Effect of deep learning-based reconstruction on high-resolution three-dimensional T2-weighted fast asymmetric spin-echo imaging in the preoperative evaluation of cerebellopontine angle tumors.

PURPOSE: We aimed to evaluate the effect of deep learning-based reconstruction (DLR) on high-spatial-resolution three-dimensional T2-weighted fast asymmetric spin-echo (HR-3D T2-FASE) imaging in the preoperative evaluation of cerebellopontine angle (CPA) tumors. METHODS: This study included 13 consecutive patients who underwent preoperative HR-3D T2-FASE imaging using a 3 T MRI scanner. The reconstruction voxel size of HR-3D T2-FASE imaging was 0.23 × 0.23 × 0.5 mm. The contrast-to-noise ratios (CNRs) of the structures were compared between HR-3D T2-FASE images with and without DLR. The observers' preferences based on four categories on the tumor side on HR-3D T2-FASE images were evaluated. The facial nerve in relation to the tumor on HR-3D T2-FASE images was assessed with reference to intraoperative findings. RESULTS: The mean CNR between the tumor and trigeminal nerve and between the cerebrospinal fluid and trigeminal nerve was significantly higher for DLR images than non-DLR-based images (14.3 ± 8.9 vs. 12.0 ± 7.6, and 66.4 ± 12.0 vs. 53.9 ± 8.5, P < 0.001, respectively). The observer's preference for the depiction and delineation of the tumor, cranial nerves, vessels, and location relation on DLR HR-3D T2FASE images was superior to that on non-DLR HR-3D T2FASE images in 7 (54%), 6 (46%), 6 (46%), and 6 (46%) of 13 cases, respectively. The facial nerves around the tumor on HR-3D T2-FASE images were visualized accurately in five (38%) cases with DLR and in four (31%) without DLR. CONCLUSION: DLR HR-3D T2-FASE imaging is useful for the preoperative assessment of CPA tumors.

Intracranial aneurysms treated with stent-assisted coil embolization: evaluation with four-dimensional ultrashort-TE MR angiography.

OBJECTIVES: As a novel follow-up method for intracranial aneurysms treated with stent-assisted coil embolization (SACE), we developed four-dimensional magnetic resonance angiography (MRA) with minimized acoustic noise utilizing ultrashort-echo time (4D mUTE-MRA). We aimed to assess whether 4D mUTE-MRA is useful for the evaluation of intracranial aneurysms treated with SACE. METHODS: This study included 31 consecutive patients with intracranial aneurysm treated with SACE who underwent 4D mUTE-MRA at 3 T and digital subtraction angiography (DSA). For 4D mUTE-MRA, five dynamic MRA images with a spatial resolution of 0.5 × 0.5 × 0.5 mm3 were obtained every 200 ms. Two readers independently reviewed the 4D mUTE-MRA images to evaluate the aneurysm occlusion status (total occlusion, residual neck, and residual aneurysm) and the flow in the stent using a 4-point scale (from 1 [not visible] to 4 [excellent]). The interobserver and intermodality agreement was assessed using κ statistics. RESULTS: On DSA images, 10 aneurysms were classified as total occlusion, 14 as residual neck, and 7 as residual aneurysm. In terms of aneurysm occlusion status, the intermodality and interobserver agreement was excellent (κ = 0.92 and κ = 0.96, respectively). For the flow in the stents on 4D mUTE-MRA, the mean score was significantly higher for single stents than multiple stents (p < .001) and for open-cell type stents than closed-cell type (p < .01). CONCLUSIONS: 4D mUTE-MRA is a useful tool with a high spatial and temporal resolution for the evaluation of intracranial aneurysms treated with SACE. KEY POINTS: • In the evaluation of intracranial aneurysms treated with SACE on 4D mUTE-MRA and DSA, the intermodality and interobserver agreement in aneurysm occlusion status was excellent. • 4D mUTE-MRA shows good to excellent visualization of flow in the stents, especially for cases treated with a single or open-cell stent. • 4D mUTE-MRA can provide hemodynamic information related to embolized aneurysms and the distal arteries to stented parent arteries.

Arterial Spin Labeling for Pediatric Central Nervous System Diseases: Techniques and Clinical Applications.

