A novel method combining gated SPECT and vectorcardiography to guide left ventricular lead placement to improve response to cardiac resynchronization therapy: A proof of concept study.

BACKGROUND: The segment of the latest mechanical contraction (LMC) does not always overlap with the site of the latest electrical activation (LEA). By integrating both mechanical and electrical dyssynchrony, this proof-of-concept study aimed to propose a new method for recommending left ventricular (LV) lead placements, with the goal of enhancing response to cardiac resynchronization therapy (CRT). METHODS: The LMC segment was determined by single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) phase analysis. The LEA site was detected by vectorcardiogram. The recommended segments for LV lead placement were as follows: (1) the LMC viable segments that overlapped with the LEA site; (2) the LMC viable segments adjacent to the LEA site; (3) If no segment met either of the above, the LV lateral wall was recommended. The response was defined as ≥15% reduction in left ventricular end-systolic volume (LVESV) 6-months after CRT. Patients with LV lead located in the recommended site were assigned to the recommended group, and those located in the non-recommended site were assigned to the non-recommended group. RESULTS: The cohort comprised of 76 patients, including 54 (71.1%) in the recommended group and 22 (28.9%) in the non-recommended group. Among the recommended group, 74.1% of the patients responded to CRT, while 36.4% in the non-recommended group were responders (P = .002). Compared to pacing at the non-recommended segments, pacing at the recommended segments showed an independent association with an increased response by univariate and multivariable analysis (odds ratio 5.00, 95% confidence interval 1.73-14.44, P = .003; odds ratio 7.33, 95% confidence interval 1.53-35.14, P = .013). Kaplan-Meier curves showed that pacing at the recommended LV lead position demonstrated a better long-term prognosis. CONCLUSION: Our findings indicate that pacing at the recommended segments, by integrating of mechanical and electrical dyssynchrony, is significantly associated with an improved CRT response and better long-term prognosis.

A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support.

PURPOSE: Cardiac resynchronization therapy (CRT) has been established as an important therapy for heart failure. Mechanical dyssynchrony has the potential to predict responders to CRT. The aim of this study was to report the development and the validation of machine learning models which integrate ECG, gated SPECT MPI (GMPS), and clinical variables to predict patients' response to CRT. METHODS: This analysis included 153 patients who met criteria for CRT from a prospective cohort study. The variables were used to model predictive methods for CRT. Patients were classified as "responders" for an increase of LVEF ≥ 5% at follow-up. In a second analysis, patients were classified as "super-responders" for an increase of LVEF ≥ 15%. For ML, variable selection was applied, and Prediction Analysis of Microarrays (PAM) approach was used to model response while Naïve Bayes (NB) was used to model super-response. These ML models were compared to models obtained with guideline variables. RESULTS: PAM had AUC of 0.80 against 0.72 of partial least squares-discriminant analysis with guideline variables (p = 0.52). The sensitivity (0.86) and specificity (0.75) were better than for guideline alone, sensitivity (0.75) and specificity (0.24). Neural network with guideline variables was better than NB (AUC = 0.93 vs. 0.87) however without statistical significance (p = 0.48). Its sensitivity and specificity (1.0 and 0.75, respectively) were better than guideline alone (0.78 and 0.25, respectively). CONCLUSIONS: Compared to guideline criteria, ML methods trended toward improved CRT response and super-response prediction. GMPS was central in the acquisition of most parameters. Further studies are needed to validate the models.

Organocatalytic Diastereoselective (4 + 1) Cycloaddition of o-Hydroxyphenyl-Substituted Secondary Phosphine Oxides.

The first organocatalytic diastereoselective (4 + 1) cycloaddition of o-hydroxyphenyl-substituted secondary phosphine oxides (SPOs) has been established, which makes use of o-hydroxyphenyl substituted SPOs as suitable four-atom phosphorus-containing 1,4-dinucleophiles and 3-indolylformaldehydes as competent 1,1-dielectrophiles under BroÌ·nsted acid catalysis. The reaction mechanism was suggested to involve the formation of 3-indolylmethanol intermediates and vinyliminium intermediates, which played an important role in controlling the reactivity and diastereoselectivity of the (4 + 1) cycloaddition under BroÌ·nsted acid catalysis. By this approach, a series of benzo oxaphospholes bearing P- and C-stereocenters were synthesized in moderate to good yields (50%-95% yields) with excellent diastereoselectivities (all >95:5 dr). This reaction not only represents the first organocatalytic diastereoselective (4 + 1) cycloaddition of o-hydroxyphenyl-substituted SPOs but also provides an efficient and diastereoselective method for the construction of phosphorus-containing benzo five-membered heterocyclic skeletons bearing both P-stereocenter and C-stereocenter.

