Role of CT attenuation correction gated SPECT MPI in prediction of pulmonary hypertension.

OBJECTIVE: To elucidate the value of gated SPECT-MPI using CT attenuation correction (AC) for prediction of pulmonary hypertension (PHT) in coronary patients by estimation of reliability of non-contrast CT in measurement of main pulmonary artery diameter (MPAd) as well as by assessment of potential predictive role of gated parameters as beneficial accessory findings. BACKGROUND: Contrast-enhanced CT is known as an accurate tool for assessment of MPAd to predict PHT. [1] The low-dose non-contrast CT which is used for AC in MPI study, however, has an unclear value in precise vascular diameter measurement; it is also uncertain whether gated parameters could help to predict PHT. METHODS AND PATIENTS: A total of 207 patients, who had a transthoracic echocardiography and MPI with an interval of maximum one month, underwent this retrospective study. PHT was defined as a RVSP ≥36 mmHg by echocardiography; peak tricuspid regurgitation velocity (PTRV) was also calculated to use as a criterion for PHT. Of all subjects, 120 had RVSP ≥ 36 and 87 showed RVSP < 36; there also were 191 and 16 patients with PTRV ≤ 3.4 m/s and >3.4 m/s, respectively. Comparison was made unconnectedly between each group regarding the echocardiography results with the MPI parameters, with and without CT-AC, including MPAd derived from CT as well as RV/LV uptake ratio, shape index and septal wall motion and thickening scores to define the best indicators of PHT. RESULTS: There was a significant association between established benchmark of PHT in echocardiography (RVSP), with MPAd derived from non-contrast CT as well as with LV shape index from gated study and RV/LV uptake ratio acquired from non-AC SPECT-MPI. Also, stress and rest RV/LV uptake ratio, MPAd, LV end-systolic and LV end-diastolic shape indexes are significantly higher in patients with RVSP ≥ 36 mmHg compare to patients with RVSP < 36 mmHg. CONCLUSIONS: Gated-SPECT-MPI using CT-AC can predict PHT by reliable estimation of MPAd as well as by defining RV/LV uptake ratio and shape index, providing an added clinical value for this invaluable modality in cardiac patients.

Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.

Heart failure caused by iron deposits in the myocardium is the primary cause of mortality in beta-thalassemia major patients. Cardiac magnetic resonance imaging (CMRI) T2* is the primary screening technique used to detect myocardial iron overload, but inherently bears some limitations. In this study, we aimed to differentiate beta-thalassemia major patients with myocardial iron overload from those without myocardial iron overload (detected by T2*CMRI) based on radiomic features extracted from echocardiography images and machine learning (ML) in patients with normal left ventricular ejection fraction (LVEF > 55%) in echocardiography. Out of 91 cases, 44 patients with thalassemia major with normal LVEF (> 55%) and T2* ≤ 20 ms and 47 people with LVEF > 55% and T2* > 20 ms as the control group were included in the study. Radiomic features were extracted for each end-systolic (ES) and end-diastolic (ED) image. Then, three feature selection (FS) methods and six different classifiers were used. The models were evaluated using various metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Maximum relevance-minimum redundancy-eXtreme gradient boosting (MRMR-XGB) (AUC = 0.73, ACC = 0.73, SPE = 0.73, SEN = 0.73), ANOVA-MLP (AUC = 0.69, ACC = 0.69, SPE = 0.56, SEN = 0.83), and recursive feature elimination-K-nearest neighbors (RFE-KNN) (AUC = 0.65, ACC = 0.65, SPE = 0.64, SEN = 0.65) were the best models in ED, ES, and ED&ES datasets. Using radiomic features extracted from echocardiographic images and ML, it is feasible to predict cardiac problems caused by iron overload.

Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.

