Deep Learning of Cancer Stem Cell Morphology.

Knowledge regarding cancer stem cell (CSC) morphology is limited, and more extensive studies are therefore required. Image recognition technologies using artificial intelligence (AI) require no previous expertise in image annotation. Herein, we describe the construction of AI models that recognize the CSC morphology in cultures and tumor tissues. The visualization of the AI deep learning process enables insight to be obtained regarding unrecognized structures in an image.

Imaging of 17O-labeled Water Using Fast T2 Mapping with T2-preparation: A Phantom Study.

17O-labeled water is a T2-shortening contrast agent used in proton MRI and is a promising method for visualizing cerebrospinal fluid (CSF) dynamics because it provides long-term tracking of water molecules. However, various external factors reduce the accuracy of 17O-concentration measurements using conventional signal-intensity-based methods. In addition, T2 mapping, which is expected to provide a stable assessment, is generally limited to temporal-spatial resolution. We developed the T2-prepared based on T2 mapping used in cardiac imaging to adapt to long T2 values and tested whether it could accurately measure 17O-concentration in the CSF using a phantom. The results showed that 17O-concentration in a fluid mimicking CSF could be evaluated with an accuracy comparable to conventional T2-mapping (Carr-Purcell-Meiboom-Gill multi-echo spin-echo method). This method allows 17O-imaging with a high temporal resolution and stability in proton MRI. This imaging technique may be promising for visualizing CSF dynamics using 17O-labeled water.

Automated Detection of Cerebral Microbleeds on Two-dimensional Gradient-recalled Echo T2* Weighted Images Using a Morphology Filter Bank and Convolutional Neural Network.

PURPOSE: We present a novel algorithm for the automated detection of cerebral microbleeds (CMBs) on 2D gradient-recalled echo T2* weighted images (T2*WIs). This approach combines a morphology filter bank with a convolutional neural network (CNN) to improve the efficiency of CMB detection. A technical evaluation was performed to ascertain the algorithm's accuracy. METHODS: In this retrospective study, 60 patients with CMBs on T2*WIs were included. The gold standard was set by three neuroradiologists based on the Microbleed Anatomic Rating Scale guidelines. Images with CMBs were extracted from the training dataset comprising 30 cases using a morphology filter bank, and false positives (FPs) were removed based on the threshold of size and signal intensity. The extracted images were used to train the CNN (Vgg16). To determine the effectiveness of the morphology filter bank, the outcomes of the following two methods for detecting CMBs from the 30-case test dataset were compared: (a) employing the morphology filter bank and additional FP removal and (b) comprehensive detection without filters. The trained CNN processed both sets of initial CMB candidates, and the final CMB candidates were compared with the gold standard. The sensitivity and FPs per patient of both methods were compared. RESULTS: After CNN processing, the morphology-filter-bank-based method had a 95.0% sensitivity with 4.37 FPs per patient. In contrast, the comprehensive method had a 97.5% sensitivity with 25.87 FPs per patient. CONCLUSION: Through effective CMB candidate refinement with a morphology filter bank and FP removal with a CNN, we achieved a high CMB detection rate and low FP count. Combining a CNN and morphology filter bank may facilitate the accurate automated detection of CMBs on T2*WIs.

Improved image quality in contrast-enhanced 3D-T1 weighted sequence by compressed sensing-based deep-learning reconstruction for the evaluation of head and neck.

PURPOSE: To assess the utility of deep learning (DL)-based image reconstruction with the combination of compressed sensing (CS) denoising cycle by comparing images reconstructed by conventional CS-based method without DL in fat-suppressed (Fs)-contrast enhanced (CE) three-dimensional (3D) T1-weighted images (T1WIs) of the head and neck. MATERIALS AND METHODS: We retrospectively analyzed the cases of 39 patients who had undergone head and neck Fs-CE 3D T1WI applying reconstructions based on conventional CS and CS augmented by DL, respectively. In the qualitative assessment, we evaluated overall image quality, visualization of anatomical structures, degree of artifacts, lesion conspicuity, and lesion edge sharpness based on a five-point system. In the quantitative assessment, we calculated the signal-to-noise ratios (SNRs) of the lesion and the posterior neck muscle and the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. RESULTS: For all items of the qualitative analysis, significantly higher scores were awarded to images with DL-based reconstruction (p < 0.001). In the quantitative analysis, DL-based reconstruction resulted in significantly higher values for both the SNR of lesions (p < 0.001) and posterior neck muscles (p < 0.001). Significantly higher CNRs were also observed in images with DL-based reconstruction (p < 0.001). CONCLUSION: DL-based image reconstruction integrating into the CS-based denoising cycle offered superior image quality compared to the conventional CS method. This technique will be useful for the assessment of patients with head and neck disease.

