Normative Analysis of Individual Brain Differences Based on a Population MRI-Based Atlas of Cynomolgus Macaques.

The developmental trajectory of the primate brain varies substantially with aging across subjects. However, this ubiquitous variability between individuals in brain structure is difficult to quantify and has thus essentially been ignored. Based on a large-scale structural magnetic resonance imaging dataset acquired from 162 cynomolgus macaques, we create a species-specific 3D template atlas of the macaque brain, and deploy normative modeling to characterize individual variations of cortical thickness (CT) and regional gray matter volume (GMV). We observed an overall decrease in total GMV and mean CT, and an increase in white matter volume from juvenile to early adult. Specifically, CT and regional GMV were greater in prefrontal and temporal cortices relative to early unimodal areas. Age-dependent trajectories of thickness and volume for each cortical region revealed an increase in the medial temporal lobe, and decreases in all other regions. A low percentage of highly individualized deviations of CT and GMV were identified (0.0021%, 0.0043%, respectively, P < 0.05, false discovery rate [FDR]-corrected). Our approach provides a natural framework to parse individual neuroanatomical differences for use as a reference standard in macaque brain research, potentially enabling inferences regarding the degree to which behavioral or symptomatic variables map onto brain structure in future disease studies.

Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?

Clinical symptoms and inflammatory markers cannot reliably distinguish the etiology of CAP, and chest radiographs have abundant information related with CAP. Hence, we developed a context-fusion convolution neural network (CNN) to explore the application of chest radiographs to distinguish the etiology of CAP in children. This retrospective study included 1769 cases of pediatric pneumonia (viral pneumonia, n = 487; bacterial pneumonia, n = 496; and mycoplasma pneumonia, n = 786). The chest radiographs of the first examination, C-reactive protein (CRP), and white blood cell (WBC) were collected for analysis. All patients were stochastically divided into training, validation, and test cohorts in a 7:1:2 ratio. Automatic lung segmentation and hand-crafted pneumonia lesion segmentation were performed, from which three image-based models including a full-lung model, a local-lesion model, and a context-fusion model were built; two clinical characteristics were used to build a clinical model, while a logistic regression model combined the best CNN model and two clinical characteristics. Our experiments showed that the context-fusion model which integrated the features of the full-lung and local-lesion had better performance than the full-lung model and local-lesion model. The context-fusion model had area under curves of 0.86, 0.88, and 0.93 in identifying viral, bacterial, and mycoplasma pneumonia on the test cohort respectively. The addition of clinical characteristics to the context-fusion model obtained slight improvement. Mycoplasma pneumonia was more easily identified compared with the other two types. Using chest radiographs, we developed a context-fusion CNN model with good performance for noninvasively diagnosing the etiology of community-acquired pneumonia in children, which would help improve early diagnosis and treatment.

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis.

BACKGROUND: The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even more sensitive than laboratory test results. OBJECTIVE: To evaluate image quality improvement in CTA of children diagnosed with TAK using a deep learning image reconstruction (DLIR) in comparison to other image reconstruction algorithms. METHODS: hirty-two TAK patients (9.14±4.51 years old) underwent neck, chest and abdominal CTA using 100 kVp were enrolled. Images were reconstructed at 0.625 mm slice thickness using Filtered Back-Projection (FBP), 50%adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. RESULTS: There was no significant difference in CT number across images reconstructed using different algorithms. Image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 using FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P > 0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P < 0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, however, only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). CONCLUSION: DLIR-H improves CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection.

BACKGROUND: To evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 mm thin-slice images. METHODS: Low-dose axial head CT scans of 50 children with 120 kV, 0.8 s rotation and age-dependent 150-220 mA tube current were selected. Images were reconstructed at 5 mm and 0.625 mm slice thickness using Filtered back projection (FBP), Adaptive statistical iterative reconstruction-v at 50% strength (50%ASIR-V) (as reference standard), 100%ASIR-V and DLIR-high (DL-H). The CT attenuation and standard deviation values of the gray and white matters in the basal ganglia were measured. The clarity of sulci/cisterns, boundary between white and gray matters, and overall image quality was subjectively evaluated. The number of lesions in each reconstruction group was counted. RESULTS: The 5 mm FBP, 50%ASIR-V, 100%ASIR-V and DL-H images had a subjective score of 2.25 ± 0.44, 3.05 ± 0.23, 2.87 ± 0.39 and 3.64 ± 0.49 in a 5-point scale, respectively with DL-H having the lowest image noise of white matter at 2.00 ± 0.34 HU; For the 0.625 mm images, only DL-H images met the diagnostic requirement. The 0.625 mm DL-H images had similar image noise (3.11 ± 0.58 HU) of the white matter and overall image quality score (3.04 ± 0.33) as the 5 mm 50% ASIR-V images (3.16 ± 0.60 HU and 3.05 ± 0.23). Sixty-five lesions were recognized in 5 mm 50%ASIR-V images and 69 were detected in 0.625 mm DL-H images. CONCLUSION: DL-H improves the head CT image quality for children compared with ASIR-V images. The 0.625 mm DL-H images improve lesion detection and produce similar image noise as the 5 mm 50%ASIR-V images, indicating a potential 85% dose reduction if current image quality and slice thickness are desired.

Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in "double low" chest CTA in children: a feasibility study.

BACKGROUND: Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounter limitations under low radiation dose and conventional 100 kVp tube voltage may not provide adequate enhancement under low contrast dose. PURPOSE: To evaluate the performance of a deep learning image reconstruction (DLIR) algorithm in conjunction with lower tube voltage in chest CTA in children under reduced radiation and contrast medium (CM) dose. MATERIALS AND METHODS: 46 Children (age 5.9 ± 4.2 years) in the study group underwent chest CTA with 70 kVp and CM dose of 0.8-1.2 ml/kg. Images were reconstructed at 0.625 mm using a high setting DLIR (DLIR-H). The control group consisted of 46 age-matching children scanned with 100 kVp, CM dose of 1.3-1.8 ml/kg and images reconstructed with 50% and 100% adaptive statistical iterative reconstruction-V. Two radiologists evaluated images subjectively for overall image noise, vessel contrast and vessel margin clarity separately on a 5-point scale (5, excellent and 1, not acceptable). CT value and image noise of aorta and erector spinae muscle were measured. RESULTS: Compared to the control group, the study group reduced the dose-length-product by 11.2% (p = 0.01) and CM dose by 24% (p < 0.001), improved the enhancement in aorta (416.5 ± 113.1HU vs. 342.0 ± 57.6HU, p < 0.001) and reduced noise (15.1 ± 3.5HU vs. 18.6 ± 4.4HU, p < 0.001). The DLIR-H images provided acceptable scores on all 3 aspects of the qualitative evaluation. CONCLUSION: "Double low" chest CTA in children using 70 kVp and DLIR provides high image quality with reduced noise and improved vessel enhancement for diagnosis while further reduces radiation and CM dose.

Application of 70 kVp in abdominal CT angiography to reduce both radiation and contrast dosage and improve patient comfort for children.

BACKGROUND: Low-tube voltage scanning improves CT attenuation value of contrast medium (CM). Thus, we hypothesized that 70 kVp in pediatric abdominal CT angiography (CTA) could be used to reduce both radiation and CM dose and improve patient comfort at the same time. OBJECTIVE: To evaluate the feasibility of using 70 kVp in pediatric abdominal CTA to reduce radiation dose and CM dose and improve patient care for children. MATERIALS AND METHODS: Forty-six children needing abdominal CTA were enrolled in the study group using low-dose scanning protocol with 70 kVp and 0.7-1.1 ml/kg contrast dose, and reconstructed with 50%ASIR-V. They were compared with other 46 children in control group with matching body weight and underwent conventional CT scans with 100 kVp, 1.2-1.8 ml/kg contrast dose and reconstructed using 50%ASIR. Image quality of large vessels was evaluated using a 5-point scale. CT value and standard deviation of descending aorta (Ao) was measured, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Radiation dose, contrast dose, the maximum injection pressure between the two groups were also compared. RESULTS: Score for displaying large vessels by 70 kVp images was 3.91±0.28, lower than that (4.17±0.38) of the control group (p < 0.05), but fully met the diagnostic requirements. CT value of Ao was 390.87±86.79HU in study group, which is higher than 343.93±49.94HU in control group, while there was no difference in SNR and CNR between two groups; the radiation dose, contrast dosage and injection pressure of the study group were 1.23±0.39mGy, 12.67±7.27 ml and 43.83±17.16psi, respectively, which are significantly lower than the 1.95±0.37mGy, 22.67±7.39 ml, and 77.59±19.68psi of control group. CONCLUSION: Use of 70 kVp in pediatric abdominal CTA provides diagnostic quality images while significantly reduce radiation and contrast dose, as well as injection pressure to improve patient comfort for children.

