Cysteine- and glycine-rich protein 1 predicts prognosis and therapy response in patients with acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease with a poor prognosis. The current risk stratification system is essential but remains insufficient to select the best schedules. Cysteine-rich protein 1 (CSRP1) is a member of the CSRP family and associated with poor clinicopathological features in many tumors. This study aimed to explore the clinical significance and molecular mechanisms of cysteine- and glycine-rich protein 1 (CSRP1) in AML. RT-qPCR was used to detect the relative expression of CSRP1 in our clinical cohort. Functional enrichment analysis of CSRP1-related differentially expressed genes was carried out by GO/KEGG enrichment analysis, immune cell infiltration analysis, and protein-protein interaction (PPI) network. The OncoPredict algorithm was implemented to explore correlations between CSRP1 and drug resistance. CSRP1 was highly expressed in AML compared with normal samples. High CSRP1 expression was an independent poor prognostic factor. Functional enrichment analysis showed neutrophil activation and apoptosis were associated with CSRP1. In the PPI network, 19 genes were present in the most significant module, and 9 of them were correlated with AML prognosis. The high CSRP1 patients showed higher sensitivity to 5-fluorouracil, gemcitabine, rapamycin, cisplatin and lower sensitivity to fludarabine. CSRP1 may serve as a potential prognostic marker and a therapeutic target for AML in the future.

Dihydroartemisinin enhances the anti-tumour effect of photodynamic therapy by targeting PKM2-mediated glycolysis in oesophageal cancer cell.

Photodynamic therapy (PDT) has been demonstrated to provide immediate relief of oesophageal cancer patients' re-obstruction and extend their lifespan. However, tumour regrowth may occur after PDT due to enhanced aerobic glycolysis. Previous research has confirmed the inhibitory effect of Dihydroartemisinin (DHA) on aerobic glycolysis. Therefore, the current study intends to investigate the function and molecular mechanism of DHA targeting tumour cell aerobic glycolysis in synergia PDT. The combined treatment significantly suppressed glycolysis in vitro and in vivo compared to either monotherapy. Exploration of the mechanism through corresponding experiments revealed that pyruvate kinase M2 (PKM2) was downregulated in treated cells, whereas overexpression of PKM2 nullified the inhibitory effects of DHA and PDT. This study proposes a novel therapeutic strategy for oesophageal cancer through DHA-synergized PDT treatment, targeting inhibit PKM2 to reduce tumour cell proliferation and metastasis.

The intellectual base and global trends in inflammation of diabetic kidney disease: a bibliometric analysis.

Diabetic kidney disease (DKD) is a severe complication of diabetes mellitus (DM). The literature on DKD inflammation research has experienced substantial growth. However, there is a lack of bibliometric analyses. This study aimed to examine the existing research on inflammation in DKD by analyzing articles published in the Web of Science Core Collection (WOSCC) over the past 30 years. We conducted a visualization analysis using several software, including CiteSpace and VOSviewer. We found that the literature on inflammation research in DKD has experienced substantial growth, indicating a rising interest in this developing area of study. In this field, Navarro-Gonzalez, JF is the most frequently cited author, Kidney International is the most frequently cited journal, China had the highest number of publications in the field of DKD inflammation, and Monash University emerged as the institution with the most published research. The research area on inflammation in DKD primarily centers around the investigation of 'Glycation end-products', 'chronic kidney disease', and 'diabetic nephropathy'. The emerging research trends in this field will focus on the 'Gut microbiota', 'NLRP3 inflammasome', 'autophagy', 'pyroptosis', 'sglt2 inhibitor', and 'therapeutic target'. Future research on DKD may focus on further exploring the inflammatory response, identifying specific therapeutic targets, studying biomarkers, investigating stem cell therapy and tissue engineering, and exploring gene therapy and gene editing. In summary, this study examines the main areas of study, frontiers, and trends in DKD inflammation, which have significant implications for future research.

Alternative splicing of HOXB-AS3 underlie the promoting effect of nuclear m6A reader YTHDC1 on the self-renewal of leukemic stem cells in acute myeloid leukemia.

