Cerebral small vessel disease (CSVD) includes vascular lesions detected on brain MRI, such as white matter hyperintensities, lacunar infarctions, microbleeds, or enlarged perivascular spaces. There is accumulating evidence that vascular changes may play an important role in development of Alzheimer’s disease (AD), and CSVD lesions detected on brain MRI were reported to be associated with β-amyloid and tau proteins accumulation. As the vascular contribution has therapeutic potential, it is important to understand the association of CSVD with AD and AD biomarkers. This review begins with a brief introduction of AD and AD biomarkers, explains the association between AD and vascular changes, and then details the pathogenesis and MR imaging findings of CSVD. Afterwards, we discuss the association of CSVD with AD and AD biomarkers.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To evaluate pharmacokinetic variables from contrast-enhancing lesions (CELs) and non-enhancing T2 high signal intensity lesions (NE-T2HSILs) on dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging for predicting progression-free survival (PFS) in glioblastoma (GBM) patients. Materials and Methods: Sixty-four GBM patients who had undergone preoperative DCE MR imaging and received standard treatment were retrospectively included. We analyzed the pharmacokinetic variables of the volume transfer constant (Ktrans) and volume fraction of extravascular extracellular space within the CEL and NE-T2HSIL of the entire tumor. Univariate and multivariate Cox regression analyses were performed using preoperative clinical characteristics, pharmacokinetic variables of DCE MR imaging, and postoperative molecular biomarkers to predict PFS. Results: The increased mean Ktrans of the CEL, increased 95th percentile Ktrans of the CELs, and absence of methylated O6- methylguanine-DNA methyltransferase promoter were relevant adverse variables for PFS in the univariate analysis (p = 0.041, p = 0.032, and p = 0.083, respectively). The Kaplan-Meier survival curves demonstrated that PFS was significantly shorter in patients with a mean Ktrans of the CEL > 0.068 and 95th percentile Ktrans of the CEL > 0.223 (log-rank p = 0.038 and p = 0.041, respectively). However, only mean Ktrans of the CEL was significantly associated with PFS (p = 0.024; hazard ratio, 553.08; 95% confidence interval, 2.27–134756.74) in the multivariate Cox proportional hazard analysis. None of the pharmacokinetic variables from NE-T2HSILs were significantly related to PFS. Conclusion Among the pharmacokinetic variables extracted from CELs and NE-T2HSILs on preoperative DCE MR imaging, the mean Ktrans of CELs exhibits potential as a useful imaging predictor of PFS in GBM patients.

OBJECTIVE: To determine whether histogram values of the normalized apparent diffusion coefficient (nADC) and normalized cerebral blood volume (nCBV) maps obtained in contrast-enhancing lesions detected on immediate post-operative MR imaging can be used to predict the patient response to concurrent chemoradiotherapy (CCRT) with temozolomide (TMZ). MATERIALS AND METHODS: Twenty-four patients with GBM who had shown measurable contrast enhancement on immediate post-operative MR imaging and had subsequently undergone CCRT with TMZ were retrospectively analyzed. The corresponding histogram parameters of nCBV and nADC maps for measurable contrast-enhancing lesions were calculated. Patient groups with progression (n = 11) and non-progression (n = 13) at one year after the operation were identified, and the histogram parameters were compared between the two groups. Receiver operating characteristic (ROC) analysis was used to determine the best cutoff value for predicting progression. Progression-free survival (PFS) was determined with the Kaplan-Meier method and the log-rank test. RESULTS: The 99th percentile of the cumulative nCBV histogram (nCBV C99) on immediate post-operative MR imaging was a significant predictor of one-year progression (p = 0.033). ROC analysis showed that the best cutoff value for predicting progression after CCRT was 5.537 (sensitivity and specificity were 72.7% and 76.9%, respectively). The patients with an nCBV C99 of < 5.537 had a significantly longer PFS than those with an nCBV C99 of ≥ 5.537 (p = 0.026). CONCLUSION: The nCBV C99 from the cumulative histogram analysis of the nCBV from immediate post-operative MR imaging may be feasible for predicting glioblastoma response to CCRT with TMZ.
Adult ; Aged ; Antineoplastic Agents, Alkylating/*therapeutic use ; Brain/pathology/radiography ; Brain Neoplasms/*drug therapy/mortality/radiography ; Chemoradiotherapy ; Dacarbazine/*analogs & derivatives/therapeutic use ; Diffusion Magnetic Resonance Imaging ; Disease Progression ; Disease-Free Survival ; Female ; Glioblastoma/*drug therapy/mortality/radiography ; Humans ; Kaplan-Meier Estimate ; Male ; Middle Aged ; Proportional Hazards Models ; ROC Curve ; Retrospective Studies