Association of gestational age with MRI-based biometrics of brain development in fetuses.

BACKGROUND: Reported date of last menstrual period and ultrasonography measurements are the most commonly used methods for determining gestational age in antenatal life. However, the mother cannot always determine the last menstrual period with certainty, and ultrasonography measurements are accurate only in the first trimester. We aimed to assess the ability of various biometric measurements on magnetic resonance imaging (MRI) in determining the accurate gestational age of an individual fetus in the second half of gestation. METHODS: We used MRI to scan a total of 637 fetuses ranging in age from 22 to 40 gestational weeks. We evaluated 9 standard fetal 2D biometric parameters, and regression models were fitted to assess normal fetal brain development. A stepwise linear regression model was constructed to predict gestational age, and measurement accuracy was determined in a held-out, unseen test sample (n = 49). RESULTS: A second-order polynomial regression model was found to be the best descriptor of biometric measures including brain bi-parietal diameter, head circumference, and fronto-occipital diameter in relation to normal fetal growth. Normal fetuses showed divergent growth patterns for the cerebrum and cerebellum, where the cerebrum undergoes rapid growth in the second trimester, while the cerebellum undergoes rapid growth in the third trimester. Moreover, a linear model based on biometrics of brain bi-parietal diameter, length of the corpus callosum, vermis area, transverse cerebellar diameter, and cerebellar area accurately predicted gestational age in the second and third trimesters (cross-validation R2 = 0.822, p < 0.001). CONCLUSIONS: These results support the use of MRI biometry charts to improve MRI evaluation of fetal growth and suggest that MRI biometry measurements offer a potential estimation model of fetal gestational age in the second half of gestation, which is vital to any assessment of pregnancy, fetal development, and neonatal care.

Performance evaluation of two iterative reconstruction algorithms, MBIR and ASIR, in low radiation dose and low contrast dose abdominal CT in children.

BACKGROUND: The adverse effect of low-dose CT on image quality may be mitigated using iterative reconstructions. The purpose of this study was to evaluate the performance of the full model-based iterative reconstruction (MBIR) and adaptive statistical reconstruction (ASIR) algorithms in low radiation dose and low contrast dose abdominal contrast-enhanced CT (CECT) in children. METHODS: A total of 59 children (32 males and 27 females) undergoing low radiation dose (100kVp) and low contrast dose (270 mgI/ml) abdominal CECT were enrolled. The median age was 4.0 years (ranging from 0.3 to 13 years). The raw data were reconstructed with MBIR, ASIR and filtered back-projection (FBP) algorithms into 6 groups (MBIR, 100%ASIR, 80%ASIR, 60%ASIR, 40%ASIR and FBP). The CT numbers, standard deviations, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of liver, pancreas, kidney and abdominal aorta were measured. Two radiologists independently evaluated the subjective image quality including the overall image noise and structure display ability on a 4-point scale with 3 being clinically acceptable. The measurements among the reconstruction groups were compared using one-way ANOVA. RESULTS: The overall image noise score and display ability were 4.00 ± 0.00 and 4.00 ± 0.00 with MBIR and 3.27 ± 0.33 and 3.25 ± 0.43 with ASIR100%, respectively, which met the diagnostic requirement; other reconstructions couldn't meet the diagnostic requirements. Compared with FBP images, the noise of MBIR images was reduced by 62.86-65.73% for the respective organs (F = 48.15-80.47, P < 0.05), and CNR increased by 151.38-170.69% (F = 22.94-38.02, P < 0.05). CONCLUSIONS: MBIR or ASIR100% improves the image quality of low radiation dose and contrast dose abdominal CT in children to meet the diagnostic requirements, and MBIR has the best performance.

Application of metal artifact reduction algorithm in reducing metal artifacts in post-surgery pediatric low radiation dose spine computed tomography (CT) images.

