Harmonic field extension for QSM with reduced spatial coverage using physics-informed generative adversarial network.

Quantitative susceptibility mapping (QSM) is frequently employed in investigating brain iron related to brain development and diseases within deep gray matter (DGM). Nonetheless, the acquisition of whole-brain QSM data is time-intensive. An alternative approach, focusing the QSM specifically on areas of interest such as the DGM by reducing the field-of-view (FOV), can significantly decrease scan times. However, severe susceptibility value underestimations have been reported during QSM reconstruction with a limited FOV, largely attributable to artifacts from incorrect background field removal in the boundary region. This presents a considerable barrier to the clinical use of QSM with small spatial coverages using conventional methods alone. To mitigate the propagation of these errors, we proposed a harmonic field extension method based on a physics-informed generative adversarial network. Both quantitative and qualitative results demonstrate that our method outperforms conventional methods and delivers results comparable to those obtained with full FOV. Furthermore, we demonstrate the versatility of our method by applying it to data acquired prospectively with limited FOV and to data from patients with Parkinson's disease. The method has shown significant improvements in local field results, with QSM outcomes. In a clear illustration of its feasibility and effectiveness in real clinical environments, our proposed method addresses the prevalent issue of susceptibility underestimation in QSM with small spatial coverage.

Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI.

Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient movement due to the relatively long data acquisition time. This could cause severe degradation of image quality and therefore affect the overall diagnosis. In this paper, we develop an efficient retrospective 2D deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in 3D brain MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns the missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving the spatial image details and improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. The proposed network is optimized by minimizing the loss of structural similarity (SSIM) using the synthesized motion-corrupted images from 83 real motion-free subjects. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans. The overall image quality of the motion-corrected images via the proposed motion correction network significantly improves SSIM from 71.66% to 95.03% and declines the mean square error from 99.25 to 29.76. These results indicate the high similarity of the brain's anatomical structure in the corrected images compared to the motion-free data. The motion-corrected results of both the simulated and real motion data showed the potential of the proposed motion correction network to be feasible and applicable in clinical practices.

Salt-and-Pepper Noise Sign on Fat-Fraction Maps by Chemical-Shift-Encoded MRI: A Useful Sign to Differentiate Bone Islands From Osteoblastic Metastases-A Preliminary Study.

OBJECTIVE. The objective of our study was to assess the diagnostic utility of the "salt-and-pepper noise" sign on fat-fraction maps by chemical-shift-encoded MRI (CSE-MRI) compared with the halo sign on fat-suppressed T2-weighted imaging and mean attenuation on CT for differentiating bone islands from osteoblastic metastases. MATERIALS AND METHODS. Twenty-nine patients with 43 sclerotic vertebral bone marrow lesions (26 osteoblastic metastases, 17 bone islands) were included retrospectively. All patients underwent CT and MRI, including a CSE-MRI sequence on a 1.5-T MRI system, from November 2016 to January 2019. The salt-and-pepper noise sign was defined as the speckled appearance of white and black pixels that is similar to the appearance of background air on a fat-fraction map. ROC curves were analyzed to compare the diagnostic performance of the salt-and-pepper noise sign, halo sign, and mean CT attenuation between the two groups. RESULTS. The salt-and-pepper noise sign was significantly associated with bone islands (p < 0.001). The sensitivity, specificity, and accuracy for discriminating bone islands from osteoblastic metastases were 92.31-96.15%, 100%, and 95.35-97.67% for the salt-and-pepper noise sign; 88.46-92.31%, 88.24-94.12%, and 90.70% for the halo sign; and 96.15%, 94.12-100%, and 95.35-97.67% for mean CT attenuation, respectively. There was no statistically significant difference of diagnostic performances among the imaging characteristics for differentiating between bone islands and osteoblastic metastases (p > 0.05). Interobserver agreement for the salt-and-pepper noise sign, halo sign, and mean CT attenuation was almost perfect (κ ≥ 0.953, κ = 0.905, and ICC = 0.966, respectively). CONCLUSION. The salt-and-pepper noise sign is present in bone islands on fat-fraction maps by CSE-MRI and can aid in differentiating bone islands from osteoblastic metastases.

Data-driven synthetic MRI FLAIR artifact correction via deep neural network.