Dynamic susceptibility contrast (DSC) and arterial spin labeling (ASL) are techniques used to evaluate brain perfusion using MRI. DSC requires dynamic image acquisition with a rapid administration of gadolinium-based contrast agent. In contrast, ASL obtains brain perfusion information using magnetically labeled blood water as an endogenous tracer. For the evaluation of brain perfusion in pediatric neurological diseases, ASL has a significant advantage compared to DSC, CT, and single-photon emission CT/positron emission tomography because of the lack of radiation exposure and contrast agent administration. However, in ASL, optimization of several parameters, including the type of labeling, image acquisition, background suppression, and postlabeling delay, is required, because they have a significant effect on the quantification of cerebral blood flow (CBF).In this article, we first review recent technical developments of ASL and age-dependent physiological characteristics in pediatric brain perfusion. We then review the clinical implementation of ASL in pediatric neurological diseases, including vascular diseases, brain tumors, acute encephalopathy with biphasic seizure and late reduced diffusion (AESD), and migraine. In moyamoya disease, ASL can be used for brain perfusion and vessel assessment in pre- and post-treatment. In arteriovenous malformations, ASL is sensitive to detect small degrees of shunt. Furthermore, in vascular diseases, the implementation of ASL-based time-resolved MR angiography is described. In neoplasms, ASL-derived CBF has a high diagnostic accuracy for differentiation between low- and high-grade pediatric brain tumors. In AESD and migraine, ASL may allow for accurate early diagnosis and provide pathophysiological information.

Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method.

OBJECTIVES: This study aimed to evaluate the efficacy of a combined wavelet and deep-learning reconstruction (DLR) method for under-sampled pituitary MRI. METHODS: This retrospective study included 28 consecutive patients who underwent under-sampled pituitary T2-weighted images (T2WI). Images were reconstructed using either the conventional wavelet denoising method (wavelet method) or the wavelet and DLR methods combined (hybrid DLR method) at five denoising levels. The signal-to-noise ratio (SNR) of the CSF, hypothalamic, and pituitary images and the contrast between structures were compared between the two image types. Noise quality, contrast, sharpness, artifacts, and overall image quality were evaluated by two board-certified radiologists. The quantitative and the qualitative analyses were performed with robust two-way repeated analyses of variance. RESULTS: Using the hybrid DLR method, the SNR of the CSF progressively increased as denoising levels increased. By contrast, with the wavelet method, the SNR of the CSF, hypothalamus, and pituitary did not increase at higher denoising levels. There was a significant main effect of denoising methods (p < 0.001) and denoising levels (p < 0.001), and an interaction between denoising methods and denoising levels (p < 0.001). For all five qualitative scores, there was a significant main effect of denoising methods (p < 0.001) and an interaction between denoising methods and denoising levels (p < 0.001). CONCLUSIONS: The hybrid DLR method can provide higher image quality for T2WI of the pituitary with compressed sensing (CS) than the wavelet method alone, especially at higher denoising levels. KEY POINTS: • The signal-to-noise ratios of cerebrospinal fluid progressively increased with the hybrid DLR method, with an increase in the denoising level for cerebrospinal fluid in pituitary T2WI with CS. • The signal-to-noise ratios of cerebrospinal fluid using the conventional wavelet method did not increase at higher denoising levels. • All qualitative scores of hybrid deep-learning reconstructions at all denoising levels were higher than those for the wavelet denoising method.

Laterality on FDG-PET/CT in clinically node-negative early-stage oral squamous cell carcinoma: a retrospective analysis of patients with late neck metastasis.

This study aimed to assess the role of preoperative 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) for predicting late neck metastasis in clinically node-negative (cN0) early-stage oral squamous cell carcinoma (OSCC). We retrospectively investigated the standardized uptake value (SUV) parameters in patients with late neck metastasis based on the neck node level. The study population consisted of 16 patients with cT1N0 or cT2N0 oral SCC who were evaluated with dual-phase FDG-PET/CT and were treated with local resection of the primary tumor and watchful waiting for neck management. The SUV at each level was measured on the early and delayed images, and the laterality of the SUV was calculated. The laterality on the delayed images significantly differed between positive and negative pairs at the levels Ib (p = 0.002) and IIb (p = 0.013); a cut-off value of 1.4 yielded a true-positive rate of 50% and a false-positive rate of 6%. The laterality of FDG-uptake should be used to stratify the risk for nodal-level metastasis.

