Spoiled Gradient-recalled Echo Research Articles

Quantifying Brain Myelin Water Fraction in a Guinea Pig Model of Spontaneous Intrauterine Growth Restriction.

Intrauterine growth restriction (IUGR) is an obstetrical condition where a fetus has not achieved its genetic potential. A consequence of IUGR is a decrease in brain myelin content. Myelin water imaging (MWI) has been used to assess fetal myelin water fraction (MWF) and might potentially assess myelination changes associated with IUGR. To quantify and compare the MWF of non-IUGR and IUGR fetal guinea pigs (GPs) in late gestation. Prospective animal model. Dunkin-Hartley GP model of spontaneous IUGR (mean ± SD: 60 ± 1.2 days gestation; 19 IUGR, 52 control). Eight spoiled gradient-recalled (SPGR) gradient echo volumes (flip angles [α]: 2°-16°), and two sets of eight balanced steady-state free precession (bSSFP) gradient echo volumes (α: 8° - 64°), at 0° and 180° phase increments, at 3.0 T. MWF maps were generated for each fetal GP brain using multicomponent driven equilibrium single pulse observation of T1 /T2 (mcDESPOT). MWF was quantified in the fetal corpus callosum (CC), fornix (FOR), parasagittal white matter (PSW), and whole fetal brain. Linear regression was performed between five fetal IUGR markers (body volume, body-to-pregnancy volume ratio, brain-to-liver volume ratio, brain-to-placenta volume ratio, and brain-to-body volume ratio) and MWF (coefficient of determination, R2 ). A t-test with a linear mixed model compared the MWF of non-IUGR and IUGR fetal GPs (significance was determined at α < 0.05). The MWF of the control fetuses are (mean ± SD): 0.23 ± 0.02 (CC), 0.31 ± 0.02 (FOR), 0.28 ± 0.02 (PSW), and 0.20 ± 0.01 (whole brain). The MWF of the IUGR fetuses are (mean ± SD): 0.19 ± 0.02 (CC), 0.27 ± 0.01 (FOR), 0.24 ± 0.03 (PSW), and 0.16 ± 0.01 (whole brain). Significant differences in MWF were found between the non-IUGR and IUGR fetuses in every comparison. The mean MWF of IUGR fetal GPs is significantly lower than non-IUGR fetal GPs. 2 TECHNICAL EFFICACY: Stage 1.

Hemifacial spasm associated with posterior inferior cerebellar artery aneurysm: illustrative case.

Hemifacial spasms (HFSs) complicated by intracranial aneurysms are rare. Recently, endovascular treatment has been widely used for this disease entity and can allow the cessation of intracranial aneurysm arterial pulsation, leading to recovery from the HFS. Here, the authors present a case of HFS associated with an ipsilateral posterior inferior cerebellar artery (PICA) aneurysm successfully treated with open surgery. A 68-year-old woman was annually followed-up for an incidentally found right PICA aneurysm. Over 3 years, the PICA aneurysm gradually increased in size, which eventually led to right HFS. An axial fast spoiled gradient-recalled echo sequence with gadolinium enhancement showed the PICA aneurysm compressing the root exit zone (REZ), which was attributed as the cause of the HFS. However, a fusion image of the three-dimensional T1-weighted fast spin-echo sequence and magnetic resonance angiogram clearly showed a direct contact between the REZ and the anterior inferior cerebellar artery (AICA), which was located at the apex of the PICA aneurysm. Intraoperatively, the AICA was found compressing the REZ; hence, microvascular decompression with aneurysmal clipping was performed. The HFS resolved immediately after surgery. In cases of HFS associated with an ipsilateral intracranial aneurysm, a detailed neuroradiological assessment to identify the responsible lesion is important to use the most optimal treatment of choice.

Data adaptive regularization with reference tissue constraints for liver quantitative susceptibility mapping.

