Development of [64Cu]Cu-NODAGA-RGD-BBN as a Novel Radiotracer for Dual Integrin and GRPR-targeted Tumor PET Imaging.

BACKGROUND: In this study, [64Cu]Cu-NODAGA-RGD-BBN was prepared and its preclinical assessments were evaluated for PET imaging of GRPR overexpressing tumors. METHODS: NODAGA-RGD-BBN heterodimer peptide was successfully labeled with cyclotronproduced copper-64 at optimized conditions. The radiochemical purity of the radiotracer was checked by HPLC and RTLC methods. The stability of the radiolabeled compound was assessed in PBS (4°C) and in human blood serum (37°C). Binding affinity and internalization of [64Cu]Cu-NODAGA-RGD-BBN were studied on PC3, LNCaP, and CHO cell lines. The biodistribution of the radiotracer was evaluated in normal and tumor-bearing mice. RESULTS: [64Cu]Cu-NODAGA-RGD-BBN was prepared with radiochemical purity >99 ± 0.7% (HPLC/ITLC) and specific activity of 18.5 ± 2.2 TBq/mmol. The radiotracer showed high stability in PBS (95 ± 1.05%) and in human blood serum (96 ± 1.24%) and, high affinity to the GRP expressing tumor cells. [64Cu]Cu-NODAGA-RGD-BBN showed hydrophilic (log p = -1.14) and agonistic nature. The biodistribution and imaging studies demonstrated high uptake at the tumor site at all intervals post-injection and 3-4 h post-injection can be considered an appropriate time of imaging. CONCLUSION: The results indicated that [64Cu]Cu-NODAGA-RGD-BBN radiolabeled heterodimer peptide can be considered as a high-potential agent for PET imaging of GRPRoverexpressing tumors.

An automatic segmentation framework for computer-assisted renal scintigraphy procedure.

One of the techniques for achieving unique and reliable information in medicine is renal scintigraphy. A key step for quantitative renal scintigraphy is segmentation of the kidneys. Here, an automatic segmentation framework was proposed for computer-aided renal scintigraphy procedures. To extract kidney boundary in dynamic renal scintigraphic images, a multi-step approach was proposed. This technique is featured with key steps, namely, localization and segmentation. At first, the ROI of each kidney was estimated using Otsu's thresholding, anatomical constraint, and integral projection, which is done in an automatic process. Afterwards, the ROI obtained for the kidneys was used as the initial contours to create the final counter of kidneys using geometric active contours. At this step and for the segmentation, an improved variational level set was utilized through Mumford-Shah formulation. Using e.cam gamma camera system (SIEMENS), 30 data sets were used to assess the proposed method. By comparing the manually outlined borders, the performance of the proposed method was shown. Different measures were used to examine the performance. It was found that the proposed segmentation method managed to extract the kidney boundary in renal scintigraphic images. The proposed technique achieved a sensitivity of 95.15% and a specificity of 95.33%. In addition, the section under the curve in the ROC analysis was equal to 0.974. The proposed technique successfully segmented the renal contour in dynamic renal scintigraphy. Using all the data sets, a correct segmentation of the kidney was performed. In addition, the technique was successful with noisy and low-resolution images and challenging cases with close interfering activities such as liver and spleen activities.

Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts.

Background: This study evaluated the performances of neural networks in terms of denoizing metal artifacts in computed tomography (CT) images to improve diagnosis based on the CT images of patients. Methods: First, head-and-neck phantoms were simulated (with and without dental implants), and CT images of the phantoms were captured. Six types of neural networks were evaluated for their abilities to reduce the number of metal artifacts. In addition, 40 CT patients' images with head-and-neck cancer (with and without teeth artifacts) were captured, and mouth slides were segmented. Finally, simulated noisy and noise-free patient images were generated to provide more input numbers (for training and validating the generative adversarial neural network [GAN]). Results: Results showed that the proposed GAN network was successful in denoizing artifacts caused by dental implants, whereas more than 84% improvement was achieved for images with two dental implants after metal artifact reduction (MAR) in patient images. Conclusion: The quality of images was affected by the positions and numbers of dental implants. The image quality metrics of all GANs were improved following MAR comparison with other networks.