This research sought to elucidate the mechanism underlying the self-renewal capacity of leukemic stem cells (LSCs) to offer new insights into the treatment of acute myeloid leukemia (AML). The expression of HOXB-AS3 and YTHDC1 in the AML samples was screened and verified in THP-1 cells and LSCs. The relationship between HOXB-AS3 and YTHDC1 was determined. HOXB-AS3 and YTHDC1 were knocked down through cell transduction to examine the effect of HOXB-AS3 and YTHDC1 on LSCs isolated from THP-1 cells. Tumor formation in mice was used to verify fore experiments. HOXB-AS3 and YTHDC1 were robustly induced in AML, in correlation with adverse prognosis in patients with AML. We found YTHDC1 bound HOXB-AS3 and regulated its expression. Overexpression of YTHDC1 or HOXB-AS3 promoted the proliferation of THP-1 cells and LSCs and impaired their apoptosis, increasing the number of LSCs in the blood and bone marrow of AML mice. YTHDC1 could upregulate the expression of HOXB-AS3 spliceosome NR_033205.1 via the m6A modification of HOXB-AS3 precursor RNA. By this mechanism, YTHDC1 accelerated the self-renewal of LSCs and the subsequent AML progression. This study identifies a crucial role for YTHDC1 in the regulation of LSC self-renewal in AML and suggests a new perspective for AML treatment.

Four New Species of Russula Subsection Sardoninae from China.

Four new species of Russula subsection Sardoninae from northern and southwestern China under coniferous and deciduous trees are proposed as R. begonia, R. photinia, R. rhodochroa, and R. rufa. Illustrations and descriptions of R. gracillima, R. leucomarginata, R. roseola, and the above four new species are provided based on evidence of morphological characters and phylogenetic analyses of the internal transcribed spacer (ITS), as well as the multi-locus of mtSSU, nLSU, rpb1, rpb2 and tef1-α. The relationships between these new species and allied taxa are discussed.

Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application.

Objective. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET activity (λ-MLAA) and attenuation (µ-MLAA) maps was proposed to solve those issues using the time-of-flight PET raw data only. However,λ-MLAA still suffers from quantification error as compared to reconstruction using the gold-standard CT-based attenuation map (µ-CT). Recently, a deep learning (DL)-based framework was proposed to improve MLAA by predictingµ-DL fromλ-MLAA andµ-MLAA using an image domain loss function (IM-loss). However, IM-loss does not directly measure the AC errors according to the PET attenuation physics. Our preliminary studies showed that an additional physics-based loss function can lead to more accurate PET AC. The main objective of this study is to optimize the attenuation map generation framework for clinical full-dose18F-FDG studies. We also investigate the effectiveness of the optimized network on predicting attenuation maps for synthetic low-dose oncological PET studies.Approach. We optimized the proposed DL framework by applying different preprocessing steps and hyperparameter optimization, including patch size, weights of the loss terms and number of angles in the projection-domain loss term. The optimization was performed based on 100 skull-to-toe18F-FDG PET/CT scans with minimal misalignment. The optimized framework was further evaluated on 85 clinical full-dose neck-to-thigh18F-FDG cancer datasets as well as synthetic low-dose studies with only 10% of the full-dose raw data.Main results. Clinical evaluation of tumor quantification as well as physics-based figure-of-merit metric evaluation validated the promising performance of our proposed method. For both full-dose and low-dose studies, the proposed framework achieved <1% error in tumor standardized uptake value measures.Significance. It is of great clinical interest to achieve CT-less PET reconstruction, especially for low-dose PET studies.

Quantification of intramyocardial blood volume using 99mTc-RBC SPECT/CT: a pilot human study.

