Enhanced parameter estimation in multiparametric arterial spin labeling using artificial neural networks.

PURPOSE: Multiparametric arterial spin labeling (MP-ASL) can quantify cerebral blood flow (CBF) and arterial cerebral blood volume (CBVa). However, its accuracy is compromised owing to its intrinsically low SNR, necessitating complex and time-consuming parameter estimation. Deep neural networks (DNNs) offer a solution to these limitations. Therefore, we aimed to develop simulation-based DNNs for MP-ASL and compared the performance of a supervised DNN (DNNSup), physics-informed unsupervised DNN (DNNUns), and the conventional lookup table method (LUT) using simulation and in vivo data. METHODS: MP-ASL was performed twice during resting state and once during the breath-holding task. First, the accuracy and noise immunity were evaluated in the first resting state. Second, CBF and CBVa values were statistically compared between the first resting state and the breath-holding task using the Wilcoxon signed-rank test and Cliff's delta. Finally, reproducibility of the two resting states was assessed. RESULTS: Simulation and first resting-state analyses demonstrated that DNNSup had higher accuracy, noise immunity, and a six-fold faster computation time than LUT. Furthermore, all methods detected task-induced CBF and CBVa elevations, with the effect size being larger with the DNNSup (CBF, p = 0.055, Δ = 0.286; CBVa, p = 0.008, Δ = 0.964) and DNNUns (CBF, p = 0.039, Δ = 0.286; CBVa, p = 0.008, Δ = 1.000) than that with LUT (CBF, p = 0.109, Δ = 0.214; CBVa, p = 0.008, Δ = 0.929). Moreover, all the methods exhibited comparable and satisfactory reproducibility. CONCLUSION: DNNSup outperforms DNNUns and LUT with respect to estimation performance and computation time.

Comparison between supervised and physics-informed unsupervised deep neural networks for estimating cerebral perfusion using multi-delay arterial spin labeling MRI.

This study aimed to implement a physics-informed unsupervised deep neural network (DNN) to estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multi-delay arterial spin labeling (ASL), and compare its performance with that of a supervised DNN and the conventional method. Supervised and unsupervised DNNs were trained using simulation data. The accuracy and noise immunity of the three methods were compared using simulations and in vivo data. The simulation study investigated the differences between the predicted and ground-truth values and their variations with the noise level. The in vivo study evaluated the predicted values from the original images and noise-induced variations in the predicted values from the synthesized noisy images by adding Rician noise to the original images. The simulation study showed that CBF estimated using the supervised DNN was not biased by noise, whereas that estimated using other methods had a positive bias. Although the ATT with all methods exhibited a similar behavior with noise increase, the ATT with the supervised DNN was less biased. The in vivo study showed that CBF and ATT with the supervised DNN were the most accurate and that the supervised and unsupervised DNNs had the highest noise immunity in CBF and ATT estimations, respectively. Physics-informed unsupervised learning can estimate CBF and ATT from multi-delay ASL signals, and its performance is superior to that of the conventional method. Although noise immunity in ATT estimation was superior with unsupervised learning, other performances were superior with supervised learning.

Effects of the Training Data Condition on Arterial Spin Labeling Parameter Estimation Using a Simulation-Based Supervised Deep Neural Network.

OBJECTIVE: A simulation-based supervised deep neural network (DNN) can accurately estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multidelay arterial spin labeling signals. However, the performance of deep learning depends on the characteristics of the training data set. We aimed to investigate the effects of the ground truth (GT) ranges of CBF and ATT on the performance of the DNN when training data were prepared using arterial spin labeling signal simulation. METHODS: Deep neural networks were individually trained using 36 patterns of the training data sets. Simulation test data (1,000,000 points), 17 healthy volunteers, and 1 patient with moyamoya disease were included. The simulation test data were used to evaluate accuracy, precision, and noise immunity of the DNN. The best-performing DNN was determined by the normalized mean absolute error (NMAE), normalized root mean squared error (NRMSE), and normalized coefficient of variation over repeated training (CV Net ). Cerebral blood flow and ATT values and their histograms were compared between the GT and predicted values. For the in vivo data, the dependency of the predicted values on the GT ranges was visually evaluated by comparing CBF and ATT maps between the best-performing DNN and the other DNNs. Moreover, using the synthesized noisy images, noise immunity was compared between the best-performing DNN based on the simulation study and a conventional method. RESULTS: The simulation study showed that a network trained by the GT of CBF and ATT in the ranges of 0 to 120 mL/100 g/min and 0 to 4500 milliseconds, respectively, had the highest performance (NMAE CBF , 0.150; NRMSE CBF , 0.231; CV NET CBF , 0.028; NMAE ATT , 0.158; NRMSE ATT , 0.257; and CV NET ATT , 0.028). Although the predicted CBF and ATT varied with the GT range of the training data sets, the appropriate settings preserved the accuracy, precision, and noise immunity of the DNN. In addition, the same results were observed in in vivo studies. CONCLUSIONS: The GT ranges to prepare the training data affected the performance of the simulation-based supervised DNNs. The predicted CBF and ATT values depended on the GT range; inappropriate settings degraded the accuracy, whereas appropriate settings of the GT range provided accurate and precise estimates.