Human Dose Assessment of 68Ga-NODAGA-RGD-BBN Heterodimer Peptide based on Animal Data.

Aims: Calculation of the absorbed dose in human organs is one of the first steps for developing new radiopharmaceuticals. The aim of this study is to estimate the human absorbed dose of a newly developed 68Ga-NODAGA-RGD-BBN radiolabeled compound. Materials and Methods: 68Ga-NODAGA-RGD-BBN was prepared by varying different parameters at optimized conditions. The stability of the radiolabeled peptide in phosphate-buffered saline (PBS) and in human serum was evaluated for 120 min. Afterward, the biodistribution of the complex was assessed in normal and tumor-bearing mice, at least for 120 min postinjection. Finally, the human absorbed dose of 68Ga-NODAGA-RGD-BBN was estimated based on mice data using Radiation Dose Assessment Resource and Spark method. Results: 68Ga-NODAGA-RGD-BBN was produced with radiochemical purity of more than 98% (high-performance liquid chromatography/ radio thin layer chromatography (RTLC)) with high stability in PBS buffer and in human serum at least for 2 h. The complex demonstrated high uptake in gastrin-releasing peptide receptor-expressing tumors compared to other nontarget organs. Furthermore, the dose assessment for the complex showed that the kidneys receive the highest absorbed dose in comparison with other organs. Conclusion: The result of this study showed that 68Ga-NODAGA-RGD-BBN is an effective and radiolabeled ligand for tumor detection, however more studies are still needed.

Investigating dose homogeneity in radiotherapy of oral cancers in the presence of a dental implant system: an in vitro phantom study.

BACKGROUND: Materials with high atomic numbers are part of the composition of dental implant systems. In radiotherapy of oral cavity cancers, an implant can cause dose perturbations that affect target definition, dose calculation, and dose distribution. In consequence, this may result in poor tumor control and higher complications. In this study, we evaluated dose homogeneity when a dental implant replaced a normal tooth. We also aimed to evaluate the concordance of dose calculations with dose measurements. MATERIALS AND METHODS: In this study, 2 sets of planning CT scans of a phantom with a normal tooth and the same phantom with the tooth replaced by a Z1 TBR dental implant system were used. The implant system was composed of a porcelain-fused-to-metal crown and titanium with a zirconium collar. Three radiotherapy plans were designed when the density of the implant material was corrected to match their elements, or when all were set to the density of water, or when using the default density conversion. Gafchromic EBT-3 films at the level of isocenter and crowns were used for measurements. RESULTS: At the level of crowns, upstream and downstream dose calculations were reduced when metal kernels were applied (M-plan). Moreover, relatively measured dose distribution patterns were most similar to M-plan. At this level, relative to the non-implanted phantom, mean doses values were higher with the implant (215.93 vs. 192.25), also, new high-dose areas appeared around a low-dose streak forward to the implant (119% vs. 95%). CONCLUSIONS: Implants can cause a high dose to the oral cavity in radiotherapy because of extra scattered radiation. Knowledge of the implant dimensions and defining their material enhances the accuracy of calculations.

Samarium-153-(4-[((bis (phosphonomethyl)) carbamoyl) methyl]-7,10-bis (carboxymethyl)-1,4,7,10-tetraazacyclododec-1-yl) acetic acid: A novel agent for bone pain palliation therapy.