PURPOSE: Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers. MATERIALS AND METHODS: After applying our exclusion criteria on 7188 patients from three different centers, 3772 and 2248 patients were enrolled for the first and second analyses, respectively. Data were split into two parts, including training and testing, while a fraction of training data were considered for validation. Ten different CNN models were applied to single- and dual-view input (posterior and anterior views) modes to find the optimal model for each analysis. In addition, three different methods, including squeeze-and-excitation (SE), spatial pyramid pooling (SPP), and attention-augmented (AA), were used to aggregate the features for dual-view input models. Model performance was reported through area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity and was compared with the DeLong test applied to ROC curves. The test dataset was evaluated by three nuclear medicine physicians (NMPs) with different levels of experience to compare the performance of AI and human observers. RESULTS: DenseNet121_AA (DensNet121, with dual-view input aggregated by AA) and InceptionResNetV2_SPP achieved the highest performance (AUC = 0.72) for the first and second analyses, respectively. Moreover, on average, in the first analysis, Inception V3 and InceptionResNetV2 CNN models and dual-view input with AA aggregating method had superior performance. In addition, in the second analysis, DenseNet121 and InceptionResNetV2 as CNN methods and dual-view input with AA aggregating method achieved the best results. Conversely, the performance of AI models was significantly higher than human observers for the first analysis, whereas their performance was comparable in the second analysis, although the AI model assessed the scans in a drastically lower time. CONCLUSION: Using the models designed in this study, a positive step can be taken toward improving and optimizing WBS interpretation. By training DL models with larger and more diverse cohorts, AI could potentially be used to assist physicians in the assessment of WBS images.

Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition.

A U-shaped contraction pattern was shown to be associated with a better Cardiac resynchronization therapy (CRT) response. The main goal of this study is to automatically recognize left ventricular contractile patterns using machine learning algorithms trained on conventional quantitative features (ConQuaFea) and radiomic features extracted from Gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). Among 98 patients with standard resting GSPECT MPI included in this study, 29 received CRT therapy and 69 did not (also had CRT inclusion criteria but did not receive treatment yet at the time of data collection, or refused treatment). A total of 69 non-CRT patients were employed for training, and the 29 were employed for testing. The models were built utilizing features from three distinct feature sets (ConQuaFea, radiomics, and ConQuaFea + radiomics (combined)), which were chosen using Recursive feature elimination (RFE) feature selection (FS), and then trained using seven different machine learning (ML) classifiers. In addition, CRT outcome prediction was assessed by different treatment inclusion criteria as the study's final phase. The MLP classifier had the highest performance among ConQuaFea models (AUC, SEN, SPE = 0.80, 0.85, 0.76). RF achieved the best performance in terms of AUC, SEN, and SPE with values of 0.65, 0.62, and 0.68, respectively, among radiomic models. GB and RF approaches achieved the best AUC, SEN, and SPE values of 0.78, 0.92, and 0.63 and 0.74, 0.93, and 0.56, respectively, among the combined models. A promising outcome was obtained when using radiomic and ConQuaFea from GSPECT MPI to detect left ventricular contractile patterns by machine learning.

Breathing-induced Errors in Quantification and Description of Dominant Intra-Prostatic Lesions (Dils) in PET Images: A Simulation Study by Means of The 4D NCAT Phantom.

Background: Respiratory movement and the motion range of the diaphragm can affect the quality and quantity of prostate images. Objective: This study aimed to investigate the magnitude of respiratory-induced errors to determine Dominant Intra- prostatic Lesions (DILs) in positron emission tomography (PET) images. Material and Methods: In this simulation study, we employed the 4D NURBS-based cardiac-torso (4D-NCAT) phantom with a realistic breathing model to simulate the respiratory cycles of a patient to assess the displacement, volume, maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), signal to noise ratio (SNR), and the contrast of DILs in frames within the respiratory cycle. Results: Respiration in a diaphragm motion resulted in the maximum superior-inferior displacement of 3.9 and 6.1 mm, and the diaphragm motion amplitudes of 20 and 35 mm. In a no-motion image, the volume measurement of DILs had the smallest percentage of errors. Compared with the no-motion method, the percentages of errors in the average method in 20 and 35 mm- diaphragm motion were 25% and 105%, respectively. The motion effect was significantly reduced in terms of the values of SUVmax and SUVmean in comparison with the values of SUVmax and SUVmean in no- motion images. The contrast values in respiratory cycle frames were at a range of 3.3-19.2 mm and 6.5-46 for diaphragm movements' amplitudes of 20 and 35 mm. Conclusion: The respiratory movement errors in quantification and delineation of DILs were highly dependent on the range of motion, while the average method was not suitable to precisely delineate DILs in PET/CT in the dose-painting technique.

