Cardiac amyloidosis (CA) is a severe condition characterized by amyloid fibril deposition in the myocardium, leading to restrictive cardiomyopathy and heart failure. Differentiating between amyloidosis subtypes is crucial due to distinct treatment strategies. The individual conventional diagnostic methods lack the accuracy needed for effective subtype identification. This study aimed to evaluate the efficacy of combining ¹¹C-PiB PET/CT and ^99mTc-DPD scintigraphy in detecting CA and distinguishing between its main subtypes, light chain (AL) and transthyretin (ATTR) amyloidosis while assessing the association of imaging findings with patient prognosis. We retrospectively evaluated the diagnostic efficacy of combining ¹¹C-PiB PET/CT and ^99mTc-DPD scintigraphy in a cohort of 50 patients with clinical suspicion of CA. Semi-quantitative imaging markers were extracted from the images. Diagnostic performance was calculated against biopsy results or genetic testing. Both machine learning models and a rationale-based model were developed to detect CA and classify subtypes. Survival prediction over five years was assessed using a random survival forest model. Prognostic value was assessed using Kaplan-Meier estimators and Cox proportional hazards models. The combined imaging approach significantly improved diagnostic accuracy, with ¹¹C-PiB PET and ^99mTc-DPD scintigraphy showing complementary strengths in detecting AL and ATTR, respectively. The machine learning model achieved an AUC of 0.94 (95% CI 0.93-0.95) for CA subtype differentiation, while the rationale-based model demonstrated strong diagnostic ability with AUCs of 0.95 (95% CI 0.88-1.00) for ATTR and 0.88 (95% CI 0.770-0.961) for AL. Survival prediction models identified key prognostic markers, with significant stratification of overall mortality based on predicted survival (p value = 0.006; adj HR 2.43 [95% CI 1.03-5.71]). The integration of ¹¹C-PiB PET/CT and ^99mTc-DPD scintigraphy, supported by both machine learning and rationale-based models, enhances the diagnostic accuracy and prognostic assessment of cardiac amyloidosis, with significant implications for clinical practice.

The aim of this study was to develop a method for personalized training of Deep Neural Networks by means of an MRI simulator to improve MOLLI native T₁ estimates relative to conventional fitting methods. The proposed Personalized Training Neural Network (PTNN) for T₁ mapping was based on a neural network which was trained with simulated MOLLI signals generated for each individual scan, taking into account both the pulse sequence parameters and the heart rate triggers of the specific healthy volunteer. Experimental data from eleven phantoms and ten healthy volunteers were included in the study. In phantom studies, agreement between T₁ reference values and those obtained with the PTNN yielded a statistically significant smaller bias than conventional fitting estimates (-26.69 ± 29.5ms vs. -65.0 ± 33.25ms, p < 0.001). For in vivo studies, T₁ estimates derived from the PTNN yielded higher T₁ values (1152.4 ± 25.8ms myocardium, 1640.7 ± 30.6ms blood) than conventional fitting (1050.8 ± 24.7ms myocardium, 1597.2 ± 39.9ms blood). For PTNN, shortening the acquisition time by eliminating the pause between inversion pulses yielded higher myocardial T₁ values (1162.2 ± 19.7ms with pause vs. 1127.1 ± 19.7ms, p = 0.01 myocardium), (1624.7 ± 33.9ms with pause vs. 1645.4 ± 18.7ms, p = 0.16 blood). For conventional fitting statistically significant differences were found. Compared to T₁ maps derived by conventional fitting, PTNN is a post-processing method that yielded T₁ maps with higher values and better accuracy in phantoms for a physiological range of T₁ and T₂ values. In normal volunteers PTNN yielded higher T₁ values even with a shorter acquisition scheme of eight heartbeats scan time, without deploying new pulse sequences.

