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.

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.

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.

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).

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.

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.

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.

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.

Changes in sensorimotor-related thalamic diffusion properties and cerebrospinal fluid hydrodynamics predict gait responses to tap test in idiopathic normal-pressure hydrocephalus.

OBJECTIVES: To compare diffusion tensor (DT)-derived indices from the thalamic nuclei and cerebrospinal fluid (CSF) hydrodynamic parameters for the prediction of gait responsiveness to the CSF tap test in early iNPH patients. METHODS: In this study, 22 patients with iNPH and 16 normal controls were enrolled with the approval of an institutional review board. DT imaging and phase-contrast magnetic resonance imaging were performed in patients and controls to determine DT-related indices of the sensorimotor-related thalamic nuclei and CSF hydrodynamics. Gait performance was assessed in patients using gait scale before and after the tap test. The Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were applied to compare group differences between patients and controls and assess the predictive performance of gait responsiveness to the tap test in the patients. RESULTS: Fractional anisotropy (FA) and axial diffusivity showed significant increases in the ventrolateral (VL) and ventroposterolateral (VPL) nuclei of the iNPH group compared with those of the control group (p < 0.05). The predictions of gait responsiveness of ventral thalamic FA alone (area under the ROC curve [AUC] < 0.8) significantly outperformed those of CSF hydrodynamics alone (AUC < 0.6). The AUC curve was elevated to 0.812 when the CSF peak systolic velocity and FA value were combined for the VPL nucleus, yielding the highest sensitivity (0.769) and specificity (0.778) to predict gait responses. CONCLUSIONS: Combined measurements of sensorimotor-related thalamic FA and CSF hydrodynamics can provide potential biomarkers for gait response to the CSF tap test in patients with iNPH. KEY POINTS: • Ventrolateral and ventroposterolateral thalamic FA may predict gait responsiveness to tap test. • Thalamic neuroplasticity can be assessed through DTI in idiopathic normal-pressure hydrocephalus. • Changes in the CST associated with gait control could trigger thalamic neuroplasticity. • Activities of sensorimotor-related circuits could alter in patients with gait disturbance. • Management of patients with iNPH could be more appropriate.

Effects of B Value on Quantification of Rapid Diffusion Kurtosis Imaging in Normal and Acute Ischemic Brain Tissues.

PURPOSE: Diffusion kurtosis imaging (DKI) has been widely used to characterize brain tissue alterations. Diffusion-weighting factor or b value plays an important role in the measurement of rapid DKI and may have influential effects on them. The purpose of this study was to investigate the effects of b value on rapid DKI indices in normal and acute ischemic brain tissues. MATERIALS AND METHODS: This study enrolled 10 healthy subjects and 4 acute ischemic stroke patients. Three repeated DKI data with 6 high b values (500, 750, 1000, 1500, 2000, 3000 s/mm) were acquired from healthy subjects, whereas nonrepeated DKI data with 3 high b values (1000, 2000, 3000 s/mm) were acquired from ischemic stroke patients. The DKI datasets were decomposed into several rapid DKI datasets consisting of 1 b0 and 2 high b values for comparisons. RESULTS: The results showed that b value significantly impacted the reproducibility and accuracy of DKI indices. The comparisons demonstrated that DKI with b = (0, 1000, 3000) s/mm exhibited more reproducible and accurate DKI indices than other DKI datasets in normal brain tissues, and similar results were noticed in acute ischemic brain tissue. CONCLUSIONS: We concluded that b value significantly impacted the quantification of DKI indices in both normal and acute ischemic brain tissues.

TLG-S criteria are superior to both EORTC and PERCIST for predicting outcomes in patients with metastatic lung adenocarcinoma treated with erlotinib.

PURPOSE: In this retrospective review of prospectively collected data, we sought to investigate whether early FDG-PET assessment of treatment response based on total lesion glycolysis measured using a systemic approach (TLG-S) would be superior to either local assessment with EORTC (European Organization for Research and Treatment of Cancer) criteria or single-lesion assessment with PERCIST (PET Response Criteria in Solid Tumors) for predicting clinical outcomes in patients with metastatic lung adenocarcinoma treated with erlotinib. We also examined the effect of bone flares on tumor response evaluation by single-lesion assessment with PERCIST in patients with metastatic bone lesions. METHODS: We performed a retrospective review of prospectively collected data from 23 patients with metastatic lung adenocarcinoma treated with erlotinib. All participants underwent FDG-PET imaging at baseline and on days 14 and 56 after completion of erlotinib treatment. In addition, diagnostic CT scans were performed at baseline and on day 56. FDG-PET response was assessed with TLG-S, EORTC, and PERCIST criteria. Response assessment based on RECIST 1.1 (Response Evaluation Criteria in Solid Tumors) from diagnostic CT imaging was used as the reference standard. Two-year progression-free survival (PFS) and overall survival (OS) served as the main outcome measures. RESULTS: We identified 13 patients with bone metastases. Of these, four (31 %) with persistent bone uptake due to bone flares on day 14 were erroneously classified as non-responders according to the PERCIST criteria, but they were correctly classified as responders according to both the EORTC and TLG-S criteria. Patients who were classified as responders on day 14 based on TLG-S criteria had higher rates of 2-year PFS (26.7 % vs. 0 %, P = 0.007) and OS (40.0 % vs. 7.7 %, P = 0.018). Similar rates were observed in patients who showed a response on day 56 based on CT imaging according to the RECIST criteria. Patients classified as responders on day 14 according to the EORTC criteria on FDG-PET imaging had better rates of 2-year OS than did non-responders (36.4 % vs. 8.3 %, P = 0.015). CONCLUSIONS: TLG-S criteria may be of greater help in predicting survival outcomes than other forms of assessment. Bone flares, which can interfere with the interpretation of treatment response based on PERCIST criteria, are not uncommon in patients with metastatic lung adenocarcinoma treated with erlotinib.