To improve repeatability and reproducibility across acquisition parameters and reduce bias in quantitative susceptibility mapping (QSM) of the liver, through development of an optimized regularized reconstruction algorithm for abdominal QSM. An optimized approach to estimation of magnetic susceptibility distribution is formulated as a constrained reconstruction problem that incorporates estimates of the input data reliability and anatomical priors available from chemical shift-encoded imaging. The proposed data-adaptive method was evaluated with respect to bias, repeatability, and reproducibility in a patient population with a wide range of liver iron concentration (LIC). The proposed method was compared to the previously proposed and validated approach in liver QSM for two multi-echo spoiled gradient-recalled echo protocols with different acquisition parameters at 3T. Linear regression was used for evaluation of QSM methods against a reference FDA-approved -based LIC measure and measurements; repeatability/reproducibility were assessed by Bland-Altman analysis. The data-adaptive method produced susceptibility maps with higher subjective quality due to reduced shading artifacts. For both acquisition protocols, higher linear correlation with both - and -based measurements were observed for the data-adaptive method ( for , for ) than the standard method ( and ). For both protocols, the data-adaptive method enabled better test-retest repeatability (repeatability coefficients 0.19/0.30 ppm for the data-adaptive method, 0.38/0.47 ppm for the standard method) and reproducibility across protocols (reproducibility coefficient 0.28 vs. 0.53ppm) than the standard method. The proposed data-adaptive QSM algorithm may enable quantification of LIC with improved repeatability/reproducibility across different acquisition parameters as 3T.

Characterization of Microvascular Invasion in Hepatocellular Carcinoma Using Computational Modeling of Interstitial Fluid Pressure and Velocity.

Most solid tumors show increased interstitial fluid pressure (IFP), and this increased IFP is an obstacle to treatment. A noninvasive model for measuring IFP in hepatocellular carcinoma (HCC) is an unresolved issue. To develop a noninvasive model to measure IFP and interstitial fluid velocity (IFV) in HCC and to characterize the microvascular invasion (MVI) status by using this model. Retrospective. A total of 97 HCC patients (mean age 57.6± 10.9 years, 77.3% males), 53 of them with MVI and 44 of them without MVI. A 3-T, three-dimensional spoiled gradient-recalled echo. MVI was defined as microscopic vascular invasion of small vessels within the peritumoral liver tissue. The volumes of interest (VOIs) were manually delineated and enclosed the tumor lesion and healthy liver parenchyma, respectively. The extended Tofts model (ETM) was used to estimate permeability parameters from all the VOIs. Subsequently, the continuity partial differential equation (PDE) was implemented and IFP and IFV were acquired. Wilcoxon signed-ranks tests, histogram analysis, Mann-Whitney U test, Fisher's exact test, least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC), Youden index, DeLong test, and Benjamini-Hochberg correction. A P value <0.05 was considered statistically significant. The HCC lesions exhibited elevated IFP and reduced IFV. There were no significant differences in any measured demographic and clinical features between the MVI-positive and MVI-negative groups, except for tumor size. Nine IFP histogram analysis-derived parameters and seven IFV histogram analysis-derived parameters could be used to characterize the MVI status. LASSO regression selected five features: IFP maximum, IFP 10th percentile, IFP 90th percentile, IFV SD, and IFV 10th percentile. The combination of these features showed the highest AUC (0.781) and specificity (77.3%). A noninvasive IFP and IFV measurement model for HCC was developed. Specific IFP- and IFV-derived parameters exhibited significant association with the MVI status. 3. Stage 2.

Functional and Morphological Brain Alterations in Dysthyroid Optic Neuropathy: A Combined Resting-State fMRI and Voxel-Based Morphometry Study.

Increasing evidence has indicated that the entire visual pathway from retina to visual cortex may be involved in dysthyroid optic neuropathy (DON) pathological mechanisms. To explore the functional and morphological brain characteristics in DON and their relationship with ophthalmologic performance. Retrospective. A total of 30 DON patients, 40 thyroid-associated ophthalmopathy (TAO) without DON patients and 21 healthy-controls (HCs). A 3.0 T, 3D T1-weighted spoiled gradient-recalled echo and gradient-recalled echo-planar imaging. Functional and structural alterations in brain regions were evaluated with fractional amplitude of low-frequency fluctuations, degree centrality (DC), and gray matter volume (GMV). Clinical activity score (CAS) is assessed across patients. One-way analysis of variance with post hoc two sample t-tests (GRF-corrected, voxel level: P< 0.005, cluster level: P< 0.05) and correlation analysis (significance level: P< 0.05). Compared to HCs, DON patients had significantly decreased DC values in the bilateral BA17 and BA18 regions. Compared to the TAO group, DON patients had decreased GMV in the left anterior cingulate cortex, left middle frontal gyrus, left lingual gyrus, left parietal gyrus, right Rolandic operculum, left supplementary motor area, and right middle temporal gyrus. In addition, GMV in the right Rolandic operculum was significantly positively correlated with CAS (correlation coefficient: r= 0.448). This study showed significant morphological and functional alterations in visual cortex and morphological alterations in partial default mode network regions of DON patients, which may provide insights into the mechanism of vision loss and may facilitate the diagnosis and treatment of DON. 3. Stage 3.