This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos. A multi-level motion feature guided diffusion model, equipped with a specialized Spatio-Temporal Motion Encoder (STME) to extract fine-grained motion conditioning, is then developed to improve synthesis quality and fidelity. We evaluate our method, MFD-V2V, on a comprehensive cardiac dataset, demonstrating superior performance over the state-of-the-art in both quantitative metrics and qualitative assessments. Furthermore, we show the benefits of our synthesized cine CMRs improving downstream clinical and analytical tasks, underscoring the broader impact of our approach. Our code is publicly available at https://github.com/SwaksharDeb/MFD-V2V.

Deep learning image reconstruction (DLIR) technology effectively improves the image quality while maintaining spatial resolution. The impact of DLIR on the quantification of coronary artery calcium (CAC) is still unclear. The purpose of this study was to investigate the effect of DLIR on the quantification of coronary calcium in high-risk populations. A retrospective study was conducted on patients who underwent coronary artery CT angiography (CCTA) at our hospital(China) from February 2022 to September 2022. Raw data were reconstructed with filtered back projection (FBP) reconstruction, 40% and 80% level adaptive statistical iterative reconstruction-veo (ASiR-V 40%, ASiR-V 80%) and low, medium and high-level deep learning algorithm (DLIR-L, DLIR-M, and DLIR-H). Calculate and compare the signal-to-noise and contrast-to-noise ratio, volumetric score, mass scores, and Agaston score of 6 sets of images. There were 178 patients, female (107), mean age (62.43 ± 9.26), and mean BMI (25.33 ± 3.18) kg/m². Compared with FBP, the image noise of ASiR-V and DLIR was significantly reduced (P < 0.001). There was no significant difference in Agaston score, volumetric score, and mass scores among the six reconstruction algorithms (all P > 0.05). Bland-Altman diagram indicated that the Agatston scores of the five reconstruction algorithms showed good agreement with FBP, with DLIR-L(AUC, 110.08; 95% CI: 26.48, 432.92;)and ASIR-V40% (AUC,110.96; 95% CI: 26.23, 431.34;) having the highest consistency with FBP. Compared with FBP, DLIR and ASiR-V improve CT image quality to varying degrees while having no impact on Agatston score-based risk stratification. CACS is a powerful tool for cardiovascular risk stratification, and DLIR can improve image quality without affecting CACS, making it widely applicable in clinical practice.

We aimed to evaluate the diagnostic performance of artificial intelligence (AI) in detecting coronary artery stenosis and calcified plaque on CT angiography (CTA), comparing its diagnostic performance with that of radiologists. A thorough search of the literature was performed using PubMed, Web of Science, and Embase, focusing on studies published until October 2024. Studies were included if they evaluated AI models in detecting coronary artery stenosis and calcified plaque on CTA. A bivariate random-effects model was employed to determine combined sensitivity and specificity. Study heterogeneity was assessed using I² statistics. The risk of bias was assessed using the revised quality assessment of diagnostic accuracy studies-2 tool, and the evidence level was graded using the Grading of Recommendations Assessment, Development and Evalutiuon (GRADE) system. Out of 1071 initially identified studies, 17 studies with 5560 patients and images were ultimately included for the final analysis. For coronary artery stenosis ≥50%, AI showed a sensitivity of 0.92 (95% CI: 0.88-0.95), specificity of 0.87 (95% CI: 0.80-0.92), and AUC of 0.96 (95% CI: 0.94-0.97), outperforming radiologists with sensitivity of 0.85 (95% CI: 0.67-0.94), specificity of 0.84 (95% CI: 0.62-0.94), and AUC of 0.91 (95% CI: 0.89-0.93). For stenosis ≥70%, AI achieved a sensitivity of 0.88 (95% CI: 0.70-0.96), specificity of 0.96 (95% CI: 0.90-0.99), and AUC of 0.98 (95% CI: 0.96-0.99). In calcified plaque detection, AI demonstrated a sensitivity of 0.93 (95% CI: 0.84-0.97), specificity of 0.94 (95% CI: 0.88-0.96), and AUC of 0.98 (95% CI: 0.96-0.99)." AI-based CT demonstrated superior diagnostic performance compared to clinicians in identifying ≥50% stenosis in coronary arteries and showed excellent diagnostic performance in recognizing ≥70% coronary artery stenosis and calcified plaque. However, limitations include retrospective study designs and heterogeneity in CTA technologies. Further external validation through prospective, multicenter trials is required to confirm these findings. The original findings of this research are included in the article. For additional inquiries, please contact the corresponding authors.