BACKGROUND: FLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow, which are difficult to correct by analytical modeling. In this study, a deep learning (DL)-based synthetic FLAIR method was developed, which does not require analytical modeling of the signal. PURPOSE: To correct artifacts in synthetic FLAIR using a DL method. STUDY TYPE: Retrospective. SUBJECTS: A total of 80 subjects with clinical indications (60.6 ± 16.7 years, 38 males, 42 females) were divided into three groups: a training set (56 subjects, 62.1 ± 14.8 years, 25 males, 31 females), a validation set (1 subject, 62 years, male), and the testing set (23 subjects, 57.3 ± 20.4 years, 13 males, 10 females). FIELD STRENGTH/SEQUENCE: 3 T MRI using a multiple-dynamic multiple-echo acquisition (MDME) sequence for synthetic MRI and a conventional FLAIR sequence. ASSESSMENT: Normalized root mean square (NRMSE) and structural similarity (SSIM) were computed for uncorrected synthetic FLAIR and DL-corrected FLAIR. In addition, three neuroradiologists scored the three FLAIR datasets blindly, evaluating image quality and artifacts for sulci/periventricular and intraventricular/cistern space regions. STATISTICAL TESTS: Pairwise Student's t-tests and a Wilcoxon test were performed. RESULTS: For quantitative assessment, NRMSE improved from 4.2% to 2.9% (P < 0.0001) and SSIM improved from 0.85 to 0.93 (P < 0.0001). Additionally, NRMSE values significantly improved from 1.58% to 1.26% (P < 0.001), 3.1% to 1.5% (P < 0.0001), and 2.7% to 1.4% (P < 0.0001) in white matter, gray matter, and cerebral spinal fluid (CSF) regions, respectively, when using DL-corrected FLAIR. For qualitative assessment, DL correction achieved improved overall quality, fewer artifacts in sulci and periventricular regions, and in intraventricular and cistern space regions. DATA CONCLUSION: The DL approach provides a promising method to correct artifacts in synthetic FLAIR. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1413-1423.

Spatiotemporal variations of magnetic susceptibility in the deep gray matter nuclei from 1 month to 6 years: A quantitative susceptibility mapping study.

BACKGROUND: Quantitative susceptibility mapping (QSM) is emerging as a technique that quantifies the paramagnetic nonheme iron in brain tissue. Brain iron quantification during early development provides insights into the underlying mechanism of brain maturation. PURPOSE: To quantify the spatiotemporal variations of brain iron-related magnetic susceptibility in deep gray matter nuclei during early development by using QSM. STUDY TYPE: Retrospective. SUBJECTS: Eighty-seven infants and children aged 1 month to 6 years. FIELD STRENGTH/SEQUENCE: Enhanced T2 *-weighted angiography using a 3D gradient-echo sequence at 3.0T. ASSESSMENT: QSM was calculated by modified sophisticated harmonic artifact reduction for phase data and sparse linear equations and sparse least squares-based algorithm. Means of susceptibility in deep gray matter nuclei (caudate nucleus, putamen, globus pallidus, thalamus) relative to that in splenium of corpus callosum were measured. STATISTICAL TESTS: Relationships of mean susceptibility with age and referenced iron concentration were tested by Pearson correlation. Differences of mean susceptibility between the selected nuclei in each age group were compared by one-way analysis of variance (ANOVA) and Fisher's Linear Significant Difference (LSD) test. RESULTS: Positive correlations of susceptibility with both referenced iron concentration and age were found (P < 0.0001); particularly, globus pallidus showed the highest correlation with age (correlation coefficient, 0.882; slope, 1.203; P < 0.001) and greatest susceptibility (P < 0.05) among the selected nuclei. DATA CONCLUSION: QSM allows the feasible quantification of iron deposition in deep gray matter nuclei in infants and young children, which exhibited gradual accumulation at different speeds. The fastest and highest iron accumulation was observed in the globus pallidus with increasing age during early development. LEVEL OF EVIDENCE: 4 Technical Efficacy:Stage 2 J. Magn. Reson. Imaging 2018.

Imaging of nigrosome 1 in substantia nigra at 3T using multiecho susceptibility map-weighted imaging (SMWI).