Magnetic Resonance Water Tracer Imaging Using 17 O-Labeled Water.

ABSTRACT: Magnetic resonance imaging (MRI) is a crucial imaging technique for visualizing water in living organisms. Besides proton MRI, which is widely available and enables direct visualization of intrinsic water distribution and dynamics in various environments, MR-WTI (MR water tracer imaging) using 17 O-labeled water has been developed, benefiting from the many advancements in MRI software and hardware that have substantially improved the signal-to-noise ratio and made possible faster imaging. This cutting-edge technique allows the generation of novel and valuable images for clinical use. This review elucidates the studies related to MRI water tracer techniques centered around 17 O-labeled water, explaining the fundamental principles of imaging and providing clinical application examples. Anticipating continued progress in studies involving isotope-labeled water, this review is expected to contribute to elucidating the pathophysiology of various diseases related to water dynamics abnormalities and establishing novel imaging diagnostic methods for associated diseases.

Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck.

OBJECTIVES: To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI). MATERIALS AND METHODS: We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. RESULTS: Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001). DISCUSSION: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.

Blockage of CSF Outflow in Rats after Deep Cervical Lymph Node Ligation Observed Using Gd-based MR Imaging.

PURPOSE: To investigate whether deep cervical lymph node (DCLN) ligation alters intracranial cerebrospinal fluid (CSF) tracer dynamics and outflow using a rat model with intrathecal dynamic contrast-enhanced (DCE) MRI. METHODS: Six bilateral DCLN-ligated and six sham-operated rats were subjected to DCE MRI with Gd-BTDO3A, and dynamic T1-weighted images were acquired. ROIs were collected from the CSF at the C1 level (CSF_C1), CSF between the olfactory bulbs (CSF_OB), CSF at the pituitary recess (CSF_PitR), and CSF at the pineal recess (CSF_PinR), upper nasal turbinate (UNT), olfactory bulbs, cerebrum, and the jugular region. Time-intensity curves were evaluated, and the maximum slope, peak timing, peak signal ratio, and elimination half-life for the four CSF ROIs and UNT were calculated and compared. RESULTS: Delayed tracer arrival in the rostral CSF space and the nasal cavity with tracer retention in the ventral CSF space were observed in the ligation group. The maximum slopes were smaller in the ligation group at UNT (sham: 0.075 ± 0.0061, ligation: 0.044 ± 0.0086/min, P = 0.011). A significant difference was not detected in peak timings. The peak signal ratio values were lower in the ligation group at UNT (sham: 2.12 ± 0.19, ligation: 1.72 ± 0.11, P = 0.011). The elimination half-life was delayed in the ligation group at CSF_C1 (sham: 30.5 ± 2.70, ligation: 44.4 ± 12.6 min, P = 0.043), CSF_OB (sham: 30.2 ± 2.67, ligation: 44.8 ± 7.47 min, P = 0.021), and CSF_PitR (sham: 30.2 ± 2.49, ligation: 41.3 ± 7.57 min, P = 0.021). CONCLUSION: The DCLN ligation in rats blocked CSF outflow into the nasal cavity and caused CSF retention.

Rapid and Reliable Steatosis Rat Model Shrsp5-Dmcr for Cold Storage Experiment.

