Self-Assembly of Hierarchical High-χ Fluorinated Block Copolymers with an Orthogonal Smectic-within-Lamellae 3 nm Sublattice and Vertical Surface Orientation.

Hierarchical structure-within-structure assemblies offer a route toward increasingly complex and multifunctional materials while pushing the limits of block copolymer self-assembly. We present a detailed study of the self-assembly of a series of fluorinated high-χ block copolymers (BCPs) prepared via postmodification of a single poly(styrene)-block-poly(glycidyl methacrylate) (S-b-G) parent polymer with the fluorinated alkylthiol pendent groups containing 1, 6, or 8 fluorinated carbons (termed trifluoro-ethanethiol (TFET), perfluoro-octylthiol (PFOT), and perfluoro-decylthiol (PFDT), respectively). Bulk X-ray scattering of thermally annealed samples demonstrates hierarchical molecular assembly with phase separation between the two blocks and within the fluorinated block. The degree of ordering within the fluorinated block is highly sensitive to synthetic variation; a lamellar sublattice was formed for S-b-GPFOT and S-b-GPFDT. Thermal analyses of S-b-GPFOT reveal that the fluorinated block exhibits liquid crystal-like ordering. The complex thin-film self-assembly behavior of an S-b-GPFOT polymer was investigated using real-space (atomic force microscopy and scanning electron microscopy) and reciprocal-space (resonant soft X-ray scattering (RSoXS), grazing incidence small- and wide-angle scattering) measurements. After thermal annealing in nitrogen or vacuum, films thicker than 1.5 times the primary lattice spacing exhibit a 90-degree grain boundary, exposing a thin layer of vertical lamellae at the free interface, while exhibiting horizontal lamellae on the preferential (polystyrene brush) substrate. RSoXS measurements reveal the near-perfect orthogonality between the primary and sublattice orientations, demonstrating hierarchical patterning at the nanoscale.

Self-brushing for nanopatterning: achieving perpendicular domain orientation in block copolymer thin films.

The self-assembly of thin films of block copolymers (BCPs) with perpendicular domain orientation offers a promising approach for nanopatterning on a variety of substrates, which is required by advanced applications such as ultrasmall transistors in integrated circuits, nanopatterned materials for tissue engineering, and electrocatalysts for fuel cell applications. In this study, we created BCPs with an A-b-(B-r-C) architecture that have blocks with equal surface energy (γair) and that can bind to the substrate, effectively creating a non-preferential substrate coating via self-brushing that enables the formation of through-film perpendicular domains in thin films of BCPs. We employed a thiol-epoxy click reaction to functionalize polystyrene-block-poly(glycidyl methacrylate) with a pair of thiols to generate an A-b-(B-r-C) BCP and tune γair of the B-r-C block. The secondary hydroxyl and thiol ether functionality generated by the click reaction was utilized to bind the BCP to the substrates. Scanning electron microscopy revealed that perpendicular orientation was achieved by simply annealing a thin film of the BCP on the bare substrate without the usual extra step of coating a random copolymer brush on the substrate. The self-brushing capability of the BCP was also examined on gold, platinum, titanium, aluminum nitride, and silicon nitride surfaces. These results demonstrate that self-brushing is a promising approach for achieving perpendicular domain orientation in thin films of BCP for nanopatterning on a variety of useful surfaces.

Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education.

Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source medical image analysis platform, has become a valuable tool in medical imaging education due to its integration of various AI applications. Through its open-source architecture, students can gain practical experience with diverse medical images and the latest AI technology, reinforcing their understanding of anatomy and imaging technology while fostering independent learning and clinical reasoning skills. The implementation of this platform improves instruction quality and nurtures skilled professionals who can meet the demands of clinical practice, research institutions, and technology innovation enterprises. AI algorithms' application in medical image processing have facilitated their translation from the lab to practical clinical applications and education.

A multifunctional fluorescent sensor for Ag+ and Hg2+ detection in seawater.

