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The investigation of the bottom-oxide thickness ([T.sub.bo]) and program/erase (P/E) stress effects on charge retention in silicon-oxide-nitride-oxide-silicon (SONOS) Flash memory cells with FN programming is presented. The dominant mechanism is found to be the trapped-charge direct tunneling for thinner oxides and the Frenkel-Poole (FP) emission followed by positive oxide charge-assisted tunneling has explained the retention characteristics in thicker oxide cells.

A modified charge pumping technique is used to characterize the programmed charge lateral distribution in a hot electron program/hot hole erase, two-bit storage nitride Flash memory cell. The result shows that the second programmed bit has a broader stored charge distribution than the first programmed bit.

The authors propose a technique to extract a silicon nitride trap density from stress induced leakage current in a polycrystalline silicon-oxide-nitride-oxide-silicon flash memory cell. An analytical model based on the Frenkel-Poole emission is developed to correlate a nitride trap density with stress induced leakage current. The extracted nitride trap density is 7.0×1012 cm-2 eV-1. They find that nitride trapped charges have a rather uniform distribution in an energy range of measurement (∼0.2 eV). [ABSTRACT FROM AUTHOR]

Objective: Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) are closely related neurodegenerative conditions within the Lewy body spectrum. The relationship between DLB and PDD remains debated, with ongoing discussion about whether they are distinct diseases or different manifestations of the same disorder. This study aimed to identify differences in cerebral perfusion patterns between DLB and PDD patients., Methods: Single-photon emission computed tomography (SPECT) ethyl cysteinate dimer (ECD) imaging was performed on patients diagnosed with DLB ( n  = 46) and PDD ( n  = 30). The data were acquired by two SPECT/computed tomography systems from two affiliated centers and were retrospectively recruited under the local ethics approval. Relative tracer uptake levels were calculated across 47 automatically determined regions of interest and 240 subregions. A two-sided Welch's t -test was employed to evaluate mean perfusion differences, with results further confirmed through a voxel-wise t -test mapping. The Benjamini-Hochberg procedure was used to adjust P -values for multiple comparisons., Results: The greatest difference in perfusion was in the left associative visual cortex in planes 34 and 36 and the right primary visual cortex in plane 34, with lower mean perfusion observed in PDD patients than those with DLB ( P  = 0.005)., Conclusion: No statistically significant differences in ECD perfusion were found between DLB and PDD patients. The greatest difference was in the visual cortices, where PDD patients suffered a greater loss of perfusion., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Aims: Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associated cost and time for data acquisition. The purpose of this study is to create a multi-stage machine learning model to predict cardiac resynchronization therapy (CRT) response for heart failure (HF) patients. This model exploits uncertainty quantification to recommend additional collection of single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) variables if baseline clinical variables and features from electrocardiogram (ECG) are not sufficient., Methods: 218 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 a 6±1 month follow-up. A multi-stage ML model was created by combining two ensemble models: Ensemble 1 was trained with clinical variables and ECG; Ensemble 2 included Ensemble 1 plus SPECT MPI features. Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary. The performance of the multi-stage model was compared with that of Ensemble models 1 and 2., Results: The response rate for CRT was 55.5% (n = 121) with overall male gender 61.0% (n = 133), an average age of 62.0±11.8, and LVEF of 27.7±11.0. The multi-stage model performed similarly to Ensemble 2 (which utilized the additional SPECT data) with AUC of 0.75 vs. 0.77, accuracy of 0.71 vs. 0.69, sensitivity of 0.70 vs. 0.72, and specificity 0.72 vs. 0.65, respectively. However, the multi-stage model only required SPECT MPI data for 52.7% of the patients across all folds., Conclusions: By using rule-based logic stemming from uncertainty quantification, the multi-stage model was able to reduce the need for additional SPECT MPI data acquisition without sacrificing performance., Competing Interests: Conflict of Interest Disclosure Statement All authors declare that there are no conflicts of interest.

Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD.

Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in computer-aided coronary artery disease (CAD) diagnosis. However, separating and identifying individual coronary arterial segments is challenging because morphological similarities of different branches on the coronary arterial tree and human-to-human variabilities exist. Inspired by the training procedure of interventional cardiologists for interpreting the structure of coronary arteries, we propose an association graph-based graph matching network (AGMN) for coronary arterial semantic labeling. We first extract the vascular tree from invasive coronary angiography (ICA) and convert it into multiple individual graphs. Then, an association graph is constructed from two individual graphs where each vertex represents the relationship between two arterial segments. Thus, we convert the arterial segment labeling task into a vertex classification task; ultimately, the semantic artery labeling becomes equivalent to identifying the artery-to-artery correspondence on graphs. More specifically, the AGMN extracts the vertex features by the embedding module using the association graph, aggregates the features from adjacent vertices and edges by graph convolution network, and decodes the features to generate the semantic mappings between arteries. By learning the mapping of arterial branches between two individual graphs, the unlabeled arterial segments are classified by the labeled segments to achieve semantic labeling. A dataset containing 263 ICAs was employed to train and validate the proposed model, and a five-fold cross-validation scheme was performed. Our AGMN model achieved an average accuracy of 0.8264, an average precision of 0.8276, an average recall of 0.8264, and an average F1-score of 0.8262, which significantly outperformed existing coronary artery semantic labeling methods. In conclusion, we have developed and validated a new algorithm with high accuracy, interpretability, and robustness for coronary artery semantic labeling on ICAs., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest.

Coronary artery disease (CAD) is one of the primary causes leading deaths worldwide. The presence of atherosclerotic lesions in coronary arteries is the underlying pathophysiological basis of CAD, and accurate extraction of individual arterial branches using invasive coronary angiography (ICA) is crucial for stenosis detection and CAD diagnosis. However, deep-learning-based models face challenges in generating semantic segmentation for coronary arteries due to the morphological similarity among different types of arteries. To address this challenge, we propose an innovative approach called the Edge Attention Graph Matching Network (EAGMN) for coronary artery semantic labeling. Inspired by the learning process of interventional cardiologists in interpreting ICA images, our model compares arterial branches between two individual graphs generated from different ICAs. We begin with extracting individual graphs based on the vascular tree obtained from the ICA. Each node in the individual graph represents an arterial segment, and the EAGMN aims to learn the similarity between nodes from the two individual graphs. By converting the coronary artery semantic segmentation task into a graph node similarity comparison task, identifying the node-to-node correspondence would assign semantic labels for each arterial branch. More specifically, the EAGMN utilizes the association graph constructed from the two individual graphs as input. A graph attention module is employed for feature embedding and aggregation, while a decoder generates the linear assignment for node-to-node semantic mapping. Based on the learned node-to-node relationships, unlabeled coronary arterial segments are classified using the labeled coronary arterial segments, thereby achieving semantic labeling. A dataset with 263 labeled ICAs is used to train and validate the EAGMN. Experimental results indicate the EAGMN achieved a weighted accuracy of 0.8653, a weighted precision of 0.8656, a weighted recall of 0.8653 and a weighted F1-score of 0.8643. Furthermore, we employ ZORRO to provide interpretability and explainability of the graph matching for artery semantic labeling. These findings highlight the potential of the EAGMN for accurate and efficient coronary artery semantic labeling using ICAs. By leveraging the inherent characteristics of ICAs and incorporating graph matching techniques, our proposed model provides a promising solution for improving CAD diagnosis and treatment., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Attenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on 99m Tc-TRODAT-1 brain SPECT using clinical patient data on two different scanners., Methods: Two hundred and sixty patients who underwent 99m Tc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-AC μ ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (μ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-AC μ ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-AC μ ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated μ-maps from (c/e)DL-AC μ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods., Results: The NMSE for Chang's method, DL-AC μ , DL-AC, cDL-AC μ , cDL-AC, eDL-AC μ , and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-AC μ are closer to CT-AC, Followed by DL-AC, eDL-AC μ , cDL-AC μ , cDL-AC, eDL-AC, Chang's method, and NAC., Conclusion: All DL-based AC methods are superior to Chang-AC. DL-AC μ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for 99m Tc-TRODAT-1 brain SPECT., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Du, Jiang, Lin, Peng, Sun, Chiu, Hung and Mok.)

Background : Cross-calibration of 123 I-labeled meta -iodobenzylguanidine ( m IBG) myocardial-derived indices is essential to extrapolate findings from several clinical centers. Here, we conducted a phantom study to generate conversion coefficients for the calibration of heart-to-mediastinum ratios and compare them between Taiwan and Europe. Methods : We used an acrylic phantom dedicated to 123 I- m IBG planar imaging to calculate the conversion coefficients of 136 phantom images derived from 36 Taiwanese institutions. A European phantom image database including 191 images from 27 institutions was used. Conversion coefficients were categorized into five collimator types: low-energy (LE) high-resolution (LEHR), LE general-purpose (LEGP), extended LEGP (ELEGP), medium-energy (ME) GP (MEGP), and ME low-penetration (MELP) collimators. Results : The conversion coefficients were 0.53 ± 0.039, 0.59 ± 0.032, 0.79 ± 0.032, 0.96 ± 0.038, and 0.99 ± 0.050 for LEHR, LEGP, ELEGP, MEGP, and MELP collimators, respectively. The Taiwanese and European conversion coefficients for the LEHR, LEGP, and MELP collimators did not significantly differ. The coefficient of variation was slightly higher for the Taiwanese than the European conversion coefficients (3.7%-7.5% vs. 2.3%-5.6%). Conclusions : We calculated conversion coefficients for various types of collimators used in Taiwan using a 123 I- m IBG phantom. In general, the Taiwanese and European conversion coefficients were comparable. These findings further corroborated and highlighted the need for 123 I- m IBG standardization using the phantom-determined conversion coefficients., Competing Interests: K. Nakajima and K. Okuda collaborate with PDRadiopharma Inc., Tokyo, Japan, and K. Nakajima belongs to an endowed department partly funded by PDRadiopharma Inc. C. Kitamura is an employee of PDRadiopharma Inc., Tokyo, Japan., (© The Japanese Society of Nuclear Cardiology 2023.)