POCS-based reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE): A general algorithm for reducing motion-related artifacts.

PURPOSE: A projection onto convex sets reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE) is developed to reduce motion-related artifacts, including respiration artifacts in abdominal imaging and aliasing artifacts in interleaved diffusion-weighted imaging. THEORY: Images with reduced artifacts are reconstructed with an iterative projection onto convex sets (POCS) procedure that uses the coil sensitivity profile as a constraint. This method can be applied to data obtained with different pulse sequences and k-space trajectories. In addition, various constraints can be incorporated to stabilize the reconstruction of ill-conditioned matrices. METHODS: The POCSMUSE technique was applied to abdominal fast spin-echo imaging data, and its effectiveness in respiratory-triggered scans was evaluated. The POCSMUSE method was also applied to reduce aliasing artifacts due to shot-to-shot phase variations in interleaved diffusion-weighted imaging data corresponding to different k-space trajectories and matrix condition numbers. RESULTS: Experimental results show that the POCSMUSE technique can effectively reduce motion-related artifacts in data obtained with different pulse sequences, k-space trajectories and contrasts. CONCLUSION: POCSMUSE is a general post-processing algorithm for reduction of motion-related artifacts. It is compatible with different pulse sequences, and can also be used to further reduce residual artifacts in data produced by existing motion artifact reduction methods.

Discriminating pyogenic brain abscesses, necrotic glioblastomas, and necrotic metastatic brain tumors by means of susceptibility-weighted imaging.

OBJECTIVES: To investigate the feasibility of using susceptibility-weighted imaging (SWI) to discriminate abscesses and necrotic tumours. METHODS: Twenty-one patients with pyogenic abscesses, 21 patients with rim-enhancing glioblastomas and 23 patients with rim-enhancing metastases underwent SWI. Intralesional susceptibility signal (ILSS) was analyzed employing both qualitative (QL) and semi-quantitative (SQ) methods. Logistic regression models and receiver operating characteristic analysis were used to demonstrate the discriminating power. RESULTS: In QL analysis, ILSSs were seen in 12 of 21 abscesses, in 20 of 21 glioblastomas, and in 16 of 23 metastases. In SQ analysis, a low degree of ILSS (85.8 %) was in the majority of abscesses and a high degree of ILSS (76.2 %) was in the majority of glioblastomas. SQ model was significantly better than QL model in distinguishing abscesses from glioblastomas (P < .001). A derived ILSS cutoff grade of 1 or less was quantified as having a sensitivity of 85.7 %, specificity of 90.5 %, accuracy of 88.1 %, PPV of 90.0 %, and NPV of 86.4 % in distinguishing abscesses from glioblastomas. CONCLUSIONS: A high-grade ILSS may help distinguish glioblastomas from abscesses and necrotic metastatic brain tumours. The lack of ILSS or low-grade ILSS can be a more specific sign in the imaging diagnosis of abscesses. KEY POINTS: • ILSS of SWI can contribute to differential diagnosis of rim-enhanced mass. • Low-grade ILSS can be a more specific sign in abscesses. • High-grade ILSS may help distinguish necrotic glioblastomas from abscesses. • ILSS spreads across the four ILSS categories in metastases.

Effects of gender, age, and body mass index on fat contents and apparent diffusion coefficients in healthy parotid glands: an MRI evaluation.

OBJECTIVES: To establish standard apparent diffusion coefficient (ADC) and the fat content as a function of age, gender and body mass index (BMI) in healthy parotid glands, and to address the influences of fat suppression on ADC measurements. METHODS: A total of 100 healthy adults (gender and age evenly distributed) were prospectively recruited, with parotid fat content measured from gradient-echo images with fat-water separated using iterative decomposition with echo asymmetry and least squares (IDEAL). The ADCs were estimated using both fat-saturated and non-fat-saturated diffusion-weighted imaging via a periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique. RESULTS: Parotid fat content was larger in men than in women by about 10 percentage points (P < 0.005), and positively associated with BMI and age for both genders (mostly with P < 0.001). ADCs estimated with non-fat-saturated PROPELLER were significantly lower in men than in women (P < 0.005), but showed no gender difference if measured using fat-saturated PROPELLER (P = 0.840). The negative association between parotid ADC and age/BMI/fat (P < 0.001) showed greater regression slopes in non-fat-saturated PROPELLER than in fat-saturated data. CONCLUSIONS: Parotid fat content in healthy adults correlates positively with both age and BMI; the correlation with age is gender-dependent. Parotid ADC measurements are strongly influenced by fat saturation. KEY POINTS: Parotid fat content in healthy adults correlates positively with age and BMI. The rate of aging-related increase in fat contents is gender-dependent. Parotid ADC measurements are strongly influenced by fat saturation.

