Dosimetric comparison of advanced radiation techniques for scalp-sparing in low-grade gliomas.

BACKGROUND: Alopecia causes significant distress for patients and negatively impacts quality of life for low-grade glioma (LGG) patients. We aimed to compare and evaluate variations in dose distribution for scalp-sparing in LGG patients with proton therapy and photon therapy, namely intensity-modulated proton therapy (IMPT), intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), and helical tomotherapy (HT). METHODS: This retrospective study utilized a dataset comprising imaging data from 22 patients with LGG who underwent postoperative radiotherapy. Treatment plans were generated for each patient with scalp-optimized (SO) approaches and scalp-non-optimized (SNO) approaches using proton techniques and photons techniques; all plans adhered to the same dose constraint of delivering a total radiation dose of 54.04 Gy to the target volume. All treatment plans were subsequently analyzed. RESULTS: All the plans generated in this study met the dose constraints for the target volume and OARs. The SO plans resulted in reduced maximum scalp dose (Dmax), mean scalp dose (Dmean), and volume of the scalp receiving 30 Gy (V30) and 40 Gy (V40) compared with SNO plans in all radiation techniques. Among all radiation techniques, the IMPT plans exhibited superior performance compared to other plans for dose homogeneity as for SO plans. Also, IMPT showed lower values for Dmean and Dmax than all photon radiation techniques. CONCLUSION: Our study provides evidence that the SO approach is a feasible technique for reducing scalp radiation dose. However, it is imperative to conduct prospective trials to assess the benefits associated with this approach.

Gut Microbiota Moderates Multimodal Brain Structure-Function Integration and Behavioral Cognition in Growth Hormone Deficient Children.

INTRODUCTION: Previous brain studies of growth hormone deficiency (GHD) often used single-modal neuroimaging, missing the complexity captured by multimodal data. Growth hormone affects gut microbiota and metabolism in GHD. However, from a gut-brain axis (GBA) perspective, the relationship between abnormal GHD brain development and microbiota alterations remains unclear. The ultimate goal is to uncover the manifestations underlying GBA abnormalities in GHD and idiopathic short stature (ISS). METHODS: Participants included 23 GHD and 25 ISS children. The fusion independent component analysis was applied to integrate multimodal brain data (high-resolution structural, diffusion tensor, and resting-state functional MRI) covering regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and white matter fractional anisotropy (FA). Gut microbiome diversity and metabolites were analyzed using 16S sequencing and proton nuclear magnetic resonance (1H-NMR). Associations between multimodal neuroimaging and cognition were assessed using moderation analysis. RESULTS: Six independent components (IC) of ReHo, ALFF, and FA differed significantly between GHD and ISS patients, with three functional components linked to the processing speed index. GHD individuals showed higher levels of acetate, nicotinate, and lysine in microbiota metabolism. Higher alpha diversity in GHD strengthened connections between ReHo-IC1, ReHo-IC5, ALFF-IC1, and the processing speed index, while increasing agathobacter levels in ISS weakened the link between ALFF-IC1 and the speech comprehension index. CONCLUSIONS: Our findings uncover differing brain structure and functional fusion in GHD, alongside microbiota metabolism of short-chain fatty acids. Additionally, microbiome influences connections between neuroimaging and cognition, offering insight into diverse GBA patterns in GHD and ISS, enhancing our understanding of the disease's pathophysiology and interventions.

Real-Time Image-Guided Navigation in the Management of Alveolar Cleft Repair.