An analysis of the correlation between diabetic retinopathy and preretinal oxygen tension using three-dimensional spoiled gradient-recalled echo sequence imaging

BackgroundThe aims of this study were to evaluate the levels of preretinal oxygen tension in patients with diabetes who did not have hypertension by using three-dimensional spoiled gradient-recalled (3D-SPGR) echo sequence imaging and to explore the correlation between diabetic retinopathy (DR) and changes in preretinal oxygen tension.MethodThis study involved 15 patients with type 2 diabetes without hypertension, who were divided into a diabetic retinopathy (DR) group (n = 10 eyes) and a diabetic non-retinopathy (NDR) group (n = 20 eyes), according to the results of a fundus photography test. Another healthy control group (n = 14 eyes) also participated in the study. The preretinal vitreous optic disc area, nasal side, and temporal side signal intensity of the eyes was assessed before and after oxygen inhalation with the use of 3D-SPGR echo magnetic resonance imaging (MRI). The signal acquisition time was 10, 20, 30, 40, and 50 min after oxygen inhalation.ResultsThe results showed that, in the DR and NDR groups, the preretinal vitreous oxygen tension increased rapidly at 10 min after oxygen inhalation and peaked at 30–40 min, and the increased slope of the DR group was higher than that of the NDR group. The oxygen tension of the preretinal vitreous gradually increased after oxygen inhalation, and the difference between the DR and NDR groups and the control group was statistically significant (P < 0.05). The preretinal vitreous oxygen tension was higher in the optic disc, temporal side, and nasal side in the NDR group than in the control group, and the difference was statistically significant (P < 0.05). The maximum slope ratios of the optic disc and the temporal side of the DR group were greater than those of the control group, and the difference was statistically significant (P < 0.05).ConclusionThree-dimensional-SPGR echo MRI sequencing technology is useful for detecting preretinal oxygen tension levels in patients with diabetes. It can be used as one of the functional and imaging observation indicators for the early diagnosis of DR.

High Signal Intensity of the Cochlear Modiolus on Unenhanced T1-Weighted Images in Classical Infratentorial Superficial Siderosis.

Superficial siderosis (SS) results from chronic bleeding in the subarachnoid space. SS can be classified as infratentorial SS (i-SS) and supratentorial SS (s-SS). The cochlear modiolus (CM) normally shows low signal intensity (SI) on Tl-weighted images (T1WI). We noticed persistently high SI of the CM on unenhanced thin-sliced T1WI in patients with i-SS. The purpose of this study was to evaluate the correlation between SS and high SI of the CM on unenhanced T1WI. This retrospective study analyzed three cases with i-SS, eight cases with s-SS, and 23 normal controls (NC) evaluated on unenhanced thin-sliced T1WI with a three-dimensional spoiled gradient-recalled echo sequence. CM-T1SI scores of 0, 1, and 2 indicated low, iso, and high SI, respectively. In cases with scores of 2 evaluated several times, all scores were reviewed for each case. The CM-T1SI ratio was defined as the contrast ratio between the CM and the cerebellum. Differences between the three groups were statistically analyzed based on the CM-T1SI score and ratio. Receiver operative curve (ROC) analysis was used to determine the cut-off values for differentiating the i-SS group from the NC group based on the CM-T1SI ratio. Two patients with i-SS had a score of 2 on all evaluations. The CM-T1SI score and ratio differed significantly between the i-SS and NC groups. The accuracy of the CM-T1SI ratio for discriminating i-SS from NC was 98.9% at a cutoff value of 0.628. High SI of the CM on unenhanced TIWI can be an additional characteristic finding of i-SS.

Associations between brain volumetry and relaxometry signatures and the Edmonton Frail Scale in frailty.