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., (© 2021. American Society of Nuclear Cardiology.)

Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it with the striatal/background ratio (SBR) using semiquantification for diagnosing LBD in two other centers. Patients and Methods: This was a retrospective analysis of data from a history-based computerized dementia diagnostic system. MT-VS and SBR among normal controls (NCs) and patients with PD, PD with dementia (PDD), DLB, or Alzheimer’s disease (AD) were compared. Results: We included 715 individuals, including 122 NCs, 286 patients with PD, 40 with AD, 179 with DLB, and 88 with PDD. Compared with NCs, patients with PD exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. Compared with the AD group, PDD and DLB groups exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. The distribution of ML-VS was significantly different between PD and NC, DLB and AD, and PDD and AD groups (all p < 0.001). The correlation coefficient of ML-VS/SBR in all participants was 0.679. Conclusions: The ML-VS designed in one center is useful for differentiating PD from NC, DLB from AD, and PDD from AD in other centers. Its correlation with traditional approaches using different scanning machines is also acceptable. Future studies should develop models using data pools from multiple centers for increasing diagnostic accuracy.

Emergency department visits (EDV) are common among older adults with and without dementia. The risk factors and demands of EDVs for people with dementia have been well studied; however, the association between EDVs and conversion to dementia among people with predementia has not been thoroughly explored. To study the predictive value of EDVs in predementia's progression to dementia. The baseline predementia cohort registered from September 2015 to August 2017, with longitudinal follow-up in the History-based Artificial Intelligent Clinical Dementia Diagnostic System database, was retrospectively analyzed. The rates of conversion among the different EDVs were compared. Multivariate logistic regression and Cox proportional hazards analyses were applied to study the influence of EDVs on progression. Age, education, sex, neuropsychological tests, activities of daily living, neuropsychiatric symptoms, parkinsonism, and multiple vascular risk factors were adjusted for. A total of 512 participants were analyzed, including 339 (66.2%) non-converters and 173 (33.8%) converters with a mean follow-up of 3.3 (range 0.4-6.1) and 2.8 (range 0.5-5.9) years, respectively. Compared to people without EDV (EDV 0), the hazard ratios for conversion to dementia were 3.6, 5.9, and 6.9 in those with EDV once (EDV 1), twice (EDV 2), and more than twice (EDV >2), respectively. In addition, older age, lower education, poorer cognition, poorer ADL performance, and longer follow-up periods also increased the conversion rates. EDVs in the predementia stages highly predict progression to dementia. Therefore, a sound public health as well as primary healthcare system that provide strategies for better management of mental and physical condition might help prevention of EDVs among older people in the predementia stages., Competing Interests: The authors have declared that no competing interests exist.

Background: An abnormal increase of α-synuclein in the brain is the hallmark of dementia with Lewy bodies (DLB). However, the diagnostic power of plasma α-synuclein in DLB is not yet confirmed. Parkinsonism is highly associated with and is one of the core clinical features of DLB. We studied plasma α-synuclein and developed a novel tool that combined plasma α-synuclein level and Motor Dysfunction Questionnaire (MDQ), namely Synuclein Motor Dysfunction Composite Scale (SMDCS), for the clinical discrimination of DLB from Alzheimer's disease (AD)., Methods: This cross-sectional study analyzed participants' demographical data, plasma α-synuclein level, MDQ, structured clinical history questionnaire, neuropsychological and motor function tests, and neuroimaging studies. The power of plasma α-synuclein level, MDQ, and SMDCS for discriminating DLB from non-demented controls (NC) or AD were compared., Results: Overall, 121 participants diagnosed as 58 DLB, 31 AD, and 31 NC were enrolled. Patients with DLB had significantly higher mean plasma α-synuclein level (0.24 ± 0.32 pg/ml) compared to the NC group (0.08 ± 0.05 pg/ml) and the AD group (0.08 ± 0.05 pg/ml). The DLB group demonstrated higher MDQ (2.95 ± 1.60) compared to the NC (0.42 ± 0.98) or AD (0.44 ± 0.99) groups. The sensitivity/specificity of plasma α-synuclein level, MDQ, and SMDCS for differentiating DLB from non-DLB were 0.80/0.64, 0.83/0.89, and 0.88/0.93, respectively., Conclusion: Both plasma α-synuclein and MDQ were significantly higher in patients with DLB compared to the NC or AD groups. The novel SMDCS, significantly improved accuracy for the clinical differentiation of DLB from AD or NC., Competing Interests: S-YY was employed by the MagQu Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Orimo, Wei, Hung, Yang and Chiu.)