Interventions for liver grafts with moderate macrovesicular steatosis have been important in enlarging donor pools. Here, we tested a high-fat and cholesterol (HFC) diet to create a steatosis model for cold hepatic preservation and reperfusion experiments. The aim of the present study was to assess the steatosis model's reliability and to show the resulting graft's quality for cold preservation and reperfusion experiment. Male SHRSP5-Dmcr rats were raised with an HFC diet for up to 2 weeks. The fat content was evaluated using magnetic resonance imaging (MRI) proton density fat fraction (PDFF). The nonalcoholic fatty liver disease activity score (NAS) was evaluated after excision. Steatosis created by 2 weeks of HFC diet was subjected to 24-hour cold storage in the University of Wisconsin and the original test solution (new sol.). Grafts were applied to isolated perfused rat livers for simulating reperfusion. The NAS were 2.2 (HFC 5 days), 3.3 (HFC 1 week), and 5.0 (HFC 2 weeks). Ballooning and fibrosis were not observed in any group. An MRI-PDFF showed 0.2 (HFC 0 days), 12.0 (HFC 1 week), and 18.9 (HFC 2 weeks). The NAS and MRI-PDFF values correlated. Many indices in the isolated perfused rat liver experiment tended to improve in the new sol. group but were insufficient. Although the new sol. failed to be effective, it acted at multiple sites under difficult conditions. In conclusion, the HFC diet for 2 weeks in SHRSP5-Dmcr rats, together with MRI-PDFF evaluation, is a reliable method for creating simple steatosis and provides good-quality cold preservation and reperfusion experiments.

Deep Learning of Phase-Contrast Images of Cancer Stem Cells Using a Selected Dataset of High Accuracy Value Using Conditional Generative Adversarial Networks.

Artificial intelligence (AI) technology for image recognition has the potential to identify cancer stem cells (CSCs) in cultures and tissues. CSCs play an important role in the development and relapse of tumors. Although the characteristics of CSCs have been extensively studied, their morphological features remain elusive. The attempt to obtain an AI model identifying CSCs in culture showed the importance of images from spatially and temporally grown cultures of CSCs for deep learning to improve accuracy, but was insufficient. This study aimed to identify a process that is significantly efficient in increasing the accuracy values of the AI model output for predicting CSCs from phase-contrast images. An AI model of conditional generative adversarial network (CGAN) image translation for CSC identification predicted CSCs with various accuracy levels, and convolutional neural network classification of CSC phase-contrast images showed variation in the images. The accuracy of the AI model of CGAN image translation was increased by the AI model built by deep learning of selected CSC images with high accuracy previously calculated by another AI model. The workflow of building an AI model based on CGAN image translation could be useful for the AI prediction of CSCs.

Non-Gaussian model-based diffusion-weighted imaging of oral squamous cell carcinoma: associations with Ki-67 proliferation status.

OBJECTIVES: To investigate possible associations between diffusion-weighted imaging (DWI) parameters derived from a non-Gaussian model fitting and Ki-67 status in patients with oral squamous cell carcinoma (OSCC). METHODS: Twenty-four patients with newly diagnosed OSCC were prospectively recruited. DWI was performed using six b-values (0-2500). The diffusion-related parameters of kurtosis value (K), kurtosis-corrected diffusion coefficient (DK), diffusion heterogeneity (α), distributed diffusion coefficient (DDC), slow diffusion coefficient (Dslow), and apparent diffusion coefficient (ADC) were calculated from four diffusion fitting models. Ki-67 status was categorized as low (Ki-67 percentage score < 20%), middle (20-50%), or high (> 50%). Kruskal-Wallis tests were performed between each non-Gaussian diffusion model parameters and Ki-67 grade. RESULTS: The Kruskal-Wallis tests revealed that multiple parameters (K, ADC, Dk, DDC and Dslow) showed statistically significant differences between the three levels of Ki-67 status (K: p = 0.020, ADC: p = 0.012, Dk: p = 0.027, DDC: p = 0.007 and Dslow: p = 0.026). CONCLUSIONS: Several non-Gaussian diffusion model parameters and ADC values were significantly associated with Ki-67 status and have potential as promising prognostic biomarkers in patients with OSCC.

Establishment of a New Qualitative Evaluation Method for Articular Cartilage by Dynamic T2w MRI Using a Novel Contrast Medium as a Water Tracer.