BACKGROUND: Quantification of intramyocardial blood volume (IMBV), the fraction of myocardium that is occupied by blood, is a promising Index to measure microcirculatory functions. In previous large animal SPECT/CT studies injected with 99mTc-labeled Red Blood Cell (RBC) and validated by ex vivo microCT, we have demonstrated that accurate IMBV can be measured. In this study, we report the data processing methods and results of the first-in-human pilot study. METHODS: Data from three subjects have been included to date. Each subject underwent rest and adenosine-induced stress 99mTc-RBC SPECT/CT on a dedicated cardiac system with both non-contrast and contrast-enhanced CT acquired. Corrections of attenuation (AC) and scatter (SC), respiratory and cardiac gating, and partial volume correction (PVC) were applied. We also performed automatic segmentation and registration approach based on the blood pool topology in both SPECT and CT images. RESULTS: The quantified IMBV across all subjects under resting conditions were 35.0% ± 3.3% for the end-diastolic phase and 24.1% ± 2.7% for the end-systolic phase. The cycle-dependent change in IMBV (ΔIMBV) between diastolic and systolic phases was 31.5% ± 3.0%. Under stress, IMBV were 40.6% ± 4.2% for the end-diastolic phase and 26.5% ± 2.8% for the end-systolic phase, and ΔIMBV was 34.7% ± 7.4%. CONCLUSIONS: It is feasible to quantify IMBV in resting and stress conditions in human studies using SPECT/CT with 99mTc-RBC.

Segmentation-Free PVC for Cardiac SPECT Using a Densely-Connected Multi-Dimensional Dynamic Network.

In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective. However, such anatomical-guided methods typically require tedious image registration and segmentation steps. Accurately segmented organ templates are also hard to obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid SPECT/CT scanners with high-end CT and associated motion artifacts. Slight mis-registration/mis-segmentation would result in severe degradation in image quality after PVC. In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation. The proposed network involves a densely-connected multi-dimensional dynamic mechanism, allowing the convolutional kernels to be adapted based on the input images, even after the network is fully trained. Intramyocardial blood volume (IMBV) is introduced as an additional clinical-relevant loss function for network optimization. The proposed network demonstrated promising performance on 28 canine studies acquired on a GE Discovery NM/CT 570c dedicated cardiac SPECT scanner with a 64-slice CT using Technetium-99m-labeled red blood cells. This work showed that the proposed network with densely-connected dynamic mechanism produced superior results compared with the same network without such mechanism. Results also showed that the proposed network without anatomical information could produce images with statistically comparable IMBV measurements to the images generated by anatomical-guided PVC methods, which could be helpful in clinical translation.

TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction.

The rapid tracer kinetics of rubidium-82 (82Rb) and high variation of cross-frame distribution in dynamic cardiac positron emission tomography (PET) raise significant challenges for inter-frame motion correction, particularly for the early frames where conventional intensity-based image registration techniques are not applicable. Alternatively, a promising approach utilizes generative methods to handle the tracer distribution changes to assist existing registration methods. To improve frame-wise registration and parametric quantification, we propose a Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) to transform the early frames into the late reference frame using an all-to-one mapping. Specifically, a feature-wise linear modulation layer encodes channel-wise parameters generated from temporal tracer kinetics information, and rough cardiac segmentations with local shifts serve as the anatomical information. We validated our proposed method on a clinical 82Rb PET dataset and found that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, motion estimation accuracy and clinical myocardial blood flow (MBF) quantification were improved compared to using the original frames. Our code is published at https://github.com/gxq1998/TAI-GAN.

Downregulation of SMIM3 inhibits growth of leukemia via PI3K-AKT signaling pathway and correlates with prognosis of adult acute myeloid leukemia with normal karyotype.