A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

BACKGROUND: Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT myocardial perfusion imaging (MPI) have their own statistical limitations in predicting cardiac resynchronization therapy (CRT) response. The purpose of this study is to discover new predictors from the polarmaps of LVMD by deep learning to help select heart failure patients with a high likelihood of response to CRT. METHODS: One hundred and fifty-seven patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at 6 [Formula: see text] 1 month follow up. The autoencoder (AE) technique, an unsupervised deep learning method, was applied to the polarmaps of LVMD to extract new predictors characterizing LVMD. Pearson correlation analysis was used to explain the relationships between new predictors and existing clinical parameters. Patients from the IAEA VISION-CRT trial were used for an external validation. Heatmaps were used to interpret the AE-extracted feature. RESULTS: Complete data were obtained in 130 patients, and 68.5% of them were classified as CRT responders. After variable selection by feature importance ranking and correlation analysis, one AE-extracted LVMD predictor was included in the statistical analysis. This new AE-extracted LVMD predictor showed statistical significance in the univariate (OR 2.00, P = .026) and multivariate (OR 1.11, P = .021) analyses, respectively. Moreover, the new AE-extracted LVMD predictor not only had incremental value over PBW and significant clinical variables, including QRS duration and left ventricular end-systolic volume (AUC 0.74 vs 0.72, LH 7.33, P = .007), but also showed encouraging predictive value in the 165 patients from the IAEA VISION-CRT trial (P < .1). The heatmaps for calculation of the AE-extracted predictor showed higher weights on the anterior, lateral, and inferior myocardial walls, which are recommended as LV pacing sites in clinical practice. CONCLUSIONS: AE techniques have significant value in the discovery of new clinical predictors. The new AE-extracted LVMD predictor extracted from the baseline gated SPECT MPI has the potential to improve the prediction of CRT response.

Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images.

BACKGROUND: Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI. METHODS: A total of 254 patients were enrolled, including 226 stress SPECT MPIs and 247 rest SPECT MPIs. Fivefold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing. The rigid transformation parameters (translation and rotation) from manual reorientation were annotated by an experienced nuclear cardiologist and used as the reference standard. A convolutional neural network (CNN) was designed to predict the transformation parameters. Then, the derived transform was applied to the grid generator and sampler in spatial transformer network (STN) to generate the reoriented image. A loss function containing mean absolute errors for translation and mean square errors for rotation was employed. A three-stage optimization strategy was adopted for model optimization: (1) optimize the translation parameters while fixing the rotation parameters; (2) optimize rotation parameters while fixing the translation parameters; (3) optimize both translation and rotation parameters together. RESULTS: In the test set, the Spearman determination coefficients of the translation distances and rotation angles between the model prediction and the reference standard were 0.993 in X axis, 0.992 in Y axis, 0.994 in Z axis, 0.987 along X axis, 0.990 along Y axis and 0.996 along Z axis, respectively. For the 46 stress MPIs in the test set, the Spearman determination coefficients were 0.858 in percentage of profusion defect (PPD) and 0.858 in summed stress score (SSS); for the 46 rest MPIs in the test set, the Spearman determination coefficients were 0.9 in PPD and 0.9 in summed rest score (SRS). CONCLUSIONS: Our deep learning-based LV reorientation method is able to accurately generate the SA images. Technical validations and subsequent evaluations of measured clinical parameters show that it has great promise for clinical use.

Protein Kinase D1 Correlates with Less Lymph Node Metastasis Risk, Enhanced 5-FU Sensitivity, and Better Prognosis in Colorectal Cancer.