In order to detect Ag+ and Hg2+ in seawater, we explored a multifunctional fluorescence sensor. A multifunctional Ag+ and Hg2+ sensor was designed by using gold nanoparticles (AuNPs) as quenching agent, PicoGreen dye as fluorescent probe of base pairing double-stranded deoxyribonucleic acid (DNA), and combining the characteristics of Ag+ making C base mismatch and Hg2+ making T base mismatch. Meanwhile, the DNA logic gate was constructed by establishing logic circuit, truth table, and logic formula. The relevant performances of the sensor were investigated. The results revealed that the sensor can detect Ag+ in the range of 100 to 700 nM with R2 = 0.98129, and its detection limit is 16.88 nM (3σ/slope). The detection range of Hg2+is 100-900 nM with R2 = 0.99725, and the detection limit is 5.59 nM (3σ/slope). An AND-AND-NOR-AND molecular logic gate has been successfully designed. With the characteristics of high sensitivity, multifunction, and low cost, the recommended detection method has the potential to be applied to the detection of Ag+ and Hg2+ in seawater.

Correlation between 18F-FDG PET-derived parameters and quantitative pathological characteristics of soft tissue sarcoma.

Background: 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) has been widely used for evaluating patients with soft tissue sarcoma (STS). However, uncertainties and overlap among individuals may be observed, and the relevance of these findings remains to be further explored. The present study was aimed at investigating the correlation between PET metabolic parameters and quantitative pathological characteristics in STS. Methods: We retrospectively collected 39 patients with STS who underwent 18F-FDG PET/computed tomography (CT) examination before treatment. Metabolic parameters including the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and intratumoral FDG uptake heterogeneity (IFH) were measured. Histological grading was performed according to the French Federation of Cancer Centers grading system. Continuous staining of tissue sections and digital quantitative analysis methods were used, the characteristics of tumor nucleated cells were observed through hematoxylin-eosin staining, and the expression of CD163, CD68, CD8, and CD4 in tumor tissues was determined by immunohistochemistry (IHC), then the correlation between FDG metabolic parameters and the above quantitative pathological characteristics in patients with STS were evaluated. Results: The SUVmax of 18F-FDG PET/CT in STS was positively correlated with the total nuclear area (r=0.355, P=0.027). SUVmax was also positively correlated with the expression levels of CD163, CD68, CD8, and CD4 (r=0.582, 0.485, 0.343, and 0.324, with P<0.001, 0.002, 0.032, and 0.044, respectively), but was not significantly correlated with cell count and mean nuclear area (all P>0.05). However, MTV, TLG, and IFH were not significantly correlated with the above quantitative pathological characteristics. Further multivariate logistic regression analysis indicated that only CD163 expression and histological grade were independently correlated with SUVmax. Moreover, SUVmax remained positively correlated with CD163 expression in the low-grade STS (r=0.820, P=0.001) and high-grade STS groups (r=0.430, P=0.028). Conclusions: 18F-FDG uptake was positively correlated with the quantitative pathological features of soft tissue tumors. SUVmax may be a meaningful method reflecting the level of M2 macrophage infiltration and may provide additional valuable information for preclinical evaluation of STS.

The relationship between myodural bridge, atrophy and hyperplasia of the suboccipital musculature, and cerebrospinal fluid dynamics.

The Myodural Bridge (MDB) is a physiological structure that is highly conserved in mammals and many of other tetrapods. It connects the suboccipital muscles to the cervical spinal dura mater (SDM) and transmits the tensile forces generated by the suboccipital muscles to the SDM. Consequently, the MDB has broader physiological potentials than just fixing the SDM. It has been proposed that MDB significantly contributes to the dynamics of cerebrospinal fluid (CSF) movements. Animal models of suboccipital muscle atrophy and hyperplasia were established utilizing local injection of BTX-A and ACE-031. In contrast, animal models with surgical severance of suboccipital muscles, and without any surgical operation were set as two types of negative control groups. CSF secretion and reabsorption rates were then measured for subsequent analysis. Our findings demonstrated a significant increase in CSF secretion rate in rats with the hyperplasia model, while there was a significant decrease in rats with the atrophy and severance groups. We observed an increase in CSF reabsorption rate in both the atrophy and hyperplasia groups, but no significant change was observed in the severance group. Additionally, our immunohistochemistry results revealed no significant change in the protein level of six selected choroid plexus-CSF-related proteins among all these groups. Therefore, it was indicated that alteration of MDB-transmitted tensile force resulted in changes of CSF secretion and reabsorption rates, suggesting the potential role that MDB may play during CSF circulation. This provides a unique research insight into CSF dynamics.