The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer's disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD.

Objective: Characteristic parkinsonism is the major comorbidity of dementia with Lewy bodies (DLB). We aimed to differentiate DLB from Alzheimer's disease (AD) with motor dysfunction using a composite scale with a characteristic motor dysfunction questionnaire (MDQ) and dopamine transporter (DAT) imaging. It could help detect DLB easily in healthcare settings without movement disorder specialists. Methods: This is a two-phase study. In the design phase, seven questions were selected and composed of a novel MDQ. In the test phase, all participants with DLB, AD, or non-dementia (ND) control completed dementia and parkinsonism survey, the novel designed questionnaire, DAT imaging, and composite scales of MDQ and DAT. The cutoff scores of the MDQ, semiquantitative analysis of the striatal-background ratio (SBR) and visual rating of DAT, and the composite scale of MDQ and DAT for discriminating DLB from AD or ND were derived and compared. Results: A total of 277 participants were included in this study (126 with DLB, 86 with AD, and 65 with ND). Compared with the AD or ND groups, the DLB group showed a significantly higher frequency in all seven items in the MDQ and a significantly lower SBR. For discrimination of DLB from non-DLB with MDQ, SBR, and composite scale, the cutoff scores of 3/2, 1.37/1.38, and 6/5 were suggested for the diagnosis of DLB with the sensitivities/specificities of 0.91/0.72, 0.91/0.80, and 0.87/0.93, respectively. The composite scale significantly improved the accuracy of discrimination compared with either the MDQ or SBR. Conclusion: This study showed that the novel designed simple questionnaire was a practical screening tool and had similar power to DAT scanning to detect DLB. The questionnaire can be applied in clinical practice and population studies for screening DLB. In addition, the composite scale of MDQ and DAT imaging further improved the diagnostic accuracy, indicating the superiority of the dual-model diagnostic tool., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Chiu, Wei, Hung and Wu.)

Objectives: Gated myocardial perfusion SPECT (GMPS) provides a one-stop-shop evaluation for cardiac resynchronization therapy (CRT). However, conflicting results have been observed regarding whether the baseline left-ventricular (LV) mechanical dyssynchrony as assessed by phase analysis on GMPS was predictive of therapeutic response to CRT. Since dyssynchrony parameters by phase analysis spuriously increased by scarred myocardium, the purpose of this study was to explore the value of dyssynchrony after stripping off the scar region in correlation to mechanical response to CRT., Methods: Forty-seven patients following standard indications for CRT received GMPS with phase analysis as pre-CRT evaluation. A decrease of end-systolic volume (ESV) > 15% on follow-up echocardiography after CRT was considered as a mechanical response to CRT. Myocardial regions with less than 50% of maximal activity on GMPS were considered as a scar. The phase standard deviation (PSD) and histogram bandwidth (BW) without or with stripping off scar were assessed by phase analysis of GMPS and were used for evaluation of LV dyssynchrony of all myocardium or only the viable myocardium, respectively., Results: No significant difference was noted between mechanical responders (31 of 47 patients, 66%) and nonresponders ( 16 of 47 patients, 34%) for PSD (48.6° ± 19.4° vs 43.9° ± 20.7°, p = 0.46) and BW (225° ± 91.1° vs 163.5° ± 94.6°, p = 0.38) of the entire myocardium. However, responders had significantly larger PSD (40.5° ± 15.7° vs 30.5° ± 13.2°, p = 0.03) and borderlinely larger BW (215° ± 91.2° vs. 139.5° ± 78.2°, p = 0.05) than non-responders after stripping off scar. Logistic regression analysis showed that scar area and PSD after stripping off scar were independent predictors of mechanical response., Conclusions: Our result showed that LV dyssynchrony of the entire myocardium did not predict response to CRT. However, LV dyssynchrony only in the viable myocardium was a significant predictor of CRT mechanical response., (© 2021. The Japanese Society of Nuclear Medicine.)