Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.

Virtual MOLLI Target: Generative Adversarial Networks Toward Improved Motion Correction in MRI Myocardial T1 Mapping.

BACKGROUND: The modified Look-Locker inversion recovery (MOLLI) sequence is commonly used for myocardial T1 mapping. However, it acquires images with different inversion times, which causes difficulty in motion correction for respiratory-induced misregistration to a given target image. HYPOTHESIS: Using a generative adversarial network (GAN) to produce virtual MOLLI images with consistent heart positions can reduce respiratory-induced misregistration of MOLLI datasets. STUDY TYPE: Retrospective. POPULATION: 1071 MOLLI datasets from 392 human participants. FIELD STRENGTH/SEQUENCE: Modified Look-Locker inversion recovery sequence at 3 T. ASSESSMENT: A GAN model with a single inversion time image as input was trained to generate virtual MOLLI target (VMT) images at different inversion times which were subsequently used in an image registration algorithm. Four VMT models were investigated and the best performing model compared with the standard vendor-provided motion correction (MOCO) technique. STATISTICAL TESTS: The effectiveness of the motion correction technique was assessed using the fitting quality index (FQI), mutual information (MI), and Dice coefficients of motion-corrected images, plus subjective quality evaluation of T1 maps by three independent readers using Likert score. Wilcoxon signed-rank test with Bonferroni correction for multiple comparison. Significance levels were defined as P < 0.01 for highly significant differences and P < 0.05 for significant differences. RESULTS: The best performing VMT model with iterative registration demonstrated significantly better performance (FQI 0.88 ± 0.03, MI 1.78 ± 0.20, Dice 0.84 ± 0.23, quality score 2.26 ± 0.95) compared to other approaches, including the vendor-provided MOCO method (FQI 0.86 ± 0.04, MI 1.69 ± 0.25, Dice 0.80 ± 0.27, quality score 2.16 ± 1.01). DATA CONCLUSION: Our GAN model generating VMT images improved motion correction, which may assist reliable T1 mapping in the presence of respiratory motion. Its robust performance, even with considerable respiratory-induced heart displacements, may be beneficial for patients with difficulties in breath-holding. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

Neurovascular Correlates of Cobalamin, Folate, and Homocysteine in Dementia.

BACKGROUND: Cobalamin (Cbl) and folate are common supplements clinicians prescribe as an adjuvant therapy for dementia patients, on the presumption of their neurotrophic and/or homocysteine (Hcy) lowering effect. However, the treatment efficacy has been found mixed and the effects of Cbl/folate/Hcy on the human brain remain to be elucidated. OBJECTIVE: To explore the neurovascular correlates of Cbl/folate/Hcy in Alzheimer's disease (AD) and subcortical ischemic vascular dementia (SIVD). METHODS: Sixty-seven AD patients and 57 SIVD patients were prospectively and consecutively recruited from an outpatient clinic. Multimodal 3-Tesla magnetic resonance imaging was performed to quantitatively evaluate cerebral blood flow (CBF) and white matter integrity. The relationship between neuroimaging metrics and the serum levels of Cbl/folate/Hcy was examined by using the Kruskal-Wallis test, partial correlation analysis, and moderation analysis, at a significance level of 0.05. RESULTS: As a whole, CBF mainly associated with Cbl/folate while white matter hyperintensities exclusively associated with Hcy. As compared with AD, SIVD exhibited more noticeable CBF correlates (spatially widespread with Cbl and focal with folate). In SIVD, a bilateral Cbl-moderated CBF coupling was found between medial prefrontal cortex and ipsilateral basal ganglia, while in the fronto-subcortical white matter tracts, elevated Hcy was associated with imaging metrics indicative of increased injury in both axon and myelin sheath. CONCLUSIONS: We identified the neurovascular correlates of previously reported neurotrophic effect of Cbl/folate and neurotoxic effect of Hcy in dementia. The correlates exhibited distinct patterns in AD and SIVD. The findings may help improving the formulation of supplemental Cbl/folate treatment for dementia.

Thalamocortical coherence predicts persistent postconcussive symptoms.