Protein kinase D1 (PKD1) controls tumor growth and invasion of gastrointestinal tract-related cancers, but its prognostic role in colorectal cancer (CRC) is not clear yet. Therefore, this research intended to assess the potential of PKD1 as a marker for CRC patients' management, also to evaluate its effect on 5-fluorouracil (5-FU) chemosensitivity in CRC cell lines. PKD1 protein and mRNA expressions were measured by immunohistochemistry and reverse transcription-quantitative polymerase chain reaction assays in 214 CRC patients, respectively. The PKD1 overexpression plasmids and negative control (NC) plasmids were transfected into the HCT-116 and LoVo cell lines followed by 0-16 µM 5-FU treatment. PKD1 protein (P < 0.001) and mRNA expressions (P < 0.001) were both descended in tumor tissues compared to tumor-adjacent tissues. Meanwhile, tumor PKD1 protein and mRNA expressions were both negatively related to lymph node metastasis, N stage, and tumor-node-metastasis (TNM) stage (all P < 0.05). Prognostically, high expressions of PKD1 protein and mRNA were linked with prolonged disease-free survival (DFS) and overall survival (OS) (all P < 0.05). After adjustment by multivariate Cox analyses, PKD1 mRNA high expression independently forecasted longer DFS [hazard ratio (HR) = 0.199, P = 0.002] and OS (HR = 0.212, P = 0.022). In vitro experiments revealed that PKD1 overexpression decreased the half maximal inhibitory concentration value of 5-FU in the HCT-116 (P = 0.016) and LoVo (P = 0.007) cell lines. PKD1 expression links with less lymph node metastasis risk and satisfied prognosis in CRC patients, which promotes CRC cell chemosensitivity to 5-FU chemosensitivity as well.

Copper-Catalyzed Reductive Ireland-Claisen Rearrangements of Propargylic Acrylates and Allylic Allenoates.

The copper-catalyzed reductive Ireland-Claisen rearrangement of propargylic acrylates led to 3,4-allenoic acids. The use of silanes or pinacolborane as stoichiometric reducing agents and triethylphosphite as a ligand facilitated the divergent and complementary selectivity for the synthesis of diastereomeric anti- and syn-rearranged products, respectively. Copper-catalyzed reductive Ireland-Claisen rearrangement of allylic 2,3-allenoates proceeded effectively only when pinacolborane was used as a reductant to generate various 1,5-dienes in excellent yields and with good diastereoselectivities in some cases. Mechanistic studies showed that the silyl and boron enolates, rather than the copper enolate, underwent a stereospecific rearrangement via a chairlike transition state to afford the corresponding Claisen rearrangement products.

3D fusion between fluoroscopy angiograms and SPECT myocardial perfusion images to guide percutaneous coronary intervention.

BACKGROUND: Percutaneous coronary intervention (PCI) in stable coronary artery disease (CAD) is commonly triggered by abnormal myocardial perfusion imaging (MPI). However, due to the possibilities of multivessel disease, serial stenoses and variability of coronary artery perfusion distribution, an opportunity exists to better align anatomic stenosis with perfusion abnormalities to improve revascularization decisions. This study aims to develop a multi-modality fusion approach to assist decision-making for PCI. METHODS AND RESULTS: Coronary arteries from fluoroscopic angiography (FA) were reconstructed into 3D artery anatomy. Left ventricular (LV) epicardial surface was extracted from SPECT. The artery anatomy and epicardial surface were non-rigidly fused. The accuracy of the 3D fusion was evaluated via both computer simulation and real patient data. Simulated FA and MPI were integrated and then compared with the ground truth from a digital phantom. The distance-based mismatch errors between simulated fluoroscopy and phantom arteries were 1.86 ± 1.43 mm for left coronary arteries (LCA) and 2.21 ± 2.50 mm for right coronary arteries (RCA). FA and SPECT images in 30 patients were integrated and then compared with the ground truth from CT angiograms. The distance-based mismatch errors between the fluoroscopy and CT arteries were 3.84 ± 3.15 mm for LCA and 5.55 ± 3.64 mm for RCA. The presence of the corresponding fluoroscopy and CT arteries in the AHA-17-segment model agreed well with a Kappa value of 0.91 (CI 0.89-0.93) for LCA and a Kappa value of 0.80 (CI 0.67-0.92) for RCA. CONCLUSIONS: Our fusion approach is technically accurate to assist PCI decision-making and is clinically feasible to be used in the catheterization laboratory. Future studies are necessary to determine if fusion improves PCI-related outcomes.

Predictive values of left ventricular mechanical dyssynchrony for CRT response in heart failure patients with different pathophysiology.