PURPOSE: To enhance the visibility of nigrosome 1 in substantia nigra, which has recently been suggested as an imaging biomarker for Parkinson's disease (PD) at 3T magnetic resonance imaging (MRI). MATERIALS AND METHODS: The substantia nigra structure was visualized at 3T MRI using multiecho susceptibility map-weighted imaging (SMWI) in 15 healthy volunteers and 6 patients with Parkinson's disease (PD). The visibility of nigrosome 1 was further enhanced by acquiring data in an oblique-coronal imaging plane at a high spatial resolution (0.5 × 0.5 × 1.0 mm3 ). To compare the visibility, the contrast-to-noise ratios (CNR) of the nigrosome 1 structure relative to the neighboring substantia nigra structure were evaluated in the SMWI and other conventional susceptibility contrast images (magnitude, frequency, quantitative susceptibility map [QSM] and susceptibility-weighted image). RESULTS: In healthy volunteers, the CNRs of the nigrosome 1 structure were 1.04 ± 0.38, 0.84 ± 0.32, 1.04 ± 0.40, 0.86 ± 0.41, and 1.45 ± 0.48 for magnitude, frequency, quantitative susceptibility map, susceptibility-weighted image, and SMWI, respectively. Compared to conventional susceptibility contrast images, the SMWI method significantly improved the CNR of nigrosome 1 (P = 0.014 for magnitude, P = 0.030 for QSM, and P < 0.001 for frequency and SWI, respectively). The magnetic susceptibility difference between nigrosome 1 and neighboring substantia nigra structures was 0.037 ± 0.016 ppm (measured in QSM, P < 0.001) in healthy volunteers. In the PD patients, the visibility of the nigrosome 1 structures was reduced. CONCLUSION: The SMWI method enhances the visibility of nigrosome 1 structures at 3T MRI when compared to conventional susceptibility contrast images. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:528-536.

Simultaneous quantitative mapping of conductivity and susceptibility using a double-echo ultrashort echo time sequence: Example using a hematoma evolution study.

PURPOSE: The primary purpose of this study is to propose a method for the simultaneous quantitative three-dimensional (3D) mapping of conductivity and susceptibility using double-echo ultrashort echo time (UTE) imaging. The secondary purpose is to investigate the changes of these properties over time during in vitro hematoma evolution in blood samples. METHODS: The first and second set of echo data for a UTE sequence were used to perform quantitative conductivity mapping (QCM) and quantitative susceptibility mapping (QSM), respectively. A simulation study was conducted to determine the echo time (TE) range that was acceptable for QCM. Subsequently, a NaCl phantom experiment and in vivo 3D QCM and QSM demonstrations were performed. The changes in electromagnetic (EM) properties over time were studied using in vitro blood coagulation experiments with venous blood from healthy volunteers. RESULTS: Quantitative and qualitative analyses showed small differences in the QCM for TE values up to 300 µs. The estimated conductivity and susceptibility values monotonically increased during the first few hours of the hematoma evolution experiments. However, although the susceptibility values continued to increase, the conductivity values were steady after 24 h. CONCLUSION: The proposed method can be useful for determining EM property changes (including those during hemorrhage) and providing additional information about the state of the blood. Magn Reson Med 76:214-221, 2016. © 2015 Wiley Periodicals, Inc.

Volumetric R2 * mapping using z-shim multi-echo gradient echo imaging.

PURPOSE: To introduce macroscopic B0 field inhomogeneity-compensated volumetric R2 * mapping method with a three-dimensional (3D) z-shim multi-echo acquisition. METHODS: The proposed z-shim sequence acquired conventional and z-shimmed echoes alternately with bipolar readout gradients. A constant-valued z-shim gradient was applied prior to each negative readout gradient lobe. A phase combination algorithm was also proposed based on this pulse sequence, which acquires a B0 inhomogeneity-compensated field map that was shown to play a critical role for accurate R2 * mapping. A modified signal model based on recently suggested model for 3D acquisition was proposed for R2 * quantification. RESULTS: To validate the performance of the proposed method, phantom and in vivo experiments were performed and compared with other methods. An increase in the range of field inhomogeneity correction was shown in the phantom results. For in vivo studies, the proposed method showed enhanced R2 * map quality for the different subjects. CONCLUSION: The proposed method improves R2 * estimation, especially in the frontal and temporal regions.

Simultaneous imaging of in vivo conductivity and susceptibility.