The pathogenetic mechanism of persistent post-concussive symptoms (PCS) following concussion remains unclear. Thalamic damage is known to play a role in PCS prolongation while the evidence and biomarkers that trigger persistent PCS have never been elucidated. We collected longitudinal neuroimaging and behavior data from patients and rodents after concussion, complemented with rodents' histological staining data, to unravel the early biomarkers of persistent PCS. Diffusion tensor imaging (DTI) were acquired to investigated the thalamic damage, while quantitative thalamocortical coherence was derived through resting-state functional MRI for evaluating thalamocortical functioning and predicting long-term behavioral outcome. Patients with prolonged symptoms showed abnormal DTI-derived indices at the boundaries of bilateral thalami (peri-thalamic regions). Both patients and rats with persistent symptoms demonstrated enhanced thalamocortical coherence between different thalamocortical circuits, which disrupted thalamocortical multifunctionality. In rodents, the persistent DTI abnormalities were validated in thalamic reticular nucleus (TRN) through immunohistochemistry, and correlated with enhanced thalamocortical coherence. Strong predictive power of these coherence biomarkers for long-term PCS was also validated using another patient cohort. Postconcussive events may begin with persistent TRN injury, followed by disrupted thalamocortical coherence and prolonged PCS. Functional MRI-based coherence measures can be surrogate biomarkers for early prediction of long-term PCS.

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net.

BACKGROUND: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. METHODS: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant. RESULTS: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion. CONCLUSIONS: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC. KEY POINTS: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.

Single breath-hold MR T1 mapping in the heart: Hybrid MOLLI combining saturation and inversion recovery.

The native T1 values of the myocardium provide valuable information for tissue characterization and assessment of cardiomyopathies. In this study, we proposed a novel hybrid MOLLI sequence for myocardial T1 mapping. Unlike the two groups of inversion-recovery sampling of the conventional MOLLI5(3 s)3 sequence, the hybrid MOLLI sequence consisted of an inversion-recovery block followed by a saturation-recovery block. Since the second block employed a saturation pulse to spoil the longitudinal magnetization, it did not require a waiting period as MOLLI5(3 s)3 did. As a result, the hybrid MOLLI required less acquisition time leading to a practical application for patients with breath-hold difficulties. Phantom and healthy subject experiments were performed to evaluate the proposed sequence against the MOLLI5(3 s)3 sequence. The phantom study showed that the heart-rate dependency of one variant of the hybrid MOLLI sequences, hbMOLLI4, was comparable to that of MOLLI5(3 s)3. In addition, both hbMOLLI4 and MOLLI53 derived T1 values under 2% variations with simulated heart rates from 50 to 90 beats-per-minute within the range of T1 values for myocardium and blood before contrast administration. Simulation results suggested slightly reduced T1 fitting precision in hbMOLLI4 compared with MOLLI5(3 s)3, but prominently better than saturation recovery. Bland-Altman analysis on accuracy assessment revealed that hbMOLLI4 partially reduced the T1 underestimation of MOLLI5(3 s)3. In the human study, The T1 values of both methods were consistent (hbMOLLI4 vs. MOLLI5(3 s)3, slope = 1.14, R2 > 0.97), with equal reproducibility. The results supported that hybrid MOLLI produced comparable T1 mapping results in terms of accuracy, reproducibility, and heart-rate dependency, at the expense of slightly reduced precision. We concluded that the hybrid MOLLI sequence presents a competitive alternative to the MOLLI5(3 s)3 sequence when a speedy acquisition is required.

Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds.

OBJECTIVES: To examine the role of ADC threshold on agreement across observers and deep learning models (DLMs) plus segmentation performance of DLMs for acute ischemic stroke (AIS). METHODS: Twelve DLMs, which were trained on DWI-ADC-ADC combination from 76 patients with AIS using 6 different ADC thresholds with ground truth manually contoured by 2 observers, were tested by additional 67 patients in the same hospital and another 78 patients in another hospital. Agreement between observers and DLMs were evaluated by Bland-Altman plot and intraclass correlation coefficient (ICC). The similarity between ground truth (GT) defined by observers and between automatic segmentation performed by DLMs was evaluated by Dice similarity coefficient (DSC). Group comparison was performed using the Mann-Whitney U test. The relationship between the DSC and ADC threshold as well as AIS lesion size was evaluated by linear regression analysis. A p < .05 was considered statistically significant. RESULTS: Excellent interobserver agreement and intraobserver repeatability in the manual segmentation (all ICC > 0.98, p < .001) were achieved. The 95% limit of agreement was reduced from 11.23 cm2 for GT on DWI to 0.59 cm2 for prediction at an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. The segmentation performance of DLMs was improved with an overall DSC from 0.738 ± 0.214 on DWI to 0.971 ± 0.021 on an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. CONCLUSIONS: Combining an ADC threshold of 0.6 × 10-3 mm2/s with DWI reduces interobserver and inter-DLM difference and achieves best segmentation performance of AIS lesions using DLMs. KEY POINTS: • Higher Dice similarity coefficient (DSC) in predicting acute ischemic stroke lesions was achieved by ADC thresholds combined with DWI than by DWI alone (all p < .05). • DSC had a negative association with the ADC threshold in most sizes, both hospitals, and both observers (most p < .05) and a positive association with the stroke size in all ADC thresholds, both hospitals, and both observers (all p < .001). • An ADC threshold of 0.6 × 10-3 mm2/s eliminated the difference of DSC at any stroke size between observers or between hospitals (p = .07 to > .99).