Background: The commonly used methods for removing metal-induced beam hardening artifacts often rely on the use of high energy photons with either high tube voltage or high energy virtual monoenergetic images in dual-energy computed tomography (CT), the radiation dose was usually relatively high in order to generate adequate signals. This retrospective study is designed to evaluate the application of a metal artifact reduction (MAR) algorithm in reducing pedicle screw metal-caused beam hardening artifacts in post-surgery pediatric low radiation dose spine CT images. Methods: Seventy-seven children (3-15 years) who had undergone a low dose CT with 140 or 100 kV were enrolled. The radiation dose was 1.40 mGy for the 3-8 years old and 2.61 mGy for 9-15 years old children. There were 116 pedicle screws evaluated. The raw data were reconstructed with adaptive statistical iterative reconstruction-V (ASIR-V) at 50% strength, ASIR-V with MAR (AV-MAR), deep learning image reconstruction (DLIR) at high strength and DLIR with MAR (DL-MAR). The image quality concerning pedicle screws was evaluated objectively in terms of the length of beam-hardening artifact (LHA) and artifact index (AI), and subjectively using a 4-point scale (4 points: best, 3 points: acceptable). Results: Both AV-MAR and DL-MAR significantly reduced metal-induced beam hardening artifacts with smaller LHA (15.76±10.12 mm, a reduction of 57.24% and 15.66±10.49 mm, a reduction of 57.40%, respectively), and AI value (62.50±33.51, a reduction of 64.65% and 61.03±32.61, a reduction of 65.01%, respectively) compared to ASIR-V and DLIR (all P<0.01), The subjective image quality scores concerning the screws were 3.37±0.49 and 3.47±0.50 with AV-MAR and DL-MAR, respectively, higher than the respective value of 1.73±0.44 and 1.76±0.43 without MAR (all P<0.01). Conclusions: MAR significantly reduces the low-density artifacts caused by metal screws in post-surgery pediatric low-dose spine CT images, across different tube voltages, radiation dose levels and reconstruction algorithms. Combining DL-MAR further improves the overall image quality under low radiation dose conditions.

Differentiating cellular leiomyoma from uterine sarcoma and atypical leiomyoma using multi-parametric MRI.

Objectives: To evaluate the diagnostic performance of conventional magnetic resonance imaging (cMRI) combined with diffusion-weighted MRI (DWI) in discrimination of cellular leiomyoma, uterine sarcoma, and atypical leiomyoma. Methods: This retrospective study enrolled 106 patients with uterine masses, including 51 cellular leiomyomas (CLs), 32 uterine sarcomas (USs) and 23 degenerated leiomyomas (LMs) confirmed by histopathologic examination. Clinical data and imaging findings were assessed. Chi-squared test for qualitative variables and one way ANOVA analysis for quantitative variables were performed. Logistic regression analysis and the receiver operating characteristic (ROC) analysis were performed to determine the cut-off point and diagnostic performances for significant numeric values or multiple models. Results: Morphology (Odds ratio [OR] = 6.36) and margin (OR = 13.84) derived from cMRI were independent indicators for differentiating CLs from USs, and T2WI signal (OR = 0.23) were an independent indicator for differentiating CLs from degenerated LMs (all P < 0.05). The cutoff value of apparent diffusion coefficient (ADC) derived from DWI for differentiating CLs from USs was 839 ×10-6 mm2/sec and was 1239 ×10-6 mm2/sec for differentiating CLs from degenerated LMs. Compared with the use of cMRI features and ADC value alone, combination of independent indicators and ADC value achieved higher AUCs for both differentiations (all P < 0.05). Conclusions: cMRI is a reliable tool for differentiating CLs from USs and atypical leiomyoma, especially degenerated LMs. The combined use of cMRI and DWI can improve the differential diagnostic performance.

Performance evaluation of using shorter contrast injection and 70 kVp with deep learning image reconstruction for reduced contrast medium dose and radiation dose in coronary CT angiography for children: a pilot study.

