Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients.

Background: Computed tomography (CT) scan is one of the main tools to diagnose and grade COVID-19 progression. To avoid the side effects of CT imaging, low-dose CT imaging is of crucial importance to reduce population absorbed dose. However, this approach introduces considerable noise levels in CT images. Methods: In this light, we set out to simulate four reduced dose levels (60% dose, 40% dose, 20% dose, and 10% dose) of standard CT imaging using Beer-Lambert's law across 49 patients infected with COVID-19. Then, three denoising filters, namely Gaussian, bilateral, and median, were applied to the different low-dose CT images, the quality of which was assessed prior to and after the application of the various filters via calculation of peak signal-to-noise ratio, root mean square error (RMSE), structural similarity index measure, and relative CT-value bias, separately for the lung tissue and whole body. Results: The quantitative evaluation indicated that 10%-dose CT images have inferior quality (with RMSE = 322.1 ± 104.0 HU and bias = 11.44% ± 4.49% in the lung) even after the application of the denoising filters. The bilateral filter exhibited superior performance to suppress the noise and recover the underlying signals in low-dose CT images compared to the other denoising techniques. The bilateral filter led to RMSE and bias of 100.21 ± 16.47 HU and - 0.21% ± 1.20%, respectively, in the lung regions for 20%-dose CT images compared to the Gaussian filter with RMSE = 103.46 ± 15.70 HU and bias = 1.02% ± 1.68% and median filter with RMSE = 129.60 ± 18.09 HU and bias = -6.15% ± 2.24%. Conclusions: The 20%-dose CT imaging followed by the bilateral filtering introduced a reasonable compromise between image quality and patient dose reduction.

18F-Fludeoxyglucose Absorbed Dose Estimation in Fetus during Early Pregnancy.

The purpose of this study is to assess a rare case of fetal radiation absorbed dose here through 18F-Fludeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) in early pregnancy (5-week-old fetus). The fetal absorbed dose due to the radiation emitted from the mother's body, the fetus self-dose, and the dose received from CT were computed. The 35-year-old patient, weighing 85 kg, was injected with 370 MBq of 18F-FDG. Imaging started at 1 h with CT acquisition followed by PET imaging. The photon and positron self-dose was calculated by applying the Monte Carlo (MC) GATE (GEANT 4 Application for Tomographic Emission) code. The volume of absorbed dose from the mother's body organs and the absorbed dose from the CT were added to the self-dose to obtain the final dose. The volume of self-dose obtained through MC simulation for the fetus was 3.3 × 10-2 mGy/MBq, of which 2.97 × 10-2 mGy/MBq was associated with positrons and 0.33 × 10-2 mGy/MBq was associated with photons. Biologically, the absorbed dose from CT, 7.3 mGy, had to be added to the total dose. The absorbed dose by the fetus during early pregnancy was higher than the standard value of 2.2 × 10-2 mGy/MBq (MIRD DER) because, during the examinations, the mother's bladder was full. This issue was a concern during updating standards.

Comparison of different deep learning architectures for synthetic CT generation from MR images.

PURPOSE: Among the different available methods for synthetic CT generation from MR images for the task of MR-guided radiation planning, the deep learning algorithms have and do outperform their conventional counterparts. In this study, we investigated the performance of some most popular deep learning architectures including eCNN, U-Net, GAN, V-Net, and Res-Net for the task of sCT generation. As a baseline, an atlas-based method is implemented to which the results of the deep learning-based model are compared. METHODS: A dataset consisting of 20 co-registered MR-CT pairs of the male pelvis is applied to assess the different sCT production methods' performance. The mean error (ME), mean absolute error (MAE), Pearson correlation coefficient (PCC), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) metrics were computed between the estimated sCT and the ground truth (reference) CT images. RESULTS: The visual inspection revealed that the sCTs produced by eCNN, V-Net, and ResNet, unlike the other methods, were less noisy and greatly resemble the ground truth CT image. In the whole pelvis region, the eCNN yielded the lowest MAE (26.03 ± 8.85 HU) and ME (0.82 ± 7.06 HU), and the highest PCC metrics were yielded by the eCNN (0.93 ± 0.05) and ResNet (0.91 ± 0.02) methods. The ResNet model had the highest PSNR of 29.38 ± 1.75 among all models. In terms of the Dice similarity coefficient, the eCNN method revealed superior performance in major tissue identification (air, bone, and soft tissue). CONCLUSIONS: All in all, the eCNN and ResNet deep learning methods revealed acceptable performance with clinically tolerable quantification errors.