Stage-Dependent Cerebral Blood Flow and Leukoaraiosis Couplings in Subcortical Ischemic Vascular Disease and Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) and subcortical ischemic vascular disease (SIVD) have both been associated with white matter hyperintensities (WMHs) and altered cerebral blood flow (CBF) although the etiology of AD is still unclear. OBJECTIVE: To test the hypothesis that CBF and WMHs have differential effects on cognition and that the relationship between CBF and WMHs changes with the subtypes and stages of dementia. METHODS: Forty-two patients with SIVD, 50 patients with clinically-diagnosed AD, and 30 cognitively-normal subjects were included. Based on the Clinical Dementia Rating (CDR), the patients were dichotomized into early-stage (CDR = 0.5) and late-stage (CDR = 1 or 2) groups. CBF and WMH metrics were derived from magnetic resonance imaging and correlated with cognition. RESULTS: Hierarchical linear regression revealed that CBF metrics had distinct contribution to global cognition, memory, and attention, whereas WMH metrics had distinct contribution to executive function (all p < 0.05). In SIVD, the WMHs in frontotemporal areas correlated with the CBF in bilateral thalami at the early stage; the correlation then became between the WMHs in basal ganglia and the CBF in frontotemporal areas at the late stage. A similar corticosubcortical coupling was observed in AD but involved fewer areas. CONCLUSION: A stage-dependent coupling between CBF and WMHs was identified in AD and SIVD, where the extent of cortical WMHs correlated with subcortical CBF for CDR = 0.5, whereas the extent of subcortical WMHs correlated with cortical CBF for CDR = 1-2.

Intra-tumoral susceptibility signal: a post-processing technique for objective grading of astrocytoma with susceptibility-weighted imaging.

BACKGROUND: Susceptibility-weighted imaging (SWI) is sensitive to the accumulation of paramagnetic substances, such as hemorrhage and increased venous vasculature, both being frequently found in high-grade tumors. The purpose of this retrospective study is to differentiate high-grade and low-grade astrocytoma by objectively measuring quantitative intra-tumoral susceptibility signals (qITSS) on SWI. METHODS: Precontrast SWI and 3D contrast-enhanced T1WI of 65 patients with astrocytoma were collected at 1.5 Tesla. All tumors were histologically confirmed and classified into two groups: high grade (WHO grade III and IV, n=50) and low grade (WHO grade II, n=15). After manual delineation of the tumor on T1WI, normalized contrast (NC) was calculated voxel by voxel within the tumor by using the concept of contrast to noise ratio. Thresholding on NC was applied to detect qITSS, and the volumetric percentage of qITSS can be obtained for each tumor. Two-sample t-test was applied to examine significant difference of qITSS percentage between high-grade and low-grade astrocytoma for different NC thresholds, ranging from 4 to 20. Receiver operating characteristic analysis was performed to evaluate the performance of differentiation. RESULTS: P value was less than 0.01 for a large range of NC thresholds [4-20], reflecting significant difference of qITSS percentage between high-grade and low-grade astrocytoma. The area under the receiver operating characteristic curve was larger than 0.9 at NC thresholds from 8 to 16 and peaks at 0.949 with a NC threshold of 14. It was shown that astrocytoma grading by qITSS percentage is successful for a wide range of NC threshold, demonstrating robustness on threshold selection. CONCLUSIONS: Without relying on the selection of slice position and at the same time providing objective identification of hypointense signal in SWI, the qITSS percentage can be used to distinguish high-grade and low-grade astrocytoma reliably.