Objectives: To present the use of dynamic navigation system in the repair of alveolar cleft. Patients and Participants: A total of three non-syndromic patients with unilateral alveolar cleft were involved in this study. Real-time computer-aided navigation were used to achieve restoration and reconstruction with standardized surgical technique. Methods: With the individual virtual 3-dimensional (3-D) modeling based on computed tomography (CT) data, preoperative planning and surgical simulation were carried out with the navigation system. During preoperative virtual planning, the defect volume or the quantity of graft is directly assessed at the surgical region. With the use of this system, the gingival periosteum flap incision can be tracked in real-time, and the bone graft can be navigated under the guidance of the 3-D views until it matches the preoperatively planned position. Results: Three patients with alveolar cleft were successfully performed under navigation guidance. Through the model alignment procedure, accurate matches between the actual intraoperative position and the CT images were achieved within the systematic error of 0.3 mm. The grafted bone was implanted according to the preoperative plan with the aid of instrument- and probe-based navigation. All the patients were healed well without serious complications. Conclusions: These findings suggest that image-guided surgical navigation, including preoperative planning, surgical simulation, postoperative assessment, and computer-assisted navigation was feasible and yielded good clinical outcomes. Clinical relevance: This dynamic navigation could be proved to be a valuable option for this complicated surgical procedure in the management of alveolar cleft repair.

CDC6 is a prognostic biomarker and correlated with immune infiltrates in glioma.

BACKGROUND: Cell division cycle 6 (CDC6) has been proven to be associated with the initiation and progression of human multiple tumors. However, it's role in glioma, which is ranked as one of the common primary malignant tumor in the central nervous system and is associated with high morbidity and mortality, is unclear. METHODS: In this study, we explored CDC6 gene expression level in pan-cancer. Furthermore, we focused on the relationships between CDC6 expression, its prognostic value, potential biological functions, and immune infiltrates in glioma patients. We also performed vitro experiments to assess the effect of CDC6 expression on proliferative, apoptotic, migrant and invasive abilities of glioma cells. RESULTS: As a result, CDC6 expression was upregulated in multiple types of cancer, including glioma. Moreover, high expression of CDC6 was significantly associated with age, IDH status, 1p/19q codeletion status, WHO grade and histological type in glioma (all p < 0.05). Meanwhile, high CDC6 expression was associated with poor overall survival (OS) in glioma patients, especially in different clinical subgroups. Furthermore, a univariate Cox analysis showed that high CDC6 expression was correlated with poor OS in glioma patients. Functional enrichment analysis indicated that CDC6 was mainly involved in pathways related to DNA transcription and cytokine activity, and Gene Set Enrichment Analysis (GSEA) revealed that MAPK pathway, P53 pathway and NF-κB pathway in cancer were differentially enriched in glioma patients with high CDC6 expression. Single-sample gene set enrichment analysis (ssGSEA) showed CDC6 expression in glioma was positively correlated with Th2 cells, Macrophages and Eosinophils, and negative correlations with plasmacytoid dendritic cells, CD8 T cells and NK CD56bright cells, suggesting its role in regulating tumor immunity. Finally, CCK8 assay, flow cytometry and transwell assays showed that silencing CDC6 could significantly inhibit proliferation, migration, invasion, and promoted apoptosis of U87 cells and U251 cells (p < 0.05). CONCLUSION: In conclusion, high CDC6 expression may serve as a promising biomarker for prognosis and correlated with immune infiltrates, presenting to be a potential immune therapy target in glioma.

[The Effects of Different Adaptive Statistical Iterative Reconstruction-V and Convolution Kernel Parameters on Auto-Segmentation Stability in CT Images].

Objective The study aims to investigate the effects of different adaptive statistical iterative reconstruction-V（ ASiR-V） and convolution kernel parameters on stability of CT auto-segmentation which is based on deep learning. Method Twenty patients who have received pelvic radiotherapy were selected and different reconstruction parameters were used to establish CT images dataset. Then structures including three soft tissue organs (bladder, bowelbag, small intestine) and five bone organs (left and right femoral head, left and right femur, pelvic) were segmented automatically by deep learning neural network. Performance was evaluated by dice similarity coefficient（ DSC） and Hausdorff distance, using filter back projection(FBP) as the reference. Results Auto-segmentation of deep learning is greatly affected by ASIR-V, but less affected by convolution kernel, especially in soft tissues. Conclusion The stability of auto-segmentation is affected by parameter selection of reconstruction algorithm. In practical application, it is necessary to find a balance between image quality and segmentation quality, or improve segmentation network to enhance the stability of auto-segmentation.