Bone Age Assessment of Iranian Children in an Automatic Manner.

BACKGROUND: Bone age assessment (BAA) is a radiological process with the aim of identifying growth disorders in children. The objective of this study is to assess the bone age of Iranian children in an automatic manner. METHODS: In this context, three computer vision techniques including histogram of oriented gradients (HOG), local binary pattern (LBP), and scale-invariant feature transform (SIFT) are applied to extract appropriate features from the carpal and epiphyseal regions of interest. Two different datasets are applied here: the University of Southern California hand atlas for training this computer-aided diagnosis (CAD) system and Iranian radiographs for evaluating the performance of this system for BAA of Iranian children. In this study, the concatenation of HOG, LBP, and dense SIFT feature vectors and background subtraction are applied to improve the performance of this approach. Support vector machine (SVM) and K-nearest neighbor are used here for classification and the better results yielded by SVM. RESULTS: The accuracy of female radiographs is 90% and of male is 71.42%. The mean absolute error is 0.16 and 0.42 years for female and male test radiographs, respectively. Cohen's kappa coefficients are 0.86 and 0.6, P < 0.05, for female and male radiographs, respectively. The results indicate that this proposed approach is in substantial agreement with the bone age reported by the experienced radiologist. CONCLUSION: This approach is easy to implement and reliable, thus qualified for CAD and automatic BAA of Iranian children.

A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI.

PURPOSE: Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in radiation therapy, MRI-guided radiation treatment planning is limited by the fact that MRI does not directly provide the electron density map required for absorbed dose calculation. In this work, a new deep convolutional neural network model with efficient learning capability, suitable for applications where the number of training subjects is limited, is proposed to generate accurate synthetic computed tomography (sCT) images from MRI. METHODS: This efficient convolutional neural network (eCNN) is built upon a combination of the SegNet architecture (a 13-layer encoder-decoder structure similar to the U-Net network) without softmax layers and the residual network. Moreover, maxpooling indices and high resolution features from the encoding network were incorporated into the corresponding decoding layers. A dataset containing 15 co-registered MRI-CT pairs of male pelvis (1861 two-dimensional images) were used for training and evaluation of MRI to CT synthesis process using a fivefold cross-validation scheme. The performance of the eCNN model was compared to an atlas-based sCT generation technique as well as the original U-Net model considering CT images as reference. The mean error (ME), mean absolute error (MAE), Pearson correlation coefficient (PCC), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) metrics were calculated between sCT and ground truth CT images. RESULTS: The eCNN model exhibited effective learning capability using only 12 training subjects. The model achieved a ME and MAE of 2.8 ± 10.3 and 30.0 ± 10.4 HU, respectively, which is substantially lower than values achieved by the atlas-based (-0.8 ± 35.4 and 64.6 ± 21.2) and U-Net (7.4 ± 11.9 and 44.0 ± 8.8) methods, respectively. CONCLUSION: The proposed eCNN model exhibited efficient convergence rate with a low number of training subjects, while providing accurate synthetic CT images. The eCNN model outperformed the original U-Net model and showed superior performance to the atlas-based technique.

Assessing the Bone Age of Children in an Automatic Manner Newborn to 18 Years Range.