Effect of polyethylene terephthalate particles on filamentous bacteria involved in activated sludge bulking and improvement in sludge settleability.

Excessive proliferation of filamentous bacteria within activated sludge leads to sludge structural instability and diminished settling properties, which is a prevalent issue in tannery wastewater treatment. Based on available information, there is a lack of research on the impact of particle addition on filamentous bacteria in activated sludge, specifically with respect to a reduction in sludge bulking. Therefore, polyethylene terephthalate (PET) was selected as the test material to elucidate the effect of particles on sludge bulking. The results illustrate that particles measuring 0.1 mm in diameter have a profound influence on both the quantity and morphological characteristics of filamentous bacteria in activated sludge. In an anaerobic-aoxic-oxic (AAO) reactor, the use of 4000 particles/L led to a significant decrease in the sludge volume index (SVI), reducing it from 358 mg/L to 198 mg/L. The results offer significant insights for resolving sludge bulking problems in tannery wastewaters. Moreover, the results are significant as a reference point for future investigations on the efficacy of incorporating diverse particulate materials to ameliorate issues associated with activated sludge bulking.

Wetting Behavior of A-block-(B-random-C) Copolymers with Equal Block Surface Energies on Surfaces Functionalized with B-random-C Copolymers.

To form nanopatterns with self-assembled block copolymers (BCPs), it is desirable to have through-film domains that are oriented perpendicular to the substrate. The domain orientation is determined by the interfacial interactions of the BCP domains with the substrate and with the free surface. Here, we use thin films of two different sets of BCPs with A-block-(B-random-C) architecture matched with a corresponding B-random-C copolymer nanocoating on the substrate to demonstrate two distinct wetting behaviors. The two sets of A-b-(B-r-C) BCPs are made by using thiol-epoxy click chemistry to functionalize polystyrene-block-poly(glycidyl methacrylate) with trifluoroethanethiol (TFET) and either 2-mercaptopyridine (2MP) or methyl thioglycolate (MTG). For each set of BCPs, the composition ratio of the two thiols in the BCP (φ1) is found that results in the two blocks of the modified BCP having equal surface energies (Δγair = 0). The corresponding B-r-C random copolymers were synthesized and used to modify the substrate, and the composition ratio (φ2) values that resulted in the two blocks of the BCP having equal interfacial energy with the substrate (Δγsub = 0) were determined with scanning electron microscopy. The correlation between each block's γsub value and the interaction parameter, χ, is employed to explain the different wetting behaviors of the two sets of BCPs. For the thiol pair 2MP and TFET, the values of φ1 and φ2 that lead to Δγair = 0 and Δγsub = 0, respectively, are significantly different. A similar difference was observed between the φ1 and φ2 values that lead to Δγair = 0 and Δγsub = 0 for the BCPs made with the thiol pair MTG and TFET. In the latter case, for Δγsub = 0 two windows of φ2 are identified, which can be explained by the thermodynamic interactions of the specific thiol pair and the A-b-(B-r-C) architecture.