A preliminary study of deep learning-based reconstruction specialized for denoising in high-frequency domain: usefulness in high-resolution three-dimensional magnetic resonance cisternography of the cerebellopontine angle.

PURPOSE: Deep learning-based reconstruction (DLR) has been developed to reduce image noise and increase the signal-to-noise ratio (SNR). We aimed to evaluate the efficacy of DLR for high spatial resolution (HR)-MR cisternography. METHODS: This retrospective study included 35 patients who underwent HR-MR cisternography. The images were reconstructed with or without DLR. The SNRs of the CSF and pons, contrast of the CSF and pons, and sharpness of the normal-side trigeminal nerve using full width at half maximum (FWHM) were compared between the two image types. Noise quality, sharpness, artifacts, and overall image quality of these two types of images were qualitatively scored. RESULTS: The SNRs of the CSF and pons were significantly higher with DLR than without DLR (CSF 21.81 ± 7.60 vs. 15.33 ± 4.03, p < 0.001; pons 5.96 ± 1.38 vs. 3.99 ± 0.48, p < 0.001). There were no significant differences in the contrast of the CSF and pons (p = 0.225) and sharpness of the normal-side trigeminal nerve using FWHM (p = 0.185) without and with DLR, respectively. Noise quality and the overall image quality were significantly higher with DLR than without DLR (noise quality 3.95 ± 0.19 vs. 2.53 ± 0.44, p < 0.001; overall image quality 3.97 ± 0.17 vs. 2.97 ± 0.12, p < 0.001). There were no significant differences in sharpness (p = 0.371) and artifacts (p = 1) without and with DLR. CONCLUSION: DLR can improve the image quality of HR-MR cisternography by reducing image noise without sacrificing contrast or sharpness.

Diagnostic Performance of 123I-FPCIT SPECT Specific Binding Ratio in Progressive Supranuclear Palsy: Use of Core Clinical Features and MRI for Comparison.

OBJECTIVE. Progressive supranuclear palsy (PSP) is listed as a core clinical feature in the Movement Disorder Society 2017 criteria, along with ocular motor dysfunction, postural instability, akinesia, and cognitive dysfunction. Imaging evidence shows predominant mid-brain atrophy and postsynaptic striatal dopaminergic degeneration as two supportive features. The purpose of this study was to investigate the diagnostic performance of 123I-N- ω-fluoropropyl-2ß-carbomethoxy-3ß-(4-iodophenyl) nortropane (123I-FP-CIT) SPECT by comparing it with evaluation of core clinical features and MRI in the diagnosis of PSP. MATERIALS AND METHODS. The study included 53 patients with clinically suspected PSP who had undergone 123I-FP-CIT SPECT and MRI examinations. MR parkinsonism index (MRPI) was used as the MRI index. For the 123I-FP-CIT SPECT index, specific binding ratio (SBR) was calculated as the average of the right and left SBRs. RESULTS. In regard to core clinical features, ocular motor dysfunction was present in 15 of 20 (75.0%) patients with the diagnosis of probable PSP (p < 0.0001). Calculation of the diagnostic performance of the imaging parameters showed that MRPI (cutoff > 11.6) had 85.0% sensitivity, 100% specificity, and 94.3% accuracy. SBR (cutoff < 3.7) had 95.0% sensitivity, 36.4% specificity, and 58.5% accuracy. CONCLUSION. Iodine-123-labeled FP-CIT SPECT has high sensitivity, and MRI has high specificity in the diagnosis of PSP. Because these tools have complementary roles, reach ing a more confident clinical diagnosis of PSP may be possible when both are used.

Microsecretory adenocarcinoma of the hard palate: A case report of a recently described entity.