PURPOSE: Approaches for quantitative mapping of electric conductivity and magnetic susceptibility using MRI have been developed independently. The purpose of this study is to present a method to simultaneously acquire information on conductivity and susceptibility and to produce images based on these properties. METHODS: A 3D multiecho gradient-echo sequence was used. Phase evolution during the multiecho was used to produce quantitative susceptibility maps, while the phase value at zero echo time was retrieved, and used to generates quantitative conductivity maps. Electromagnetic simulations were performed to evaluate the phase distribution due to conductivity variations. Phantom and in vivo data were also acquired to assess the quality of images produced. RESULTS: Simulations demonstrated that phase differences across objects increases with size and conductivity. For an accurate conductivity estimate, the maximum echo time was approximately equal to the true T2* value in order to achieve signal-to-noise ratio maximization. The most accurate susceptibility was obtained when separating phase contribution from conductivity. Phantom and in vivo results showed good quality images representing the electromagnetic properties. CONCLUSION: A simultaneous quantitative electromagnetic property imaging approach is demonstrated here. The approach not only improves the efficiency of mapping electromagnetic properties, but can also improve the accuracy of susceptibility mapping by separating image phases introduced by conductivity and susceptibility.

Susceptibility map-weighted imaging (SMWI) for neuroimaging.

PURPOSE: To propose a susceptibility map-weighted imaging (SMWI) method by combining a magnitude image with a quantitative susceptibility mapping (QSM) -based weighting factor thereby providing an alternative contrast compared with magnitude image, susceptibility-weighted imaging, and QSM. METHODS: A three-dimensional multi-echo gradient echo sequence is used to obtain the data. The QSM was transformed to a susceptibility mask that varies in amplitude between zero and unity. This mask was multiplied several times with the original magnitude image to create alternative contrasts between tissues with different susceptibilities. A temporal domain denoising method to enhance the signal-to-noise ratio was further applied. Optimal reconstruction processes of the SMWI were determined from simulations. RESULTS: Temporal domain denoising enhanced the signal-to-noise ratio, especially at late echoes without spatial artifacts. From phantom simulations, the optimal number of multiplication and threshold values was chosen. Reconstructed SMWI created different contrasts based on its weighting factors made from paramagnetic or diamagnetic susceptibility tissue and provided an excellent delineation of microhemorrhage without blooming artifacts typically caused by the nonlocal property of phase. CONCLUSION: SMWI presents an alternative contrast for susceptibility-based imaging. The validity of this method was demonstrated using in vivo data. This proposed method together with denoising allows high-quality reconstruction of susceptibility-weighted image of human brain in vivo.

A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI.

Patient movement during Magnetic Resonance Imaging (MRI) scan can cause severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several echoes are typically measured during a single repetition period, where the earliest echoes show less contrast between various tissues, while the later echoes are more susceptible to artifacts and signal dropout. In this paper, we propose a knowledge interaction paradigm that jointly learns feature details from multiple distorted echoes by sharing their knowledge with unified training parameters, thereby simultaneously reducing motion artifacts of all echoes. This is accomplished by developing a new scheme that boosts a Single Encoder with Multiple Decoders (SEMD), which assures that the generated features not only get fused but also learned together. We called the proposed method Knowledge Interaction Learning between Multi-Echo data (KIL-ME-based SEMD). The proposed KIL-ME-based SEMD allows to share information and gain an understanding of the correlations between the multiple echoes. The main purpose of this work is to correct the motion artifacts and maintain image quality and structure details of all motion-corrupted echoes towards generating high-resolution susceptibility enhanced contrast images, i.e., SWI, using a weighted average of multi-echo motion-corrected acquisitions. We also compare various potential strategies that might be used to address the problem of reducing artifacts in multi-echoes data. The experimental results demonstrate the feasibility and effectiveness of the proposed method, reducing the severity of motion artifacts and improving the overall clinical image quality of all echoes with their associated SWI maps. Significant improvement of image quality is observed using both motion-simulated test data and actual volunteer data with various motion severity strengths. Eventually, by enhancing the overall image quality, the proposed network can increase the effectiveness of the physicians' capability to evaluate and correctly diagnose brain MR images.

Change of iron content in brain regions after intravenous iron therapy in restless legs syndrome: quantitative susceptibility mapping study.