OBJECTIVE: In the early stages of cartilage damage, diagnostic methods focusing on the mechanism of maintaining the hydrostatic pressure of cartilage are thought to be useful. 17O-labeled water, which is a stable isotope of oxygen, has the advantage of no radiation exposure or allergic reactions and can be detected by magnetic resonance imaging (MRI). This study aimed to evaluate MRI images using 17O-labeled water in a rabbit model. DESIGN: Contrast MRI with 17O-labeled water and macroscopic and histological evaluations were performed 4 and 8 weeks after anterior cruciate ligament transection surgery in rabbits. A total of 18 T2-weighted images were acquired, and 17O-labeled water was manually administered on the third scan. The 17O concentration in each phase was calculated from the signal intensity at the articular cartilage. Macroscopic and histological grades were evaluated and compared with the 17O concentration. RESULTS: An increase in 17O concentration in the macroscopic and histologically injured areas was observed by MRI. Macroscopic evaluation showed that the 17O concentration significantly increased in the damaged site group. Histological evaluations also showed that 17O concentrations significantly increased at 36 minutes 30 seconds after initiating MRI scanning in the Osteoarthritis Research Society International (OARSI) grade 3 (0.493 in grade 0, 0.659 in grade 1, 0.4651 in grade 2, and 0.9964 in grade 3, P < 0.05). CONCLUSION: 17O-labeled water could visualize earlier articular cartilage damage, which is difficult to detect by conventional methods.

The utility of diffusion-weighted T2 mapping for the prediction of histological tumor grade in patients with head and neck squamous cell carcinoma.

Background: In head and neck cancers, histopathological information is important for the determination of the tumor characteristics and for predicting the prognosis. The aim of this study was to assess the utility of diffusion-weighted T2 (DW-T2) mapping for the evaluation of tumor histological grade in patients with head and neck squamous cell carcinoma (SCC). Methods: The cases of 41 patients with head and neck SCC (21 well/moderately and 17 poorly differentiated SCC) were retrospectively analyzed. All patients received MR scanning using a 3-Tesla MR unit. The conventional T2 value, DW-T2 value, ratio of DW-T2 value to conventional T2 value, and apparent diffusion coefficient (ADC) were calculated using signal information from the DW-T2 mapping sequence with a manually placed region of interest (ROI). Results: ADC values in the poorly differentiated SCC group were significantly lower than those in the moderately/well differentiated SCC group (P<0.05). The ratio of DW-T2 value to conventional T2 value was also significantly different between poorly and moderately/well differentiated SCC groups (P<0.01). Receiver operating characteristic (ROC) curve analysis of ADC values showed a sensitivity of 0.76, specificity of 0.67, positive predictive value (PPV) of 0.62, negative predictive value (NPV) of 0.8, accuracy of 0.71 and area under the curve (AUC) of 0.73, whereas the ROC curve analysis of the ratio of DW-T2 value to conventional T2 value showed a sensitivity of 0.76, specificity of 0.83, PPV of 0.76, NPV of 0.83, accuracy of 0.8 and AUC of 0.82. Conclusions: DW-T2 mapping might be useful as supportive information for the determination of tumor histological grade in patients with head and neck SCC.

Amide proton transfer imaging for the determination of human papillomavirus status in patients with oropharyngeal squamous cell carcinoma.

The aim of this study was to investigate the utility of amide proton transfer (APT) imaging for the determination of human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (SCC). Thirty-one patients with oropharyngeal SCC were retrospectively evaluated. All patients underwent amide proton transfer imaging using a 3T magnetic resonance (MR) unit. Patients were divided into HPV-positive and -negative groups depending on the pathological findings in their primary tumor. In APT imaging, the primary tumor was delineated with a polygonal region of interest (ROI). Signal information in the ROI was used to calculate the mean, standard deviation (SD) and coefficient of variant (CV) of the APT signals (APT mean, APT SD, and APT CV, respectively). The value of APT CV in the HPV-positive group (0.43 ±â€…0.04) was significantly lower than that in the HPV-negative group (0.48 ±â€…0.04) (P = .01). There was no significant difference in APT mean (P = .82) or APT SD (P = .13) between the HPV-positive and -negative groups. Receiver operating characteristic (ROC) curve analysis of APT CV had a sensitivity of 0.75, specificity of 0.8, positive predictive value of 0.75, negative predictive value of 0.8, accuracy of 0.77 and area under the curve (AUC) of 0.8. The APT signal in the HPV-negative group was considered heterogeneous compared to the HPV-positive group. This information might be useful for the determination of HPV status in patients with oropharyngeal SCC.

Temporal and Locational Values of Images Affecting the Deep Learning of Cancer Stem Cell Morphology.