Frailty is a geriatric condition characterized by a decreased reserve. The Edmonton frailty scale (EFS) has been widely used as an assessment tool in clinical practice. However, the brain's underlying pathophysiological changes in frailty and their associations with the EFS remain unclear. This study aimed to explore the associations between brain volumetry and relaxometry signatures and the EFS (and each domain score of the EFS) in frailty. A total of 40 non-demented subjects were enrolled in this prospective study. Frailty assessment was performed for each subject according to the EFS. All subjects underwent synthetic magnetic resonance imaging (MRI) (MAGnetic resonance image Compilation, MAGiC) and three-dimensional fast spoiled gradient-recalled echo (3D-FSPGR) T1-weighted structural image acquisitions on a 3.0 T MR scanner. Brain segmentation was performed based on quantitative values obtained from the MAGiC and 3D-FSPGR images. Volumetry and relaxometry of the global brain and regional gray matter (GM) were also obtained. The associations between the total EFS score (and the score of each domain) and the brain's volumetry and relaxometry were investigated by partial correlation while eliminating the effects of age. Multiple comparisons of regional GM volumetry and relaxometry analyses were controlled by false discovery rate (FDR) correction. All data were analyzed using the SPSS 13.0 statistical package (IBM, Armonk, NY, USA) and MATLAB (MathWorks, Natick, MA, USA). For global volumetry, significant correlations were found between multiple global volumetry parameters and the EFS, as well as the cognition score, functional independence score, nutrition score, and functional performance score (P<0.05). For global relaxometry, notable positive correlations were found between the T2 values of gray and white matter (WM) and the EFS (r=0.357, P=0.026; r=0.357, P=0.026, respectively). Significant correlations were also identified between the T2 value of GM, the T1, T2, and PD values of WM, and the cognition score (r=0.426, P=0.007; r=0.456, P=0.003; r=0.377, P=0.018; r=0.424, P=0.007, respectively), functional independence score (r=-0.392, P=0.014; r=-0.611, P<0.001; r=-0.367, P=0.022; r=-0.569, P<0.001, respectively), and functional performance score (r=0.337, P=0.036; r=0.472, P=0.002; r=0.354, P=0.027; r=0.376, P=0.018, respectively). For regional GM volumetry, multiple regions showed significant negative correlations with the EFS (P<0.05). Notable negative correlations were found between multiple regional GM volume and the functional independence score (P<0.05). For regional GM relaxometry, the T1 and T2 values of several regions showed significant negative correlations with the functional independence score (T1 value of caudate, r=-0.617, P<0.001; T2 value of insula, r=-0.510, P=0.015; T2 value of caudate, r=-0.633, P<0.001, respectively). No significant correlation was found between the domain scores of the EFS and regional GM PD values (P>0.05). In conclusion, brain volumetry and relaxometry signatures showed strong associations with the EFS and some EFS domain scores in frailty. These associations may reveal the possible underlying pathophysiology of the EFS and different domains of the EFS.

Tissue Probability Based Registration of Diffusion-Weighted Magnetic Resonance Imaging.

Current registration methods for diffusion-MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types. To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs). Retrospective longitudinal study. Thirty-two healthy participants were scanned twice (legacy data), divided into a training-set (n=16) and a test-set (n=16), and 35 randomly-selected participants from the Human Connectome Project. 3.0T, diffusion-weighted spin-echo echo-planar sequence; T1-weighted spoiled gradient-recalled echo (SPGR) sequence. A joint segmentation-registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine-learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools. Classifiers used for segmentation were evaluated using leave-one-out cross-validation and scored using Dice-index. Registration success was evaluated by voxel-wise variance, normalized cross-correlation of registered DTI maps, intra- and inter-subject similarity of the registered TPMs, and region-based intra-subject similarity using an anatomical atlas. One-way ANOVAs were performed to compare between our method and other registration tools. The proposed method outperformed mainstream registration tools as indicated by lower voxel-wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1-0.2; P < 0.05). A joint segmentation-registration approach based on diffusion-driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a "translation" of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images. 1 Technical Efficacy Stage: 1.

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.

Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious.Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance.Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations.Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only −0.605% (CI 95% = −0.847 to −0.363%) and agreement limits of −3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s.Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP.

Effectively Measuring Exercise-Related Variations in T1ρ and T2 Relaxation Times of Healthy Articular Cartilage.