STUDY OBJECTIVES: The pathomechanism of restless legs syndrome (RLS) is related to brain iron deficiency and iron therapy is effective for RLS; however, the effect of iron therapy on human brain iron state has never been studied with magnetic resonance imaging. This study aimed to investigate the change of brain iron concentrations in patients with RLS after intravenous iron therapy using quantitative susceptibility mapping (QSM). METHODS: We enrolled 31 RLS patients and 20 healthy controls. All participants underwent initial baseline (t0) assessment using brain magnetic resonance imaging, serum iron status, and sleep questionnaires including international RLS Study Group rating scale (IRLS). RLS patients underwent follow-up tests at 6 and 24 weeks (t1 and t2) after receiving 1000 mg ferric carboxymaltose. Iron content of region-of-interest on QSM images was measured for 13 neural substrates using the fixed-shaped method. RESULTS: RLS symptoms evaluated using IRLS were significantly improved after iron treatment (t0: 29.7 ± 6.5, t1: 19.5 ± 8.5, t2: 21.3 ± 10.1; p < .001). There was no significant difference in susceptibility values between the controls and RLS patients at t0. In the caudate nucleus, putamen, and pulvinar thalamus of RLS patients, the QSM values differed significantly for three timepoints (p = .035, .048, and .032, respectively). The post-hoc analysis revealed that the QSM values increased at t1 in the caudate nucleus (66.8 ± 18.0 vs 76.4 ± 16.6, p = .037) and decreased from t1 to t2 in the putamen (69.4 ± 16.3 vs 62.5 ± 13.6, p = .025). Changes in the QSM values for the pulvinar and caudate nuclei at t1 were positively and negatively correlated with symptomatic improvement, respectively (r = 0.361 and -0.466, respectively). CONCLUSIONS: Intravenous iron treatment results in changes in brain iron content which correlate to reductions in RLS severity. This suggests a connection between symptom improvement and the associated specific brain regions constituting the sensorimotor network.

Fast and tissue-optimized mapping of magnetic susceptibility and T2* with multi-echo and multi-shot spirals.

Gradient-echo MRI of resonance-frequency shift and T2* values exhibit unique tissue contrast and offer relevant physiological information. However, acquiring 3D-phase images and T2* maps with the standard spoiled gradient echo (SPGR) sequence is lengthy for routine imaging at high-spatial resolution and whole-brain coverage. In addition, with the standard SPGR sequence, optimal signal-to-noise ratio (SNR) cannot be achieved for every tissue type given their distributed resonance frequency and T2* value. To address these two issues, a SNR optimized multi-echo sequence with a stack-of-spiral acquisition is proposed and implemented for achieving fast and simultaneous acquisition of image phase and T2* maps. The analytical behavior of the phase SNR is derived as a function of resonance frequency, T2* and echo time. This relationship is utilized to achieve tissue optimized SNR by combining phase images with different echo times. Simulations and in vivo experiments were designed to verify the theoretical predictions. Using the multi-echo spiral acquisition, whole-brain coverage with 1 mm isotropic resolution can be achieved within 2.5 min, shortening the scan time by a factor of 8. The resulting multi-echo phase map shows similar SNR to that of the standard SPGR. The acquisition can be further accelerated with non-Cartesian parallel imaging. The technique can be readily extended to other multi-shot readout trajectories besides spiral. It may provide a practical acquisition strategy for high resolution and simultaneous 3D mapping of magnetic susceptibility and T2*.

Association of Cerebral Blood Flow and Brain Tissue Relaxation Time With Neurodevelopmental Outcomes of Preterm Neonates: Multidelay Arterial Spin Labeling and Synthetic MRI Study.

OBJECTIVES: Both cerebral blood flow (CBF) and brain tissue relaxation times are known to reflect maturation in the neonatal brain. However, we do not yet know if these factors are associated with neurodevelopmental outcomes. The objective of this study was to acquire CBF and relaxation time in preterm neonates, using multidelay arterial spin labeling and synthetic magnetic resonance imaging (MRI), and show their association with later neurodevelopmental outcomes. MATERIALS AND METHODS: In this prospective study, preterm neonates were recruited, and multidelay arterial spin labeling and synthetic MRI were performed between September 2017 and December 2018. These neonates underwent the Bayley Scales of Infant Development test at 18 months of age, and both cognitive and motor outcome scores were measured. Transit time-corrected CBF and T1 and T2 relaxation time values were measured for different brain regions. The measured values were correlated with gestational age (GA) at birth and corrected GA at the MRI scan. Simple and multiple linear regression analyses were performed for the measured values and neurodevelopmental outcome scores. RESULTS: Forty-nine neonates (median [interquartile range] GA, 30 [2] weeks, 209 [17] days; 28 boys) underwent MRI scans at or near term-equivalent age (median [interquartile range] corrected GA, 37 [2] weeks, 258 [14] days). Transit time-corrected CBF (coefficient, 0.31-0.59) and relaxation time (coefficient, -0.39 to -0.86) values showed significant correlation with corrected GA but not with GA. After controlling for GA, the frontal white matter CBF in preterm neonates showed a negative relationship with cognitive outcome scores (ß = -0.97; P = 0.029). Frontal white matter T1 relaxation times showed a positive relationship with cognitive outcome scores (ß = 0.03; P = 0.025) after controlling for GA. CONCLUSIONS: Higher CBF values and lower T1 relaxation times in frontal white matter were associated with poorer cognitive outcomes. As quantitative neuroimaging markers, CBF and relaxation times may help predict neurodevelopmental outcomes in preterm neonates.