Effects of microvascular permeability changes on contrast-enhanced T1 and pharmacokinetic MR imagings after ischemia.

BACKGROUND AND PURPOSE: Brain enhancement on contrast-enhanced T1-weighted imaging (CET1-WI) after ischemic stroke is generally accepted as an indicator of the blood-brain barrier disruption. However, this phenomenon usually starts to become visible at the subacute phase. The purpose of this study was to evaluate the time-course profiles of K(trans), cerebral blood volume (vp), and CET1-WI with early detection of blood-brain barrier changes on K(trans) maps and their role for prediction of subsequent hemorrhagic transformation in acute middle cerebral arterial infarct. METHODS: Twenty-six patients with acute middle cerebral arterial stroke and early spontaneous reperfusion, whose MR images were obtained at predetermined stroke stages, were included. T2*-based MR perfusion-weighted images were acquired using the first-pass pharmacokinetic model to derive K(trans) and vp. Parenchymal enhancement observed on maps of K(trans), vp, and CET1-WI at each stage was compared. Association among these measurements and hemorrhagic transformation was analyzed. RESULTS: K(trans) map showed significantly higher parenchymal enhancement in ischemic parenchyma as compared with that of vp map and CET1-WI at early stroke stages (P<0.05). The increased K(trans) at acute stage was not associated with parenchymal enhancement in CET1-WI at the same stage. Parenchymal enhancement in CET1-WI started to occur at the late subacute stage and tended to be luxury reperfusion-dependent. Patients with hemorrhagic transformation showed higher mean K(trans) values as compared with patients without hemorrhagic transformation (P=0.02). CONCLUSIONS: Postischemic brain enhancement on routine CET1-WI seems to be closely related to the luxury reperfusion at the late subacute stage and is not dependent on microvascular permeability changes at the acute stage.

Parotid fat contents in healthy subjects evaluated with iterative decomposition with echo asymmetry and least squares fat-water separation.

PURPOSE: To evaluate the effectiveness of three fat measurement methods for parotid glands in healthy subjects, with or without metallic dental implants. MATERIALS AND METHODS: The institutional review board approved this study, with informed consent obtained from 114 volunteers undergoing magnetic resonance (MR) imaging at 1.5 T. Fat-saturated (FS) and non-fat-saturated (NFS) fast spin-echo T1-weighted imaging (T1 method), FS and NFS T2-weighted periodically rotated overlapping parallel lines with enhanced reconstruction fast spin-echo imaging (T2 method), and gradient-echo imaging with fat-water separation using iterative decomposition with echo asymmetry and least squares (IDEAL) method were used to derive parotid fat contents. Two raters examined the homogeneity of fat saturation to determine whether parotid fat quantification was successful, with the success rate in the 114 subjects recorded for each protocol. In subjects whose fat quantification was successful with all three imaging methods, linear regression was used to analyze the correlation between any pair of the three parotid fat content measurement methods. RESULTS: Success rates in parotid fat measurements by using T1, T2, and IDEAL methods were 87.7% (100 of 114), 87.7% (100 of 114), and 100% (114 of 114), respectively. The means of measured parotid fat contents revealed significant differences (P < .001) between any pair of the three measurement methods. The parotid fat contents measured with the three methods were significantly correlated with each other between any pair of combinations. CONCLUSION: The IDEAL method provided a high success rate for parotid fat measurements, even in subjects with metallic dental implants.

Quantitative comparison of post-processing methods for reduction of frequency modulation sidebands in non-water suppression 1H MRS.

Non-water suppression MRS (NWS MRS) has several advantages. First, the unsuppressed water signal can be used as internal calibration for metabolite quantification and as a reliable frequency/phase reference for retrospective motion correction. Second, it avoids the potential artifacts caused by incomplete water suppression (WS) and extra radiofrequency deposition from WS pulses. However, the frequency modulation (FM) sidebands originating from a large water signal will distort the spectrum. Among the methods proposed to solve the problems caused by FM sidebands, post-acquisition processing methods are superior in flexibility for general use compared with experimental methods. In this study, we propose two algorithms based on advanced matrix decomposition to remove the FM sidebands. These methods, the simultaneous diagonalization (QZ) algorithm and its subsequent variant, the simultaneously generalized Schur decomposition (SGSD) algorithm, were numerically evaluated using computer simulations. In addition, we quantitatively compared the performance of these methods and the modulus method in an in vitro experiment and in vivo NWS MRS against conventional WS data. Our results show that the proposed SGSD algorithm can reduce the FM sidebands to achieve superior estimation of concentration on three major metabolites. This method can be applied directly to spectra pre-acquired under various experimental conditions without modifying the acquisition sequences.