BACKGROUND: Cardiac resynchronization therapy (CRT) patients with different pathophysiology may influence mechanical dyssynchrony and get different ventricular resynchronization and clinical outcomes. METHODS: Ninety-two dilated cardiomyopathy (DCM) and fifty ischemic cardiomyopathy (ICM) patients with gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) were included in this retrospective study. Patients were classified based on the concordance between the left ventricular (LV) lead and the latest contraction or relaxation position. If the LV lead was located on or adjacent to both the latest contraction and relaxation position, the patient was categorized into the both match group; if the LV lead was located on or adjacent to the latest contraction or relaxation position, the patient was classified into the one match group; if the LV lead was located on or adjacent to neither the latest contraction nor relaxation position, the patient was categorized to the neither group. CRT response was defined as [Formula: see text] improvement of LV ejection fraction at the 6-month follow-up. Variables with P < .05 in the univariate analysis were included in the stepwise multivariate model. RESULTS: During the follow-up period, 58.7% (54 of 92) for DCM patients and 54% (27 of 50) for ICM patients were CRT responders. The univariate analysis and stepwise multivariate analysis showed that QRS duration, systolic phase bandwidth (PBW), diastolic PBW, diastolic phase histogram standard deviation (PSD), and left ventricular mechanical dyssynchrony (LVMD) concordance were independent predictors of CRT response in DCM patients; diabetes mellitus and left ventricular end-systolic volume were significantly associated with CRT response in ICM patients. The intra-group comparison revealed that the CRT response rate was significantly different in the both match group of DCM (N = 18, 94%) and ICM (N = 24, 62%) patients (P = .016). However, there was no significant difference between DCM and ICM in the one match and neither group. For the inter-group comparison, Kruskal-Wallis H-test revealed that CRT response was significantly different in all the groups of DCM patients (P < .001), but not in ICM patients (P = .383). CONCLUSIONS: Compared with ICM patients, systolic PBW, diastolic PBW and PSD have better predictive and prognostic values for the CRT response in DCM patients. Placing the LV lead in or adjacent to the latest contraction and relaxation position can improve the clinical outcomes of DCM patients, but it does not apply to ICM patients.

Incremental value of left ventricular shape parameters measured by gated SPECT MPI in predicting the super-response to CRT.

BACKGROUND: The purpose of this study was to evaluate the predictive value of left ventricular (LV) shape parameters measured by gated SPECT myocardial perfusion imaging (MPI) in super-responders enrolled in the VISION-CRT trial. METHODS: One hundred and ninety-nine patients who met standard criteria for CRT from multiple centers were enrolled in this study. End-systolic eccentricity (ESE) and end-diastolic eccentricity (EDE) were measures of LV shape. Super-responders were the patients who had a relative increase in left ventricular ejection fraction (LVEF) ≥ 15%. RESULTS: Complete data were obtained in 165 patients, and 43.6% of them were classified as super-responders. ESE was an independent predictor of CRT super-responders in univariate (OR 12.59, 95% CI 1.56-101.35, P = .017) and multivariate analysis (OR 35.71, 95% CI 1.66-766.03, P = .006). ESE had an incremental value over significant clinical and SPECT imaging variables, including angiotensin-converting enzyme inhibitors or angiotensin II receptor blocker, coronary artery disease, myocardial infarction, LVEF, end-diastolic volume index, and scar burden (AUC 0.82 vs. 0.80, sensitivity 0.68 vs. 0.65, specificity 0.82 vs. 0.78). CONCLUSIONS: LV shape parameters derived from gated SPECT MPI have the promise to improve the prediction of the super-response to CRT. Moreover, ESE provides incremental value over existing clinical and nuclear imaging variables.

Clinical impacts of scar reduction on gated myocardial perfusion SPECT after cardiac resynchronization therapy.