Feasibility of Quantitative MRI Using 3D-QALAS for Discriminating Immunohistochemical Status in Invasive Ductal Carcinoma of the Breast.

BACKGROUND: Two-dimensional synthetic MRI of the breast has limited spatial coverage. Three-dimensional (3D) synthetic MRI could provide volumetric quantitative parameters that may reflect the immunohistochemical (IHC) status in invasive ductal carcinoma (IDC) of the breast. PURPOSE: To evaluate the feasibility of 3D synthetic MRI using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (QALAS) for discriminating the IHC status, including hormone receptor (HR), human epidermal growth factor receptor 2 (HER 2), and Ki-67 expression in IDC. STUDY TYPE: Prospective observational study. POPULATION: A total of 33 females with IDC of the breast (mean, 52.3 years). FIELD STRENGTH/SEQUENCE: A 3-T, 3D-QALAS gradient-echo and fat-suppressed T1-weighted 3D fast spoiled gradient-echo sequences. ASSESSMENT: Two radiologists semiautomatically delineated 3D regions of interest (ROIs) of the whole tumors on the dynamic MRI that was registered to the synthetic T1-weighted images acquired from 3D-QALAS. The mean T1 and T2 were measured for each IDC. STATISTICAL TESTS: Intraclass correlation coefficient for assessing interobserver agreement. Mann-Whitney U test to determine the relationship between the mean T1 or T2 and the IHC status. Multivariate logistic regression analysis followed by receiver operating characteristics (ROC) analysis for discriminating IHC status. A P value <0.05 was considered statistically significant. RESULTS: The interobserver agreement was good to excellent. There was a significant difference in the mean T1 between HR-positive and HR-negative lesions, while the mean T2 value differed between HR-positive and HR-negative lesions, between the triple-negative and HR-positive or HER2-positive lesions, and between the Ki-67 level > 14% and ≤ 14%. Multivariate analysis showed that the mean T2 was higher in HR-negative IDC than in HR-positive IDC. ROC analysis revealed that the mean T2 was predictive for discriminating HR status, triple-negative status, and Ki-67 level. DATA CONCLUSION: 3D synthetic MRI using QALAS may be useful for discriminating IHC status in IDC of the breast. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

The Utility of Arterial Transit Time Measurement for Evaluating the Hemodynamic Perfusion State of Patients with Chronic Cerebrovascular Stenosis or Occlusive Disease: Correlative Study between MR Imaging and 15O-labeled H2O Positron Emission Tomography.

PURPOSE: To verify whether arterial transit time (ATT) mapping can correct arterial spin labeling-cerebral blood flow (ASL-CBF) values and to verify whether ATT is a parameter that correlates with positron emission tomography (PET)-oxygen extraction fraction (OEF) and PET-mean transit time (MTT). METHODS: Eleven patients with unilateral major cerebral artery stenosis or occlusion underwent MRI and PET in the chronic or asymptomatic phase. ASL-MRI acquisitions were conducted with each of two post-label delay (PLD) settings (0.7s and 2.0s) using a pseudo-continuous ASL pulse sequence and 3D-spin echo spiral readout with vascular crusher gradient. ATT maps were obtained using a low-resolution pre-scan approach with five PLD settings. Using the ASL perfusion images and ATT mapping, ATT-corrected ASL-CBF images were obtained. Four kinds of ASL-CBF methods (PLD 0.7s with or without ATT correction and PLD 2.0s with or without ATT correction) were compared to PET-CBF, using vascular territory ROIs. ATT and OEF were compared for all ROIs, unaffected side ROIs, and affected side ROIs, respectively. ATT and MTT were compared by the ratio of the affected side to the unaffected side. Transit time-based ROIs were used for the comparison with ATT. RESULTS: Comparing ASL-CBF and PET-CBF, the correlation was higher with ATT correction than without correction, and for a PLD of 2.0s compared with 0.7s. The best correlation was for PLD of 2.0s with ATT correction (R2 = 0.547). ROIs on the affected side showed a low but significant correlation between ATT and PET-OEF (R2 = 0.141). There was a low correlation between the ATT ratio and the MTT ratio (R2 = 0.133). CONCLUSION: Low-resolution ATT correction may increase the accuracy of ASL-CBF measurements in patients with unilateral major cerebral artery stenosis or occlusion. In addition, ATT itself might have a potential role in detecting compromised hemodynamic state.