BACKGROUND: Acute myeloid leukemia (AML) patients with normal karyotype (NK-AML) have significant variabilities in outcomes. The European Leukemia Net stratification system and some prognostic models have been used to evaluate risk stratification. However, these common standards still have some limitations. The biological functions and mechanisms of Small Integral Membrane Protein 3 (SMIM3) have seldomly been investigated. To this date, the prognostic value of SMIM3 in AML has not been reported. This study aimed to explore the clinical significance, biological effects and molecular mechanisms of SMIM3 in AML. METHODS: RT-qPCR was applied to detect the expression level of SMIM3 in bone marrow specimens from 236 newly diagnosed adult AML patients and 23 healthy volunteers. AML cell lines, Kasumi-1 and THP-1, were used for lentiviral transfection. CCK8 and colony formation assays were used to detect cell proliferation. Cell cycle and apoptosis were analyzed by flow cytometry. Western blot was performed to explore relevant signaling pathways. The biological functions of SMIM3 in vivo were validated by xenograft tumor mouse model. Survival rate was evaluated by Log-Rank test and Kaplan-Meier. Cox regression model was used to analyze multivariate analysis. The correlations between SMIM3 and drug resistance were also explored. RESULTS: Through multiple datasets and our clinical group, SMIM3 was shown to be significantly upregulated in adult AML compared to healthy subjects. SMIM3 overexpression conferred a worse prognosis and was identified as an independent prognostic factor in 95 adult NK-AML patients. Knockdown of SMIM3 inhibited cell proliferation and cell cycle progression, and induced cell apoptosis in AML cells. The reduced SMIM3 expression significantly suppressed tumor growth in the xenograft mouse model. Western blot analysis showed downregulation of p-PI3K and p-AKT in SMIM3-knockdown AML cell lines. SMIM3 may also be associated with some PI3K-AKT and first-line targeted drugs. CONCLUSIONS: SMIM3 was highly expressed in adult AML, and such high-level expression of SMIM3 was associated with a poor prognosis in adult AML. Knockdown of SMIM3 inhibited the proliferation of AML through regulation of the PI3K-AKT signaling pathway. SMIM3 may serve as a potential prognostic marker and a therapeutic target for AML in the future.

Relevance of the pyroptosis-related inflammasome drug targets in the Chuanxiong to improve diabetic nephropathy.

BACKGROUND: A chronic inflammatory disease caused by disturbances in metabolism, diabetic nephropathy (DN) is a chronic inflammatory disease. Pyroptosis is a novel form of programmed cell death in many inflammation-related diseases, including DN. Therefore, pyroptosis could be a promising target for DN therapy. METHODS: To get the components and pharmacodynamic targets of Chuanxiong, we identified by searching TCMID, TCMSP, ETCM and HERB databases. Then, from the Molecular Signatures Database (MSigDB) and Gene Ontology (GO) database, pyroptosis genes were collected. Identification of critical genes in DN by bioinformatics analysis and then using the ConsensusClusterPlus package to divide the express data of diff genes into some subgroups with different levels of pyroptosis; the WGCNA machine algorithm was used to simulate the mechanism Chuanxiong improving DN. RESULTS: In this study, we found DHCR24, ANXA1, HMOX1, CDH13, ALDH1A1, LTF, CHI3L1, CACNB2, and MTHFD2 interacted with the diff genes of DN. We used GSE96804 as a validation set to evaluate the changes of APIP, CASP6, CHMP2B, CYCS, DPP8, and TP53 in four different cell proapoptotic states. WGCNA analysis showed that DHCR24, CHI3L1, and CACNB2 had significant changes in different cell proapoptotic levels. In the experimental stage, we also confirmed that the active ingredients of Chuanxiong could improve the inflammatory state and the levels of pyroptosis under high glucose. CONCLUSION: The improvement of DN by Chuanxiong is related to the change of pyroptosis.

Identification and Exploration of Serine Peptidase Inhibitor Kazal Type I (SPINK1) as a Potential Biomarker Correlated with the Progression of Non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Although significant advances have been achieved in the treatment of NSCLC during the past two decades, the 5-year survival rate of patients with NSCLC remains <20%. Thus, there is an urgent requirement to further understand the molecular mechanisms that promote NSCLC development and to identify novel therapeutic targets. In the present study, the gene expression profiles of patients with NSCLC from The Cancer Genome Atlas database were carefully analyzed and SPINK1 was identified as a tumor-inducing factor. SPINK1 expression level was found to be increased in both NSCLC tissues and cell lines. Moreover, SPINK1 promoted cell proliferation in A549 and H1299 cells. Knockdown of SPINK1 could activate cell autophagy and apoptosis. Mechanistically, SPINK1 was demonstrated to induce the proliferation of NSCLC via activating the MEK/ERK signaling pathway. In conclusion, these findings suggested that SPINK1 may serve as a potential biomarker in NSCLC.

Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study.

Attenuation correction (AC) is important for accurate interpretation of SPECT myocardial perfusion imaging (MPI). However, it is challenging to perform AC in dedicated cardiac systems not equipped with a transmission imaging capability. Previously, we demonstrated the feasibility of generating attenuation-corrected SPECT images using a deep learning technique (SPECTDL) directly from non-corrected images (SPECTNC). However, we observed performance variability across patients which is an important factor for clinical translation of the technique. In this study, we investigate the feasibility of overcoming the performance variability across patients for the direct AC in SPECT MPI by proposing to develop an advanced network and a data management strategy. To investigate, we compared the accuracy of the SPECTDL for the conventional U-Net and Wasserstein cycle GAN (WCycleGAN) networks. To manage the training data, clustering was applied to a representation of data in the lower-dimensional space, and the training data were chosen based on the similarity of data in this space. Quantitative analysis demonstrated that DL model with an advanced network improves the global performance for the AC task with the limited data. However, the regional results were not improved. The proposed data management strategy demonstrated that the clustered training has potential benefit for effective training.

Periodontitis induced by orthodontic wire ligature drives oral microflora dysbiosis and aggravates alveolar bone loss in an improved murine model.

Aim: To assess the contribution of polymicrobial disruption of host homeostasis to periodontitis progression in orthodontic wire ligation murine model. Methods: Orthodontic wire rings were inserted between the first and second molars of mice for 18 days for the orthodontic wire ligation mouse model, and Pg injection model and Pg-LPS injection model were used as controls. Alveolar bone loss and periodontal inflammation were analyzed by micro-CT, histological staining and qRT-PCR. Further, pyrosequencing of 16S rRNA gene amplicon was used to analyze the development of oral microorganism dysbiosis in the mice. Results: Micro-CT, TRAP staining and qRT-PCR showed that orthodontic wire ligation model led to more severe alveolar bone loss than Pg and Pg-LPS models.H&E staining and qRT-PCR demonstrated that stronger inflammatory response was induced by the orthodontic wire treatment compared to the other models. In addition, pyrosequencing of 16S rRNA gene amplicons revealed that the composition of oral microbiota presented a transition as the disease progressed and significant differences emerged in oral microbiota communities between orthodontic ligature mice and healthy controls. Furthermore, antibiotic treatment decreased both inflammation and alveolar bone loss in response to microbial community dysbiosis. However, no significant difference in bacterial community composition was observed in Pg and Pg-LPS models. Conclusions: Orthodontic wire ligation drove oral microbial community transitions that mimicked polymicrobial communities characterized by polymicrobial synergy and dysbiosis. Our improved model is suitable for further study of pathogenesis of periodontitis and exploration of corresponding treatment strategies.

MZB1 targeted by miR-185-5p inhibits the migration of human periodontal ligament cells through NF-κB signaling and promotes alveolar bone loss.