Bone age assessment (BAA) is a radiological process to identify the growth disorders in children. Although this is a frequent task for radiologists, it is cumbersome. The objective of this study is to assess the bone age of children from newborn to 18 years old in an automatic manner through computer vision methods including histogram of oriented gradients (HOG), local binary pattern (LBP), and scale invariant feature transform (SIFT). Here, 442 left-hand radiographs are applied from the University of Southern California (USC) hand atlas. In this experiment, for the first time, HOG-LBP-dense SIFT features with background subtraction are applied to assess the bone age of the subject group. For this purpose, features are extracted from the carpal and epiphyseal regions of interest (ROIs). The SVM and 5-fold cross-validation are used for classification. The accuracy of female radiographs is 73.88% and of the male is 68.63%. The mean absolute error is 0.5 years for both genders' radiographs. The accuracy a within 1-year range is 95.32% for female and 96.51% for male radiographs. The accuracy within a 2-year range is 100% and 99.41% for female and male radiographs, respectively. The Cohen's kappa statistical test reveals that this proposed approach, Cohen's kappa coefficients are 0.71 for female and 0.66 for male radiographs, p value < 0.05, is in substantial agreement with the bone age assessed by experienced radiologists within the USC dataset. This approach is robust and easy to implement, thus, qualified for computer-aided diagnosis (CAD). The reduced processing time and number of ROIs facilitate BAA.

Mathematical Modeling of Preclinical Alpha-Emitter Radiopharmaceutical Therapy.

Preclinical studies, in vivo, and in vitro studies, in combination with mathematical modeling can help optimize and guide the design of clinical trials. The design and optimization of alpha-particle emitter radiopharmaceutical therapy (αRPT) is especially important as αRPT has the potential for high efficacy but also high toxicity. We have developed a mathematical model that may be used to identify trial design parameters that will have the greatest impact on outcome. The model combines Gompertzian tumor growth with antibody-mediated pharmacokinetics and radiation-induced cell killing. It was validated using preclinical experimental data of antibody-mediated 213Bi and 225Ac delivery in a metastatic transgenic breast cancer model. In modeling simulations, tumor cell doubling time, administered antibody, antibody specific-activity, and antigen-site density most impacted median survival. The model was also used to investigate treatment fractionation. Depending upon the time-interval between injections, increasing the number of injections increased survival time. For example, two administrations of 200 nCi, 225Ac-labeled antibody, separated by 30 days, resulted in a simulated 31% increase in median survival over a single 400 nCi administration. If the time interval was 7 days or less, however, there was no improvement in survival; a one-day interval between injections led to a 10% reduction in median survival. Further model development and validation including the incorporation of normal tissue toxicity is necessary to properly balance efficacy with toxicity. The current model is, however, useful in helping understand preclinical results and in guiding preclinical and clinical trial design towards approaches that have the greatest likelihood of success. SIGNIFICANCE: Modeling is used to optimize αRPT.

Effect of beam configuration with inaccurate or incomplete small field output factors on the accuracy of treatment planning dose calculation.

PURPOSE: To evaluate the effect of beam configuration with inaccurate or incomplete small field output factors on the accuracy of dose calculations in treatment planning systems. METHODS: Output factors were measured using various detectors and for a range of field sizes. Three types of treatment machines were configured in two treatment planning systems. In the first (corrected) machine, the Exradin W1 scintillator was used to determine output factors. In the second (uncorrected) machine, the measured output factors by the A1SL ion chamber without considering output correction factors for small field sizes were utilized. In the third (clinical) machine, measured output factors by the Exradin W1 were used but not for field sizes smaller than 2 × 2 cm2 . The dose computed by the anisotropic analytical algorithm (AAA), Acuros XB (AXB) and collapsed cone convolution/superposition (CCC) algorithms in the three machines were delivered using static (jaw-, MLC-, and jaw/MLC-defined), and composite [intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT)] fields. The differences between measured and calculated dose values were analyzed. RESULTS: For static fields, the percentage differences between measured and calculated doses by the three algorithms in three configured machines were <2% for field sizes larger than 2 × 2 cm2 . In jaw- and jaw/MLC-defined fields smaller than 2 × 2 cm2 , the corrected machine presented better agreement with measurement. Considering output correction factors in MLC-defined fields, among the three configured machines, the accuracy of calculation improved to within ±0.5%. For MLC-defined field size of 1 × 1 cm2 , AXB showed the smallest percentage difference (1%). In IMRT and VMAT plans, the percentage differences between measured and calculated doses at the isocenter, as well as the gamma analysis of different plans, which include field sizes larger than 3 × 3 cm2 , did not vary noticeably. For smaller field sizes, using the corrected machine influences dose calculation accuracy. CONCLUSION: Configuration with corrected output factors improves accuracy of dose calculation for static field sizes smaller than 2 × 2 cm2 . For very small fields, the robustness of the dose calculation algorithm affects the accuracy of dose as well. In IMRT and VMAT plans, which include small subfields, the size of the jaw-defined field is an important factor and using corrected output factors increases dose calculation accuracy.