Multi-shot Diffusion-Weighted MRI With Multiplexed Sensitivity Encoding (MUSE) in the Assessment of Active Inflammation in Crohn's Disease.

BACKGROUND: Single-shot diffusion-weighted imaging (ssDWI) has been shown useful for detecting active bowel inflammation in Crohn's disease (CD) without MRI contrast. However, ssDWI suffers from geometric distortion and low spatial resolution. PURPOSE: To compare conventional ssDWI with higher-resolution ssDWI (HR-ssDWI) and multi-shot DWI based on multiplexed sensitivity encoding (MUSE-DWI) for evaluating bowel inflammation in CD, using contrast-enhanced MR imaging (CE-MRI) as the reference standard. STUDY TYPE: Prospective. SUBJECTS: Eighty nine patients with histological diagnosis of CD from previous endoscopy (55 male/34 female, age: 17-69 years). FIELD STRENGTH/SEQUENCES: ssDWI (2.7 mm × 2.7 mm), HR-ssDWI (1.8 mm × 1.8 mm), MUSE-DWI (1.8 mm × 1.8 mm) based on echo-planar imaging, T2-weighted imaging, and CE-MRI sequences, all at 1.5 T. ASSESSMENT: Five raters independently evaluated the tissue texture conspicuity, geometry accuracy, minimization of artifacts, diagnostic confidence, and overall image quality using 5-point Likert scales. The diagnostic performance (sensitivity, specificity and accuracy) of each DWI sequences was assessed on per-bowel-segment basis. STATISTICAL TESTS: Inter-rater agreement for qualitative evaluation of each parameter was measured by the intra-class correlation coefficient (ICC). Paired Wilcoxon signed-rank tests were performed to evaluate the statistical significance of differences in qualitative scoring between DWI sequences. A P value <0.05 was considered to be statistically significant. RESULTS: Tissue texture conspicuity, geometric distortions, and overall image quality were significantly better for MUSE-DWI than for ssDWI and HR-ssDWI with good agreement among five raters (ICC: 0.70-0.89). HR-ssDWI showed significantly poorer performance to ssDWI and MUSE-DWI for all qualitative scores and had the worst diagnostic performance (sensitivity of 57.0% and accuracy of 87.3%, with 36 undiagnosable cases due to severe artifacts). MUSE-DWI showed significantly higher sensitivity (97.5% vs. 86.1%) and accuracy (98.9% vs. 95.1%) than ssDWI for detecting bowel inflammation. DATA CONCLUSION: MUSE-DWI was advantageous in assessing bowel inflammation in CD, resulting in improved spatial resolution and image quality. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Evaluation of the Swallow-Tail Sign and Correlations of Neuromelanin Signal with Susceptibility and Relaxations.

The presence of a swallow-tail sign in the nigrosome-1 with hyperintense signal shown on the susceptibility-weighted imaging (SWI) has been shown to be sensitive in detecting the abnormal iron deposits in this area. A systematic evaluation in healthy subjects is required before this tool can be recommended in a widespread application. We evaluated a simple and practical SWI approach using a multiecho gradient-echo sequence with an improved contrast-to-noise ratio (CNR). We also evaluated the association of the neuromelanin imaging contrast behavior with the susceptibility and relaxation measurements. Twenty-five older and 23 young healthy adults were evaluated. The CNRs of the nigrosome-1 were compared along with method and group. Correlations of the nigrosome-1 neuromelanin signal in the neuromelanin-sensitive imaging with CNRs in the susceptibility, T1 and T2 mappings were examined. Two different coils were used to confirm the reproducibility. Compared with the single-echo, multiecho SWI can improve the CNR of the swallow-tail sign. We found significant correlations between neuromelanin signal and CNRs in the susceptibility and T2 mappings, and T1 value. The older subjects exhibited increased CNRs compared with the young adults. No significant difference was observed in the measurements between 20 and 64 channels. The multiecho technique allows the high-quality nigrosome-1 images in SWI and allows for a joint analysis of T2* and quantitative-susceptibility mapping at high spatial resolution. The correlations of neuromelanin-sensitive imaging with susceptibility and T2 imply that the iron content in the nigrosome-1 may have significant influences on the hyperintensity of neuromelanin in the magnetization transfer-related contrast.