Evaluation of Dose-Painting in the Dominant Intraprostatic Lesions by Radiobiological Parameters using 68Ga- PSMA PET/CT.

Background: Patients diagnosed with dominant intraprostatic lesions (DIL) may need radiation doses over than 80 Gy. Dose-painting by contours (DPC) is a useful technique which helps the patients. Dose-painting approach need to be evaluated. Objective: To evaluate the DCP technique in the case of boosting the DILs by radiobiological parameters, tumor control probability (TCP), and normal tissue complication probability (NTCP) via PET/CT images traced by 68Ga-PSMA. Material and Methods: In this analytical study, 68Ga-PSMA PET/CT images were obtained from patients with DILs that were delineated using the Fuzzy c-mean (FCM) algorithm and thresholding methods. The protocol of therapy included two phases; at the first phase (ph1), a total dose of 72 Gy in 36 fractions were delivered to the planning target volume (PTV1); the seconds phase consisted of the application of variable doses to the PTV2. Moreover, two concepts were also considered to calculate the TCP using the Zaider-Minerbo model. Results: The lowest volume in DILs belonged to the DIL1 extracted by the FCM method. According to dose-volume parameters of the rectum and bladder, by the increase in the PTV dose higher than 92 Gy, the amounts of rectum and bladder doses are increased. There was no difference between the TCPs of DILs at doses higher than 86 Gy and 100 Gy for ordinary and high clone density, respectively. Conclusion: Consequently, our dose-painting approach for DILs, extracted by the FCM method via PET/CT images, can reduce the total dose for prostate radiation with 100% tumor control and less normal tissue complications.

Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms.

The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.5-T MRI scanner. All images were segmented by two expert radiologists (in consensus). Prior to extraction of radiomics features, all MR images were resampled to an isotropic voxel size of 1.8 × 1.8 × 1.8 mm3. Subsequently, intensities were quantized to 64 discretized gray levels and a total of 93 features were extracted. The applied algorithms included a smoothly clipped absolute deviation (SCAD)-penalized support vector machine (SVM) and the recursive partitioning (RP) algorithm as a robust classifier for binary classification in this high-dimensional and non-sparse data. All models were validated with repeated fivefold cross-validation and 10,000 bootstrapping resamples. Ten and seven features were selected with SCAD-penalized SVM and RP algorithm, respectively, for CABG responder/non-responder classification. Considering univariate analysis, the GLSZM gray-level non-uniformity-normalized feature achieved the best performance (AUC: 0.62, 95% CI: 0.53-0.76) with SCAD-penalized SVM. Regarding multivariable modeling, SCAD-penalized SVM obtained an AUC of 0.784 (95% CI: 0.64-0.92), whereas the RP algorithm achieved an AUC of 0.654 (95% CI: 0.50-0.82). In conclusion, different radiomics texture features alone or combined in multivariate analysis using machine learning algorithms provide prognostic information regarding myocardial function in patients after CABG.

Amino-modified-silica-coated gadolinium-copper nanoclusters, conjugated to AS1411 aptamer and radiolabeled with technetium-99 m as a novel multimodal imaging agent.