Estimation of Cerebral Blood Flow and Arterial Transit Time From Multi-Delay Arterial Spin Labeling MRI Using a Simulation-Based Supervised Deep Neural Network.

BACKGROUND: An inherently poor signal-to-noise ratio (SNR) causes inaccuracy and less precision in cerebral blood flow (CBF) and arterial transit time (ATT) when using arterial spin labeling (ASL). Deep neural network (DNN)-based parameter estimation can solve these problems. PURPOSE: To reduce the effects of Rician noise on ASL parameter estimation and compute unbiased CBF and ATT using simulation-based supervised DNNs. STUDY TYPE: Retrospective. POPULATION: One million simulation test data points, 17 healthy volunteers (five women and 12 men, 33.2 ± 14.6 years of age), and one patient with moyamoya disease. FIELD STRENGTH/SEQUENCE: 3.0 T/Hadamard-encoded pseudo-continuous ASL with a three-dimensional fast spin-echo stack of spirals. ASSESSMENT: Performances of DNN and conventional methods were compared. For test data, the normalized mean absolute error (NMAE) and normalized root mean squared error (NRMSE) between the ground truth and predicted values were evaluated. For in vivo data, baseline CBF and ATT and their relative changes with respect to SNR using artificial noise-added images were assessed. STATISTICAL TESTS: One-way analysis of variance with post-hoc Tukey's multiple comparison test, paired t-test, and the Bland-Altman graphical analysis. Statistical significance was defined as P < 0.05. RESULTS: For both CBF and ATT, NMAE and NRMSE were lower with DNN than with the conventional method. The baseline values were significantly smaller with DNN than with the conventional method (CBF in gray matter, 66 ± 10 vs. 71 ± 12 mL/100 g/min; white matter, 45 ± 6 vs. 46 ± 7 mL/100 g/min; ATT in gray matter, 1424 ± 201 vs. 1471 ± 154 msec). CBF and ATT increased with decreasing SNR; however, their change rates were smaller with DNN than were those with the conventional method. Higher CBF in the prolonged ATT region and clearer contrast in ATT were identified by DNN in a clinical case. DATA CONCLUSION: DNN outperformed the conventional method in terms of accuracy, precision, and noise immunity. EVIDENCE LEVEL: 3 Technical Efficacy: Stage 1.

Simultaneous relaxometry and morphometry of human brain structures with 3D magnetic resonance fingerprinting: a multicenter, multiplatform, multifield-strength study.

Relaxation times and morphological information are fundamental magnetic resonance imaging-derived metrics of the human brain that reflect the status of the underlying tissue. Magnetic resonance fingerprinting (MRF) enables simultaneous acquisition of T1 and T2 maps inherently aligned to the anatomy, allowing whole-brain relaxometry and morphometry in a single scan. In this study, we revealed the feasibility of 3D MRF for simultaneous brain structure-wise morphometry and relaxometry. Comprehensive test-retest scan analyses using five 1.5-T and three 3.0-T systems from a single vendor including different scanner types across 3 institutions demonstrated that 3D MRF-derived morphological information and relaxation times are highly repeatable at both 1.5 T and 3.0 T. Regional cortical thickness and subcortical volume values showed high agreement and low bias across different field strengths. The ability to acquire a set of regional T1, T2, thickness, and volume measurements of neuroanatomical structures with high repeatability and reproducibility facilitates the ability of longitudinal multicenter imaging studies to quantitatively monitor changes associated with underlying pathologies, disease progression, and treatments.

Time resolved DCE-MRI of the kidneys: Evaluation of the renal vasculatures and tumors using F-DISCO with and without compressed sensing in normal and wide-bore 3T systems.

Dynamic contrast-enhanced MR imaging (DCE-MRI) has been widely used for the evaluation of renal arteries. This method is also useful for tumor and renal parenchyma characterization. The very fast MRI may provide stable and precise information regarding vasculature and soft tissues. The purpose of this study was to evaluate the ability of DCE-MRI to assess renal vasculatures and tumor perfusions using Differential subsampling with Cartesian ordering with spectrally selected inversion recovery with adiabatic pulses (F-DISCO) with and without compressed sensing (CS) in normal and wide-bore 3T systems. Fifty-one patients who underwent DCE-MRI using F-DISCO with or without CS for evaluation of renal or adrenal regions were included. Image quality, artifacts, fat saturation, and selective visual recognition of renal vasculatures were assessed by using a 5-point scale. Tumor recognition was verified by using a 5-point scale of confidence level. Signal intensities of each structure were also measured. In all cases, the temporal resolution of each phase for DCE-MRI was 1.9 to 2.0 seconds. Image quality, artifacts, fat saturation, and selective visual recognition of vasculatures were all acceptable (mean score 4.2-4.9). The selective visualization of renal arteries and veins was successfully accomplished (mean score 4.0-4.9). Contrast media perfusion for renal vasculature, renal parenchyma, and tumors was also recognized. DCE-MRI for the evaluation of renal vasculatures and tumors using F-DISCO with or without CS can be performed with high temporal and spatial resolutions in normal and wide-bore 3T systems. This information can be obtained in a stable fashion throughout the dynamic contrast study. CS can additionally provide benefits that the total imaging time may be shorter than without CS.