Influence of the hypothalamus-pituitary-gonadal axis reactivation and pubertal hormones on gray matter volume in early pubertal girls.

OBJECTIVE: Puberty is a sensitive period of brain development accompany with pubertal hormones fluctuation. However, the underlying mechanisms of the impact of hypothalamus-pituitary-gonadal (HPG) axis reactivation and associated elevated pubertal hormones on brain structure are still unclear. Here, we investigated the brain structure differences between girls with and without HPG axis reactivation and the influence of pubertal hormones on these brain regions. METHODS: 126 girls aged 8-9.5 years underwent a gonadotropin-releasing hormone (GnRH) stimulation test to identify the HPG axis status and categorized into HPG+ group (n = 80) and HPG- group (n = 46). T1-weighted gradient echo three dimensional MRI was performed using a 3.0-Tesla scanner to assess the difference in GMV between the two groups. Correlation analyses were conducted to explore the relations between the brain regions showing significant GMV differences and serum hormone concentrations. RESULT: The HPG+ group showed significantly higher GMV in the bilateral lingual gyrus and lower GMV within the right orbital inferior frontal gyrus compare to the HPG - group. Furthermore, GMV in the right orbital inferior frontal gyrus was positively associated with plasma concentrations of follicle stimulating hormone (FSH) in HPG+ group. CONCLUSION: The present study suggests that the reactivated HPG axis could affects regional structural brain changes in early pubertal girls. FSH production play an important role in bilateral lingual gyrus, which are involved in vision processing, semantic processing and emotional expression.

Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy.

PURPOSE AND BACKGROUND: The magnetic resonance (MR)-only radiotherapy workflow is urged by the increasing use of MR image for the identification and delineation of tumors, while a fast generation of synthetic computer tomography (sCT) image from MR image for dose calculation remains one of the key challenges to the workflow. This study aimed to develop a neural network to generate the sCT in brain site and evaluate the dosimetry accuracy. MATERIALS AND METHODS: A generative adversarial network (GAN) was developed to translate T1-weighted MRI to sCT. First, the "U-net" shaped encoder-decoder network with some image translation-specific modifications was trained to generate sCT, then the discriminator network was adversarially trained to distinguish between synthetic and real CT images. We enrolled 37 brain cancer patients acquiring both CT and MRI for treatment position simulation. Twenty-seven pairs of 2D T1-weighted MR images and rigidly registered CT image were used to train the GAN model, and the remaining 10 pairs were used to evaluate the model performance through the metric of mean absolute error. Furthermore, the clinical Volume Modulated Arc Therapy plan was calculated on both sCT and real CT, followed by gamma analysis and comparison of dose-volume histogram. RESULTS: On average, only 15 s were needed to generate one sCT from one T1-weighted MRI. The mean absolute error between synthetic and real CT was 60.52 ± 13.32 Housefield Unit over 5-fold cross validation. For dose distribution on sCT and CT, the average pass rates of gamma analysis using the 3%/3 mm and 2%/2 mm criteria were 99.76% and 97.25% over testing patients, respectively. For parameters of dose-volume histogram for both target and organs at risk, no significant differences were found between both plans. CONCLUSION: The GAN model can generate synthetic CT from one single MRI sequence within seconds, and a state-of-art accuracy of CT number and dosimetry was achieved.

[Feasibility of Evaluating Result of Auto-segmentation of Target Volumes in Radiotherapy with Medical Consideration Index].