OBJECTIVE: To explore the role of Marginal Zone B and B-1 Cell-Specific Protein (MZB1), a novel molecule associated with periodontitis, in migration of human periodontal ligament cells (hPDLCs) and alveolar bone orchestration. BACKGROUND: MZB1 is an ER-localized protein and its upregulation has been found to be associated with a variety of human diseases. However, few studies have investigated the effect and mechanism of MZB1 on hPDLCs in periodontitis. METHODS: Gene expression profiles in human gingival tissues were acquired from the Gene Expression Omnibus (GEO) database, and candidate molecules were then selected through bioinformatic analysis. Subsequently, we identified the localization and expression of MZB1 in human gingival tissues, mice, and hPDLCs by immunofluorescence, RT-qPCR, and Western blot. Dual-luciferase reporter assay was applied to assess the binding of miR-185-5p to MZB1. Furthermore, the effects of MZB1 on cell migration, proliferation, and apoptosis in vitro were investigated by wound-healing assay, transwell assay, CCK-8 assay, and flow cytometry analysis. Finally, Micro-CT analysis and H&E staining were performed to examine the effects of MZB1 on alveolar bone loss in vivo. RESULTS: Bioinformatic analysis discovered that MZB1 was one of the most significantly increased genes in periodontitis patients. MZB1 was markedly increased in the gingival tissues of periodontitis patients, in the mouse models, and in the hPDLCs treated with lipopolysaccharide of Porphyromonas gingivalis (LPS-PG). Furthermore, in vitro experiments showed that MZB1, as a target gene of miR-185-5p, inhibited migration of hPDLCs. Overexpression of MZB1 specifically upregulated the phosphorylation of p65, while pretreatment of MZB1-overexpressed hPDLCs with PDTC (NF-κB inhibitor) notably reduced the p-p65 level and promoted cell migration. In addition, the mRNA expression levels of alkaline phosphatase (ALP) and Runt-related transcription factor 2 (Runx2) were inhibited in MZB1-overexpressed hPDLCs and miR-185-5p inhibitor treated hPDLCs, respectively. In vivo experiments showed that knockdown of MZB1 alleviated the loss of alveolar bone. CONCLUSION: As a target gene of miR-185-5p, MZB1 plays a crucial role in inhibiting the migration of hPDLCs through NF-κB signaling pathway and deteriorating alveolar bone loss.

Tumor-suppressive MEG3 induces microRNA-493-5p expression to reduce arabinocytosine chemoresistance of acute myeloid leukemia cells by downregulating the METTL3/MYC axis.

BACKGROUND: Chemoresistance serves as a huge obstacle for acute myeloid leukemia (AML) patients. To counteract the chemoresistance in AML cells, we discussed the role of maternally expressed gene 3 (MEG3) in arabinocytosine (AraC) chemoresistance in AML cells. METHODS: MEG3, microRNA (miR)-493-5p, methyltransferase-like 3 (METTL3) and MYC expression in AML cells was determined and then their interactions were also analyzed. Then, the viability and apoptosis of AML cells were determined through loss- and gain- function assay. The level of m6A modification in AML cells was examined. AML mouse models were also established to validate the potential roles of MEG3. RESULTS: MEG3 and miR-493-5p were downregulated in AML cells, and they were lower in resistant cells than in parental cells. MEG3 led to elevated expression of miR-493-5p which targeted METTL3. METTL3 increased expression of MYC by promoting its m6A levels. Overexpression of MEG3 and miR-493-5p or knockdown of METTL3 inhibited HL-60 and Molm13 cell proliferation and promoted their apoptosis. Overexpressed MEG3 induced heightened sensitivity of AML cells to AraC. However, the suppression of miR-493-5p reversed the effects of overexpressed MEG3 on AML cells. CONCLUSIONS: Collectively, MEG3 could upregulate miR-493-5p expression and suppress the METTL3/MYC axis through MYC m6A methylation, by which MEG3 promoted the chemosensitivity of AML cells.

Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine.

A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps estimated by the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a modified U-net neural network with a novel imaging physics-based loss function to learn a CT-derived attenuation map (µ-CT). METHODS: Clinical whole-body PET/CT datasets of 18F-FDG (N = 113), 68 Ga-DOTATATE (N = 76), and 18F-Fluciclovine (N = 90) were used to train and test tracer-specific neural networks. For each tracer, forty subjects were used to train the neural network to predict attenuation maps (µ-DL). µ-DL and µ-MLAA were compared to the gold-standard µ-CT. PET images reconstructed using the OSEM algorithm with µ-DL (OSEMDL) and µ-MLAA (OSEMMLAA) were compared to the CT-based reconstruction (OSEMCT). Tumor regions of interest were segmented by two radiologists and tumor SUV and volume measures were reported, as well as evaluation using conventional image analysis metrics. RESULTS: µ-DL yielded high resolution and fine detail recovery of the attenuation map, which was superior in quality as compared to µ-MLAA in all metrics for all tracers. Using OSEMCT as the gold-standard, OSEMDL provided more accurate tumor quantification than OSEMMLAA for all three tracers, e.g., error in SUVmax for OSEMMLAA vs. OSEMDL: - 3.6 ± 4.4% vs. - 1.7 ± 4.5% for 18F-FDG (N = 152), - 4.3 ± 5.1% vs. 0.4 ± 2.8% for 68 Ga-DOTATATE (N = 70), and - 7.3 ± 2.9% vs. - 2.8 ± 2.3% for 18F-Fluciclovine (N = 44). OSEMDL also yielded more accurate tumor volume measures than OSEMMLAA, i.e., - 8.4 ± 14.5% (OSEMMLAA) vs. - 3.0 ± 15.0% for 18F-FDG, - 14.1 ± 19.7% vs. 1.8 ± 11.6% for 68 Ga-DOTATATE, and - 15.9 ± 9.1% vs. - 6.4 ± 6.4% for 18F-Fluciclovine. CONCLUSIONS: The proposed framework provides accurate and robust attenuation correction for whole-body 18F-FDG, 68 Ga-DOTATATE and 18F-Fluciclovine in tumor SUV measures as well as tumor volume estimation. The proposed method provides clinically equivalent quality as compared to CT in attenuation correction for the three tracers.

Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT.

PURPOSE: Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (µ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without µ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. METHODS: For dedicated SPECT, we developed strategies to predict truncated µ-maps from NAC images reconstructed with a small matrix, or full µ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict µ-maps or AC images. RESULTS: For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full µ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. CONCLUSIONS: We developed strategies of generating µ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.

Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation.

BACKGROUND: Attenuation correction can improve the quantitative accuracy of single-photon emission computed tomography (SPECT) images. Existing SPECT-only systems normally can only provide non-attenuation corrected (NC) images which are susceptible to attenuation artifacts. In this work, we developed a post-reconstruction attenuation correction (PRAC) approach facilitated by a deep learning-based attenuation map for myocardial perfusion SPECT imaging. METHODS: In the PRAC method, new projection data were estimated via forwardly projecting the scanner-generated NC image. Then an attenuation map, generated from NC image using a pretrained deep learning (DL) convolutional neural network, was incorporated into an offline reconstruction algorithm to obtain the attenuation-corrected images from the forwardly projected projections. We evaluated the PRAC method using 30 subjects with a DL network trained with 40 subjects, using the vendor-generated AC images and CT-based attenuation maps as the ground truth. RESULTS: The PRAC methods using DL-generated and CT-based attenuation maps were both highly consistent with the scanner-generated AC image. The globally normalized mean absolute errors were 1.1% ± .6% and .7% ± .4% and the localized absolute percentage errors were 8.9% ± 13.4% and 7.8% ± 11.4% in the left ventricular (LV) blood pool, respectively, and - 1.3% ± 8.0% and - 3.8% ± 4.5% in the LV myocardium for PRAC methods using DL-generated and CT-based attenuation maps, respectively. The summed stress scores after PRAC using both attenuation maps were more consistent with the ground truth than those of the NC images. CONCLUSION: We developed a PRAC approach facilitated by deep learning-based attenuation maps for SPECT myocardial perfusion imaging. It may be feasible for this approach to provide AC images for SPECT-only scanner data.

CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network.

BACKGROUND: Attenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC. METHODS: CT-free AC was implemented using our customized Dual Squeeze-and-Excitation Residual Dense Network (DuRDN). 172 anonymized clinical hybrid SPECT/CT stress/rest myocardial perfusion studies were used in training, validation, and testing. Additional body mass index (BMI), gender, and scatter-window information were encoded as channel-wise input to further improve the network performance. RESULTS: Quantitative and qualitative analysis based on image voxels and 17-segment polar map showed the potential of our approach to generate consistent SPECT AC images. Our customized DuRDN showed superior performance to conventional network design such as U-Net. The averaged voxel-wise normalized mean square error (NMSE) between the predicted AC images by DuRDN and the ground-truth AC images was 2.01 ± 1.01%, as compared to 2.23 ± 1.20% by U-Net. CONCLUSIONS: Our customized DuRDN facilitates dedicated cardiac SPECT AC without CT scanning. DuRDN can efficiently incorporate additional patient information and may achieve better performance compared to conventional U-Net.