Rigid real-time prospective motion-corrected three-dimensional multiparametric mapping of the human brain.

PURPOSE: To develop a rigid real-time prospective motion-corrected multiparametric mapping technique and to test the performance of quantitative estimates. METHODS: Motion tracking and correction were performed by integrating single-shot spiral navigators into a multiparametric imaging technique, three-dimensional quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). The spiral navigator was optimized, and quantitative measurements were validated using a standard system phantom. The effect of motion correction on whole-brain T1 and T2 mapping under different types of head motion during the scan was evaluated in 10 healthy volunteers. Finally, six patients with Parkinson's disease, which is known to be associated with a high prevalence of motion artifacts, were scanned to evaluate the effectiveness of our method in the real world. RESULTS: The phantom study demonstrated that the proposed motion correction method did not introduce quantitative bias. Improved parametric map quality and repeatability were shown in volunteer experiments with both in-plane and through-plane motions, comparable to the no-motion ground truth. In real-life validation in patients, the approach showed improved parametric map quality compared to images obtained without motion correction. CONCLUSIONS: Real-time prospective motion-corrected multiparametric relaxometry based on 3D-QALAS provided robust and repeatable whole-brain multiparametric mapping.

Accelerated Two-Point Dixon MR Angiography Improves Diagnostic Performance for Cervical Artery Diseases.

BACKGROUND: Nonenhanced MR angiography (MRA) studies are often used to manage acute and chronic large cervical artery disease, but lengthy scan times limit their clinical usefulness. PURPOSE: To develop an accelerated cervical MRA and test its diagnostic performance. STUDY TYPE: Prospective. POPULATION: Patients with cervical artery disease (n = 32, 17 males). FIELD STRENGTH/SEQUENCE: 3.0 T; accelerated two-point Dixon three-dimensional Cartesian spoiled gradient-echo (FLEXA) and conventional time-of-flight MRA (cMRA) sequences. ASSESSMENT: All patients underwent FLEXA (1'28″) and cMRA (6'47″) acquisitions. Quantitative evaluation (artery-to-background signal ratio and a blur metric) and qualitative evaluation using diagnostic performance measured by the sensitivity, specificity, and positive/negative predictive values (PPV/NPV), and vessel and plaque visualization scores from three board-certified radiologists' (with 10, 11, and 12 years of experience) independent readings using maximum intensity projection (MIP) for luminal diseases and axial images for plaque. The reference standards were contrast-enhanced angiography and fat-saturated T1-weighted images, respectively. STATISTICAL TESTS: All measures were compared between FLEXA and cMRA using the paired t, Wilcoxon signed-rank, McNemar's, or chi-squared test, as appropriate. Interreader agreement was assessed using Cohen's κ. P < 0.05 was considered statistically significant. RESULTS: The artery-to-background signal ratio was significantly higher for FLEXA (FLEXA: 7.20 ± 1.63 [fat]; 4.26 ± 0.52 [muscle]; cMRA: 2.57 ± 0.49 [fat]), while image blurring was significantly less (FLEXA: 0.24 ± 0.016; cMRA: 0.30 ± 0.029). In luminal disease detection, sensitivity (FLEXA: 0.97/0.91/0.91; cMRA:0.71/0.69/0.63), specificity (FLEXA: 0.98/0.93/0.98; cMRA:0.93/0.85/0.92), PPV (FLEXA: 0.92/0.86/0.86; cMRA: 0.64/0.5/0.58), and NPV (FLEXA: 0.99/0.98/0.98; cMRA: 0.92/0.91/0.9) were significantly higher for FLEXA. interreader agreement was substantial to almost perfect for FLEXA (κ = 0.82/0.86/0.78) and moderate to substantial for cMRA (κ = 0.67/0.56/0.57). MIP visualization scores were significantly higher for FLEXA, with substantial to almost perfect interreader agreement (FLEXA: κ = 0.83/0.86/0.82; cMRA: κ = 0.89/0.79/0.79). In plaque detection, sensitivity (FLEXA: 0.9/0.9/0.7; cMRA: 0.3/0.6/0.2) and specificity (FLEXA: 1/0.87/1; cMRA: 0.93/0.63/0.97) were significantly higher for FLEXA in two of three readers. The interreader plaque detection agreement was fair to substantial (FLEXA: κ = 0.63/0.69/0.48; cMRA: κ = 0.21/0.45/0.20). Side-by-side plaque and vessel wall visualization was superior for FLEXA in all readers, with moderate to substantial interreader agreement (plaque: κ = 0.73/0.73/0.77; vessel wall: κ = 0.57/0.40/0.39). DATA CONCLUSION: FLEXA enhanced visualization of the cervical arterial system and improved diagnostic performance for luminal abnormalities and plaques in patients with cervical artery diseases. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.