Monte Carlo assessment of beam deflection and depth dose equivalent variation of a carbon-ion beam in a perpendicular magnetic field.

PURPOSE: To evaluate beam deflection and dose equivalent perturbation of carbon-ion (C-ion) versus depth in a perpendicular magnetic field with the motivation of application to potential future development of MRI-guided carbon therapy. METHODS: A therapeutic beamline, a rectangular water phantom (homogeneous) and a multi-layer tissue phantom were simulated by applying the FLUKA Monte Carlo simulation code. The C-ion beam deflection variation against depth inside the water phantom at 100, 220 and 310 MeV/nucleon (MeV/n) was calculated in the presence of 0.5, 1.5 and 3 T magnetic fields. The 220 MeV/n primary ion depth dose equivalent variations induced by a 1.5 T field were calculated inside the homogeneous and multi-layer phantoms. RESULTS: The calculated deflections were ranging from 0 to 10.5 mm. The Bragg depth did not change by applying a 1.5 T field to both phantoms under study at 220 MeV/n energy. The dose equivalent in the Bragg depth inside the homogeneous and multi-layer tissue phantoms was found to be reduced by 5.1% and 2.95%, respectively. A dose equivalent reduction of 5.77% in the Bragg depth was obtained when an air layer of 1.8 cm thick was added to the multi-layer phantom. CONCLUSION: Dose equivalent perturbation and beam deflection become important at energies above 100 MeV/n, in both phantoms affected by a 1.5 T magnetic field.

Reliability of sonography in the assessment of lumbar stabilizer muscles size in healthy subjects and patients with scoliosis.

OBJECTIVE: The purpose of the present study was to compare the reliability of sonography in the evaluation of abdominal and multifidus muscles size between healthy subjects and patients with scoliosis. METHODS: In this study, 20 healthy males and 20 male patients with scoliosis (20-50 years old) were recruited. Multifidus and abdominal muscles (transversus abdominis, internal and external oblique) size were assessed by sonography. Three images were recorded; the first and second images were taken on the same day with an hour interval to evaluate within-day reliability, and the third image was taken one-week later to assess between-day reliability. RESULTS: Intraclass correlation coefficient (ICC = 0.82-0.91) demonstrated high within-day reliability of sonography in the assessment of abdominal muscle thickness in both groups. In addition, high between-day reliability was observed for these muscles in both healthy and patient groups (ICC = 0.80-0.89). Within-day and also between-day reliability of multifidus muscle were shown to be high in the healthy group (ICC = 0.81-0.88) and the patient group (ICC = 0.78-0.85). Overall, within-day reliability was higher than between-day reliability and also the reliability of sonography in healthy subjects was greater than of those suffering from scoliosis. CONCLUSIONS: According to the results, sonography was shown to be a highly reliable imaging technique for assessment of abdominal and multifidus muscle size in healthy males and those suffering from scoliosis.

Technical Note: Impact of region of interest size and location in Gafchromic film dosimetry.