Determining the compositional response of articular cartilage to dynamic joint-loading using MRI may be a more sensitive assessment of cartilage status than conventional static imaging. However, distinguishing the effects of joint-loading vs. inherent measurement variability remains difficult, as the repeatability of these quantitative methods is often not assessed or reported. To assess exercise-induced changes in femoral, tibial, and patellar articular cartilage composition and compare these against measurement repeatability. Prospective observational study. Phantom and 19 healthy participants. 3T; 3D fat-saturated spoiled gradient recalled-echo; T1ρ - and T2 -prepared pseudosteady-state 3D fast spin echo. The intrasessional repeatability of T1ρ and T2 relaxation mapping, with and without knee repositioning between two successive measurements, was determined in 10 knees. T1ρ and T2 relaxation mapping of nine knees was performed before and at multiple timepoints after a 5-minute repeated, joint-loading stepping activity. 3D surface models were created from patellar, femoral, and tibial articular cartilage. Repeatability was assessed using root-mean-squared-CV (RMS-CV). Using Bland-Altman analysis, thresholds defined as the smallest detectable difference (SDD) were determined from the repeatability data with knee repositioning. Without knee repositioning, both surface-averaged T1ρ and T2 were very repeatable on all cartilage surfaces, with RMS-CV <1.1%. Repositioning of the knee had the greatest effect on T1ρ of patellar cartilage with the surface-averaged RMS-CV = 4.8%. While T1ρ showed the greatest response to exercise at the patellofemoral cartilage region, the largest changes in T2 were determined in the lateral femorotibial region. Following thresholding, significant (>SDD) average exercise-induced in T1ρ and T2 of femoral (-8.0% and -5.3%), lateral tibial (-6.9% and -5.9%), medial tibial (+5.8% and +2.9%), and patellar (-7.9% and +2.8%) cartilage were observed. Joint-loading with a stepping activity resulted in T1ρ and T2 changes above background measurement error. 2 TECHNICAL EFFICACY STAGE: 1 J. MAGN. RESON. IMAGING 2020;52:1753-1764.

Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy

BackgroundStructure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and automatise this procedure, obtaining promising results. With the advent of magnetic resonance imaging (MRI)-guided radiotherapy, MR-based segmentation is becoming increasingly relevant. However, the majority of the studies investigated automatic contouring based on computed tomography (CT).PurposeIn this study, we investigate the feasibility of clinical use of deep learning-based automatic OARs delineation on MRI.Materials and methodsWe included 150 patients diagnosed with prostate cancer who underwent MR-only radiotherapy. A three-dimensional (3D) T1-weighted dual spoiled gradient-recalled echo sequence was acquired with 3T MRI for the generation of the synthetic-CT. The first 48 patients were included in a feasibility study training two 3D convolutional networks called DeepMedic and dense V-net (dV-net) to segment bladder, rectum and femurs. A research version of an atlas-based software was considered for comparison. Dice similarity coefficient, 95% Hausdorff distances (HD95), and mean distances were calculated against clinical delineations. For eight patients, an expert RTT scored the quality of the contouring for all the three methods. A choice among the three approaches was made, and the chosen approach was retrained on 97 patients and implemented for automatic use in the clinical workflow. For the successive 53 patients, Dice, HD95 and mean distances were calculated against the clinically used delineations.ResultsDeepMedic, dV-net and the atlas-based software generated contours in 60 s, 4 s and 10-15 min, respectively. Performances were higher for both the networks compared to the atlas-based software. The qualitative analysis demonstrated that delineation from DeepMedic required fewer adaptations, followed by dV-net and the atlas-based software. DeepMedic was clinically implemented. After retraining DeepMedic and testing on the successive patients, the performances slightly improved.ConclusionHigh conformality for OARs delineation was achieved with two in-house trained networks, obtaining a significant speed-up of the delineation procedure. Comparison of different approaches has been performed leading to the succesful adoption of one of the neural networks, DeepMedic, in the clinical workflow. DeepMedic maintained in a clinical setting the accuracy obtained in the feasibility study.

Four-angle method for practical ultra-high-resolution magnetic resonance mapping of brain longitudinal relaxation time and apparent proton density