Separating spin compartments in arterial spin labeling using delays alternating with nutation for tailored excitation (DANTE) pulse: A validation study using T2 -relaxometry and application to arterial cerebral blood volume imaging.

PURPOSE: To clarify the type of spin compartment in arterial spin labeling (ASL) that is eliminated by delays alternating with nutation for tailored excitation (DANTE) pulse using T2 -relaxometry, and to demonstrate the feasibility of arterial cerebral blood volume (CBVa ) imaging using DANTE-ASL in combination with a simplified two-compartment model. METHOD: The DANTE and T2 -preparation modules were combined into a single ASL sequence. T2 values under the application of DANTE were determined to evaluate changes in T2 , along with the post-labeling delay (PLD) and the relationship between transit time without DANTE (TTnoVS ) and T2 . The reference tissue T2 (T2_ref ) was also obtained. Subsequently, the DANTE module was embedded into the Hadamard-encoded ASL. Cerebral blood flow (CBF) and CBVa were computed using two Hadamard-encoding datasets (with and without DANTE) in a rest and breath-holding (BH) task. RESULTS: While T2 without DANTE (T2_noVS ) decreased as the PLD increased, T2 with DANTE (T2_DANTE ) was equivalent to T2_ref and did not change with the PLD. Although there was a significant positive correlation between TTnoVS and T2_noVS with short PLD, T2_DANTE was not correlated with TTnoVS nor PLD. Baseline CBVa values obtained at rest were 0.64 ± 0.12, 0.64 ± 0.11, and 0.58 ± 0.15 mL/100 g for anterior, middle, and posterior cerebral arteries, respectively. Significant CBF and CBVa elevations were observed in the BH task. CONCLUSION: Microvascular compartment signals were eliminated from the total ASL signals by DANTE. CBVa can be measured using Hadamard-encoded DANTE-ASL in combination with a simplified two-compartment model.

Arterial Transit Time-Based Multidelay Combination Strategy Improves Arterial Spin Labeling Cerebral Blood Flow Measurement Accuracy in Severe Steno-Occlusive Diseases.

BACKGROUND: Although perfusion imaging plays a key role in the management of steno-occlusive diseases, the clinical usefulness of arterial spin labeling (ASL) is limited by technical issues. PURPOSE: To examine the effect of arterial transit time (ATT) prolongation on cerebral blood flow (CBF) measurement accuracy and identify the best CBF measurement protocol for steno-occlusive diseases. STUDY TYPE: Prospective. POPULATION: Moyamoya (n = 10) and atherosclerotic diseases (n = 8). FIELD STRENGTH/SEQUENCE: A 3.0T/3DT1 -weighted and ASL. ASSESSMENT: Hadamard-encoded multidelay ASL scans with/without vessel suppression (VS) and single-delay ASL scans with long-label duration (LD) and long postlabeling delay (PLD), referred to as long-label long-delay (LLLD), were acquired. CBF measurement accuracy and its ATT dependency, measured as the correlation between the relative CBF measurement difference (ASL-single-photon emission computed tomography [SPECT]) and ATT, were compared among 1) Combo (incorporating multidelay and LLLD data based on ATT), 2) standard (LD/PLD = 1333/2333 msec), and 3) LLLD (LD/PLD = 4000/4000 msec) protocols, using whole-brain voxel-wise correlation with reference standard SPECT CBF. The effect of VS on CBF measurement accuracy was also assessed. STATISTICAL TESTS: Pearson's correlation coefficient, repeated-measures analysis of variance, t-test. P< 0.05 was considered significant. RESULTS: Pearson's correlation coefficients between ASL and SPECT CBF measurements were as follows: Combo = 0.55 ± 0.09; standard = 0.52 ± 0.12; LLLD = 0.41 ± 0.10. CBF measurement was least accurate in LLLD and most accurate in Combo. VS significantly improved overall CBF measurement accuracy in the standard protocol and in moyamoya patients for the Combo. ATT dependency analysis revealed that, compared with Combo, the standard and LLLD protocols showed significantly lower and negative and significantly higher and positive correlations, respectively (standard = -0.12 ± 0.04, Combo = -0.04 ± 0.03, LLLD = 0.17 ± 0.03). DATA CONCLUSION: By using ATT-corrected CBF derived from LD/PLD = 1333/2333 msec as a base and by compensating underestimation in delayed regions using multidelay scans, the ATT-based Combo strategy improves CBF measurement accuracy compared with single-delay protocols in severe steno-occlusive diseases. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Reliability and Sensitivity to Longitudinal CBF Changes in Steno-Occlusive Diseases: ASL Versus 123 I-IMP-SPECT.