PURPOSE: To evaluate dependence of measured dose on size and location of region of interest (ROI) in Gafchromic EBT3 film dosimetry. METHODS: Gafchromic EBT3 films were irradiated perpendicularly using the 6MV beam from a linear accelerator at 10 cm depth (100 cm SSD) of a 30 × 30 × 20 cm3 solid water phantom for a range of field sizes of 6 × 6 to 100 × 100 mm2 . ImageJ software was used for reading pieces of film. The appropriate location of ROIs in scanned films was found by two methods. First, the ROI was visually placed at the center of image. Second, the profile of pixel value versus distance was plotted and the center of profile was used for drawing ROI. Each scanned film was read using both methods and for three ROI sizes (1, 2, and 4 mm). A plastic scintillator, Exradin W1, was used as the reference dosimeter. RESULTS: Comparing the three ROI sizes using both methods showed that there was less than 2% difference from reference in output factor measurements for field sizes larger or equal to 10 × 10 mm2 . The percentage differences were increased in field sizes smaller than 10 × 10 mm2 and for ROI size of 4 × 4 mm2 for both centered-ROI and profiled-ROI methods. The mean percentage differences from reference measurements, for field sizes of 100 × 100 to 20 × 20 mm2 , were smaller than 1% in both methods of ROI positioning. For field sizes of 15 × 15 and 10 × 10 mm2 , the smaller mean percentage differences were observed in profiled-ROI (4 × 4 mm2 ) and centered-ROI (4 × 4 mm2 ). For the field sizes of 8 × 8 and 6 × 6 mm2 , the profiled-ROI (2 × 2 mm2 ) had smallest mean percentage difference, which was 0.88%. CONCLUSION: The ROI size of 4 × 4 mm2 is appropriate for dose measurements in field sizes of 100 × 100 mm2 to 10 × 10 mm2 , regardless of the method of finding location of ROI. In field sizes smaller than 10 × 10 mm2 , finding location of the ROI by profile of pixel values increases the accuracy of measurement, and ROI size of 2 × 2 mm2 has the smallest difference from the reference dose measurements.

Automatic Region-Based Brain Classification of MRI-T1 Data.

Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness.

A New Method to Segment the Multiple Sclerosis Lesions on Brain Magnetic Resonance Images.

Automatic segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance imaging (MRI) has been widely investigated in the recent years with the goal of helping MS diagnosis and patient follow-up. In this research work, Gaussian mixture model (GMM) has been used to segment the MS lesions in MRIs, including T1-weighted (T1-w), T2-w, and T2-fluid attenuation inversion recovery. Usually, GMM is optimized by using expectation-maximization (EM) algorithm. The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected. So, GMM is time-consuming and not too much efficient. To overcome these limitations, in this research study, at the first step, GMM was applied to segment only T1-w images by using 100 various starting points when the maximum number of iterations was considered to be 50. Then segmentation results were used to calculate the parameters of the other two images. Furthermore, FAST-trimmed likelihood estimator algorithm was applied to determine which voxels should be rejected. The output result of the segmentation was classified in three classes; White and Gray matters, cerebrospinal fluid, and some rejected voxels which prone to be MS. In the next phase, MS lesions were detected by using some heuristic rules. This new method was applied on the brain MRIs of 25 patients from two hospitals. The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82. The results showed a better performance for the proposed approach, in comparison to those of previous works with less time-consuming.

Magnetic resonance image tissue classification using an automatic method.

BACKGROUND: Brain segmentation in magnetic resonance images (MRI) is an important stage in clinical studies for different issues such as diagnosis, analysis, 3-D visualizations for treatment and surgical planning. MR Image segmentation remains a challenging problem in spite of different existing artifacts such as noise, bias field, partial volume effects and complexity of the images. Some of the automatic brain segmentation techniques are complex and some of them are not sufficiently accurate for certain applications. The goal of this paper is proposing an algorithm that is more accurate and less complex). METHODS: In this paper we present a simple and more accurate automated technique for brain segmentation into White Matter, Gray Matter and Cerebrospinal fluid (CSF) in three-dimensional MR images. The algorithm's three steps are histogram based segmentation, feature extraction and final classification using SVM. The integrated algorithm has more accurate results than what can be obtained with its individual components. To produce much more efficient segmentation method our framework captures different types of features in each step that are of special importance for MRI, i.e., distributions of tissue intensities, textural features, and relationship with neighboring voxels or spatial features. RESULTS: Our method has been validated on real images and simulated data, with desirable performance in the presence of noise and intensity inhomogeneities. CONCLUSIONS: The experimental results demonstrate that our proposed method is a simple and accurate technique to define brain tissues with high reproducibility in comparison with other techniques. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_207.