We report a case of microsecretory adenocarcinoma of the hard palate. The patient is a 37-year-old woman with a 15 mm submucosal tumor, which was incidentally found by her primary care dentist, in her hard palate. Preoperative magnetic resonance imaging revealed a tumor exhibiting high signal on T2-weighted image, which was gradually enhanced on dynamic study. Histologically, the tumor border was ill-defined without fibrous capsule and adjoined minor salivary gland with permeative infiltration at the tumor periphery. The tumor comprised intercalated duct-like cells with polygonal narrow eosinophilic to clear cytoplasm and small, uniform oval nuclei. These cells formed small infiltrative microcysts, tubules and fascicular cords collecting pale basophilic secretions and small vacuoles setting in an abundant fibromyxoid stroma. The tumor cells were positive for CK AE1+AE3, S-100 protein, and p63, while are completely negative for p40, alpha-SMA, and calponin. The MEF2C-SS18 fusion was identified by reverse transcriptase-polymerase chain reaction followed by Sanger sequencing. The combination of characteristic histology, immunophenotype, and presence of MEF2C-SS18 fusion indicated the diagnosis of microsecretory adenocarcinoma of the hard palate, an entity described only recently. Post-operative course was uneventful and there was no evidence of disease at 4 months after surgery.

Hemodynamic study about cortical hyperintensity belt sign after direct bypass surgery for moyamoya disease.

Transient neurological events (TNEs) are observed after direct bypass surgery in patients with moyamoya disease (MMD). Although a correlation between cortical hyperintensity belt signs (CHBs) and TNEs has been reported, the pathophysiology of CHBs is still unknown. The purpose of this study was to reveal the pathophysiology of CHBs by using dynamic susceptibility contrast-magnetic resonance imaging. Thirty patients with MMD were included in this study. We provided scores (0-2) for the existence of CHBs on postoperative FLAIR images. We placed the ROI for the presented area of CHBs in the images of cerebral blood flow, CBV, and MTT. We calculated the change of the hemodynamic parameters (increase ratio, IR) and analyzed the relationship between IRs, CHB scores, and TNEs. TNEs were observed in 15 cases (50%) and CHBs were detected in 28 cases (93%). TNEs showed significantly higher CHB scores than those without (p < 0.05). The group of CHB score 2 showed a significantly higher CBV IR than the group with of score 0 (p < 0.05). Patients with TNEs showed a significantly higher CBV IR than those without (p < 0.05). As for the cut-off level to predict an appearance of TNEs, the CBV IR was 1.36 by the Receiver Operating Characteristic analysis, and the sensitivity and specificity were 80% respectively. We hypothesize that the pathophysiology of the CHBs are vasogenic edemas because the postoperative CBV increase correlated with the CHBs.

Hybrid of Compressed Sensing and Parallel Imaging Applied to Three-dimensional Isotropic T2-weighted Turbo Spin-echo MR Imaging of the Lumbar Spine.

PURPOSE: The hybrid compressed sensing (hybrid-CS) technique can shorten the acquisition time compared with the sensitivity encoding (SENSE) technique in lumbar MRI. To evaluate the feasibility of a hybrid-CS technique in comparison with 3D isotropic T2-weighted turbo spin-echo (3D volume isotropic turbo spin-echo acquisition [VISTA]) MRI of the lumbar spine. MATERIALS AND METHODS: The Institutional Review Board approved this study and informed consent was obtained from participants prior to study entry. Sixteen healthy volunteers underwent lumbar spine 3D VISTA with conventional parallel imaging for SENSE and hybrid-CS at 3T. We recorded the image acquisition times of SENSE and hybrid-CS. We compared the signal-to-noise ratio (SNR) in spine, cerebrospinal fluid (CSF), lumbar disc, epidural fat, and erector spinae muscle, and the contrast of spine, CSF, and disc, and performed qualitative image analysis assessment, between the two image sequences. RESULTS: The image acquisition time for hybrid-CS was 39.2% shorter than that of SENSE (218.4/358.8 s). The contrast of CSF and SNR of the spine was significantly higher with hybrid-CS than with SENSE (P < 0.05). The SNR of the disc and muscle was significantly higher with SENSE than with hybrid-CS (P < 0.05). There were no significant differences in the contrast of spine, disc, and fat, and SNR of CSF and fat between hybrid-CS and SENSE. There were no significant differences in the qualitative evaluation between hybrid-CS and SENSE. CONCLUSION: Compared with SENSE, hybrid-CS for 3D VISTA can shorten image acquisition time without sacrificing image quality.