Changes in longitudinal relaxation time (T1) and proton density (PD) are sensitive indicators of microstructural alterations associated with various central nervous system diseases as well as brain maturation and aging. In this work, we introduce a new approach for rapid and accurate high-resolution (HR) or ultra HR (UHR) mapping of T1 and apparent PD (APD) of the brain with correction of radiofrequency field, B1, inhomogeneities. The four-angle method (FAM) uses four spoiled-gradient recalled-echo (SPGR) images acquired at different flip angles (FA) and short repetition times (TRs). The first two SPGR images are acquired at low-spatial resolution and used to accurately map the active B1+ field with the recently introduced steady-state double angle method (SS-DAM). The estimated B1+ map is used in conjunction with the two other SPGR images, acquired at HR or UHR, to map T1 and APD. The method is evaluated with numerical, phantom, and in-vivo imaging measurements. Furthermore, we investigated imaging acceleration methods to further shorten the acquisition time. Our results indicate that FAM provides an accurate method for simultaneous HR or UHR mapping of T1 and APD in human brain in clinical high-field MRI. Derived parameter maps without B1+correction suffer from large inaccuracies, but this issue is well-corrected through use of the SS-DAM. Furthermore, the use of SPGR imaging with short TR and phased-array coil acquisition permits substantial imaging acceleration and enables robust HR or UHR T1 and APD mapping in a clinically acceptable time frame, with whole brain coverage obtained in less than 2 min or 5 min, respectively. The method exhibits high reproducibility and benefits from the use of the conventional SPGR sequence, available in all preclinical and clinical MRI machines, and very simple modeling to address a critical outstanding issue in neuroimaging.

Altered gray matter volume and disrupted functional connectivity of dorsolateral prefrontal cortex in men with heroin dependence.

Chronic heroin use can cause various neuropathological characteristics that may compromise brain function. The present study evaluated the alteration of gray matter volume (GMV) and its resting-state functional connectivity (rsFC) over the dorsolateral prefrontal cortex (DLPFC) among male heroin users. Thirty heroin-dependent men undergoing methadone maintenance therapy and 30 educational-level- and age-matched male controls were recruited for this study. To assess their GMV and rsFC, the participants were evaluated using spoiled gradient echo and gradient-recalled echo planar imaging sequences with a 3-Tesla General Electric MR scanner under resting state. The heroin-dependent men showed lower GMV over the right DLPFC in comparison with the controls. Further evaluation of the rsFC of the right DLPFC revealed a marked decrease in interhemispheric DLPFC connectivity among those with heroin dependence under control of head movement and GMV of the right DLPFC. Although the mechanism remains unclear, the present study shows that chronic heroin use is associated with alteration of morphology as well as rsFC over the right DLPFC. As the DLPFC plays an imperative role in various domains of cognitive function, service providers for heroin users should consider the impacts of possible DLPFC-related cognitive deficits on treatment effectiveness.

Technical report: gadoxetate-disodium-enhanced 2D R2* mapping: a novel approach for assessing bile ducts in living donors.

Gadoxetate-disodium (Gd-EOB-DTPA)-enhanced 3D T1- weighted (T1w) MR cholangiography (MRC) is an efficient method to evaluate biliary anatomy due to T1 shortening of excreted contrast in the bile. A method that exploits both T1 shortening and T2* effects may produce even greater bile duct conspicuity. The aim of our study is to determine feasibility and compare the diagnostic performance of two-dimensional (2D) T1w multi-echo (ME) spoiled gradient-recalled-echo (SPGR) derived R2* maps against T1w MRC for bile duct visualization in living liver donor candidates. Ten potential living liver donor candidates underwent pretransplant 3T MRI and were included in our study. Following injection of Gd-EOBDTPA and a 20-min delay, 3D T1w MRC and 2D T1w ME SPGR images were acquired. 2D R2* maps were generated inline by the scanner assuming exponential decay. The 3D T1w MRC and 2D R2* maps were retrospectively and independently reviewed in two separate sessions by three radiologists. Visualization of eight bile duct segments was scored using a 4-point ordinal scale. The scores were compared using mixed effects regression model. Imaging was tolerated by all donors and R2* maps were successfully generated in all cases. Visualization scores of 2D R2* maps were significantly higher than 3D T1w MRC for right anterior (p=0.003) and posterior (p=0.0001), segment 2 (p<0.0001), segment 3 (p=0.0001), and segment 4 (p<0.0001) ducts. Gd-EOB-DTPA-enhanced 2D R2* mapping is a feasible method for evaluating the bile ducts in living donors and may be a valuable addition to the living liver donor MR protocol for delineating intrahepatic biliary anatomy.

Two-Dimensional Spoiled Gradient-Recalled Echo Magnetic Resonance Imaging of the Liver Using Respiratory Navigator-Gating Techniques.