Hybridimagingtechnology has the potential to provide reliable imagingand accurate detection of cancer cells by combining the advantages and overcoming the shortages of various clinical imaging tools. Nanomaterials with unique targeting properties and their small size have improved biomedical imaging. Indeed, their small size determines local contrast agent concentrations in tumors by enhanced permeability and retention (EPR) effect. In this work, amino-modified silica-coated Gadolinium-Copper Nanoclusters were fabricated and conjugated to AS1411 aptamer (Apt-ASGCuNCs) and radiolabeled with technetium-99 m (99mTc) for in vivo fluorescence imaging, magnetic resonance imaging (MRI) and single-photon emission computed tomography (SPECT). The synthesized nanoconjugate was fully characterized by transmission electron microscopy (TEM), element mapping, fluorescence spectroscopy, and Fourier-transform infrared spectroscopy. Moreover, XTT assay, and apoptosis and necrosis methods were applied to study toxicity. Radiochemical yield was calculated 93% that revealed a great potential for complex formation between Apt-ASGCuNCs and 99mTcO4-. Also, good stability of 99mTc-Apt-ASGCuNCs was found in the human serum up to 4 h. Both Apt-ASGCuNCs and 99mTc-Apt-ASGCuNCs indicated a considerable tumor-targeting in in vivo fluorescence imaging, MRI and SPECT with 4T1 tumor-bearing BALB/c mice. The biodistribution results showed no undesirable accumulation of 99mTc-Apt-ASGCuNCs in the liver, and spleen as it circulated freely in the blood pool. Meanwhile, 99mTc-Apt-ASGCuNCs were removed from the body through the renal clearance system, making it more convenient for future multimodality imaging applications.

Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection.

OBJECTIVE: Robust differentiation between infarcted and normal tissue is important for clinical diagnosis and precision medicine. The aim of this work is to investigate the radiomic features and to develop a machine learning algorithm for the differentiation of myocardial infarction (MI) and viable tissues/normal cases in the left ventricular myocardium on non-contrast Cine Cardiac Magnetic Resonance (Cine-CMR) images. METHODS: Seventy-two patients (52 with MI and 20 healthy control patients) were enrolled in this study. MR imaging was performed on a 1.5 T MRI using the following parameters: TR = 43.35 ms, TE = 1.22 ms, flip angle = 65°, temporal resolution of 30-40 ms. N4 bias field correction algorithm was applied to correct the inhomogeneity of images. All images were segmented and verified simultaneously by two cardiac imaging experts in consensus. Subsequently, features extraction was performed within the whole left ventricular myocardium (3D volume) in end-diastolic volume phase. Re-sampling to 1 × 1 × 1 mm3 voxels was performed for MR images. All intensities within the VOI of MR images were discretized to 64 bins. Radiomic features were normalized to obtain Z-scores, followed by Student's t-test statistical analysis for comparison. A p-value < 0.05 was used as a threshold for statistically significant differences and false discovery rate (FDR) correction performed to report q-value (FDR adjusted p-value). The extracted features were ranked using the MSVM-RFE algorithm, then Spearman correlation between features was performed to eliminate highly correlated features (R2 > 0.80). Ten different machine learning algorithms were used for classification and different metrics used for evaluation and various parameters used for models' evaluation. RESULTS: In univariate analysis, the highest area under the curve (AUC) of receiver operating characteristic (ROC) value was achieved for the Maximum 2D diameter slice (M2DS) shape feature (AUC = 0.88, q-value = 1.02E-7), while the average of univariate AUCs was 0.62 ± 0.08. In multivariate analysis, Logistic Regression (AUC = 0.93 ± 0.03, Accuracy = 0.86 ± 0.05, Recall = 0.87 ± 0.1, Precision = 0.93 ± 0.03 and F1 Score = 0.90 ± 0.04) and SVM (AUC = 0.92 ± 0.05, Accuracy = 0.85 ± 0.04, Recall = 0.92 ± 0.01, Precision = 0.88 ± 0.04 and F1 Score = 0.90 ± 0.02) yielded optimal performance as the best machine learning algorithm for this radiomics analysis. CONCLUSION: This study demonstrated that using radiomics analysis on non-contrast Cine-CMR images enables to accurately detect MI, which could potentially be used as an alternative diagnostic method for Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR).

Myocardial strain by cardiac magnetic resonance: A valuable predictor of outcome after infarct revascularization.