OBJECTIVE: To explore the feasibility of using the bidirectional local distance based medical similarity index (MSI) to evaluate automatic segmentation on medical images. METHODS: Taking the intermediate risk clinical target volume for nasopharyngeal carcinoma manually segmented by an experience radiation oncologist as region of interest, using Atlas-based and deep-learning-based methods to obtain automatic segmentation respectively, and calculated multiple MSI and Dice similarity coefficient (DSC) between manual segmentation and automatic segmentation. Then the difference between MSI and DSC was comparatively analyzed. RESULTS: DSC values for Atlas-based and deep-learning-based automatic segmentation were 0.73 and 0.84 respectively. MSI values for them varied between 0.29~0.78 and 0.44~0.91 under different inside-outside-level. CONCLUSIONS: It is feasible to use MSI to evaluate the results of automatic segmentation. By setting the penalty coefficient, it can reflect phenomena such as under-delineation and over-delineation, and improve the sensitivity of medical image contour similarity evaluation.

[Using stacked neural network to improve the auto-segmentation accuracy of Graves' ophthalmopathy target volumes for radiotherapy].

Compared with the previous automatic segmentation neural network for the target area which considered the target area as an independent area, a stacked neural network which uses the position and shape information of the organs around the target area to regulate the shape and position of the target area through the superposition of multiple networks and fusion of spatial position information to improve the segmentation accuracy on medical images was proposed in this paper. Taking the Graves' ophthalmopathy disease as an example, the left and right radiotherapy target areas were segmented by the stacked neural network based on the fully convolutional neural network. The volume Dice similarity coefficient (DSC) and bidirectional Hausdorff distance (HD) were calculated based on the target area manually drawn by the doctor. Compared with the full convolutional neural network, the stacked neural network segmentation results can increase the volume DSC on the left and right sides by 1.7% and 3.4% respectively, while the two-way HD on the left and right sides decrease by 0.6. The results show that the stacked neural network improves the degree of coincidence between the automatic segmentation result and the doctor's delineation of the target area, while reducing the segmentation error of small areas. The stacked neural network can effectively improve the accuracy of the automatic delineation of the radiotherapy target area of Graves' ophthalmopathy.

[Application and Development Trend of Medical Image Automatic Segmentation Technology in Radiation Therapy].

The development of medical image segmentation technology has been briefly reviewed. The applications of auto-segmentation of organs at risk and target volumes based on Atlas and deep learning in the field of radiotherapy have been introduced in detail, respectively. Then the development direction and product model for general automatic sketching tools or systems based on solid clinical data are discussed.

Altered Brain Structure and Functional Connectivity Associated with Pubertal Hormones in Girls with Precocious Puberty.

Pubertal hormones play an important role in brain and psychosocial development. However, the role of abnormal HPG axis states in altering brain function and structure remains unclear. The present study is aimed at determining whether there were significant differences in gray matter volume (GMV) and resting state (RS) functional connectivity (FC) patterns in girls with idiopathic central precocious puberty (CPP) and peripheral precocious puberty (PPP). We further explored the correlation between these differences and serum pubertal hormone levels. To assess this, we recruited 29 idiopathic CPP girls and 38 age-matched PPP girls. A gonadotropin-releasing hormone (GnRH) stimulation test was performed, and pubertal hormone levels (including luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol (E2), prolactin, and cortisol) were assessed. All subjects underwent multimodal magnetic resonance imaging of brain structure and function. Voxel-based morphometry (VBM) analysis was paired with seed-to-voxel whole-brain RS-FC analysis to calculate the GMV and RS-FC in idiopathic CPP and PPP girls. Correlation analyses were used to assess the effects of pubertal hormones on brain regions with structural and functional differences between the groups. We found that girls with CPP exhibited decreased GMV in the left insula and left fusiform gyrus, while connectivity between the left and right insula and the right middle frontal gyrus (MFG), as well as the left fusiform gyrus and right amygdala, was reduced in girls with CPP. Furthermore, the GMV of the left insula and peak FSH levels were negatively correlated while higher basal and peak E2 levels were associated with increased bilateral insula RS-FC. These findings suggest that premature activation of the HPG axis and pubertal hormone fluctuations alter brain structure and function involved in the cognitive and emotional process in early childhood. These findings provide vital insights into the early pathophysiology of idiopathic CPP.