Diffusion Tensor Imaging of the Spinal Cord.

Spinal cord often is regarded as one of the last territories in the central nervous system where diffusion tensor imaging (DTI) can be used to probe white matter architecture. This article reviews current progress in spinal cord DTI, starting with anatomic properties and technical challenges that make spinal cord DTI a difficult task. Several possibilities offered by advanced pulse sequences that might overcome the difficulties are addressed, with associated trade-offs and limitations. Potential clinical assistance also is discussed in various spinal cord pathologies, such as myelopathy due to external compression, spinal cord tumors, acute ischemia, traumatic injury, and so forth.

Classification of parotid gland tumors by using multimodal MRI and deep learning.

Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T2 -weighted, postcontrast T1 -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-Net, to segment and classify parotid gland tumors. The U-Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion-related parameters performed better than those with structural MR images. The performance results (n = 85) of the diffusion-based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion-weighted and contrast-enhanced T1 -weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.

Measurements of venous oxygen saturation in the superior sagittal sinus using conventional 3D multiple gradient-echo MRI: Effects of flow velocity and acceleration.

PURPOSE: This work investigates the effects of flow acceleration in the superior sagittal sinus on slice-dependent variations in venous oxygen saturation (SvO2 ) estimations using susceptibility-based MR oximetry. METHODS: Three-dimensional multiple gradient-echo images, with first-order flow compensation along the anterior-posterior readout direction for the first echo, were acquired twice from 15 healthy volunteers. For all slices, phases within the superior sagittal sinus were fitted using linear regression across four TEs to obtain the Pearson's correlation coefficients (PCCs), the largest of which corresponded to minimum acceleration influence. SvO2 derived from odd echoes on this slice was used to assess interscan difference, and compared with the central 15th slice for slice-dependent difference, both using Bland-Altman analysis. Within-scan interslice SvO2 consistency was examined versus PCC. Multislice-averaged SvO2 values were then computed from slices with PCCs above a certain threshold. RESULTS: Slice-dependent difference in SvO2 varied from -16.2% to 21.5% at two SDs, in agreement with a recent report, and about twice larger than interscan differences for the automatically selected slice (-7.5% to 10.3%) and for the central 15th slice (-8.0% to 8.8%). For slices with PCCs higher than -0.98, interslice SvO2 deviations were all found to be less than 5.0%. Multislice-averaged SvO2 with PCCs higher than -0.98 further reduced interscan difference to -4.7% to 8.2%. CONCLUSION: Slice-dependent variations in SvO2 may partly be explained by the effects of flow acceleration. Our method may enable conventional 3D multiple gradient echo to be used for SvO2 estimations in the presence of pulsatile flow.

Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model.

BACKGROUND: Recent trials have shown promise in intra-arterial thrombectomy after the first 6-24 h of stroke onset. Quick and precise identification of the salvageable tissue is essential for successful stroke management. In this study, we examined the feasibility of machine learning (ML) approaches for differentiating the ischemic penumbra (IP) from the infarct core (IC) by using diffusion tensor imaging (DTI)-derived metrics. METHODS: Fourteen male rats subjected to permanent middle cerebral artery occlusion (pMCAO) were included in this study. Using a 7 T magnetic resonance imaging, DTI metrics such as fractional anisotropy, pure anisotropy, diffusion magnitude, mean diffusivity (MD), axial diffusivity, and radial diffusivity were derived. The MD and relative cerebral blood flow maps were coregistered to define the IP and IC at 0.5 h after pMCAO. A 2-level classifier was proposed based on DTI-derived metrics to classify stroke hemispheres into the IP, IC, and normal tissue (NT). The classification performance was evaluated using leave-one-out cross validation. RESULTS: The IC and non-IC can be accurately segmented by the proposed 2-level classifier with an area under the receiver operating characteristic curve (AUC) between 0.99 and 1.00, and with accuracies between 96.3 and 96.7%. For the training dataset, the non-IC can be further classified into the IP and NT with an AUC between 0.96 and 0.98, and with accuracies between 95.0 and 95.9%. For the testing dataset, the classification accuracy for IC and non-IC was 96.0 ± 2.3% whereas for IP and NT, it was 80.1 ± 8.0%. Overall, we achieved the accuracy of 88.1 ± 6.7% for classifying three tissue subtypes (IP, IC, and NT) in the stroke hemisphere and the estimated lesion volumes were not significantly different from those of the ground truth (p = .56, .94, and .78, respectively). CONCLUSIONS: Our method achieved comparable results to the conventional approach using perfusion-diffusion mismatch. We suggest that a single DTI sequence along with ML algorithms is capable of dichotomizing ischemic tissue into the IC and IP.