OBJECTIVE: We aimed to describe the imaging findings of multidetector CT coronary angiography (MDCTA) in cases of vasospastic angina (VA) and to determine the accuracy of MDCTA in the identification of VA as compared with invasive coronary angiography with an ergonovine provocation test (CAG with an EG test). MATERIALS AND METHODS: Fifty-three patients with clinically suspected VA were enrolled in this study. Two radiologists analyzed the stenosis degree, presence or absence of plaque, plaque composition, and a remodeling index of the related-segment in CAG with an EG test, which were used as a gold standard. We evaluated the diagnostic performances of MDCTA by comparing the MDCTA findings with those of CAG with an EG test. RESULTS: Among the 25 patients with positive CAG with an EG test, all 12 patients with significant stenosis showed no definite plaque with the negative arterial remodeling. Of the six patients with insignificant stenosis, three (50%) had non-calcified plaque (NCP), two (33%) had mixed plaque, and one (17%) had calcified plaque. When the criteria for significant stenosis with negative remodeling but no definite evidence of plaque as a characteristic finding of MDCTA were used, results showed sensitivities, specificities, positive predictive values (PPV), and negative predictive values (NPV) of 48%, 100%, 100%, and 68%, respectively. CONCLUSION: Significant stenosis with negative remodeling, but no definite evidence of plaque, is the characteristic finding on MDCTA of VA. Cardiac MDCTA shows good diagnostic performance with high specificity and PPV as compared with CAG with an EG test.
Angina Pectoris/*radiography ; Chi-Square Distribution ; Comorbidity ; Contrast Media/diagnostic use ; Coronary Angiography/*methods ; Electrocardiography ; Ergonovine/diagnostic use ; Female ; Humans ; Iopamidol/analogs & derivatives/diagnostic use ; Male ; Middle Aged ; Oxytocics/diagnostic use ; Predictive Value of Tests ; Radiographic Image Interpretation, Computer-Assisted ; Retrospective Studies ; Sensitivity and Specificity ; Tomography, X-Ray Computed/*methods

Objective: Proper management of lymph nodes (LNs) with ultrasonographic (US) indeterminate features in thyroid cancerpatients remains elusive. We aimed to evaluate the malignancy risk and US findings predictive of malignancy for USindeterminate LNs in preoperative thyroid cancer patients through node-by-node correlation. Materials and Methods: A total of 348 LNs in 284 thyroid cancer patients, who underwent fine-needle aspiration or coreneedlebiopsy between December 2006 and June 2015, were included. We determined the malignancy risks for US probablybenign, indeterminate, and suspicious categories. For US indeterminate LNs, which had neither echogenic hilum nor hilarvascularity in the absence of any suspicious finding, US findings were compared between benign and metastatic LNs usingMann-Whitney U test and Fisher’s exact test. Results: US imaging diagnoses were probably benign in 20.7% (n = 72) cases, indeterminate in 23.6% (n = 82), andsuspicious in 55.7% (n = 194). Malignancy risk of US indeterminate LNs (19.5% [16/82]) differed from those of the USprobably benign (2.8% [2/72]) (p = 0.002) and US suspicious LNs (78.4% [152/194]) (p < 0.001). Among US indeterminate LNs,there were no significant differences in short, long, and long-to-short diameter (L/S) ratios between benign and metastatic LNs(3.9 vs. 3.8 mm, p = 0.619; 7.3 vs. 7.3 mm, p = 0.590; 1.9 vs. 1.9, p = 0.652). Conclusion US indeterminate LNs were frequently encountered during preoperative evaluation and had intermediate malignancyrisk. Given the lack of discriminative power of size criteria and L/S ratio, clinical factors such as surgical strategy and nodesize should be considered for proper triage of US indeterminate LNs in thyroid cancer.