BACKGROUND: It had not been reported that myocardial scar shown on gated myocardial perfusion SPECT (GMPS) might reduce after cardiac resynchronization therapy (CRT). In this study, we aim to investigate the clinical impact and characteristic of scar reduction (SR) after CRT. METHODS AND RESULTS: Sixty-one heart failure patients following standard indication for CRT received twice GMPS as pre- and post-CRT evaluations. The patients with an absolute reduction of scar ≥ 10% after CRT were classified as the SR group while the rest were classified as the non-SR group. The SR group (N = 22, 36%) showed more improvement on LV function (∆LVEF: 18.1 ± 12.4 vs 9.4 ± 9.9 %, P = 0.007, ∆ESV: - 91.6 ± 52.6 vs - 38.1 ± 46.5 mL, P < 0.001) and dyssynchrony (ΔPSD: - 26.19 ± 18.42 vs - 5.8 ± 23.0°, P < 0.001, Δ BW: - 128.7 ± 82.8 vs - 25.2 ± 109.0°, P < 0.001) than non-SR group (N = 39, 64%). Multivariate logistic regression analysis showed baseline QRSd (95% CI 1.019-1.100, P = 0.006) and pre-CRT Reduced Wall Thickening (RWT) (95% CI 1.016-1.173, P = 0.028) were independent predictors for the development of SR. CONCLUSION: More than one third of patients showed SR after CRT who had more post-CRT improvement on LV function and dyssynchrony than those without SR. Wider QRSd and higher RWT before CRT were related to the development of SR after CRT.

Kinesin family member 2A links with advanced tumor stage, reduced chemosensitivity and worse prognosis in gastric cancer.

BACKGROUND: Kinesin family member 2A (KIF2A) induces gastric cancer (GC) growth and invasion, while its clinical relevance in GC patients is not reported. This study aimed to investigate the linkage of KIF2A with clinicopathological features, prognosis, and chemosensitivity of GC. METHODS: A total of 160 surgical GC patients were reviewed, with their tumor and adjacent tissues acquired for immunohistochemical (IHC) assay to measure KIF2A expression, then scored by a semi-quantitative method (IHC score: 0-12). KIF2A siRNA or nonsense-siRNA were transfected into HGC-27 and NCI-N87 cells underwent various concentrations of capecitabine or oxaliplatin treatment followed by chemosensitivity assessment. RESULTS: Kinesin family member 2A expression was elevated in the tumor tissue compared to the adjacent tissue (IHC score: 5.6 ± 3.1 vs. 2.9 ± 1.7, p < 0.001). Besides, tumor KIF2A expression was related to larger tumor size (p = 0.014), higher N stage (p = 0.004) and TNM stage (p = 0.011); however, it was not linked with other clinicopathological features (all p > 0.05). Signally, tumor KIF2A high expression predicted poor overall survival (p = 0.037). After adjustment via multivariate Cox's regression, tumor KIF2A high expression independently linked with worse disease-free survival (p = 0.033). Finally, KIF2A knockdown improved the oxaliplatin chemosensitivity vastly but only slightly affected capecitabine chemosensitivity in HGC-27 and NCI-N87 cells. CONCLUSION: Kinesin family member 2A reflects larger tumor size, advanced TNM stage, improved chemosensitivity, and predicts unfavorable survival in GC.

Application analysis of omental flap isolation and modified pancreaticojejunostomy in pancreaticoduodenectomy (175 cases).

BACKGROUND: To explore the application value of free omental wrapping and modified pancreaticojejunostomy in pancreaticoduodenectomy (PD). METHODS: The clinical data of 175 patients who underwent pancreaticoduodenectomy from January 2015 to December 2020 were retrospectively analysed. In total, 86 cases were divided into Group A (omental wrapping and modified pancreaticojejunostomy) and 89 cases were divided into Group B (control group). The incidences of postoperative pancreatic fistula and other complications were compared between the two groups, and univariate and multivariate logistic regression analyses were used to determine the potential risk factors for postoperative pancreatic fistula. Risk factors associated with postoperative overall survival were identified using Cox regression. RESULTS: The incidences of grade B/C pancreatic fistula, bile leakage, delayed bleeding, and reoperation in Group A were lower than those in Group B, and the differences were statistically significant (P < 0.05). Group A had an earlier drainage tube extubation time, earlier return to normal diet time and shorter postoperative hospital stay than the control group (P < 0.05). The levels of C-reactive protein (CRP), interleukin-6 (IL-6), and procalcitonin (PCT) inflammatory factors 1, 3 and 7 days after surgery also showed significant. Univariate and multivariate logistic regression analyses showed that a body mass index (BMI) ≥ 24, pancreatic duct diameter less than 3 mm, no isolation of the greater omental flap and modified pancreaticojejunostomy were independent risk factors for pancreatic fistula (P < 0.05). Cox regression analysis showed that age ≥ 65 years old, body mass index ≥ 24, pancreatic duct diameter less than 3 mm, no isolation of the greater omental flap isolation and modified pancreaticojejunostomy, and malignant postoperative pathology were independent risk factors associated with postoperative overall survival (P < 0.05). CONCLUSIONS: Wrapping and isolating the modified pancreaticojejunostomy with free greater omentum can significantly reduce the incidence of postoperative pancreatic fistula and related complications, inhibit the development of inflammation, and favourably affect prognosis.