BACKGROUND: Iterative reconstruction algorithms are often used to reduce image noise in low-dose coronary computed tomography angiography (CCTA) but encounter limitations. The newly introduced deep learning image reconstruction (DLIR) algorithm may provide new opportunities. We assessed the image quality and diagnostic performance of DLIR in low radiation dose and contrast medium dose CCTA of pediatric patients with 70 kVp and a shortened injection protocol. METHODS: This was a prospective study. A total of 27 consecutive arrhythmic pediatric patients were enrolled in the study group and underwent CCTA using a prospective ECG-triggered single-beat protocol: tube voltage 70 kVp, automatic tube current modulation for a noise index (NI) of 22, and contrast dose of 0.4-0.6 mL/kg. Images were reconstructed with DLIR. They were compared with 27 matched patients in the control group scanned with 80 kVp, a lower NI setting (NI =19), and a higher contrast dose (0.8-1.2 mL/kg). The images in the control group were reconstructed using the adaptive statistical iterative reconstruction (ASIR-V) algorithm. The image contrast, image quality, and diagnostic confidence were assessed by 2 experienced radiologists using a 5-point scale (1: nondiagnostic and 5: excellent). The CT value and standard deviation of the aorta and perivascular tissue were measured, and the contrast-to-noise ratio (CNR) for the aorta was calculated. The contrast medium and radiation doses were compared. RESULTS: The study and control groups had similar image contrast scores (4.75±0.57 vs. 4.78±0.42), image quality scores (3.67±0.47 vs. 3.44±0.51), and diagnostic confidence (4.74±0.44 vs. 4.74±0.45) (all P>0.05). There was an adequate enhancement in the aorta (614.74±127.73 vs. 705.89±111.20 HU) and similar CNR (20.34±4.64 vs. 20.99±4.14) in both groups. The image noise of the study group was lower in the aorta (30.61±3.88 vs. 34.77±3.49) and similar in perivascular tissue (27.66±6.24 vs. 27.55±3.33) compared with the control group. The study group reduced the total contrast medium dose by 53% to 15.07±3.68 mL and radiation dose by 36% to 0.57±0.31 mSv. CONCLUSIONS: The DLIR algorithm in CCTA for children using 70 kVp tube voltage with a shortened contrast medium injection protocol maintains image quality and diagnostic confidence while significantly reducing contrast medium dose and radiation dose compared with the use of the conventional CCTA protocol.

Improving the image quality of pediatric chest CT angiography with low radiation dose and contrast volume using deep learning image reconstruction.

BACKGROUND: Chest CT angiography (CTA) is a common clinical examination technique for children. Iterative reconstruction algorithms are often used to reduce image noise but encounter limitations under low dose conditions. Deep learning-based image reconstruction algorithms have been developed to overcome these limitations. We assessed the quantitative and qualitative image quality of thin-slice chest CTA in children acquired with low radiation dose and contrast volume by using a deep learning image reconstruction (DLIR) algorithm. METHODS: A total of 33 children underwent chest CTA with 70 kVp and automatic tube current modulation for noise indices of 11-15 based on their age and contrast volume of 0.8-1.2 mL/kg. Images were reconstructed with 50% and 100% adaptive statistical iterative reconstruction-V (ASIR-V) and high-setting DLIR (DLIR-H) at 0.625 mm slice thickness. Two radiologists evaluated images in consensus for overall image noise, artery margin, and artery contrast separately on a 5-point scale (5, excellent; 4, good; 3, acceptable; 2, sub-acceptable, and 1, not acceptable). The CT value and image noise of the descending aorta and back muscle were measured. Radiation dose and contrast volume was recorded. RESULTS: The volume CT dose index, dose length product, and contrast volume were 1.37±0.29 mGy, 35.43±10.59 mGy·cm, and 25.43±13.32 mL, respectively. The image noises (in HU) of the aorta with DLIR-H (19.24±5.77) and 100% ASIR-V (20.45±6.93) were not significantly different (P>0.05) and were substantially lower than 50% ASIR-V (29.45±7.59) (P<0.001). The 100% ASIR-V images had over-smoothed artery margins, but only the DLIR-H images provided acceptable scores on all 3 aspects of the qualitative image quality evaluation. CONCLUSIONS: It is feasible to improve the image quality of a low radiation dose and contrast volume chest CTA in children using the high-setting DLIR algorithm.

Heightened perception of illusory motion is associated with symptom severity in schizophrenia patients.

Abnormal perceptual processing in schizophrenia may contribute to the development of positive symptoms such as hallucinations. Experimental findings suggest that such abnormalities result from impaired processing of local signals into complex cortical representations. Because complex processing is needed to generate the perception of illusory motion from local signals, deteriorated perception of illusory motion would be expected in schizophrenia. However, findings are mixed, and the relationship between complex motion processing and symptoms is unclear. Illusions with multiple flow components (e.g. rotation/expansion) are known to strongly engage specialized complex processing mechanisms that may be abnormal in schizophrenia, but have not yet been investigated. We used a recently constructed paradigm based on the Pinna-Brelstaff illusion to manipulate complex-flow illusory perception in a quantitative manner and probe associations with dimensional symptoms. In 102 patients and 90 controls, perceived speed and perceptual variability for the PBF were measured across a range of parameters. Meanwhile, eye movement was recorded and gaze parameters were analysed to examine effects on illusory perception. Our results showed that patients experienced faster illusory rotation than controls, while they made fewer eye fixations. This heightened illusory perception was significantly correlated with positive and general, but not negative, symptom scores. Our results indicate that unusual processing of complex-flow motion in patients may be specifically related to dimensional symptoms, which could provide a promising strategy for parsing heterogeneity in the schizophrenia syndrome. This further highlights the role of motion perception abnormalities in the pathophysiology of schizophrenia, thus encouraging future investigation into visual remediation therapeutics.