An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases.

PURPOSE: To evaluate the performance of a machine learning method based on texture parameters in conventional magnetic resonance imaging (MRI) in differentiating glioblastoma (GB) from brain metastases (METs). MATERIALS AND METHODS: In this retrospective study conducted between November 2008 and July 2017, we included 73 patients diagnosed with GB (n = 73) and METs (n = 53) who underwent contrast-enhanced 3 T brain MRI. Twelve histogram and texture parameters were assessed on T2-weighted images (T2WIs), apparent diffusion coefficient maps (ADCs), and contrast-enhanced T1-weighted images (CE-T1WIs). A prediction model was developed for a machine learning method, and the area under the receiver operating characteristic curve of this model was calculated through 5-fold cross-validation. Furthermore, machine learning method's performance was compared with three board-certified radiologists' judgments. RESULTS: Univariate logistic regression model showed that the area under the curve (AUC) was highest with the standard value of T2WIs (0.78), followed by the maximum value of T2WIs (0.764), minimum value of T2WIs (0.738), minimum values of CE-T1WIs and contrast of T2WIs (0.733), and mean value of T2WIs (0.724). AUC calculated using the support vector machine was comparable to that calculated by the three radiologists (0.92 vs. 0.72, p < .01; 0.92 vs. 0.73, p < .01; and 0.92 vs. 0.86, p = .096). CONCLUSION: In differentiating GB from METs on the basis of texture parameters in MRI, the performance of the machine learning method based on convention MRI was superior to that of the univariate method, and comparable to that of the radiologists.

Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers.

PURPOSE: To test whether our proposed denoising approach with deep learning-based reconstruction (dDLR) can effectively denoise brain MR images. METHODS: In an initial experimental study, we obtained brain images from five volunteers and added different artificial noise levels. Denoising was applied to the modified images using a denoising convolutional neural network (DnCNN), a shrinkage convolutional neural network (SCNN), and dDLR. Using these brain MR images, we compared the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the three denoising methods. Two neuroradiologists assessed the image quality of the three types of images. In the clinical study, we evaluated the denoising effect of dDLR in brain images with different levels of actual noise such as thermal noise. Specifically, we obtained 2D-T2-weighted image, 2D-fluid-attenuated inversion recovery (FLAIR) and 3D-magnetization-prepared rapid acquisition with gradient echo (MPRAGE) from 15 healthy volunteers at two different settings for the number of image acquisitions (NAQ): NAQ2 and NAQ5. We reconstructed dDLR-processed NAQ2 from NAQ2, then compared with SSIM and PSNR. Two neuroradiologists separately assessed the image quality of NAQ5, NAQ2 and dDLR-NAQ2. Statistical analysis was performed in the experimental and clinical study. In the clinical study, the inter-observer agreement was also assessed. RESULTS: In the experimental study, PSNR and SSIM for dDLR were statistically higher than those of DnCNN and SCNN (P < 0.001). The image quality of dDLR was also superior to DnCNN and SCNN. In the clinical study, dDLR-NAQ2 was significantly better than NAQ2 images for SSIM and PSNR in all three sequences (P < 0.05), except for PSNR in FLAIR. For all qualitative items, dDLR-NAQ2 had equivalent or better image quality than NAQ5, and superior quality to that of NAQ2 (P < 0.05), for all criteria except artifact. The inter-observer agreement ranged from substantial to near perfect. CONCLUSION: dDLR reduces image noise while preserving image quality on brain MR images.

Diagnostic accuracy of MRI parameters in pure akinesia with gait freezing.