BACKGROUND: Noninvasive cerebral blood flow (CBF) monitoring using arterial spin labeling (ASL) magnetic resonance imaging is useful for managing large cerebral artery steno-occlusive diseases. However, knowledge about its measurement characteristics in comparison with reference standard perfusion imaging is limited. PURPOSE: To evaluate perfusion in a longitudinal manner in patients with steno-occlusive disease using ASL and compare with single-photon emission computed tomography (SPECT). STUDY TYPE: Prospective. POPULATION: Moyamoya (n = 10, eight females) and atherosclerotic diseases (n = 2, two males). FIELD STRENGTH/SEQUENCE: 3.0 T; gradient-echo three-dimensional T1 -weighted and spin-echo ASL. ASSESSMENT: Multi-delay ASL and [123 I]-iodoamphetamine SPECT CBF measurements were performed both before and within 9 days of anterior-circulation revascularization. Reliability and sensitivity to whole-brain voxel-wise CBF changes (ΔCBF) and their postlabeling delay (PLD) dependency with varied PLDs (in milliseconds) of 1000, 2333, and 3666 were examined. STATISTICAL TESTS: Reliability and sensitivity to ΔCBF were examined using within-subject standard deviation (Sw) and intraclass correlation coefficients (ICCs). For statistical comparisons, standard deviation of longitudinal ΔCBF within the hemisphere contralateral to surgery, and the ratio between it and average ΔCBF within the ipsilateral regions of interest were subjected to paired t tests, respectively. P < 0.05 was considered statistically significant. RESULTS: ASL test-retest time interval was 31 ± 18 days. Test-retest reliability was significantly lower for SPECT (0.16 ± 0.02) than ASL (0.13 ± 0.04). Sensitivity to postoperative changes was significantly higher for ASL (2.71 ± 2.79) than SPECT (0.27 ± 0.62). Test-retest reliability was significantly higher for a PLD of 2333 (0.13 ± 0.04) than 3666 (0.19 ± 0.05), and sensitivity to ΔCBF was significantly higher for PLDs of 1000 (2.53 ± 2.50) and 2333 than 3666 (0.79 ± 1.88). ICC maps also showed higher reliability for ASL than SPECT. DATA CONCLUSION: Higher test-retest reliability led to better ASL sensitivity than SPECT for postoperative ΔCBF. ASL test-retest reliability and sensitivity to ΔCBF were higher with a PLD of 2333. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Accelerated Isotropic Multiparametric Imaging by High Spatial Resolution 3D-QALAS With Compressed Sensing: A Phantom, Volunteer, and Patient Study.

OBJECTIVES: The aims of this study were to develop an accelerated multiparametric magnetic resonance imaging method based on 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) combined with compressed sensing (CS) and to evaluate the effect of CS on the quantitative mapping, tissue segmentation, and quality of synthetic images. MATERIALS AND METHODS: A magnetic resonance imaging system phantom, containing multiple compartments with standardized T1, T2, and proton density (PD) values; 10 healthy volunteers; and 12 patients with multiple sclerosis were scanned using the 3D-QALAS sequence with and without CS and conventional contrast-weighted imaging. The scan times of 3D-QALAS with and without CS were 5:56 and 11:11, respectively. For healthy volunteers, brain volumetry and myelin estimation were performed based on the measured T1, T2, and PD. For patients with multiple sclerosis, the mean T1, T2, PD, and the amount of myelin in plaques and contralateral normal-appearing white matter (NAWM) were measured. Simple linear regression analysis and Bland-Altman analysis were performed for each metric obtained from the datasets with and without CS. To compare overall image quality and structural delineations on synthetic and conventional contrast-weighted images, case-control randomized reading sessions were performed by 2 neuroradiologists in a blinded manner. RESULTS: The linearity of both phantom and volunteer measurements in T1, T2, and PD values obtained with and without CS was very strong (R2 = 0.9901-1.000). The tissue segmentation obtained with and without CS also had high linearity (R2 = 0.987-0.999). The quantitative tissue values of the plaques and NAWM obtained with CS showed high linearity with those without CS (R2 = 0.967-1.000). There were no significant differences in overall image quality between synthetic contrast-weighted images obtained with and without CS (P = 0.17-0.99). CONCLUSIONS: Multiparametric imaging of the whole brain based on 3D-QALAS can be accelerated using CS while preserving tissue quantitative values, tissue segmentation, and quality of synthetic images.

Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting.