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer.

Background: Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting. Methods: We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items. Results: The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05). Conclusions: Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.

Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks.

PURPOSE: Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T1 , T2 , and proton density (PD). The study aims to develop a multicontrast reconstruction method based on joint parallel imaging (JPI) and joint deep learning (JDL) to enable further acceleration of synthetic MRI. METHODS: The JPI and JDL methods are extended and combined to improve reconstruction for better-quality, synthesized images. JPI is performed as a first step to estimate the missing k-space lines, and JDL is then performed to correct and refine the previous estimate with a trained neural network. For the JDL architecture, the original variable splitting network (VS-Net) is modified and extended to form a joint variable splitting network (JVS-Net) to apply to multicontrast reconstructions. The proposed method is designed and tested for multidynamic multiecho (MDME) images with Cartesian uniform under-sampling using acceleration factors between 4 and 8. RESULTS: It is demonstrated that the normalized root-mean-square error (nRMSE) is lower and the structural similarity index measure (SSIM) values are higher with the proposed method compared to both the JPI and JDL methods individually. The method also demonstrates the potential to produce a set of synthesized contrast-weighted images that closely resemble those from the fully sampled acquisition without erroneous artifacts. CONCLUSION: Combining JPI and JDL enables the reconstruction of highly accelerated synthetic MRIs.

Comparison of multiplexed sensitivity encoding and single-shot echo-planar imaging for diffusion-weighted imaging of the liver.

PURPOSE: To compare multiplexed sensitivity encoding (MUSE) and conventional diffusion-weighted magnetic resonance imaging (cDWI) techniques in liver MRI. METHODS: Fifty-nine patients who underwent both two-shot echo-planar DWI using MUSE and single-shot echo-planar cDWI at a 3.0-T MRI system were included. Qualitative parameters were independently evaluated by three radiologists, and quantitative parameters were calculated on the basis of region of interest measurements. Receiver operating characteristic curve analysis and McNemar's test were used to compare solid lesion characterization results and lesion detectability, respectively. RESULTS: All reviewers found less image noise, sharper liver contours, milder susceptibility artifacts, and better lesion conspicuity in MUSE-DWI than in cDWI (reader average mean, 4.1-4.5 vs. 3.5-4.0; p < 0.05). The signal-to-noise ratio (SNR) of the liver was significantly higher in MUSE-DWI than in cDWI (right lobe: mean, 9.39 vs. 8.10, p < 0.001; left lobe: mean, 8.34 vs. 7.19, p < 0.001), while the SNR of the lesion (mean, 23.72 vs. 23.88, p = 0.911) and lesion-to-liver contrast-to-noise ratio (mean, 14.65 vs. 15.41, p = 0.527) were comparable between MUSE-DWI and cDWI. Solid lesion characterization results were comparably accurate between MUSE-DWI and cDWI (reader average area under the receiver operating characteristic curve, 0.985 vs. 0.986, p = 0.480). The detectability of lesions was better in MUSE-DWI than in cDWI (reader consensus, 83.7 % [41/49] vs. 67.3 % [33/49], p = 0.021). CONCLUSION: MUSE-DWI can provide multi-shot liver DWI with less noise, fewer distortions, improved SNR of the liver, and better lesion detectability.

Clinical Feasibility of Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging with Computed Diffusion-Weighted Imaging Technique in Breast Cancer Patients.