OBJECTIVE: To determine the usefulness of MRI measurements in patients with pure akinesia with gait freezing (PAGF), Richardson's syndrome, and Parkinson's disease for diagnosis. METHODS: We obtained MRI measurements for patients with PAGF, Richardson's syndrome, or Parkinson's disease: 9 patients with PAGF, 26 with Richardson's syndrome, and 93 with Parkinson's disease. We measured the area of the pons and midbrain on midsagittal MRIs and the midbrain width on axial MRIs. We also calculated the mean values of the superior cerebellar peduncle, middle cerebellar peduncle, and cerebral crus width; the pons area-to-midbrain area ratio; the middle cerebellar peduncle width-to-superior cerebellar peduncle width ratio; and the magnetic resonance (MR) Parkinsonism index. RESULTS: The Richardson's syndrome group had the highest pons area-to-midbrain area ratio and MR Parkinsonism index; the Parkinson's disease group had the lowest values. The Parkinson's disease group also had the highest midbrain width and cerebral crus width, with the lowest values being seen in the Richardson's syndrome group. The PAGF group had the intermediate values of the pons area-to-midbrain area ratio and MR Parkinsonism index between the Richardson's syndrome group and the Parkinson's disease group, whereas significant differences were found only in the pons area-to-midbrain area ratio. Results from receiver operating characteristic curve analyses showed that the pons area-to-midbrain area ratio has a higher sensitivity, specificity, and accuracy than the MR Parkinsonism index. CONCLUSIONS: The pons area-to-midbrain area ratio is more useful to distinguish PAGF from Richardson's syndrome and Parkinson's disease than the MR Parkinsonism index.

Perfusion abnormality on three-dimensional arterial spin labeling in patients with acute encephalopathy with biphasic seizures and late reduced diffusion.

PURPOSE: Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) is the most common encephalopathy subtype in Japanese children. Few case reports have shown perfusion abnormality on arterial spin labeling (ASL) in patients with AESD. The present study aimed to review the chronological change of cerebral perfusion on three-dimensional (3D) ASL in patients with AESD. METHODS: Twenty consecutive patients with AESD were enrolled; the patients underwent MRI including 3D ASL. The clinical course of AESD was divided into four phases according to the time from occurrence of seizures to MRI. Two neuroradiologists independently assessed presence or absence, distribution, and severity of perfusion abnormality using ASL and qualitatively scored perfusion abnormality using a five-point grading system. The level of interobserver agreement in the evaluation was analyzed using weighted κ statistics. Additionally, the signal ratio of abnormal perfusion region and peri-central sulcus region on ASL was semi-quantitatively evaluated. Moreover, we qualitatively compared the distribution between perfusion abnormality on ASL and bright tree appearance (BTA) on diffusion-weighted image (DWI). RESULTS: ASL showed hypoperfusion from 8.5 to 22 h after early seizures (ESs) and hyperperfusion within 24 h after late seizures (LSs). Various perfusions were found >3 days after LSs. Interobserver agreement for qualitative scored perfusion abnormality was good (κ = 0.77). The distribution of abnormal perfusion was relatively consistent with BTA. CONCLUSION: In AESD, cerebral perfusion changes with time. ASL showed hypoperfusion from 8.5 to 22 h after ESs, hyperperfusion within 24 h after LSs in patients with AESD.

Combining quantitative susceptibility mapping to the morphometric index in differentiating between progressive supranuclear palsy and Parkinson's disease.

PURPOSE: To determine whether the susceptibility value in the deep gray matter obtained by quantitative susceptibility mapping (QSM) provides additive value to the morphometric index for differentiating progressive supranuclear palsy (PSP) from Parkinson's disease (PD). MATERIALS AND METHODS: PSP- (n = 8) and PD patients (n = 18) and 18 age-matched healthy controls who underwent QSM and 3D magnetization-prepared rapid gradient echo (MPRAGE) sequences. The mean susceptibility values (MSVs) of the deep gray matter structures on QSM- and areas of the midbrain (morphometric index, MI) on 3D MPRAGE images were measured by two neuroradiologists. Analysis of variance, the Scheffe test and receiver operating characteristic (ROC) analysis were conducted to assess differences and discriminate among PSP, PD and controls by the MSVs and the MI. Using the MSV of a structure with the best area under the curve (AUC) and the MI, we created a decision tree to differentiate between PSP and PD. RESULTS: The MSVs of the globus pallidus (GP) and substantia nigra (SN) were significantly higher in PSP than PD and the controls (p < .05). By ROC analysis (PSP vs PD), AUC was greatest (0.903) for the GP. The MI was significantly smaller in PSP than PD and the controls (p < .05); AUC (PSP vs PD) was 0.917. The decision tree using cutoff values of 244 parts per billion for MSV of the GP and 74.0 mm2 for MI served to completely differentiate between PSP and PD. CONCLUSION: The MSV in the GP on QSM images adds value to the MI for differentiating PSP from PD.