Application of Deep Learning for Prediction of Alzheimer's Disease in PET/MR Imaging.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is a promising technique that combines the advantages of PET and MR to provide both functional and structural information of the brain. Deep learning (DL) is a subfield of machine learning (ML) and artificial intelligence (AI) that focuses on developing algorithms and models inspired by the structure and function of the human brain's neural networks. DL has been applied to various aspects of PET/MR imaging in AD, such as image segmentation, image reconstruction, diagnosis and prediction, and visualization of pathological features. In this review, we introduce the basic concepts and types of DL algorithms, such as feed forward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. We then summarize the current applications and challenges of DL in PET/MR imaging in AD, and discuss the future directions and opportunities for automated diagnosis, predictions of models, and personalized medicine. We conclude that DL has great potential to improve the quality and efficiency of PET/MR imaging in AD, and to provide new insights into the pathophysiology and treatment of this devastating disease.

Feasibility Study of 3D FACT and IVIM Sequences in the Evaluation of Female Osteoporosis.

BACKGROUND: The aim of this study is to search for the predictive value of 3D fat analysis and calculation technique (FACT) and intravoxel incoherent motion (IVIM) parameters in identifying osteoporosis in women. METHODS: We enrolled 48 female subjects who underwent 3.0 T MRI, including 3D FACT and IVIM sequences. Bone mineral density (BMD) values and Fracture Risk Assessment (FRAX) scores were obtained. Proton density fat fraction (PDFF) in the bone marrow and the real diffusion (D) value of intervertebral discs were measured on 3D FACT and IVIM images, respectively. Accuracy and bias were assessed by linear regression analysis and Bland-Altman plots. Intraclass correlation coefficients were used to assess the measurements' reproducibility. Spearman's rank correlation was applied to explore the correlation. MRI-based parameters were tested for significant differences among the three groups using ANOVA analyses. A receiver operating characteristic (ROC) analysis was performed. RESULTS: The PDFF of the vertebral body showed a negative correlation with BMD (R = -0.393, p = 0.005) and a positive correlation with the FRAX score (R = 0.706, p < 0.001). The D value of intervertebral discs showed a positive correlation with BMD (R = 0.321, p = 0.024) and a negative correlation with the FRAX score (R = -0.334, p = 0.019). The area under the curve values from the ROC analysis showed that the 3D FACT and IVIM sequences could accurately differentiate between normal and osteoporosis (AUC = 0.88 using the PDFF; AUC = 0.77 using the D value). The PDFF value demonstrated a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 78.6%, 89.5%, 84.6%, and 85.0%, respectively, in its ability to predict osteoporosis. The D value had a sensitivity, specificity, PPV, and NPV of 63.16%, 92.9%, 65.0%, and 77.8%, respectively, for predicting osteoporosis. CONCLUSIONS: The 3D FACT- and IVIM-measured PDFF and D values are promising biomarkers in the assessment of bone quality and fracture risk.

Early-onset Alzheimer's disease with depression as the first symptom: a case report with literature review.

Background: Alzheimer's disease is a common neurodegenerative disease, and patients with early-onset Alzheimer's disease (onset age < 65 years) often have atypical symptoms, which are easily misdiagnosed and missed. Multimodality neuroimaging has become an important diagnostic and follow-up method for AD with its non-invasive and quantitative advantages. Case presentation: We report a case of a 59-year-old female with a diagnosis of depression at the age of 50 after a 46-year-old onset and a 9-year follow-up observation, who developed cognitive dysfunction manifested by memory loss and disorientation at the age of 53, and eventually developed dementia. Combined with neuropsychological scales (MMSE and MOCA scores decreased year by year and finally reached the dementia criteria) and the application of multimodal imaging. MRI showed that the hippocampus atrophied year by year and the cerebral cortex was extensively atrophied. 18F-FDG PET image showed hypometabolism in right parietal lobes, bilateral frontal lobes, bilateral joint parieto-temporal areas, and bilateral posterior cingulate glucose metabolism. The 18F-AV45 PET image showed the diagnosis of early-onset Alzheimer's disease was confirmed by the presence of Aß deposits in the cerebral cortex. Conclusion: Early-onset Alzheimer's disease, which starts with depression, often has atypical symptoms and is prone to misdiagnosis. The combination of neuropsychological scales and neuroimaging examinations are good screening tools that can better assist in the early diagnosis of Alzheimer's disease. Graphical Abstract.