AIM: Various phosphonate ligands labeled with ß--emitting radionuclides have shown good efficacy for bone pain palliation. In this study, a new agent for bone pain palliation has been developed. MATERIALS AND METHODS: Samarium-153-(4-[((bis(phosphonomethyl))carbamoyl)methyl]-7,10-bis (carboxymethyl)-1,4,7,10-tetraazacyclododec-1-yl) acetic acid (153Sm-BPAMD) complex was prepared using BPAMD ligand and samarium-153 chloride. The effect of various parameters on the labeling yield of 153Sm-BPAMD including ligand concentration, pH, temperature, and reaction time were studied. Production of 153Sm was performed at a research reactor using 152Sm (n, γ)153Sm nuclear reaction. The radiochemical purity of the radiolabeled complex was checked by instant thin layer chromatography. Stability studies of the complex in the final preparation and the presence of human serum were performed up to 48 h. Partition coefficient and hydroxyapatite (HA) binding of the complex were investigated and biodistribution studies using single photon emission computed tomography (SPECT) and scarification were performed after injection of the complex to wild-type mice. RESULTS: 153Sm-BPAMD was prepared in a high radiochemical purity >98% and specific activity of 267 GBq/mmol at the optimal conditions. The complex demonstrated significant stability at the room temperature and in human serum at least for 48 h. HA binding assay demonstrated that at the amount of more than 5 mg, approximately, all radiolabeled complex was bind to HA. At the pH 7.4, log Po/w was - 1.86 ± 0.02. Both SPECT and scarification showed major accumulation of the labeled compound in the bone tissue. CONCLUSIONS: The results show that 153Sm-BPAMD has interesting characteristics as an agent for bone pain palliation, however, further biological studies in other mammals are still needed.

Preparation, quality control and biodistribution assessment of ¹5³Sm-BPAMD as a novel agent for bone pain palliation therapy.

OBJECTIVE: Various phosphonate ligands labeled with ß(-)-emitting radionuclides have shown good efficacy for bone pain palliation. In this study, a new agent for bone pain palliation has been developed. METHODS: ¹5³Sm-(4-{[(bis(phosphonomethyl))carbamoyl]methyl}-7,10-bis(carboxymethyl)-1,4,7,10-tetraazacyclododec-1-yl) acetic acid (¹5³Sm-BPAMD) complex was prepared using BPAMD ligand and ¹5³SmCl3. The effect of various parameters on the labeling yield of ¹5³Sm-BPAMD including ligand concentration, pH, temperature and reaction time were studied. Radiochemical purity of the radiolabeled complex was checked by instant thin layer chromatography (ITLC). Stability studies of the complex in the final preparation and in the presence of human serum were performed up to 48 h. Partition coefficient and hydroxyapatite (HA) binding of the complex were investigated and biodistribution studies (SPECT imaging and scarification) were performed after injection of the complex to Syrian mice up to 48 h post-injection. The biodistribution of the complex was compared with the biodistribution of the ¹5³Sm cation in the same type mice. RESULTS: ¹5³Sm-BPAMD was prepared in high radiochemical purity >98% and specific activity of 267 GBq/mmol at the optimal conditions. The complex demonstrated significant stability at room temperature and in human serum at least for 48 h. HA binding assay demonstrated that at the amount of more than 5 mg, approximately, all radiolabeled complex was bound to HA. At the pH 7.4, LogP o/w was -1.86 ± 0.02. Both SPECT and scarification showed major accumulation of the labeled compound in the bone tissue. CONCLUSION: The results show that ¹5³Sm-BPAMD has interesting characteristics as an agent for bone pain palliation; however, further biological studies in other mammals are still needed.

Automatic left ventricle segmentation in volumetric SPECT data set by variational level set.

