Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling.

Cerebral conditions often require precise diagnosis and monitoring, necessitating advanced imaging techniques. Current modalities may not adequately detect early signs of reversible tissue damage, underlining the need for innovative diagnostic tools that can quantify changes in cerebral blood flow (CBF) with high specificity and sensitivity. This study integrates three-dimensional arterial spin labeling (3D-ASL) with structural MRI to develop comprehensive CBF atlases that cover all main functional regions of the brain. This innovative magnetic resonance imaging- arterial spin labeling (MRI-ASL) methodology provides a rapid and noninvasive means of quantifying region-specific CBF, offering a detailed view of CBF levels across different functional regions.The comparison between chronic cerebral ischemia (CCI) patients and healthy subjects revealed significantly diminished CBF across the cerebral functional regions in the constructed CBF atlases for the former. This approach not only allows for the efficient identification of CCI by analyzing concurrent decreases in CBF across critical areas relative to healthy distributions but also enables the tracking of treatment responses and rehabilitation progress through longitudinal CBF atlases.The CBF atlas developed using the MRI-ASL technique represents a novel advancement in the field of cerebral diagnostics and patient care. By comparing regional CBF levels against normative standards, this method enhances diagnostic capabilities, enabling clinicians to provide personalized care to patients with cerebral conditions.

Probabilistic Scatter Plots for visualizing carbohydrate and lipid metabolism states in Non-Alcoholic Fatty Liver Disease.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is characterized by dysregulated carbohydrate and lipid metabolism, which are its primary features. However, traditional biochemical markers pose challenges for accurate quantification and visualization of metabolic states. This study introduces a novel states-based approach for accurate NAFLD assessment. METHODS: Joint probabilistic distributions of triglycerides and glycemia were constructed using dual-indicator Probabilistic Scatter Plots based on clinical data (healthy controls: n = 1978; NAFLD patients: n = 471). Patterns of metabolic dysregulation were revealed through comparison against healthy profiles. Self-organizing feature mapping (SOFM) clustered the distributions into four dominant states. RESULTS: Healthy scatter plots demonstrated a distinct progression of sub-states ranging from very healthy to sub-healthy. In contrast, NAFLD plots exhibited shifted probability centers and outward divergence. SOFM clustering classified the states into: mild; moderate and severe lipid metabolism disorders; and carbohydrate metabolism disorders. CONCLUSIONS: Probabilistic Scatter Plots, when combined with SOFM clustering, facilitate a states-based quantification of NAFLD metabolic dysregulation. This method integrates multi-dimensional biochemical indicators and their distributions into a cohesive framework, enabling precise and intuitive visualization for personalized diagnosis and monitoring of prognostic developments.

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions.

This paper proposes a novel thyroid examination technique based on five-dimensional (5D) synchronous reconstruction of ultrasound data. The raw temporal sequences are reconstructed into 3D volumetric data reflecting anatomical structure. Triplanar visualization from three orthogonal planes is realized to provide a systematic inspection of the entire gland. Color Doppler imaging is integrated into each triplanar slice to map vascularity changes. This multi-modal fusion enables synchronous display of structural, functional, and blood flow information in the reconstructed 5D space. Compared to conventional scanning, this technique offers the benefits of flexible offline diagnosis, reduced dependency on scanning, enhanced intuitive interpretation, and comprehensive multi-aspect evaluation. By minimizing oversight errors, it could improve diagnostic accuracy, especially for novice practitioners. The proposed 5D fusion method allows rapid and precise localization of lesions for early detection. Future work will explore integration with biochemical markers to further improve diagnostic precision. The technique has considerable clinical value for advancing thyroid examination.

A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging.