Cerebral Microbleeds Are Associated with Cerebral Hypoperfusion in Patients with Alzheimer's Disease.

BACKGROUND: Although cerebral microbleeds (CMBs) are commonly observed in patients with Alzheimer's disease (AD), their clinical relevance for AD remains unclear. OBJECTIVE: We investigated the significance of CMBs in AD by examining the relationship between CMBs and cerebral blood flow (CBF) in patients with AD. METHODS: Thirty-four patients (aged 77.9±7.6 years; 17 men) with probable AD and multiple (≥8) CMBs were selected from 394 consecutive patients. For each lobe of the brain, the correlation between the number of CMBs observed on susceptibility-weighted images and the decrease in CBF observed on single-photon emission computed tomography was assessed. RESULTS: The number of microbleeds was significantly correlated with the severity of decrease in the occipital lobe (Spearman's r = 0.531, p < 0.001) and temporal lobe (r = 0.437, p < 0.001) but not in the frontal lobe (r = 0.201, p = 0.101) and parietal lobe (r = 0.178, p = 0.146). These results were unchanged in the partial correlational analysis after controlling the effect of other small vessel disease such as lacunars and white matter hyperintensities. CONCLUSION: Multiple CMBs are associated with cerebral hypoperfusion in AD. The effects of CMBs on CBF differed according to brain location, possibly reflecting different distributions of the underlying cerebral amyloid angiopathy and AD-related histopathology, such as neurofibrillary tangles.

Neonatal methionine adenosyltransferase I/III deficiency with abnormal signal intensity in the central tegmental tract.

Methionine adenosyltransferase I/III (MAT I/III) deficiency is characterized by persistent hypermethioninemia. The clinical manifestations in cases with MAT I/III deficiency vary from a complete lack of symptoms to neurological problems associated with brain demyelination. We experienced a neonatal case with MAT I/III deficiency, in which severe hypermethioninemia was detected during the newborn screening test. The patient gradually showed hyperreflexia, foot clonus, and irritability from the age of 1 month onwards, and his brain magnetic resonance imaging scans showed abnormal signal intensity in the bilateral central tegmental tracts. His neurological manifestations improved after the S-adenosylmethionine (SAMe) treatment, deteriorated after discontinuation of SAMe, and re-improved owing to re-administration of SAMe. He achieved normal neurodevelopment through SAMe and methionine restriction therapy. Lack of SAMe as well as severe hypermethioninemia were thought to contribute towards the clinical psychophysical state. Moreover, impaired MAT I/III activity contributed to the development of neurological disorder from the early neonatal period.

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL). MATERIALS AND METHODS: We included 70 patients who underwent contrast enhanced brain MRI at 3 T with brain tumors diagnosed as GBM (n = 45) and PCNSL (n = 25) in this retrospective study. Twelve histograms and texture parameters were assessed on T2-weighted images (T2WIs), apparent diffusion coefficient maps, relative cerebral blood volume (rCBV) map, and contrast-enhanced T1-weighted images (CE-T1WIs). A prediction model was developed using a machine learning method (univariate logistic regression and multivariate eXtreme gradient boosting-XGBoost) and the area under the receiver operating characteristic curve of this model was calculated via 10-fold cross validation. In addition, the performance of the machine learning method was compared with the judgments of two board certified radiologists. RESULTS: With the univariate logistic regression model, the standard deviation of rCBV offered the highest AUC (0.86), followed by mean value of rCBV (0.83), skewness of CE-T1WI (0.78), mean value of CET1 (0.78), and max value of rCBV (0.77). The AUC of the XGBoost was significantly higher than the two radiologists (0.98 vs. 0.84; p < 0.01 and 0.98 vs. 0.79; p < 0.01, respectively). CONCLUSION: The performance of machine learning based on histogram and texture features in multi-parametric MRI was superior to that of conventional cut-off method and the board certified radiologists to differentiate a GBM from a PCNSL.