Visualization of Activated Fibroblasts in Heart Failure with Preserved Ejection Fraction with [18F]AlF-NOTA-FAPI-04 PET/CT Imaging.

During the pathogenesis of heart failure with preserved ejection fraction (HFpEF), fibroblasts are activated and express the fibroblast activation protein (FAP). Targeted imaging of FAP can qualitatively and quantitatively assess the fibroblast activity. This study aimed to use [18F]AlF-NOTA-FAPI-04 (AlF = aluminum fluoride; NOTA = 1,4,7-triazacyclononane-1,4,7-triacetic acid; FAPI = FAP inhibitor) positron emission tomography/computed tomography (PET/CT) imaging to detect activated fibroblasts in a rat HFpEF model. The rat HfpEF model was established by feeding a high-fat diet plus l-NAME (Nω-nitro-l-arginine methyl ester) for 10 weeks. Blood pressure, echocardiography, and [18F]AlF-NOTA-FAPI-04 PET/CT were used to assess the progression of HfpEF. The biodistribution of [18F]AlF-NOTA-FAPI-04 in healthy rats was obtained. Cardiac tissue sections were also analyzed using Masson's, hematoxylin and eosin (H&E), and FAP immunohistochemistry (IHC) staining. The echocardiography and blood pressure data indicated that the rat HfpEF model was successfully established. [18F]AlF-NOTA-FAPI-04 PET/CT imaging showed obvious radiotracer accumulation in the left ventricular wall of the HfpEF rats from the seventh week. A biodistribution test showed that the tracer was cleared mainly via renal and intestinal excretion. Percentage of injected dose per gram tissue (% ID) of the heart and its surrounding organs was lower in normal rats, which was conducive to image analysis. Masson's and H&E stainings showed large areas of vascular and interstitial fibrosis in the HfpEF rat hearts. IHC staining also confirmed the presence of FAP-positive cardiac fibroblasts of the HfpEF rat hearts, with a good correlation with FAPI PET. Thus, [18F]AlF-NOTA-FAPI-04 PET/CT is a promising and non-invasive method to assess the progression of fibrosis in HfpEF to facilitate the clinical management.

Filtration columns containing waste iron shavings, loofah, and plastic shavings for further removal of nitrate and phosphate from wastewater effluent.

A novel pilot-scale advanced treatment system combining waste products as fillers is proposed and established to enhance the removal of nitrate (NO3--N) and phosphate (PO43--P) from secondary treated effluent. The system consists of four modular filter columns, one containing iron shavings (R1), two containing loofahs (R2 and R3), and one containing plastic shavings (R4). The monthly average concentration of total nitrogen (TN) and total phosphorus (TP) decreased from 8.87 to 2.52 mg/L and 0.607 to 0.299 mg/L, respectively. Micro-electrolysis of iron shavings produces Fe2+ and Fe3+ to remove PO43--P, while oxygen (O2) consumption creates anoxic conditions for subsequent denitrification. Gallionellaceae, iron-autotrophic Microorganisms, enriched the surface of iron shavings. The loofah served as a carbon source to remove NO3--N, and its porous mesh structure facilitated the attachment of biofilm. The plastic shavings intercepted suspended solids and degraded excess carbon sources. This system can be scaled up and installed at wastewater plants to improve the water quality of effluent cost-effectively.

Determining Structure and Thermodynamics of A-b-(B-r-C) Copolymers.