[Feasibility Study on Location of CT Images Using Convolutional Neural Networks].

OBJECTIVE: To locate CT images by using the deep learning model based on convolutional neural network. METHODS: The AlexNet network was used as a deep learning model, which was preset by the transfer learning approach. Training samples were divided into 4 categories according to the vertebral body parts and labeled, and the data augmentation was used to improve the classification accuracy. RESULTS: The accuracy of image classification after augmentation increased from 94.95% to 97.72%, and the testing time increased from 2.05 s to 3.03 s. CONCLUSIONS: It is feasible to use the convolutional neural network to locate CT images. The data augmentation approach can increase the classification accuracy but also increase the training and testing time.

Open Rhinoplasty Using Concealing Incisions for Mild Bifid Nose With Unilateral Mini-Microform Cleft Lip.

Bifid nose, an indicator of Tessier No.0, is a rare congenital malformation. Because of its rarity, few cases were reported and the optimal surgical procedure and the best time for surgery have not been widely acknowledged. In this brief report, a 9-year-old girl with mild bifid nose and unilateral mini-microform cleft lip, and its surgical management, is presented. We focused our attention on modifying the shape of the nose through open rhinoplasty without excising the surplus skin on the nasal dorsum and achieved good results.

Alterations in Cortical Thickness and White Matter Integrity in Mild-to-Moderate Communicating Hydrocephalic School-Aged Children Measured by Whole-Brain Cortical Thickness Mapping and DTI.

Follow-up observation is required for mild-to-moderate hydrocephalic patients because of the potential damage to brain. However, effects of mild-to-moderate hydrocephalus on gray and white matter remain unclear in vivo. Using structural MRI and diffusion tensor imaging (DTI), current study compared the cortical thickness and white matter integrity between children with mild-to-moderate communicating hydrocephalus and healthy controls. The relationships between cortical changes and intelligence quota were also examined in patients. We found that cortical thickness in the left middle temporal and left rostral middle frontal gyrus was significantly lower in the hydrocephalus group compared with that of controls. Fractional anisotropy in the right corpus callosum body was significantly lower in the hydrocephalus group compared with that of controls. In addition, there was no association of cortical thinning or white matter fractional anisotropy with intelligence quota in either group. Thus, our findings provide clues to that mild-to-moderate hydrocephalus could lead to structural brain deficits especially in the middle temporal and middle frontal gyrus prior to the behavior changes.

[Study of 3D-pcASL in differentiation of acute cerebral infarction and acute encephalitis].

OBJECTIVE: To investigate the value of three-dimentional pseudo-continuous arterial spin labeling (ASL) perfusion imaging in differentiating acute cerebral infarction from acute encephalitis. METHODS: From September 2013 to September 2014, 42 patients with actue stroke onset and 20 healthy volunteers underwent conventional brain MRI DWI and 3D-ASL Perfusion Imaging in our hospital. Only 20 patients whose lesions located in the middle cerebral artery (MCA) territory were enrolled in this study. Of these cases, 12 cases were diagnosed with acute cerebral infarction, 8 were diagnosed with encephalitis. First, we analyzed the imaging features of the 20 patients and 20 volunteers. Then, CBF values of the lesions in the 20 patients and the gray matter of MCA territory in the 20 volunteers were measured on 3D-pcASL images. Third, the difference of mean CBF values between patients and volunteers were analyzed. RESULTS: Out of 20 study group, 19 patients whose lesions presented high signal intensity on DWI images, 12 cases were acute cerebral infarction and 8 were encephalitis. All the lesions of 20 cases showed abnormal perfusion on 3D-pcASL images. 3D-pcASL has good consistency with DWI in diagnostic capabilities (χ² = 0.565, P = 0.01). On 3D-pcASL, 11 acute cerebral infarction patients presented perfusion defects or low perfusion, 1 acute cerebral infarction patients showed high perfusion, 8 encephalitis patients showed inhomogeneous perfusion. The mean value of CBF was (17 ± 6) ml · min⁻¹ · 100 g⁻¹ in 12 acute cerebral infarction patients, (136 ± 69) ml · min⁻¹ · 100 g⁻¹ in 8 encephalitis patients and (68 ± 12) ml · min⁻¹ · 100 g⁻¹ three in 20 healthy volunteers. The difference in mean value of CBF among the three groups was statistically significant (P < 0.01). CONCLUSION: Acute cerebral infarction often shows low perfusion and acute encephalitis shows high perfusion on 3D-pcASL images, which has a higher application value in diagnosis and differentiation of acute cerebral infarction and encephalitis.