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. The central aim is to offer a highly accurate and non-invasive means of evaluating liver fat content. The process involves the acquisition of In-phase and Water-phase images from a Dixon sequence. LFF maps are then meticulously computed voxel by voxel by dividing the Lipid Phase images by the In-phase images. Simultaneously, 3D liver contours are extracted from the In-phase images. These crucial components are seamlessly integrated to construct a comprehensive 3D-LFF distribution model. This technique is not limited to healthy livers but extends to those afflicted by hepatic steatosis. The results obtained demonstrate the remarkable effectiveness of this approach in both visualizing and quantifying liver fat content. It distinctly discerns patterns that differentiate between normal and steatotic livers. By harnessing Dixon MRI to extract the 3D structure of the liver, this method offers precise LFF assessments spanning the entirety of the organ, thereby holding great promise for the diagnosis of hepatic steatosis with remarkable effectiveness.

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules.

For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the entire lung. This is crucial for identifying the primary lesion and developing more scientifically grounded treatment plans for doctors. However, pattern recognition methods based on machine vision are susceptible to false positives and false negatives and, therefore, cannot fully meet clinical demands in this regard. Visualization methods based on maximum intensity projection (MIP) can better illustrate local and individual pulmonary nodules but lack a macroscopic and holistic description of the distribution and spatial features of multiple pulmonary nodules. Therefore, this study proposes a whole-lung 3D reconstruction method. It extracts the 3D contour of the lung using medical image processing technology against the background of the entire lung and performs 3D reconstruction of the lung, pulmonary artery, and multiple pulmonary nodules in 3D space. This method can comprehensively depict the spatial distribution and radiological features of multiple nodules throughout the entire lung, providing a simple and convenient means of evaluating the diagnosis and prognosis of multiple pulmonary nodules.

A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography.

Hepatic fibrosis is an early stage of liver cirrhosis, and there are no better non-invasive and convenient methods for the detection and evaluation of the disease. Despite the good progress made with the liver stiffness map (LSM) based on magnetic resonance elastography (MRE), there are still some limitations that need to be overcome, including manual focus determination, manual selection of regions of interest (ROIs), and discontinuous LSM data without structural information, which makes it impossible to evaluate the liver as a whole. In this study, we propose a novel three-dimensional (3D) digital model for the early diagnosis of hepatic fibrosis based on MRE. MRE is a non-invasive imaging technique that employs magnetic resonance imaging (MRI) to measure the liver stiffness at the scanning site through human-computer interaction. Studies have indicated a significant positive correlation between the LSM obtained through MRE and the degree of hepatic fibrosis. However, for clinical purposes, a comprehensive and precise quantification of the degree of hepatic fibrosis is necessary. To address this, the concept of Liver Stiffness Distribution (LSD) was proposed in this study, which refers to the 3D stiffness volume of each liver voxel obtained by the alignment of 3D liver tissue images and MRE indicators. This provides a more effective clinical tool for the diagnosis and treatment of hepatic fibrosis.

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules.

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating pulmonary nodules, and these approaches are progressively being acknowledged and adopted by physicians and patients. Nonetheless, constructing a relatively universal 3D digital model of pulmonary nodules for diagnosis and treatment is challenging due to device differences, shooting times, and nodule types. The objective of this study is to propose a new 3D digital model of pulmonary nodules that serves as a bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation. Many AI-driven pulmonary nodule detection and recognition methods employ deep learning techniques to capture the radiological features of pulmonary nodules, and these methods can achieve a good area under-the-curve (AUC) performance. However, false positives and false negatives remain a challenge for radiologists and clinicians. The interpretation and expression of features from the perspective of pulmonary nodule classification and examination are still unsatisfactory. In this study, a method of continuous 3D reconstruction of the whole lung in horizontal and coronal positions is proposed by combining existing medical image processing technologies. Compared with other applicable methods, this method allows users to rapidly locate pulmonary nodules and identify their fundamental properties while also observing pulmonary nodules from multiple perspectives, thereby providing a more effective clinical tool for diagnosing and treating pulmonary nodules.

3D Printing Model of a Patient's Specific Lumbar Vertebra.