Deep learning is being increasingly applied for obtaining digital microscopy image data of cells. Well-defined annotated cell images have contributed to the development of the technology. Cell morphology is an inherent characteristic of each cell type. Moreover, the morphology of a cell changes during its lifetime because of cellular activity. Artificial intelligence (AI) capable of recognizing a mouse-induced pluripotent stem (miPS) cell cultured in a medium containing Lewis lung cancer (LLC) cell culture-conditioned medium (cm), miPS-LLCcm cell, which is a cancer stem cell (CSC) derived from miPS cell, would be suitable for basic and applied science. This study aims to clarify the limitation of AI models constructed using different datasets and the versatility improvement of AI models. The trained AI was used to segment CSC in phase-contrast images using conditional generative adversarial networks (CGAN). The dataset included blank cell images that were used for training the AI but they did not affect the quality of predicting CSC in phase contrast images compared with the dataset without the blank cell images. AI models trained using images of 1-day culture could predict CSC in images of 2-day culture; however, the quality of the CSC prediction was reduced. Convolutional neural network (CNN) classification indicated that miPS-LLCcm cell image classification was done based on cultivation day. By using a dataset that included images of each cell culture day, the prediction of CSC remains to be improved. This is useful because cells do not change the characteristics of stem cells owing to stem cell marker expression, even if the cell morphology changes during culture.

Phase I Randomized Trial of 17 O-Labeled Water: Safety and Feasibility Study of Indirect Proton MRI for the Evaluation of Cerebral Water Dynamics.

BACKGROUND: 17 O-labeled water (PSO17) is a contrast agent developed to measure brain water dynamics and cerebral blood flow. PURPOSE: To evaluate the safety and feasibility of PSO17. STUDY TYPE: Prospective study. SUBJECTS: A total of 12 male healthy volunteers (23.1 ± 1.9 years) were assigned to three groups of four subjects: placebo (normal saline), PSO17 10%, and PSO17 20%. FIELD STRENGTH/SEQUENCE: Dynamic 3D fluid attenuated inversion recovery (FLAIR, fast spin echo with variable refocusing flip angle) scans of the brain were performed with 3-T MRI. ASSESSMENT: Contrast agents were injected 5 minutes after the start of a 10-minute scan. Any symptoms, vital signs, and blood and urine tests were evaluated at five timepoints from preinjection to 4 days after. Blood samples for pharmacokinetic analysis, including half-life (T1/2), maximum fraction (Cmax ), time-to-maximum fraction (Tmax ), and area under the curve (AUC), were collected at 13 timepoints from preinjection to 168 hours after. Regions of interest were set in the cerebral cortex (CC), basal ganglia/thalamus (BG/TM), and white matter (WM), and 17 O concentrations were calculated from signal changes and evaluated using Cmax . STATISTICAL TESTS: All items were compared among the three groups using Tukey-Kramer's honestly significant difference test. Statistical significance was defined as P < 0.5. RESULTS: No safety issues were noted with the intravenous administration of PSO17. The T1/2 was approximately 160 hours, and the AUCs were 1.77 ± 0.10 and 3.75 ± 0.36 in the PSO17 10% and 20% groups, respectively. 17 O fractions calculated from MRI signals were higher in the PSO17 20% group than in the 10% and placebo groups. Significant differences were noted between all pairs of groups in the CC and BG/TM, and between PSO17 20% and both placebo and 10% groups in the WM. DATA CONCLUSION: PSO17 might be considered safe as a contrast medium. Dynamic 3D-FLAIR might detect dose-dependent signal changes and estimate 17 O. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Variations and natural history of primary intraparenchymal lesions associated with neurofibromatosis type 2.

The study aimed to investigate the clinical implications and natural history of primary intraparenchymal lesions in patients with neurofibromatosis type 2. Radiological findings of 15 neurofibromatosis type 2 cases were retrospectively collected. Twenty-seven primary intraparenchymal lesions were observed in 7 out of 15 patients (47%). Cortical/subcortical T2 hyperintense lesions and enlarged Virchow-Robin spaces were the most common findings in five and four patients, respectively. During the follow-up period (median 84 months), one new primary intraparenchymal lesion was identified and increased lesions were observed in two cases on contrast-enhanced MRI. Surgical resection was performed in one case pathologically diagnosed with atypical meningioma. Twenty-five other lesions without contrast enhancement presented no apparent growth during follow-up. Although most primary intraparenchymal lesions are benign, a subset of cases would present newly developed or increased lesions on contrast-enhanced MRI. Careful monitoring is necessary for such cases, and pathological confirmation should be considered.