We assessed the feasibility of T1-weighted 2-dimensional spoiled gradient-recalled (2D SPGR) acquisition in steady-state imaging of the liver with various respiratory navigator gating techniques. A total of 12 healthy volunteers underwent in-phase and out-of-phase 2D SPGR imaging of the liver during breath-holding and free-breathing. Four techniques for respiratory navigation, 2 conventional navigator techniques and 2 self-navigator techniques, were used for free-breathing imaging. Good navigator waveforms were obtained in conventional navigation, whereas fluctuations were evident in self navigation. All of the 4 navigator-based methods provided better images in terms of background signals and visual image quality compared with images obtained with no respiratory control. However, differences remained in comparison with breath-holding. Superiority of self-navigation to conventional navigation was not shown. Navigator-gating techniques improved 2D SPGR images of the liver acquired during free-breathing, suggesting feasibility and beneficial effects, although navigator-based images were still inferior to breath-hold images.

Quantitative susceptibility mapping using principles of echo shifting with a train of observations sequence on 1.5 T MRI

PurposeTo evaluate the accuracy of susceptibility estimated from the principles of echo shifting with a train of observations (PRESTO) sequence using a 1.5T MRI system, we conducted experiments on the human brain using the PRESTO sequence and compared our results with the susceptibility obtained from spoiled gradient-recalled echo (GRE) sequence with flow compensation using quantitative susceptibility mapping (QSM) reconstruction. Materials and methodsExperiments on the human brain were conducted on 12 healthy volunteers (27±4years) using PRESTO and spoiled GRE sequences on a 1.5T scanner. The PRESTO sequence is an echo-shifted gradient echo sequence that allows high susceptibility sensitivity and rapid acquisition because of TE>TR compared with the spoiled GRE sequence. QSM analysis was performed on the obtained phase images using the iLSQR method. Estimated susceptibility maps were used for region of interest analyses and estimation of line profiles through iron-rich tissue and major vessels. ResultsOur results demonstrated that susceptibility maps were accurately estimated, without error, by QSM analysis of PRESTO and spoiled GRE sequences. Acquisition time in the PRESTO sequence was reduced by 43% compared with that in the spoiled GRE sequence. Differences did exist between susceptibility maps in PRESTO and spoiled GRE sequences for visualization and quantitative values of major blood vessels and the areas around them ConclusionThe PRESTO sequence enables correct estimation of tissue susceptibility with rapid acquisition and may be useful for QSM analysis of clinical use of 1.5T scanners.

Optimizing MR Scan Design for Model-Based ${T}_{1}$ , ${T}_{2}$ Estimation From Steady-State Sequences.

Rapid, reliable quantification of MR relaxation parameters T1 and T2 is desirable for many clinical applications. Steady-state sequences such as Spoiled Gradient-Recalled Echo (SPGR) and Dual-Echo Steady-State (DESS) are fast and well-suited for relaxometry because the signals they produce are quite sensitive to T1 and T2 variation. However, T1, T2 estimation with these sequences typically requires multiple scans with varied sets of acquisition parameters. This paper describes a systematic framework for selecting scan types (e.g., combinations of SPGR and DESS scans) and optimizing their respective parameters (e.g., flip angles and repetition times). The method is based on a Cramér-Rao Bound (CRB)-inspired min-max optimization that finds scan parameter combinations that robustly enable precise object parameter estimation. We apply this technique to optimize combinations of SPGR and DESS scans for T1, T2 relaxometry in white matter (WM) and grey matter (GM) regions of the human brain at 3T field strength. Phantom accuracy experiments show that SPGR/DESS scan combinations are in excellent agreement with reference measurements. Phantom precision experiments show that trends in T1,T2 pooled sample standard deviations reflect CRB-based predictions. In vivo experiments show that in WM and GM, T1 and T2 estimates from a pair of optimized DESS scans exhibit precision (but not necessarily accuracy) comparable to that of optimized combinations of SPGR and DESS scans. To our knowledge, T1 maps from DESS acquisitions alone are new. This example application illustrates that scan optimization may help reveal new parameter mapping techniques from combinations of established pulse sequences.

Anatomical pulmonary magnetic resonance imaging segmentation for regional structure-function measurements of asthma.