Prognostic value of integrative analysis of electrical and mechanical dyssynchrony in patients with acute heart failure.

BACKGROUND: Left ventricular mechanical dyssynchrony has been shown to provide significant clinical values for chronic heart failure (HF) and cardiac resynchronization therapy (CRT). The purpose of this study was to evaluate whether electrical dyssynchrony combined with mechanical dyssynchrony has an incremental benefit over electrical dyssynchrony or mechanical dyssynchrony alone to predict clinical events in patients with acute heart failure (AHF). METHODS: Ninety-six AHF patients who received standard 12-lead ECG, gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), and echocardiography were enrolled. Thirty-two normal subjects were collected as the control group to get the normal database of mechanical dyssynchrony. The end point is the composite of all-cause death and heart transplantation. Electrical dyssynchrony was defined as QRS duration > 120 ms. Mechanical dyssynchrony was defined as > mean + 2 × SD phase standard deviation (PSD) or phase bandwidth (PBW) based on our normal database. RESULTS: During the follow-up of 28 ± 10 months, complete data were obtained in 92 patients. 26 (28.3%) Patients who reached the end point were classified into the event group. There were no significant differences in PSD or PBW between the event and non-event groups. However, PBW > 77.76° was independently associated with the end point in the univariate and multivariate analysis (hazard ratio 2.92, 95% confidence interval 1.00-8.47, P = .049; hazard ratio 3.89, 95% confidence interval 1.01-14.97, P = .048). The Kaplan-Meier curve with a log-rank test showed that the end point rate was significantly higher in the patients with PBW > 77.76° (log-rank P = .039). Moreover, the ROC curve analysis showed that the area under the curve (AUC) for predicting end point events by the integrative analysis of QRS > 120 ms and PBW > 77.76° was significantly improved compared to QRS duration > 120 ms (AUC: 0.75 vs 0.68, P = .001) or PBW > 77.76° (AUC: 0.75 vs 0.62, P = .049), respectively. The model of combined electrical and mechanical dyssynchrony yielded a further significantly improved risk prediction for adverse events in the global χ2. CONCLUSIONS: The combination of QRS duration > 120 ms and PBW > 77.76° was an independent predictor of all-cause death and heart transplantation in AHF patients. The integrative analysis of electrical and mechanical dyssynchrony provides incremental prognostic value for clinical use.

Incremental value of myocardial wall motion and thickening to perfusion alone by gated SPECT myocardial perfusion imaging for viability assessment in patients with ischemic heart failure.

PURPOSE: The objective of this study was to assess the incremental value of myocardial wall motion and thickening compared with perfusion alone obtained from gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) in diagnosing myocardial viability in patients with ischemic heart failure. METHODS: Eighty-three consecutive patients with ischemic heart failure who underwent both 99mTc-MIBI gated SPECT MPI and 18F-FDG positron emission tomography (PET) myocardial metabolic imaging were retrospectively enrolled. SPECT/PET myocardial viability was defined as the reference standard. Segmental myocardial perfusion, wall motion, and thickening were measured by an automated algorithm from gated SPECT MPI. Univariate and stepwise multivariate analysis were conducted to establish an optimal multivariate model for predicting hibernating myocardium and scar. RESULTS: Among the 1411 segments evaluated, 774 segments had normal perfusion and 637 segments had decreased perfusion. The latter were classified by 18F-FDG PET into 338 hibernating segments and 299 scarred segments. The multivariate regression analysis showed that the model that combined myocardial perfusion uptake with wall motion and thickening scores had the optimal predictive efficiency to distinguish hibernating myocardium from scar in the segments with decreased perfusion. The model had the largest C-statistic (0.753 vs 0.666, P < 0.0001), and the global chi-square was increased from 53.281 to 111.234 when compared with perfusion alone (P < 0.001). CONCLUSIONS: Assessment of myocardial wall motion and thickening in addition to conventional perfusion uptake in the segments with decreased perfusion enables better differentiation of hibernating myocardium from scar in patients with ischemic heart failure. Considering wide availability and high cost-effectiveness, regional myocardial function integrated with perfusion on gated SPECT MPI has great promise to become a clinical tool in the assessment of myocardial viability.