Magnetic resonance cholangiopancreatography at 3T in a single breath-hold: comparative effectiveness between three-dimensional (3D) gradient- and spin-echo and two-dimensional (2D) thick-slab fast spin-echo acquisitions.

BACKGROUND: To compare the depiction conspicuity of three-dimensional (3D) magnetic resonance cholangiopancreatography (MRCP) based on gradient- and spin-echo (GRASE) and two-dimensional (2D) thick-slab MRCP using fast spin-echo (FSE) in different segments of hepatic and pancreatic ducts at 3T. METHODS: Both 3D GRASE and 2D thick-slab FSE MRCP, with parameters adjusted under the constraints of specific absorption rate and scan time within single breath-hold, were performed for 95 subjects (M/F =49:46; age range, 25-75) at 3T. Conspicuity of eight ductal segments was graded by two experienced raters using a 4-point score. Situations where one technique is superior or inferior to the other were recorded. RESULTS: 3D GRASE MRCP outperformed 2D thick-slab FSE MRCP in the common bile duct and common hepatic ducts (both with P<0.001), but compared inferiorly in the right hepatic ducts (P<0.001), right posterior hepatic ducts (P<0.005) and pancreatic duct distal (P<0.05). Performing both 3D and 2D MRCP would reduce the number of non-diagnostic readings in the left hepatic duct to 10 remaining (5.3%), compared with 31 (16.3%) or 21 (11.1%) out of 190 readings if using 3D GRASE or 2D thick-slab FSE alone, respectively. CONCLUSIONS: Although 3D GRASE MRCP is preferential to visualize the common bile duct and common hepatic duct within one single breath-hold, the complementary role of 2D thick-slab FSE MRCP in smaller hepatic and pancreatic ducts makes it a useful adjunct if performed additionally.

Imaging quality of PROPELLER diffusion-weighted MR imaging and its diagnostic performance in distinguishing pleomorphic adenomas from Warthin tumors of the parotid gland.

The aim of this study was to evaluate the imaging quality and diagnostic performance of fast spin echo diffusion-weighted imaging with periodically rotated overlapping parallel lines with enhanced reconstruction (FSE-PROP-DWI) in distinguishing parotid pleomorphic adenoma (PMA) from Warthin tumor (WT). This retrospective study enrolled 44 parotid gland tumors from 34 patients, including 15 PMAs and 29 WTs with waived written informed consent. All participants underwent 1.5 T diffusion-weighted imaging including FSE-PROP-DWI and single-shot echo-planar diffusion-weighted imaging (SS-EP-DWI). After imaging resizing and registration among T2WI, FSE-PROP-DWI and SS-EP-DWI, imaging distortion was quantitatively analyzed by using the Dice coefficient. Signal-to-noise ratio and contrast-to-noise ratio were qualitatively evaluated. The mean apparent diffusion coefficient (ADC) of parotid gland tumors was calculated. Wilcoxon signed-rank test was used for paired comparison between FSE-PROP-DWI versus SS-EP-DWI. Mann-Whitney U test was used for independent group comparison between PMAs versus WTs. Diagnostic performance was evaluated by receiver operating characteristics curve analysis. P < 0.05 was considered statistically significant. The Dice coefficient was statistically significantly higher on FSE-PROP-DWI than SS-EP-DWI for both tumors (P < 0.005). Mean ADC was statistically significantly higher in PMAs than WTs on both FSE-PROP-DWI and SS-EP-DWI (P < 0.005). FSE-PROP-DWI and SS-EP-DWI successfully distinguished PMAs from WTs with an AUC of 0.880 and 0.945, respectively (P < 0.05). Sensitivity, specificity, positive predictive value, negative predictive value and accuracy in diagnosing PMAs were 100%, 69.0%, 62.5%, 100% and 79.5% for FSE-PROP-DWI, and 100%, 82.8%, 75%, 100% and 88.6% for SS-EP-DWI, respectively. FSE-PROP-DWI is useful to distinguish parotid PMAs from WTs with less distortion of tumors but lower AUC than SS-EP-DWI.