The self-assembly of block copolymers (BCPs) is dictated by their segregation strength, χN, and while there are well-developed methods for determining χ in the weak and strong segregation regimes, it is challenging to accurately measure χ of copolymers with intermediate segregation strengths, especially when copolymers have inaccessible order-disorder transition temperatures. χeff is often approximated by using strong segregation theory (SST), but utilizing these values to estimate the interface width (wm) of BCPs in the intermediate segregation regime often results in predictions that deviate significantly from measured values. Therefore, we propose using the extent of mixing, quantified as the normalized interface width wm/L0, where L0 is the block copolymer pitch, as a thermodynamic parameter. We experimentally measure wm and L0 for a series of lamellar A-b-(B-r-C) copolymers via resonant soft X-ray reflectivity and extract values of χeffN based on previous data collected for A-b-B copolymers. The composition profiles measured via reflectivity match the extracted χeffN values, while those calculated with SST predict much more mixed composition profiles. The extracted χeff values agreed quantitatively between copolymers of different molecular weights. We believe that this methodology will be well-suited for block copolymers used in lithographic applications due to their inaccessible order-disorder transition temperatures, intermediate values of χN, and the importance of wm for line edge roughness metrics.

Filamentous Bacteria and Stalked Ciliates for the Stable Structure of Aerobic Granular Sludge Treating Wastewater.

Aerobic granular sludge (AGS) is a promising technology for wastewater treatment. AGS formation belongs to microbial self-aggregation. Investigation of the formation and stability of AGS is widely paid attention to, in particular the structure stability of large size granules. Two types of AGS were developed in two sequencing batch reactors fed by two different wastewaters, respectively. Through confocal laser scanning microscope (CLSM) and scanning electron microscopy (SEM), the structure and composition of granules were analyzed. Filamentous bacteria were observed in granules from synthetic wastewater reactor, while filamentous bacteria and stalked ciliates (Epistylis sp.) were simultaneously found in granules from domestic wastewater reactor. The analytic results show that filamentous bacteria and stalked ciliates acting as skeletons play important roles in the formation and stability of granules. With the bonding of extracellular polymeric substances (EPS), the filamentous bacteria and stalked ciliates could build bridges and frames to promote the aggregation of bacteria; these microorganisms could create a space grid structure around the surface layer of granules to enhance the strength of granules, and the remnants of the stalks could serve as supports to fix the steadiness of granules.

Design and Analysis of the Capacitive RF MEMS Switches with Support Pillars.

Conventional parallel capacitive RF MEMS switches have a large impact during the suction phase. In general, RF MEMS switches have to be switched on and off in a considerably fast manner. Increasing the driving voltage enables fast switching but also increases the impact force, which causes the beam membrane to be prone to failure. In the present study, the addition of two support pillars was proposed for slowing down the fall of the beam membrane based on the conventional RF MEMS parallel switch, so as to reduce the impact velocity. As such, a novel RF MEMS switch was designed. Further, simulation software was used to scan and analyze the positioning and height of the support pillars with respect to electromechanical and electromagnetic performance. The simulation results show that the optimal balance of impact velocity and pull-in time was achieved at a height of 0.8 um, a distance of 10 um from the signal line, and an applied voltage of 50 V. The impact velocity was reduced from 1.8 m/s to 1.1 m/s, decreasing by nearly 40%. The turn off time increased from 3.9 us to 4.2 us, representing an increase of only 0.05%. The insertion loss was less than 0.5 dB at 32 GHz, and the isolation was greater than 50 dB at 40 GHz.

Optimized design of block copolymers with covarying properties for nanolithography.

The ability to impart multiple covarying properties into a single material represents a grand challenge in manufacturing. In the design of block copolymers (BCPs) for directed self-assembly and nanolithography, materials often balance orthogonal properties to meet constraints related to processing, structure and defectivity. Although iterative synthesis strategies deliver BCPs with attractive properties, identifying materials with all the required attributes has been difficult. Here we report a high-throughput synthesis and characterization platform for the discovery and optimization of BCPs with A-block-(B-random-C) architectures for lithographic patterning in semiconductor manufacturing. Starting from a parent BCP and using thiol-epoxy 'click' chemistry, we synthesize a library of BCPs that cover a large and complex parameter space. This allows us to readily identify feature-size-dependent BCP chemistries for 8-20-nm-pitch patterns. These blocks have similar surface energies for directed self-assembly, and control over the segregation strength to optimize the structure (favoured at higher segregation strengths) and defectivity (favoured at lower segregation strengths).