PURPOSE: To evaluate the prognostic value of left ventricular strains by cardiac magnetic resonance feature tracking (CMR-FT) in patients with re-perfused myocardial infarction (MI). METHODS: The study enrolled 58 patients with re-vascularized MI who underwent CMR within a week from acute MI. An 18-month follow-up was carried out for the composite endpoint of major adverse cardiovascular events (MACE). A 3 to 6-month post-MI ejection fraction (EF) was also measured. The predictive value of global longitudinal, circumferential, and radial strains (GLS, GCS, and GRS, respectively) for MACE and the follow-up EF was evaluated. RESULTS: All the global strains showed significant impairment in MACE positive cases (P < 0.05 for all). On univariate regression, MACE was reversely associated with early post-MI EF (OR: 0.90, 95% CI: 0.83-0.98, P: 0.01), and directly associated with GLS (OR: 1.32, 95% CI: 1.03-1.69, P: 0.02), GCS (OR: 1.23, 95% CI: 1.00-1.50, P: 0.04) and EDVI (OR:1.02, 95 %CI: 1.00-1.04, P: 0.01). On multivariate regression model, only the interaction between EF and GLS showed a significant association with MACE (OR[CI95%]: 1.1 [1.06-1.21]). EF < 30% and GLS > -8.9% had the highest sensitivity (78.9% and 89.5%, respectively) and specificity (45.2% and 54.8%, respectively) to predict MACE. The combination of EF < 30% and GLS > -8.9% increased the sensitivity to 94.7%. In addition, the cutoff values of 35.1% for early post-MI EF and -10% for GLS could identify patients with impaired follow-up EF with more than 80% sensitivity and specificity [AUC (CI95%): 0.893(0.76-1.00) for EF and AUC (CI95%):0.836(0.67-1,00) for GLS, P < 0.05 for both)]. CONCLUSIONS: GLS by CMR-FT is a powerful prognosticator of MACE and functional recovery in MI survivors, with incremental value added to early post-MI EF alone.

Novel manganese carbon quantum dots as a nano-probe: Facile synthesis, characterization and their application in naproxen delivery (Mn/CQD/SiO2@naproxen).

This study for the first time pursues two crucial aims of using Naproxen as a non-steroidal anti-inflammatory drug in a better, non-invasive setting and introducing a simple and biocompatible nano-carrier (Mn/CQD/SiO2) which is a magneto carbon quantum dots modified with mesoporous silica probe which can be served as a drug delivery and tracer system. SiO2modification was doneby mesoporous silica which improves biocompatibility and provideslow cytotoxicity. Naproxen was conjugated to the nano-probe to form Mn/CQD/SiO2@naproxen and biodistribution was investigated. Physicochemical characteristics of the Mn/CQD/SiO2@naproxen were investigated using FT-IR, SEM, TEM, UV-Vis and BET. Antiproliferation assay using MTT assay was performed on HEK-293 cells to determine the cytotoxity of Mn/CQD/SiO2@naproxen. Relaxivity of Mn/CQD/SiO2 was examined thereafter. To investigate the imaging capability of Mn/CQD/SiO2@naproxen and biodistribution of Naproxen, fluorescent imaging was done. To confirm the data, then the levels of COX Gene expression was determined. The specific surface area, pore volume, and pore radius were 44.4 m2/g, 10.23 cm3/g, and 25.9 nm respectively. MTT assay showed no cytotoxicity. Relaxivity of Mn/CQD/SiO2 was higher than conventional Gd-based contrast agent. Fluorescence imaging of Mn/CQD/SiO2@naproxen showed the biodistribution of naproxen. COX Gene expression confirmed the biodistribution data. By increasing the accumulation in liver COX production reduced. All in all, unique features of Mn/CQD/SiO2 including biocompatibility, low toxicity, magnetic and fluorescence properties showed that it can be used in biomedical sciences.

Role of Technetium-99m-Ethylenedicysteine in the Calculation of Differential Renal Function: A Comparison Study with Dimercaptosuccinic Acid Renal Scintigraphy.

