gap. Although the sequence seems to provide stable relaxation values, identical slice thicknesses need to be used for follow-up to prevent potential T₁ changes.

Purpose: To compare the accuracy of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values between reduced FOV or so-called zonally oblique multislice (ZOOM) and conventional diffusion tensor imaging (DTI) in the cervical spinal cord.

Methods: Both ZOOM and conventional DTI were performed on 10 healthy volunteers. Intraclass correlation coefficient (ICC) was used to evaluate the reliability of the measurements obtained. Four radiologists evaluated the FA and ADC values at each cervical cord level and classified the visibility by 4 ranks. The geometric distortion ratios of the long axis and short axis were compared between ZOOM and conventional DTI. The imaging parameters were as follows: b-value = 600 s/mm²; TR = 4500 ms; TE = 81 ms; FOV = 70 × 47 mm² / 200 × 200 mm²; matrix = 80 × 51 / 128 × 126 (ZOOM and conventional DTI, respectively). The region of interest was carefully drawn inside the spinal cord margin to exclude the spinal cord component, without excluding the white matter fiber tracts.

Results: The average FA value decreased in both ZOOM and conventional DTI in lower spinal cord levels; in contrast, the ADC value increased in lower spinal cord levels. Zonally oblique multislice DTI was superior to conventional DTI with regard to inter-rater and intra-rater reliability; further, visibility was better and the standard deviation was smaller in ZOOM DTI. On both the long and short axis, the geometric distortion ratio was lower in ZOOM DTI at all cervical spinal cord levels compared with the conventional DTI. There was a significant difference in the distortion ratios of the long and short axis between ZOOM and conventional DTI.

Conclusion: Conventional DTI is unreliable owing to its susceptibility to the surrounding magnetic field. ZOOM DTI is reliable for performing highly accurate evaluations.

Purpose: It has been reported that leakage of intravenously administered gadolinium-based contrast agents (IV-GBCAs) into the cerebrospinal fluid (CSF) from the cortical veins even in healthy subjects can be detected using a highly sensitive pulse sequence such as heavily T₂-weighted 3D fluid-attenuated inversion recovery and 3D-real inversion recovery (IR). The purpose of this study was to evaluate the feasibility of MR fingerprinting to detect GBCA leakage from the cortical veins after IV-GBCA.

Materials: Fourteen patients with suspected endolymphatic hydrops (EH) who received a single dose of IV-GBCA (39–79 years old) were included. The real IR images as well as MR fingerprinting images were obtained at 4 h after IV-GBCA. T₁ and T₂ values were obtained using MR fingerprinting and analyzed in ROIs covering intense GBCA leakage, and non-leakage areas of the CSF as determined on real IR images. The scan time for real IR imaging was 10 min and that for MR fingerprinting was 41 s.

Results: The mean T₁ value of the ROI in the area of GBCA leakage was 2422 ± 261 ms and that in the non-leakage area was 3851 ± 235 ms (P < 0.01). There was no overlap between the T₁ values in the area of GBCA leakage and those in the non-leakage area.

The mean T₂ value in the area of GBCA leakage was 319 ± 90 ms and that in the non-leakage area was 670 ± 166 ms (P < 0.01). There was some overlap between the T₂ values in the area of GBCA leakage and those in the non-leakage area.

Conclusion: Leaked GBCA from the cortical veins into the surrounding CSF can be detected using MR fingerprinting obtained in <1 min.