Selective dorsal rhizotomy (SDR) is a difficult, risky, and sophisticated operation, in which a laminectomy should not only expose an adequate surgical field of view but also protect the patient's spinal nerves from injury. Digital models play an important role in the pre-and intra-operation of SDR, because they can not only make doctors more familiar with the anatomical structure of the surgical site, but also provide precise surgical navigation coordinates for the manipulator. This study aims to create a 3D digital model of a patient-specific lumbar vertebra that can be used for planning, surgical navigation, and training of the SDR operation. The 3D printing model is also manufactured for more effective work during these processes. Traditional orthopedic digital models rely almost entirely on computed tomography (CT) data, which is less sensitive to soft tissues. Fusion of the bone structure from CT and the neural structure from magnetic resonance imaging (MRI) is the key element for the model reconstruction in this study. The patient's specific 3D digital model is reconstructed for the real appearance of the surgical area and shows the accurate measurement of inter-structural distances and regional segmentation, which can effectively help in the preoperative planning and training of SDR. The transparent bone structure material of the 3D-printed model allows surgeons to clearly distinguish the relative relationship between the spinal nerve and the vertebral plate of the operated segment, enhancing their anatomical understanding and spatial sense of the structure. The advantages of the individualized 3D digital model and its accurate relationship between spinal nerve and bone structures make this method a good choice for preoperative planning of SDR surgery.

Convex and Concave Model 3D Printing for Designing Right-side Bronchial Blocker for Infants.

It is technically challenging for pediatric anesthesiologists to use bronchial blocker (BB) to isolate the lungs of infants during thoracoscopic surgery. Further, BB currently sold in the market cannot match the anatomical characteristics of the infants, especially on the right main bronchus. It may easily cause poor exhaustion of the right upper lobe, which leads to interference with the thoracoscopic surgical field. The two dimensional reconstruction data of 124 normal infants' airways were extracted from the medical image database of Beijing Children's Hospital for statistical analysis. After using linear fitting and goodness-of-fit test, a good linear relationship was detected between infant age and various parameters related to aid in designing a new BB for infants (R2=0.502). According to the growth and development rate of infants, the DICOM files of airway CT scan of 7 infants aged 30, 60, 90, 120, 180, 270, and 360 days were selected to print non-transparent convex and transparent concave 3D models. The non-transparent convex model was precisely measured to obtain the important parameters for BB design infants only, to complete the design of BB, to generate the sample, and to verify the blocking effect of produced sample in transparent concave three-dimensional (3D) model.

Genomic characterization of Chinese ovarian clear cell carcinoma identifies driver genes by whole exome sequencing.

Little is known about the genetic alterations characteristic of ovarian clear cell carcinoma (OCCC). Our aim was to identify targetable genomic alterations in this type of cancer. Forty-two OCCC formalin-fixed, paraffin-embedded (FFPE) tissue samples were analyzed by whole-exome sequencing (WES), and 74 FFPE tissue samples underwent targeted sequencing (TS) to confirm the relevant driver mutations. Cell proliferation was assessed by cell counting kit-8 (CCK8) assays. In the 42 samples, ARID1A (64.3%) and PIK3CA (28.5%) were frequently mutated, as were PPP2R1A (11.9%), PTEN (7.1%) and KRAS (4.8%), which have been reported in previous OCCC studies. We also detected mutations in MUC4 (28.6%), MAGEE1 (19%), and ARID3A (16.7%); associations with these genes have not been previously reported. The functional protein-activated pathways were associated with proliferation and survival (including the PI3K/AKT, TP53, and ERBB2 pathways) in 83% of OCCCs and with chromatin remodeling in 71% of OCCCs. Patients with alterations in MAGEE1 (64% in the targeted sequencing cohort) had worse clinical outcomes (log-rank p < 0.05). A functional study revealed that two MAGEE1 mutants, one lacking two MAGE domains and the other containing two MAGE domains, significantly decreased the proliferative capacity of OCCC cells. We successfully identified novel genetic alterations in OCCC using whole-exome sequencing and targeted sequencing of OCCC patient samples and potential therapeutic targets for the treatment of this malignancy.