Pulmonary magnetic-resonance-imaging (MRI) and x-ray computed-tomography have provided strong evidence of spatially and temporally persistent lung structure-function abnormalities in asthmatics. This has generated a shift in their understanding of lung disease and supports the use of imaging biomarkers as intermediate endpoints of asthma severity and control. In particular, pulmonary (1)H MRI can be used to provide quantitative lung structure-function measurements longitudinally and in response to treatment. However, to translate such biomarkers of asthma, robust methods are required to segment the lung from pulmonary (1)H MRI. Therefore, their objective was to develop a pulmonary (1)H MRI segmentation algorithm to provide regional measurements with the precision and speed required to support clinical studies. The authors developed a method to segment the left and right lung from (1)H MRI acquired in 20 asthmatics including five well-controlled and 15 severe poorly controlled participants who provided written informed consent to a study protocol approved by Health Canada. Same-day spirometry and plethysmography measurements of lung function and volume were acquired as well as (1)H MRI using a whole-body radiofrequency coil and fast spoiled gradient-recalled echo sequence at a fixed lung volume (functional residual capacity + 1 l). We incorporated the left-to-right lung volume proportion prior based on the Potts model and derived a volume-proportion preserved Potts model, which was approximated through convex relaxation and further represented by a dual volume-proportion preserved max-flow model. The max-flow model led to a linear problem with convex and linear equality constraints that implicitly encoded the proportion prior. To implement the algorithm, (1)H MRI was resampled into ∼3 × 3 × 3 mm(3) isotropic voxel space. Two observers placed seeds on each lung and on the background of 20 pulmonary (1)H MR images in a randomized dataset, on five occasions, five consecutive days in a row. Segmentation accuracy was evaluated using the Dice-similarity-coefficient (DSC) of the segmented thoracic cavity with comparison to five-rounds of manual segmentation by an expert observer. The authors also evaluated the root-mean-squared-error (RMSE) of the Euclidean distance between lung surfaces, the absolute, and percent volume error. Reproducibility was measured using the coefficient of variation (CoV) and intraclass correlation coefficient (ICC) for two observers who repeated segmentation measurements five-times. For five well-controlled asthmatics, forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) was 83% ± 7% and FEV1 was 86 ± 9%pred. For 15 severe, poorly controlled asthmatics, FEV1/FV C = 66% ± 17% and FEV1 = 72 ± 27%pred. The DSC for algorithm and manual segmentation was 91% ± 3%, 92% ± 2% and 91% ± 2% for the left, right, and whole lung, respectively. RMSE was 4.0 ± 1.0 mm for each of the left, right, and whole lung. The absolute (percent) volume errors were 0.1 l (∼6%) for each of right and left lung and ∼0.2 l (∼6%) for whole lung. Intra- and inter-CoV (ICC) were <0.5% (>0.91%) for DSC and <4.5% (>0.93%) for RMSE. While segmentation required 10 s including ∼6 s for user interaction, the smallest detectable difference was 0.24 l for algorithm measurements which was similar to manual measurements. This lung segmentation approach provided the necessary and sufficient precision and accuracy required for research and clinical studies.

Evaluation of a respiratory navigator-gating technique in Gd-EOB-DTPA-enhanced magnetic resonance imaging for the assessment of liver tumors

ObjectivesWe investigated the clinical usefulness of respiratory navigator-gating technique for the assessment of liver tumors in Gd-EOB-DTPA-enhanced magnetic resonance (MR) imaging. MethodsEighty patients who underwent Gd-EOB-DTPA-enhanced MR imaging to evaluate known or suspected liver tumors were enrolled. Three-dimensional spoiled gradient-recalled echo images of the liver were acquired in the hepatobiliary phase by the following three methods: breath-hold imaging, navigator-gated low-resolution imaging, and navigator-gated high-resolution imaging. Navigator-gated imaging was performed during free breathing. Spatial resolution was identical between breath-hold imaging and gated low-resolution imaging. Signal intensities in the liver, muscle, and spleen were measured in 20 patients. Image quality was visually evaluated in all 80 patients. The detection rate and lesion conspicuity were assessed for 71 malignant liver lesions identified in 29 patients. ResultsThe liver-to-muscle and liver-to-spleen signal ratios were significantly lower for gated images compared to breath-hold images. Images of acceptable quality were obtained in most patients by all three methods, and the overall image quality of axial images did not differ significantly among the imaging methods, although superior reformatted coronal images were obtained by gated high-resolution imaging. The detection rates of malignant liver lesions were similar among the three imaging methods, although lesion conspicuity was significantly better for breath-hold imaging compared to gated imaging. ConclusionsNavigator-gated imaging provided image qualities and detection rates of malignant liver lesions comparable to breath-hold imaging in Gd-EOB-DTPA-enhanced MR imaging; however, no additional benefits of high-resolution imaging were proven for lesion evaluation.