The association of myelination in the internal capsule with iron deposition in the basal ganglia in macaques: a magnetic resonance imaging study.

BACKGROUND: Iron plays a vital role in myelin synthesis and maintenance. A tight association between iron concentration and myelin content has been demonstrated in local brain regions; however, whether such a relationship exists between distant brain regions that are anatomically connected is largely unknown. METHODS: We conducted an in vivo measurement of iron and myelin content in the brains of 8 young (mean age: 7.7 years) and 8 old (mean age: 24.7 years) macaques by integrating two MRI-based techniques: quantitative susceptibility mapping (QSM) and myelin water fraction (MWF) imaging. We examined the relationship between iron deposition in components of the basal ganglia (BG), and the myelin content of the BG-connecting fiber tract internal capsule (IC) and four more white matter (WM) structures, including the optic tract, and the genu, body, and splenium of the corpus callosum, which are anatomically separate from the BG. RESULTS: Spearman's correlation analysis revealed a moderate to high (r=0.528-0.808, P<0.05) positive correlation between the magnetic susceptibility of the BG and the MWF of anatomically connected IC structures during myelin production and maintenance, but little significant correlation was found between the susceptibility of the BG and the MWF of WM structures not anatomically connected to the BG. CONCLUSIONS: These results advance the understanding of the relationship between iron and myelin, and suggest that future studies should consider the impact that iron concentration in the BG has on the myelination of WM structures that are anatomically connected to the BG.

Feasibility study of using one-tenth mSv radiation dose in young children chest CT with 80 kVp and model-based iterative reconstruction.

CT has become a routine imaging modality based on its excellent ability of displaying lung structures and diseases. But, how to reduce radiation dose of routine CT examination is a concern for radiologists. Our study aimed to evaluate the feasibility of using 80kVp and a model-based iterative reconstruction (MBIR) algorithm to achieve one-tenth mSv dose chest CT in infants and young children. Thirty-two cases (study group, average age 1.71 ± 1.01 years) underwent non-contrast chest CT examination at low dose with 80 kV, 4mAs and was reconstructed with MBIR (LD-MBIR) and the standard adaptive statistical iterative reconstruction (ASIR) algorithm (LD-ASIR); another group (control group) of 32 children underwent routine-dose chest CT with 100 kV and was reconstructed with ASIR only (RD-ASIR). The subjective and objective image quality of the three groups were measured and statistically compared. The radiation dose for the low dose scan was 0.09 ± 0.02 mSv, 6% of the routine dose. All LD-MBIR images were diagnostically acceptable. Compared with the RD-ASIR images, the LD-MBIR images were similar in noise in the left ventricle, muscles, lung field, on-par in displaying large airways, lung lucency and mediastinum, but were inferior in displaying lung marking, small airways and mediastinum. Thus, MBIR images with low dose in pediatric chest CT can be used in the diagnosis for lung field and air way disorders in infants and young children.

[MSCT perfusion analysis of nasopharyngeal cancer].

OBJECTIVE: To study MSCT perfusion imaging of nasopharyngeal cancer and its differentiated diagnosis. METHOD: Thirty cases with nasopharyngeal cancer performed multi-detector CT perfusion examination. Among them, there were 6 cases of 25 post-radiotherapy patients performed perfusion imaging with CT scan. Nasopharynx perfusion parameters include blood flow (BF), blood volume (BV), mean transit time (MTT) and permeability surface (PS). RESULT: Compared with normal region of nasopharynx, BF, BV and PS in nasopharyngeal cancer increased significantly, while MTT has not significant difference between these two areas. CONCLUSION: Nasopharynx perfusion parameters (BF, BV and PS) measured with CT were significantly altered in nasopharyngeal cancer. There was important appliance value in differentiated diagnosis of nasopharyngeal malignant neoplasms and evaluation of outcome of radiotherapy of nasopharyngeal cancer.