Left ventricular systolic and diastolic dyssynchrony to improve cardiac resynchronization therapy response in heart failure patients with dilated cardiomyopathy.

BACKGROUND: The systolic and diastolic dyssynchrony is physiologically related, but measure different left ventricular mechanisms. Left ventricular systolic mechanical dyssynchrony (systolic LVMD) has shown significant clinical values in improving cardiac resynchronization therapy (CRT) response in the heart failure patients with dilated cardiomyopathy (DCM). Our recent study demonstrated that LV diastolic dyssynchrony (diastolic LVMD) parameters have important prognostic values for DCM patients. However, there are a limited number of studies about the clinical value of diastolic LVMD for CRT. This study aims to explore the predictive values of both systolic LVMD and diastolic LVMD for CRT in DCM patients. METHODS: Eighty-four consecutive CRT patients with both DCM and complete left bundle branch block (CLBBB) who received gated resting SPECT MPI at baseline were included in the present study. The phase analysis technique was applied on resting gated short-axis SPECT MPI images to measure systolic LVMD and diastolic LVMD, characterized by phase standard deviation (PSD) and phase histogram bandwidth (PBW). CRT response was defined as ≥ 5% improvement of LVEF at 6-month follow-up. Variables with P < 0.10 in the univariate analysis were included in the multivariate cox analysis. RESULTS: During the follow-up period, 59.5% (50 of 84) patients were CRT responders. The univariate cox regression analysis showed that at baseline QRS duration, non-sustained ventricular tachycardia (NS-VT), systolic PSD, systolic PBW, diastolic PSD, diastolic PBW, scar burden and LV lead in the scarred myocardium were statistically significantly associated with CRT response. The multivariate cox regression analysis showed that QRS duration, NS-VT, systolic PSD, systolic PBW, diastolic PSD, and diastolic PBW were independent predictive factors for CRT response. Furthermore, the rate of CRT response was 94.4% (17 of 18) in patients whose LV lead was in the segments with both the first three late contraction and the first three late relaxation; by contrast, the rate of CRT response was only 6.7% (1 of 15, P < 0.000) in patients whose LV lead was in the segments with neither the first three late contraction nor the first three late relaxation. CONCLUSION: Both systolic LVMD and diastolic LVMD from gated SPECT MPI have important predictive values for CRT response in DCM patients. Pacing at LV segments with both late contraction and late relaxation has potential to increase the CRT response.

Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images.

This paper aims to develop an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features from the segmented pulmonary parenchyma regions to assist radiologists in COVID-19 diagnosis. A new segmentation method, which integrates a three-dimensional (3D) V-Net with a shape deformation module implemented using a spatial transform network (STN), was proposed to segment pulmonary parenchyma in chest CT images. The 3D V-Net was adopted to perform an end-to-end lung extraction while the deformation module was utilized to refine the V-Net output according to the prior shape knowledge. The proposed segmentation method was validated against the manual annotation generated by experienced operators. The radiomic features measured from our segmentation results were further analyzed by sophisticated statistical models with high interpretability to discover significant independent features and detect COVID-19 infection. Experimental results demonstrated that compared with the manual annotation, the proposed segmentation method achieved a Dice similarity coefficient of 0.9796, a sensitivity of 0.9840, a specificity of 0.9954, and a mean surface distance error of 0.0318 mm. Furthermore, our COVID-19 classification model achieved an area under curve (AUC) of 0.9470, a sensitivity of 0.9670, and a specificity of 0.9270 when discriminating lung infection with COVID-19 from community-acquired pneumonia and healthy controls using statistically significant radiomic features. The significant features measured from our segmentation results agreed well with those from the manual annotation. Our approach has great promise for clinical use in facilitating automatic diagnosis of COVID-19 infection on chest CT images.

A Camera Intrinsic Matrix-Free Calibration Method for Laser Triangulation Sensor.