Three-dimensional (3D) Magnetic resonance fingerprinting (MRF) permits whole-brain volumetric quantification of T1 and T2 relaxation values, potentially replacing conventional T1-weighted structural imaging for common brain imaging analysis. The aim of this study was to evaluate the repeatability and reproducibility of 3D MRF in evaluating brain cortical thickness and subcortical volumetric analysis in healthy volunteers using conventional 3D T1-weighted images as a reference standard. Scan-rescan tests of both 3D MRF and conventional 3D fast spoiled gradient recalled echo (FSPGR) were performed. For each sequence, the regional cortical thickness and volume of the subcortical structures were measured using standard automatic brain segmentation software. Repeatability and reproducibility were assessed using the within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), and mean percent difference and ICC, respectively. The wCV and ICC of cortical thickness were similar across all regions with both 3D MRF and FSPGR. The percent relative difference in cortical thickness between 3D MRF and FSPGR across all regions was 8.0 ± 3.2%. The wCV and ICC of the volume of subcortical structures across all structures were similar between 3D MRF and FSPGR. The percent relative difference in the volume of subcortical structures between 3D MRF and FSPGR across all structures was 7.1 ± 3.6%. 3D MRF measurements of human brain cortical thickness and subcortical volumes are highly repeatable, and consistent with measurements taken on conventional 3D T1-weighted images. A slight, consistent bias was evident between the two, and thus careful attention is required when combining data from MRF and conventional acquisitions.

An information theory model for optimizing quantitative magnetic resonance imaging acquisitions.

Acquisition parameter selection is currently performed empirically for many quantitative MRI (qMRI) acquisitions. Tuning parameters for different scan times, tissues, and resolutions requires some amount of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to minimize variability of quantitative maps and post-processing techniques such as synthetic image generation. The objective of this work is to introduce and evaluate a quantitative method for selecting parameters that minimize image variability. An information theory framework was developed for this purpose and applied to a 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) signal model for qMRI. In this framework, mutual information is used to measure the information gained by a measurement as a function of acquisition parameters, quantifying the information content of potential acquisitions and allowing informed parameter selection. The information theory framework was tested on artificial data generated from a representative mathematical phantom, measurements acquired on a qMRI multiparametric imaging standard phantom, and in vivo measurements in a human brain. The phantom measurements showed that higher mutual information calculated by the model correlated with smaller coefficient of variation in the reconstructed parametric maps, and in vivo measurements demonstrated that information-based calibration of acquisition parameters resulted in a decrease in parametric map variability consistent with model predictions.

Robust arterial transit time and cerebral blood flow estimation using combined acquisition of Hadamard-encoded multi-delay and long-labeled long-delay pseudo-continuous arterial spin labeling: a simulation and in vivo study.

Arterial transit time (ATT) prolongation causes an error of cerebral blood flow (CBF) measurement during arterial spin labeling (ASL). To improve the accuracy of ATT and CBF in patients with prolonged ATT, we propose a robust ATT and CBF estimation method for clinical practice. The proposed method consists of a three-delay Hadamard-encoded pseudo-continuous ASL (H-pCASL) with an additional-encoding and single-delay with long-labeled long-delay (1dLLLD) acquisition. The additional-encoding allows for the reconstruction of a single-delay image with long-labeled short-delay (1dLLSD) in addition to the normal Hadamard sub-bolus images. Five different images (normal Hadamard 3 delay, 1dLLSD, 1dLLLD) were reconstructed to calculate ATT and CBF. A Monte Carlo simulation and an in vivo study were performed to access the accuracy of the proposed method in comparison to normal 7-delay (7d) H-pCASL with equally divided sub-bolus labeling duration (LD). The simulation showed that the accuracy of CBF is strongly affected by ATT. It was also demonstrated that underestimation of ATT and CBF by 7d H-pCASL was higher with longer ATT than with the proposed method. Consistent with the simulation, the 7d H-pCASL significantly underestimated the ATT compared to that of the proposed method. This underestimation was evident in the distal anterior cerebral artery (ACA; P = 0.0394) and the distal posterior cerebral artery (PCA; 2 P = 0.0255). Similar to the ATT, the CBF was underestimated with 7d H-pCASL in the distal ACA (P = 0.0099), distal middle cerebral artery (P = 0.0109), and distal PCA (P = 0.0319) compared to the proposed method. Improving the SNR of each delay image (even though the number of delays is small) is crucial for ATT estimation. This is opposed to acquiring many delays with short LD. The proposed method confers accurate ATT and CBF estimation within a practical acquisition time in a clinical setting.

Deep Learning Approach for Generating MRA Images From 3D Quantitative Synthetic MRI Without Additional Scans.