[From microdosimetry to nanodosimetry--the link between radiobiology and radiation physics].

The link between micro- and macro-parameters for radiation interactions that take place in living biological systems is described in this paper. Meanwhile recent progress and development in microdosimetry and nanodosimetry are introduced, including the methods to measure and calculate these micro- or nano-parameters. The relationship between radiobiology and physical quantities in microdosimetry and nanodosimetry was presented. Both the current problems on their applications in radiation protection and radiotherapy and the future development direction are proposed.

Comprehensive three-dimensional cone beam computed tomography assessment unilateral alveolar cleft reconstruction using autologous iliac cancellous bone combined with deproteinized bovine bone: A clinical retrospective evaluation.

Large-volume autologous iliac cancellous bone grafting for alveolar cleft may lead to undesirable bone resorption and susceptible donor-site morbidity, whereas the addition of deproteinized bovine bone (DBB) could optimize outcomes. This study aimed to evaluate the effectiveness of combining autologous iliac bone with DBB using three-dimensional cone beam computed tomography (3D-CBCT) for better analysis of bone generation than conventional evaluation methods. Thirty-six patients with unilateral alveolar cleft were assigned into two groups. Group A (n = 21) underwent autogenous cancellous bone graft harvested from the anterior iliac crests, while Group B (n = 15) received a composite of autogenous iliac cancellous bone and DBB. Patients in Group B displayed higher bone filling rates (P < 0.0001) and lower bone absorption rates (P < 0.001) than those in Group A at both 6 months and 1 year postoperatively. Additionally, there were directional differences in bone absorption within the bone grafts, with more absorption observed on the alveolar crest and palatal sides than that on the nasal and labial sides (P < 0.001). This study demonstrates that employing a combination of DBB and autologous bone in alveolar cleft repair achieves better outcomes of bone grafting.

Altered Brain Glymphatic Function at Diffusion-Tensor MRI in Pre-cirrhotic Metabolic Dysfunction-Associated Fatty Liver Disease.

RATIONALE AND OBJECTIVES: To evaluate glymphatic function changes and their relationships with clinical features in patients with metabolic dysfunction-associated fatty liver disease (MAFLD), thereby facilitating early intervention before this disease progresses to cirrhosis. MATERIALS AND METHODS: A cross-sectional cohort of 46 pre-cirrhotic MAFLD patients and 30 age-, sex-, and education-matched controls was enrolled, with diffusion-tensor imaging (DTI) data, laboratory and neurocognitive scores collected. The DTI analysis along the perivascular space (DTI-ALPS) index was computed for qualifying glymphatic function. Generalized linear model and partial correlation analyses were applied to evaluate relationships between the ALPS index and clinical variables. RESULTS: MAFLD group exhibited a decreased ALPS index and increased diffusivity along the y-axis in the projection fiber compared to the controls. The altered ALPS index was associated with clock drawing test (CDT) score (3.931 [0.914, 6.947], P = 0.011) and was correlated with diastolic pressure level (r = -0.315, P = 0.033) in MAFLD group. The relationships of ALPS index with CDT score (6.263 [2.069, 10.458], P = 0.003) and diastolic pressure level (r = -0.518, P = 0.014) remained in the MAFLD with metabolic syndrome (MetS) group. Furthermore, the ALPS index was even associated with Auditory Verbal Learning Test-Immediate recall score (-23.853 [-45.417, -2.289], P = 0.030) in MAFLD with MetS group. CONCLUSION: MAFLD patients may have a glymphatic dysfunction prior to cirrhosis, and this alteration may be related to cognition and diastolic pressure. Glymphatic dysfunction has a more severe impact on cognition when MAFLD patient is accompanied by MetS.