Quantitative magnetic resonance imaging for evaluating of the cerebrospinal fluid kinetics with 17O-labeled water tracer: A preliminary report.

The aim of this study was to evaluate the feasibility of kinetic analysis of cerebrospinal fluid (CSF) using 17O-labeled water tracer. Four subjects (two idiopathic normal pressure hydrocephalus (iNPH) and two possible AD dementia patients) were prospectively included. Injectable formulation of 17O-labeled water containing 10 mol% of H217O (PSO17), was intrathecally administered to the subjects with the lateral decubitus position between the 3rd and 4th lumbar vertebrae. MRI acquisitions were performed in four-time points, before PSO17 administration, 1, 8, and 24 h after PSO17 administration. The 3-dimensional fast spin echo sequence was used. After image registration for all four-time points data, polygonal regions of interest (ROIs) were set in the 14 regions to obtain the signal intensity of CSF. Each signal intensity within the ROI was converted to 17O concentration [%]. The peak concentration at one hour after administration, the slope of concentration changes after PSO17 administration [%/s], and the root mean square error (RMSE) for evaluating the performance of a fitting were calculated. There was no significant difference in peak concentration between the iNPH and AD group. The slope in the AD group (-2.25 ±â€¯1.62 × 10-3 [%/h]) was significantly smaller than in the iNPH group (-1.21 ±â€¯2.31 × 10-3 [%/h]), which suggests the speed of CSF clearance in the iNPH group was slower than AD group. The RMSE indicating the fit to the concentration change in the AD group (4.86 ±â€¯4.74 × 10-3) was also significantly smaller than in the iNPH group (8.64 ±â€¯7.56 × 10-3). The kinetic evaluation of CSF using 17O-labeled water was feasible, and this preliminary study suggests that the differentiation of iNPH and possible AD dementia can be achieved using this method.

Detection of impaired cerebrovascular reactivity in patients with chronic cerebral ischemia using whole-brain 7T MRA.

BACKGROUND: Cerebrovascular reactivity (CVR) to acetazolamide (ACZ) on single-photon emission computed tomography (SPECT) can be used to assess the severity of chronic cerebral ischemia; however, this is an invasive method. We examined whether whole-brain magnetic resonance angiography (MRA) at 7T could non-invasively detect impaired CVR in patients with chronic cerebral ischemia by demonstrating the leptomeningeal collaterals (LMCs). METHODS: Fifty-seven patients with symptomatic unilateral cervical stenosis underwent whole-brain time-of-flight MRA at 7T and cerebral perfusion SPECT before/after the ACZ challenge. MRA images were visually assessed based on 6-point grading systems to evaluate the development of LMCs toward the middle cerebral artery (MCA) and antegrade flow of MCA. CVR of the affected side was calculated from the SPECT data. Subsequently, we compared the LMC grades on MRA with CVR on SPECT. RESULTS: CVR was significantly lower in grades ≥ 2 of LMCs than in grades 0-1 (P < 0.05) when applying LMCs from the anterior cerebral artery (ACA) and/or posterior cerebral artery (PCA). These differences were more evident than those in the grading of the antegrade MCA flow. The LMC grades from ACA/PCA readily detected reduced CVR (< 18.4%) with a sensitivity/specificity of 0.79/0.82. CONCLUSION: The development of LMCs on whole-brain MRA at 7T can non-invasively detect reduced CVR with a high sensitivity/specificity in patients with unilateral cervical stenosis.

Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks.

Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imaging using conditional generative adversarial networks (CGAN). Artificial intelligence (AI) was trained using fluorescence images of the Nanog-Green fluorescence protein, the expression of which was maintained in CSCs, and the phase contrast images. The AI model segmented the CSC region in the phase contrast image of the CSC cultures and tumor model. By selecting images for training, several values for measuring segmentation quality increased. Moreover, nucleus fluorescence overlaid-phase contrast was effective for increasing the values. We show the possibility of mapping CSC morphology to the condition of undifferentiation using deep-learning CGAN workflows.