PURPOSE OF THE STUDY: The aim of our study was to compare the technetium-99m (Tc-99m)-ethylenedicysteine (EC) renography calculation of differential renal function (DRF) with this measurement using Tc-99m-dimercaptosuccinic acid (DMSA) scintigraphy. MATERIALS AND METHODS: Patients referred to our department were included in our study, and both DMSA and EC scans were performed for each patient according to the standard imaging protocols. A checklist was filled for each patient. Statistical analysis was performed using correlation and regression methods. RESULTS: Forty-two patients (mean age: 3.6 ± 3.4 years), including 32 boys and 10 girls, participated in our study. The results of EC scintigraphy were significantly correlated with the values of DMSA scintigraphy (P < 0.001). Performing linear regression, EC renography significantly (P < 0.001) predicted the DRF as it was calculated by DMSA scintigraphy (R 2 = 0.92, P < 0.001). This test was significant in both male and female subgroups (P < 0.001). CONCLUSIONS: Overall, our study findings were similar to the reported results in the other reviewed studies, showing that Tc-99m-EC can be considered as an alternative for DMSA scintigraphy, providing interchangeable results.

Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study.

BACKGROUND: The aim of this work was to assess the robustness of cardiac SPECT radiomic features against changes in imaging settings, including acquisition, and reconstruction parameters. METHODS: Four commercial SPECT and SPECT/CT cameras were used to acquire images of a static cardiac phantom mimicking typical myorcardial perfusion imaging using 185 MBq of 99mTc. The effects of different image acquisition and reconstruction parameters, including number of views, view matrix size, attenuation correction, as well as image reconstruction related parameters (algorithm, number of iterations, number of subsets, type of post-reconstruction filter, and its associated parameters, including filter order and cut-off frequency) were studied. In total, 5,063 transverse views were reconstructed by varying the aforementioned factors. Eighty-seven radiomic features including first-, second-, and high-order textures were extracted from these images. To assess reproducibility and repeatability, the coefficient of variation (COV), as a widely adopted metric, was measured for each of the radiomic features over the different imaging settings. RESULTS: The Inverse Difference Moment Normalized (IDMN) and Inverse Difference Normalized (IDN) features from the Gray Level Co-occurrence Matrix (GLCM), Run Percentage (RP) from the Gray Level Co-occurrence Matrix (GLRLM), Zone Entropy (ZE) from the Gray Level Size Zone Matrix (GLSZM), and Dependence Entropy (DE) from the Gray Level Dependence Matrix (GLDM) feature sets were the only features that exhibited high reproducibility (COV ≤ 5%) against changes in all imaging settings. In addition, Large Area Low Gray Level Emphasis (LALGLE), Small Area Low Gray Level Emphasis (SALGLE) and Low Gray Level Zone Emphasis (LGLZE) from GLSZM, and Small Dependence Low Gray Level Emphasis (SDLGLE) from GLDM feature sets turned out to be less reproducible (COV > 20%) against changes in imaging settings. The GLRLM (31.88%) and GLDM feature set (54.2%) had the highest (COV < 5%) and lowest (COV > 20%) number of the reproducible features, respectively. Matrix size had the largest impact on feature variability as most of the features were not repeatable when matrix size was modified with 82.8% of them having a COV > 20%. CONCLUSION: The repeatability and reproducibility of SPECT/CT cardiac radiomic features under different imaging settings is feature-dependent. Different image acquisition and reconstruction protocols have variable effects on radiomic features. The radiomic features exhibiting low COV are potential candidates for future clinical studies.

Synthesis and evaluation of 99mTc-DOTA-ARA-290 as potential SPECT tracer for targeting cardiac ischemic region.

Objectives: Myocardial infarction caused by ischemia of heart tissue is the main reason for death worldwide; therefore, early detection can reduce mortality and treatment costs. Erythropoietin (EPO) has protection effects on ischemic tissue due to nonhematopoietic peptide (pHBSP; ARA-290) which is derived from the B-subunit of EPO. Materials and Methods: We designed and synthesized a modified DOTA-(Lys-Dabcyl6, Phe7)-ARA-290 using Fmoc solid-phase peptide synthesis strategies. To improve serum stability, Fmoc-Lys-(Dabcyl)-OH as lipophilic amino acid was synthesized along with Fmoc-Phe-OH which then were substituted with Arg6 and Ala7, respectively; they were then investigated for the ability to detect ischemic cardiac imaging. DOTA-(Lys-Dabcyl6,Phe7)-ARA-290 was labeled with technetium 99m, and its radiochemical purity (RCP), stability in the presence of human serum and, specific bind to hypoxic H9c2 cells were evaluated. In vivo studies for biodistribution and SPECT scintigraphy were checked in a normal and cardiac ischemia rat model. Results: Radiolabeling purity was obtained more than 96% by ITLC, and in vitro stability of the radiopeptide up to 6 hr was 85%. The binding of 99mTc-ARA-290 to hypoxic cells was remarkably higher than normoxic cells (3 times higher than normoxic cells at 1 hr). Biodistribution and SPECT imaging on the cardiac ischemic model showed that radiopeptide considerably accumulated in the ischemic region (cardiac ischemic-to-lung rate = 3.65 ID/g % at 0.5 hr). Conclusion: The results of studies, in vitro and in vivo, indicated that 99mTc-DOTA-(Lys-Dabcyl6,Phe7)-ARA-290 could be an appropriate candidate for early diagnosis of cardiac ischemia.