Altered cerebral white matter network topology and cognition in children with obstructive sleep apnea.

OBJECTIVES: The study aimed to explore the underlying mechanisms of OSA-related cognitive impairment by investigating the altered topology of brain white matter networks in children with OSA. METHODS: Graph theory was used to examine white matter networks' network topological properties in 46 OSA and 31 non-OSA children. All participants underwent MRI, polysomnography, and cognitive testing. The effects of the obstructive apnea-hypopnea index (OAHI) on topological properties of white matter networks and network properties on cognition were studied using hierarchical linear regression. Mediation analyses were used to explore whether white matter network properties mediated the effects of OAHI on cognition. RESULTS: Children with OSA had significantly higher assortativity than non-OSA children. Furthermore, OAHI was associated with the nodal properties of several brain regions, primarily in the frontal and temporal lobes. The relationship between OAHI and verbal comprehension index was mediated through clustering coefficients in the right temporal pole of the superior temporal gyrus. CONCLUSIONS: OSA affects the development of white matter networks in children's brains. Besides, the mediating role of white matter network properties between the OAHI and the verbal comprehension index provided neuroimaging evidence of impaired cognitive function in children with OSA.

[Relationship between gestational age and apparent diffusion coefficient values in different regions of fetal brain from middle to late trimester].

OBJECTIVE: To analyze the relationship between gestational age and apparent diffusion coefficient (ADC) values in different regions of fetal brain from middle to late trimester. METHODS: DW images performed in 70 singleton non-sedated fetuses with questionably abnormal results on sonography and normal fetal MR imaging results were retrospectively reviewed. The median gestational age was 32.4 weeks (range, 24-38).With the formula of ADC = ln (S600/S0)/(B0-B600), the mean ADC values were obtained for fetal parietal white matter (WM), frontal WM, temporal WM, occipital WM, pons, cerebellum, basal ganglia and thalamus. The relationship of mean ADC values in different regions with gestational age was analyzed with linear regression. RESULTS: The mean ADC values were 1.77 ± 0.32 mm(2)/s (SD) in fetal parietal white matter (WM), 1.71 ± 0.32 mm(2)/s in occipital WM, 1.31 ± 0.18 mm(2)/s in thalamus, 1.34 ± 0.15 mm(2)/s in basal ganglia. And the mean ADC values in cerebellum, pons, frontal WM and temporal WM were 1.17 ± 0.16, 1.41 ± 0.18, 1.87 ± 0.18 and 1.74 ± 0.19 mm(2)/s respectively. A significant negative correlation between ADC values and gestational age was found for parietal WM, occipital WM, pons, cerebellum, basal ganglia and thalamus (P < 0.05). Frontal WM ADC (P > 0.05) and temporal WM ADC (P = 0.05) did not significantly change with gestational age whereas only a downward trend was present. The correlation coefficient (r) and coefficient of regression (b) were 0.420 and -0.045 in parietal WM; 0.470 and -0.052 in occipital WM; 0.370 and -0.027 in cerebellum; 0.027 and -0.020 in pons; 0.320 and -0.027 in thalamus; 0.300 and -0.021 in basal ganglia. The mean ADC values peaked in frontal WM and lowest in pons. The mean ADC values in white matter were higher than those in deep gray nuclei, cerebellum and pons.With the development of fetal brain,ADC values declined the fastest in cerebellum and occipital WM, followed by basal ganglia and thalamus. CONCLUSION: Regional differences in non-sedated fetal brain ADC values and their evolutions with gestational age are likely to reflect variations in brain maturation.