Laser triangulation sensors (LTS) are widely used to acquire depth information in industrial applications. However, the parameters of the components, e.g., the camera, of the off-the-shelf LTS are typically unknown. This makes it difficult to recalibrate the degenerated LTS devices during regular maintenance operations. In this paper, a novel one-dimensional target-based camera intrinsic matrix-free LTS calibration method is proposed. In contrast to conventional methods that calibrate the LTS based on the precise camera intrinsic matrix, we formulate the LTS calibration as an optimization problem taking all parameters of the LTS into account, simultaneously. In this way, many pairs of the camera intrinsic matrix and the equation of the laser plane can be solved and different pairs of parameters are equivalent for displacement measurement. A closed-form solution of the position of the one-dimensional target is proposed to make the parameters of the LTS optimizable. The results of simulations and experiments show that the proposed method can calibrate the LTS without knowing the camera intrinsic matrix. In addition, the proposed approach significantly improves the displacement measurement precision of the LTS after calibration. In conclusion, the proposed method proved that the precise camera intrinsic matrix is not the necessary condition for LTS displacement measurement.

A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study.

BACKGROUND: The performance of left ventricular (LV) functional assessment using gated myocardial perfusion SPECT (MPS) relies on the accuracy of segmentation. Current methods require manual adjustments that are tedious and subjective. We propose a novel machine-learning-based method to automatically segment LV myocardium and measure its volume in gated MPS imaging without human intervention. METHODS: We used an end-to-end fully convolutional neural network to segment LV myocardium by delineating its endocardial and epicardial surface. A novel compound loss function, which encourages similarity and penalizes discrepancy between prediction and training dataset, is utilized in training stage to achieve excellent performance. We retrospectively investigated 32 normal patients and 24 abnormal patients, whose LV myocardial contours automatically segmented by our method were compared with those delineated by physicians as the ground truth. RESULTS: The results of our method demonstrated very good agreement with the ground truth. The average DSC metrics and Hausdorff distance of the contours delineated by our method are larger than 0.900 and less than 1 cm, respectively, among all 32 + 24 patients of all phases. The correlation coefficient of the LV myocardium volume between ground truth and our results is 0.910 ± 0.061 (P < 0.001), and the mean relative error of LV myocardium volume is - 1.09 ± 3.66%. CONCLUSION: These results strongly indicate the feasibility of our method in accurately quantifying LV myocardium volume change over the cardiac cycle. The learning-based segmentation method in gated MPS imaging has great promise for clinical use.

Machine learning for the diagnosis of pulmonary hypertension.

Objective This paper aims to investigate whether machine learning (ML) can be used to predict the state of pulmonary hypertension (PH), including pre-capillary and post-capillary, from echocardiographic data.Methods Two hundred and seventy-five patients with PH who underwent both echocardiography and right heart catheterization were included in the study. Mean pulmonary artery pressure, pulmonary artery wedge pressure measured by right heart catheterization were used as criteria for judging pre-capillary PH and post-capillary PH. Thirteen echocardiographic indicators were used to predict whether the PH was pre-capillary or post-capillary. Nine ML models were used to make predictions. Accuracy was used as the primary reference standard, and the performance of classification model is observed in conjunction with area under curve (AUC), specificity (Sp), sensitivity (Se), Positive Prediction Value (PPV), Negative Prediction Value (NPV), Positive Likelihood Ratio (PLR) and Negative Likelihood Ratio (NLR) and other assessment protocols.Results By comparing the accuracy (ACC), recall rate (Recall) and other model effect evaluation index of the classification under the nine ML models, it can be found that the ML model can effectively identify the pre-capillary PH and the post-capillary PH. LogitBoost performed best in nine ML models (ACC=0.87, Recall=0.83, F1score=0.85, AUC=0.87, Se=0.90, NPV=0.88, PPV=0.87, PLR=8.61 and NLR=0.18, AUC=0.83), it showed good results in identification of the pre-capillary PH (ACC=0.83, Recall=0.87, F-score=0.85); Post-vascular PH (ACC=0.90, Recall=0.88, F-score=0.89). Decision Tree (ACC=0.75, Recall=0.77, F1score=0.78, AUC=0.75, Se=0.72, NPV=0.78, PPV=0.77, PLR=3.66 and NLR=0.29, AUC=0.79) performed worst, and the accuracy of the other seven models was greater than 0.82.Conclusion The classification results of the nine ML models in this paper indicate that the ML method can effectively identify the pre-capillary PH and post-capillary PH from echocardiographic data. Compared with medical diagnosis, ML methods can distinguish between pre-capillary PH and the post-capillary PH under non-invasive conditions.