Synthesis, radiolabelling, and biological assessment of folic acid-conjugated G-3 99mTc-dendrimer as the breast cancer molecular imaging agent.

Hence, in this study, the authors aimed to develop a dendrimer-based imaging agent comprised of poly(ethylene glycol) (PEG)-citrate, technetium-99 m (99mTc), and folic acid. The dendrimer-G3 was synthesised and conjugated with folic acid, which confirmed by Fourier transform infrared, proton nuclear magnetic resonance, dynamic light scattering, and transition electron microscopy. 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-Tetrazolium-5-Carboxanilide cytotoxicity assay kit was used to measure the cellular toxicity of dendrimer. Imaging and biodistribution studies were conducted on the mice bearing tumour. The results showed that the fabricated dendrimer-G3 has a size of 90 ± 3 nm, which was increased to 100 ± 4 nm following the conjugation with folic acid. The radiostablity investigation showed that the fabricated dendrimers were stable in the human serum at various times. Toxicity assessment confirmed no cellular toxicity against HEK-293 cells at 0.25, 0.5, 1, 2, 4, and 8 mg/µl concentrations. The in vivo studies demonstrated that the synthesised dendrimers were able to provide a bright SPECT image applicable for tumour detection. In conclusion, the authors' study documented the positive aspects of PEG-citrate dendrimer conjugated with folic acid as the SPECT contrast agent for breast cancer detection.

The impact of frame numbers on cardiac ECG-gated SPECT images with interpolated extra frames using echocardiography.

Background: Cardiac echocardiography and cardiac ECG-gated single-photon emission computed tomography (SPECT) are the most common modalities for left ventricle (LV) volumes and function assessment. The temporal resolution of SPECT images is limited and an ECG provides better temporal resolution. This study investigates the impact of frame numbers on images in terms of qualitative and quantitative assessments. Methods: In this study, 5 patients underwent echocardiography and cardiac ECG-gated SPECT imaging, and 5 standard views of the LV were recorded to determine LV walls boundaries and volumes. Also, 2 original images with 8 frames and 16 frames per cardiac cycle were recorded simultaneously in a single gantry orbit. Using the data extracted from the LV model, 8 extra new frames were created with interpolation between existing frames of the original 8-frame image. Three series of images (8 and 16 original and 16 interpolated) were reconstructed separately. LV volumes and ejection fraction (EF) were calculated using Quantitative Gated SPECT (QGS) software. Results: Compared to the original 8-frame gating, original 16-frame gated images resulted in larger end-diastole volume (EDV) (mean ± SD: 68.6 ± 27.11 mL vs 66.2±25.41 mL, p<0.001), smaller end-systole volume (ESV) (mean ± SD: 24.6±8.7 mL vs 26±7.3 mL, p<0.001), and higher EF (64% vs 60.2%, p<0.001). The results for the interpolated series were also different from the original images (closer to the original 16-frame series rather than 8-frame). Conclusion: Changing the frame number from 8 to 16 in cardiac ECG-gated SPECT images caused a significant change in LV volumes and EF. Frame interpolation with sophisticated algorithms can be used to improve the temporal resolution of SPECT images.

Application of radiolabeled peptides in tumor imaging and therapy.