OBJECTIVES: Quantitative synthetic magnetic resonance imaging (MRI) enables synthesis of various contrast-weighted images as well as simultaneous quantification of T1 and T2 relaxation times and proton density. However, to date, it has been challenging to generate magnetic resonance angiography (MRA) images with synthetic MRI. The purpose of this study was to develop a deep learning algorithm to generate MRA images based on 3D synthetic MRI raw data. MATERIALS AND METHODS: Eleven healthy volunteers and 4 patients with intracranial aneurysms were included in this study. All participants underwent a time-of-flight (TOF) MRA sequence and a 3D-QALAS synthetic MRI sequence. The 3D-QALAS sequence acquires 5 raw images, which were used as the input for a deep learning network. The input was converted to its corresponding MRA images by a combination of a single-convolution and a U-net model with a 5-fold cross-validation, which were then compared with a simple linear combination model. Image quality was evaluated by calculating the peak signal-to-noise ratio (PSNR), structural similarity index measurements (SSIMs), and high frequency error norm (HFEN). These calculations were performed for deep learning MRA (DL-MRA) and linear combination MRA (linear-MR), relative to TOF-MRA, and compared with each other using a nonparametric Wilcoxon signed-rank test. Overall image quality and branch visualization, each scored on a 5-point Likert scale, were blindly and independently rated by 2 board-certified radiologists. RESULTS: Deep learning MRA was successfully obtained in all subjects. The mean PSNR, SSIM, and HFEN of the DL-MRA were significantly higher, higher, and lower, respectively, than those of the linear-MRA (PSNR, 35.3 ± 0.5 vs 34.0 ± 0.5, P < 0.001; SSIM, 0.93 ± 0.02 vs 0.82 ± 0.02, P < 0.001; HFEN, 0.61 ± 0.08 vs 0.86 ± 0.05, P < 0.001). The overall image quality of the DL-MRA was comparable to that of TOF-MRA (4.2 ± 0.7 vs 4.4 ± 0.7, P = 0.99), and both types of images were superior to that of linear-MRA (1.5 ± 0.6, for both P < 0.001). No significant differences were identified between DL-MRA and TOF-MRA in the branch visibility of intracranial arteries, except for ophthalmic artery (1.2 ± 0.5 vs 2.3 ± 1.2, P < 0.001). CONCLUSIONS: Magnetic resonance angiography generated by deep learning from 3D synthetic MRI data visualized major intracranial arteries as effectively as TOF-MRA, with inherently aligned quantitative maps and multiple contrast-weighted images. Our proposed algorithm may be useful as a screening tool for intracranial aneurysms without requiring additional scanning time.

Intravascular signal suppression and microvascular signal mapping using delays alternating with nutation for tailored excitation (DANTE) pulse for arterial spin labeling perfusion imaging.

OBJECTIVE: To optimize the delays alternating with nutation for tailored excitation (DANTE) pulse as a vascular crushing gradient to eliminate macro-and micro-vascular signals and to generate a macrovascular space-related map by applying DANTE with multiple conditions. MATERIALS AND METHODS: Numerical simulation was performed to estimate the optimal flip angle (FA) of the DANTE. A phantom study was conducted to evaluate the impact of the FA and gradient area (GA) of the DANTE with three flow velocities and various parameters of the DANTE. Finally, an in vivo study was performed to assess the optimal DANTE parameters and to map the estimated macrovascular signal of the arterial spin labeling (ASL) signal. RESULTS: Numerical simulation revealed that the decrease of magnetization plateaued at 12.5° of FA. The phantom study showed that the setting of larger FA or GA decreased the ASL signals. The decrease of the ASL signal depended on the flow velocity, and the dependence increased with decreasing GA. The in vivo study revealed that larger FA and GA decreased the perfusion signal. DISCUSSION: An optimized DANTE makes it possible to efficiently suppress the macro-and-micro vascular signals depending on the flow velocity. Moreover, macrovascular signal mapping may be useful to assess altered hemodynamic states.

Evaluation of retained products of conception using pulsed continuous arterial spin-labeling MRI: clinical feasibility and initial results.

OBJECTIVES: We evaluated the vascularity of retained products of conception (RPOC) using arterial spin-labeling magnetic resonance imaging (ASL-MRI) to clarify the clinical feasibility of this approach. MATERIALS AND METHODS: A pulsed-continuous ASL sequence with echo-planar imaging (EPI) acquisitions was used. Ten consecutive patients with RPOC were enrolled. All ASL images were evaluated visually and semiquantitatively and compared with the findings of Doppler ultrasound (US) and dynamic contrast-enhanced MRI (DCE-MRI). RESULTS: The technical success rate was 93.7% (15/16 scans). One failed case was excluded from the analysis. Six patients showed quite high signals over RPOC, while three patients showed no abnormal signals. Doppler US alone failed to detect the hypervascular area in two cases, and ASL-MRI alone failed in three. A significant linear correlation was found between semiquantitative values of ASL-MRI and DCE-MRI. All six patients showing high signals on ASL-MRI underwent follow-up MRI after therapy. High signals in five patients decreased visually and semiquantitatively, while one patient showed signal increases. CONCLUSION: Evaluation of RPOC using ASL-MRI was clinically feasible and response to therapy could be evaluated. However, the clinical advantages over conventional imaging remain unclear and need to be evaluated.