Population-specific brain [18F]-FDG PET templates of Chinese subjects for statistical parametric mapping.

Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [18F]-fluorodeoxyglucose ([18F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer's and Parkinson's patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [18F]-FDG uptake, proving the templates' effectiveness in brain function impairment analysis.

Automated brain structures segmentation from PET/CT images based on landmark-constrained dual-modality atlas registration.

Automated brain structures segmentation in positron emission tomography (PET) images has been widely investigated to help brain disease diagnosis and follow-up. To relieve the burden of a manual definition of volume of interest (VOI), automated atlas-based VOI definition algorithms were developed, but these algorithms mostly adopted a global optimization strategy which may not be particularly accurate for local small structures (especially the deep brain structures). This paper presents a PET/CT-based brain VOI segmentation algorithm combining anatomical atlas, local landmarks, and dual-modality information. The method incorporates local deep brain landmarks detected by the Deep Q-Network (DQN) to constrain the atlas registration process. Dual-modality PET/CT image information is also combined to improve the registration accuracy of the extracerebral contour. We compare our algorithm with the representative brain atlas registration methods based on 86 clinical PET/CT images. The proposed algorithm obtained accurate delineation of brain VOIs with an average Dice similarity score of 0.79, an average surface distance of 0.97 mm (sub-pixel level), and a volume recovery coefficient close to 1. The main advantage of our method is that it optimizes both global-scale brain matching and local-scale small structure alignment around the key landmarks, it is fully automated and produces high-quality parcellation of the brain structures from brain PET/CT images.

Differentiation of soft tissue and bone sarcomas from benign lesions utilizing 18F-FDG PET/CT-derived parameters.

BACKGROUND: Accurate differentiation between malignant and benign changes in soft tissue and bone lesions is essential for the prevention of unnecessary biopsies and surgical resection. Nevertheless, it remains a challenge and a standard diagnosis modality is urgently needed. The objective of this study was to evaluate the usefulness of 18F-fluorodeoxyglucose (18F-FDG) PET/CT-derived parameters to differentiate soft tissue sarcoma (STS) and bone sarcoma (BS) from benign lesions. METHODS: Patients who had undergone pre-treatment 18F-FDG PET/CT imaging and subsequent pathological diagnoses to confirm malignant (STS and BS, n = 37) and benign (n = 33) soft tissue and bone lesions were retrospectively reviewed. The tumor size, PET and low-dose CT visual characteristics, maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneous factor (HF) of each lesion were measured. Univariate and multivariate logistic regression analyses were conducted to determine the significant risk factors to distinguish sarcoma from benign lesions. To establish a regression model based on independent risk factors, and the receiver operating characteristic curves (ROCs) of individual parameters and their combination were plotted and compared. Conventional imaging scans were re-analyzed, and the diagnostic performance compared with the regression model. RESULTS: Univariate analysis results revealed that tumor size, SUVmax, MTV, TLG, and HF of 18F-FDG PET/CT imaging in the STS and BS group were all higher than in the benign lesions group (all P values were < 0.01). The differences in the visual characteristics between the two groups were also all statistically significant (P < 0.05). However, the multivariate regression model only included SUVmax and HF as independent risk factors, for which the odds ratios were 1.135 (95%CI: 1.026 ~ 1.256, P = 0.014) and 7.869 (95%CI: 2.119 ~ 29.230, P = 0.002), respectively. The regression model was constructed using the following expression: Logit (P) = - 2.461 + 0.127SUVmax + 2.063HF. The area under the ROC was 0.860, which was higher than SUVmax (0.744) and HF (0.790). The diagnostic performance of the regression model was superior to those of individual parameters and conventional imaging. CONCLUSION: The regression model including SUVmax and HF based on 18F-FDG PET/CT imaging may be useful for differentiating STS and BS from benign lesions.