Scientists are looking for new therapies to cope with the rise in cancer worldwide. Since cancer cells overexpress peptide receptors and owing to small size, easy uptake by tumor cells, easy preparation, and with no toxicity, the use of radiolabeled peptides with high specificity and affinity for accurate imaging and therapy has attracted much attention. To develop an ideal imaging or treatment radiolabeled peptide, there are some aspects in the components of radiolabeled peptide including radionuclide, peptide, chelator, and spacer that should be considered. Some peptides, including somatostatin, RGD, neurotensin, bombesin, exendin, vasoactive intestinal peptide, and gastrin are currently under (pre)clinical investigations. Today, nanoparticles are suitable tools for targeting peptide for molecular imaging and therapy of tumors with low toxicity. This paper presents some essential aspects in developing a valuable radiolabeled peptide and some radiolabeled peptides with regard to their applications in tumor imaging and therapy in pre-clinical and clinical phases.

Technetium-99 m-PEGylated dendrimer-G2-(Dabcyle-Lys6,Phe7)-pHBSP: A novel Nano-Radiotracer for molecular and early detecting of cardiac ischemic region.

In cardiac ischemic disorder, pyroglutamate helix B surface peptide (pHBSP) which derived from erythropoietin causes to increase cell stability. To improve the serum stability of pHBSP, two lipophilic amino acids Arg6, Ala7 were replaced with Fmoc-(Dabcyle)-Lys-OH and Fmoc-Phe-OH during the peptide synthesis. This peptide was subsequently conjugated to PEGylated dendrimer-G2 and labeled with 99mTcO4- to detect cardiac ischemic region. Radiochemical purity (RCP) of 99mTc-PEGylated dendrimer-G2-(Dabcyle-Lys6,Phe7)-pHBSP was evaluated by ITLC method. In addition, the radiopeptide was investigated for stability in human serum and binding affinity to hypoxic cells in myocardium H9c2 cell lines. Biodistribution and SPECT/CT scintigraphy were assessed in cardiac ischemic rats. Radiochemical yield indicated that the anionic dendrimer has a very high potential to complex formation with 99mTcO-4 (RCP > 94%) which was stable in human serum with RCP 89% up to 6 h. The binding of 99mTc- nanoconjugate to hypoxic cells was significantly more than normoxic cells (3-fold higher compared to normoxic cells at 1 h). In biodistribution studies, erythropoietin receptor-Beta common receptor (EPO-BcR)-positive uptake in the cardiac ischemic region was 3.62 ± 0.44% ID/g 30 min post injection. SPECT imaging showed a prominent uptake of 99mTc-nanoconjugate in EPO-BcR expressing ischemic heart.

Synthesis and characterization of novel 99mTc-DGC nano-complexes for improvement of heart diagnostic.

In this research, early diagnosis of cardiovascular diseases can reduce their mortality and burden. In our study, we developed a new nano-agent, 99mTc-Dendrimer Glyco Conjugate (99mTc-DGC), and assessed its safety and capability for myocardial viability scan. To develop 99mTc-DGC, we first synthesized the dendrimer and then, glucose has been conjugated. Afterwards, we measured toxicity of the product on normal cells by XTT and apoptosis/necrosis methods. We compared the myocardial viability scan (measured by SPECT and dynamic planar imaging) in two rabbit models, with and without infarction. We also assessed the biodistribution of 99mTc-DGC in rats with no infarction. DGC synthesis was confirmed by Fourier transform infrared (FT-IR), proton nuclear magnetic resonance (1H NMR), liquid chromatography-mass spectrometry (LC-MS), dynamic light scattering (DLS) and static light scattering techniques (SLS). Then radiochemical purity (RCP) was done to present the stability and potential of DGC to complex formation with 99mTc. In vitro cytotoxicity showed nontoxic concentration up to 8 mg/mL. Single Photon Emission Computed Tomography (SPECT) and dynamic planar imaging clearly showed the accumulation of 99mTc-DGC in myocardial. Biodistribution result showed the 2.60% accumulation of 99mTc-DGC in myocardial after 2 h. Our findings indicated 99mTc-DGC to be safe and can accurately diagnose myocardial infarctions at early stages. Human studies to further assess such effects are critical.