BACKGROUND: We evaluated the feasibility of the reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with computed DWI technique by comparison and analysis of the inter-method agreement among acquired rFOV DWI (rFOVA), rFOV DWI with computed DWI technique (rFOVS), and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in patients with breast cancer. METHODS: A total of 130 patients with biopsy-proven breast cancers who underwent breast MRI from April 2017 to December 2017 were included in this study. The rFOVS were reformatted by calculation of the apparent diffusion coefficient curve obtained from rFOVA b = 0 s/mm2 and b = 500 s/mm2. Visual assessment of the image quality of rFOVA b = 1000 s/mm2, rFOVS, and DCE MRI was performed using a four-point grading system. Morphologic analyses of the index cancer was performed on rFOVA, rFOVS, and DCE MRI. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast of tumor-to-parenchyma (TPC) were calculated. RESULTS: Image quality scores with rFOVA, rFOVS, and DCE MRI were not significantly different (p = 0.357). Lesion analysis of shape, margin, and size of the index cancer also did not show significant differences among the three sequences (p = 0.858, p = 0.242, and p = 0.858, respectively). SNR, CNR, and TPC of DCE MRI were significantly higher than those of rFOVA and rFOVS (p < 0.001, p = 0.001, and p = 0.016, respectively). Significant differences were not found between the SNR, CNR, and TPC of rFOVA and those of rFOVS (p > 0.999, p > 0.999, and p > 0.999, respectively). CONCLUSION: The rFOVA and rFOVS showed nearly equivalent levels of image quality required for morphological analysis of the tumors and for lesion conspicuity compared with DCE MRI.

Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment.

We investigated the capability of a trained deep learning (DL) model with a convolutional neural network (CNN) in a different scanning environment in terms of ameliorating the quality of synthetic fluid-attenuated inversion recovery (FLAIR) images. The acquired data of 319 patients obtained from the retrospective review were used as test sets for the already trained DL model to correct the synthetic FLAIR images. Quantitative analyses were performed for native synthetic FLAIR and DL-FLAIR images against conventional FLAIR images. Two neuroradiologists assessed the quality and artifact degree of the native synthetic FLAIR and DL-FLAIR images. The quantitative parameters showed significant improvement on DL-FLAIR in all individual tissue segments and total intracranial tissues than on the native synthetic FLAIR (p < 0.0001). DL-FLAIR images showed improved image quality with fewer artifacts than the native synthetic FLAIR images (p < 0.0001). There was no significant difference in the preservation of the periventricular white matter hyperintensities and lesion conspicuity between the two FLAIR image sets (p = 0.217). The quality of synthetic FLAIR images was improved through artifact correction using the trained DL model on a different scan environment. DL-based correction can be a promising solution for ameliorating the quality of synthetic FLAIR images to broaden the clinical use of synthetic magnetic resonance imaging (MRI).

The feasibility of synthetic MRI in breast cancer patients: comparison of T2 relaxation time with multiecho spin echo T2 mapping method.

OBJECTIVE:: To compare the T2 relaxation times acquired with synthetic MRI to those of multi-echo spin-echo sequences and to evaluate the usefulness of synthetic MRI in the clinical setting. METHODS:: From January 2017 to May 2017, we included 51 patients with newly diagnosed breast cancer, who underwent additional synthetic MRI and multiecho spin echo (MESE) T2 mapping sequences. Synthetic MRI technique uses a multiecho and multidelay acquisition method for the simultaneous quantification of physical properties such as T1 and T2 relaxation times and proton density image map. A radiologist with 9 years of experience in breast imaging drew region of interests manually along the tumor margins on two consecutive axial sections including the center of tumor mass and in the fat tissue of contralateral breast on both synthetic T2 map and MESE T2 map images. RESULTS:: The mean T2 relaxation time of the cancer was 84.75 ms (± 15.54) by synthetic MRI and 90.35 ms (± 19.22) by MESE T2 mapping. The mean T2 relaxation time of the fat was 129.22 ms (± 9.53) and 102.11 ms (± 13.9), respectively. Bland-Altman analysis showed mean difference of 8.4 ms for the breast cancer and a larger mean difference of 27.8 ms for the fat tissue. Spearman's correlation test showed that there was significant positive correlation between synthetic MRI and MESE sequences for the cancer (r = 0.713, p < 0.001) and for the fat (r = 0.551, p < 0.001). The positive estrogen receptor and low histologic grade were associated with little differences between two methods (p = 0.02 and = 0.043, respectively). CONCLUSION:: T2 relaxation times of breast cancer acquired with synthetic MRI showed positive correlation with those of MESE T2 mapping. Synthetic MRI could be useful for the evaluation of tissue characteristics by simultaneous acquisition of several quantitative physical properties. ADVANCES IN KNOWLEDGE:: Synthetic MRI is useful for the evaluation of T2 relaxation times of the breast cancers.