Automatic Detection of Malignant Melanoma using Macroscopic Images.

In order to distinguish between benign and malignant types of pigmented skin lesions, computerized procedures have been developed for images taken by different equipment that the most available one of them is conventional digital cameras. In this research, a new procedure to detect malignant melanoma from benign pigmented lesions using macroscopic images is presented. The images are taken by conventional digital cameras with spatial resolution higher than one megapixel and by considering no constraints and special conditions during imaging. In the proposed procedure, new methods to weaken the effect of nonuniform illumination, correction of the effect of thick hairs and large glows on the lesion and also, a new threshold-based segmentation algorithm are presented. 187 features representing asymmetry, border irregularity, color variation, diameter and texture are extracted from the lesion area and after reducing the number of features using principal component analysis (PCA), lesions are determined as malignant or benign using support vector machine classifier. According to the dermatologist diagnosis, the proposed processing methods have the ability to detect lesions area with high accuracy. The evaluation measures of classification have indicated that 13 features extracted by PCA method lead to better results than all of the extracted features. These results led to an accuracy of 82.2%, sensitivity of 77% and specificity of 86.93%. The proposed method may help dermatologists to detect the malignant lesions in the primary stages due to the minimum constraints during imaging, the ease of usage by the public and nonexperts, and high accuracy in detection of the lesion type.

Evaluation of [67Ga]-insulin for insulin receptor imaging.

BACKGROUND: Radiolabelled human recombinant insulin can be used for the imaging of insulin receptors in some tumours where FDG has natural uptake and diminishes the value of its imaging. MATERIAL AND METHODS: Insulin was successively labelled with [(67)Ga]-gallium chloride after conjugation with freshly prepared cyclic DTPA-dianhydride (HPLC radiochemical purity assay > 96%) followed by biodistribution studies in normal rats, white blood cell labelling and preliminary SPECT studies. RESULTS: In vitro studies demonstrated the retention of radiolabelled insulin receptor affinity using freshly prepared human white blood cells at different blood sugar conditions. Preliminary in vivo studies in a normal rat model was performed to determine the biodistribution of the radioimmunoconjugate at up to 44 h. SPECT images revealed high uptake of the liver. CONCLUSION: Radiolabelled insulin is stable enough to be used in biological studies in order to image insulin receptors in diabetic conditions as well as possible tumour imaging applications. The data was consistent with other radiolabelled insulin studies.

Production and biological evaluation of [18F]-6-thia-14-fluoro-heptadecanoic acid.

BACKGROUND: [(18)F]-6-thia-14-fluoro-heptadecanoic acid 3b, a free fatty acid, has been used in myocardial PET imaging. In order to establish an automated synthesis module for routine production in the country, a study was performed for optimization of the production conditions as well as making modifications. MATERIAL AND METHODS: [(18)F]Benzyl-14-Fluoro-6-thia-heptadecanoate 2b was prepared in no-carrier-added (n.c.a) form from Benzyl-14-tosyloxy-6-thia-heptadecanoate 1 in one step at 90 degrees C in Kryptofix2.2.2/[(18)F] with acetonitrile as the solvent followed by Silica column chromatography. The radiolabelled ester 2 was then hydrolysed to yield [(18)F]-6-thia-14-fluoro-heptadecanoic 3b. The final solution was concentrated using the C(18) SPE system and administered to normal rats for biodistribution and co-incidence imaging studies. RESULTS: The synthesis took 15 min with overall radiochemical yield of 15-25% (EOS) and chemical-radiochemical purity of more than 90%. Automation was performed using a two-pot synthesis. The best imaging time was shown to be 140-180 minutes post injection. CONCLUSIONS: Using this procedure a fast, reliable, automated synthesis for the cardial PET tracer, i.e. [(18)F]FTHA, can be obtained without an HPLC purification step.