INTRODUCTION: Left ventricle (LV) quantification in nuclear medicine images is a challenging task for myocardial perfusion scintigraphy. A hybrid method for left ventricle myocardial border extraction in SPECT datasets was developed and tested to automate LV ventriculography. METHODS: Automatic segmentation of the LV in volumetric SPECT data was implemented using a variational level set algorithm. The method consists of two steps: (1) initialization and (2) segmentation. Initially, we estimate the initial closed curves in SPECT images using adaptive thresholding and morphological operations. Next, we employ the initial closed curves to estimate the final contour by variational level set. The performance of the proposed approach was evaluated by comparing manually obtained boundaries with automated segmentation contours in 10 SPECT data sets obtained from adult patients. Segmented images by proposed methods were visually compared with manually outlined contours and the performance was evaluated using ROC analysis. RESULTS: The proposed method and a traditional level set method were compared by computing the sensitivity and specificity of ventricular outlines as well as ROC analysis. The results show that the proposed method can effectively segment LV regions with a sensitivity and specificity of 88.9 and 96.8%, respectively. Experimental results demonstrate the effectiveness and reasonable robustness of the automatic method. CONCLUSION: A new variational level set technique was able to automatically trace the LV contour in cardiac SPECT data sets, based on the characteristics of the overall region of LV images. Smooth and accurate LV contours were extracted using this new method, reducing the influence of nearby interfering structures including a hypertrophied right ventricle, hepatic or intestinal activity, and pulmonary or intramammary activity.

Conformal fields in prostate radiotherapy: a comparison between measurement, calculation and simulation.

AIMS: The objective of this study is to evaluate the accuracy of a treatment planning system (TPS) for calculating the dose distribution parameters in conformal fields (CF). Dosimetric parameters of CF's were compared between measurement, Monte Carlo simulation (MCNP4C) and TPS calculation. MATERIALS AND METHODS: Field analyzer water phantom was used for obtaining percentage depth dose (PDD) curves and beam profiles (BP) of different conformal fields. MCNP4C was used to model conformal fields dose specification factors and head of linear accelerator varian model 2100C/D. RESULTS: Results showed that the distance to agreement (DTA) and dose difference (DD) of our findings were well within the acceptance criteria of 3 mm and 3%, respectively. CONCLUSIONS: According to this study it can be revealed that TPS using equivalent tissue air ratio calculation method is still convenient for dose prediction in non small conformal fields normally used in prostate radiotherapy. It was also showed that, since there is a close correlation with Monte Carlo simulation, measurements and TPS, Monte Carlo can be further confirmed for implementation and calculation dose distribution in non standard and complex conformal irradiation field for treatment planning systems.

Classification and numbering of teeth in multi-slice CT images using wavelet-Fourier descriptor.

PURPOSE: Teeth arrangement is essential in face ergonomics and healthiness. In addition, they play key roles in forensic medicine. Various computer-assisted procedures for medical application in quantitative dentistry require automatic classification and numbering of teeth in dental images. METHOD: In this paper, we propose a multi-stage technique to classify teeth in multi-slice CT (MSCT) images. The proposed algorithm consists of the following three stages: segmentation, feature extraction and classification. We segment the teeth by employing several techniques including Otsu thresholding, morphological operations, panoramic re-sampling and variational level set. In the feature extraction stage, we follow a multi-resolution approach utilizing wavelet-Fourier descriptor (WFD) together with a centroid distance signature. We compute the feature vector of each tooth by employing the slice associated with largest tooth tissues. The feature vectors are employed for classification in the third stage. We perform teeth classification by a conventional supervised classifier. We employ a feed- forward neural network classifier to discriminate different teeth from each other. RESULTS: The performance of the proposed method was evaluated in the presence of 30 different MSCT data sets including 804 teeth. We compare classification results of the WFD technique with Fourier descriptor (FD) and wavelet descriptor (WD) techniques. We also investigate the invariance properties of the WFD technique. Experimental results reveal the effectiveness of the proposed method. CONCLUSION: We provided an integrated solution for teeth classification in multi-slice CT datasets. In this regard, suggested segmentation technique was successful to separate teeth from each other. The employed WFD approach was successful to discriminate and numbering of the teeth in the presence of missing teeth. The solution is independent of anatomical information such as knowing the sequence of teeth and the location of each tooth in the jaw.