PurposePrecise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in 18F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability, time-consuming, and requires significant experience. We propose a deep learning (DL) framework for automatic MC of 18F-flurpiridaz PET. MethodsThe method employs a 3D ResNet based architecture that takes 3D PET volumes and outputs motion vectors. It was validated using 5-fold cross-validation on data from 32 sites of a Phase III clinical trial (NCT01347710). Manual corrections from two experienced operators served as ground truth, and data augmentation using simulated vectors enhanced training robustness. The study compared the DL approach to both manual and standard non-AI automatic MC methods, assessing agreement and diagnostic accuracy using minimal segmental MBF and MFR. ResultsThe area under the receiver operating characteristic curves (AUC) for significant CAD were comparable between DL-MC MBF, manual-MC MBF from Operators (AUC=0.897,0.892 and 0.889, respectively; p>0.05), standard non-AI automatic MC (AUC=0.877; p>0.05) and significantly higher than No-MC (AUC=0.835; p<0.05). Similar findings were observed with MFR. The 95% confidence limits for agreement with the operator were {+/-}0.49ml/g/min (mean difference = 0.00) for MFR and {+/-}0.24ml/g/min (mean difference = 0.00) for MBF. ConclusionDL-MC is significantly faster but diagnostically comparable to manual-MC. The quantitative results obtained with DL-MC for MBF and MFR are in excellent agreement with those manually corrected by experienced operators compared to standard non-AI automatic MC in patients undergoing 18F-flurpiridaz PET-MPI.

Coronary chronic total occlusion (CTO) and subtotal occlusion (STO) pose diagnostic challenges, differing in treatment strategies. Artificial intelligence and radiomics are promising tools for accurate discrimination. This study aimed to develop deep learning (DL) and radiomics models using coronary computed tomography angiography (CCTA) to differentiate CTO from STO lesions and compare their performance with that of the conventional method. CTO and STO were identified retrospectively from a tertiary hospital and served as training and validation sets for developing and validating the DL and radiomics models to distinguish CTO from STO. An external test cohort was recruited from two additional tertiary hospitals with identical eligibility criteria. All participants underwent CCTA within 1 month before invasive coronary angiography. A total of 581 participants (mean age, 50 years ± 11 [SD]; 474 [81.6%] men) with 600 lesions were enrolled, including 403 CTO and 197 STO lesions. The DL and radiomics models exhibited better discrimination performance than the conventional method, with areas under the curve of 0.908 and 0.860, respectively, vs. 0.794 in the validation set (all p＜0.05), and 0.893 and 0.827, respectively, vs. 0.746 in the external test set (all p＜0.05). The proposed CCTA-based DL and radiomics models achieved efficient and accurate discrimination of coronary CTO and STO.

This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos. A multi-level motion feature guided diffusion model, equipped with a specialized Spatio-Temporal Motion Encoder (STME) to extract fine-grained motion conditioning, is then developed to improve synthesis quality and fidelity. We evaluate our method, MFD-V2V, on a comprehensive cardiac dataset, demonstrating superior performance over the state-of-the-art in both quantitative metrics and qualitative assessments. Furthermore, we show the benefits of our synthesized cine CMRs improving downstream clinical and analytical tasks, underscoring the broader impact of our approach. Our code is publicly available at https://github.com/SwaksharDeb/MFD-V2V.