Qualitative evaluation of filter function in brain SPECT [Persian]

Abstract Introduction: Filtering can greatly affect the quality of clinical images. Determining the best filter and the proper degree of smoothing can help to ensure the most accurate diagnosis. Methods: Forty five patient's data aquired during brain phantom SPECT studies were reconstructed using filtered back-projection technique. The ramp, Shepp-Logan, Cosine, Hamming, Hanning, Butterworth, Metz and Wiener filters were examined to find the optimum condition for each filter. For each slice image, 6200 reconstruction options were ..

Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients

Introduction Neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection is the standard treatment for locally advanced rectal cancer (LARC). Radiomics can be used as noninvasive biomarker for prediction of response to therapy. The main aim of this study was to evaluate the association of MRI texture features of LARC with nCRT response and the effect of Laplacian of Gaussian (LoG) filter and feature selection algorithm in prediction process improvement. Methods All patients underwent MRI with a 3T clinical scanner, 1 week before nCRT. For each patient, intensity, shape, and texture-based features were derived from MRI images with LoG filter using the IBEX software and without preprocessing. We identified responder from a non-responder group using 9 machine learning classifiers. Then, the effect of preprocessing LoG filters with 0.5, 1 and 1.5 value on these classification algorithms' performance was investigated. Eventually, classification algorithm's results were compared in different feature selection methods. Result Sixty-seven patients with LARC were included in the study. Patients' nCRT responses included 11 patients with Grade 0, 19 with Grade 1, 26 with Grade 2, and 11 with Grade 3 according to AJCC/CAP pathologic grading. In MR Images which were not preprocessed, the best performance was for Ada boost classifier (AUC = 74.8) with T2W MR Images. In T1W MR Images, the best performance was for aba boost classifier (AUC = 78.1) with a sigma = 1 preprocessing LoG filter. In T2W MR Images, the best performance was for naive Bayesian network classifier (AUC = 85.1) with a sigma = 0.5 preprocessing LoG filter. Also, performance of machine learning models with CfsSubsetEval (CF SUB E) feature selection algorithm was better than others. Conclusion Machine learning can be used as a response predictor model in LARC patients, but its performance should be improved. A preprocessing LoG filter can improve the machine learning methods performance and at the end, the effect of feature selection algorithm on model's performance is clear. Keywords:MRI; Rectal cancer; Radiomics; Machine learnin

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

Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics:a Preliminary Machine Learning Study

In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist. More than 1700 2D and 3D radiomics features were extracted from each patient’s scan. A cross-combination of three feature selections and seven classifier methods was implemented. Three classes of no or dis-improvement (class 1), improved EF from 0 to 5% (class 2), and improved EF over 5% (class 3) were predicted by using tenfold cross-validation. Lastly, the models were evaluated based on accuracy, AUC, sensitivity, specificity, precision, and F-score. Neighborhood component analysis (NCA) selected the most predictive feature signatures, including Gabor, first-order, and NGTDM features. Among the classifiers, the best performance was achieved by the fine KNN classifier, which yielded mean accuracy, AUC, sensitivity, specificity, precision, and F-score of 0.84, 0.83, 0.75, 0.87, 0.78, and 0.76, respectively, in 100 iterations of classification, within the 52 patients with 10-fold cross-validation. The MPI-SPECT-based radiomic features are well suited for predicting post-revascularization EF and therefore provide a helpful approach for deciding on the most appropriate treatment.</p

Respiratory Motion Detection and Correction in ECG-Gated SPECT: a New Approach

Objective: Gated myocardial perfusion single-photon emission computed tomography (GSPECT) has been established as an accurate and reproducible diagnostic and prognostic technique for the assessment of myocardial perfusion and function. Respiratory motion is among the major factors that may affect the quality of myocardial perfusion imaging (MPI) and consequently the accuracy of the examination. In this study, we have proposed a new approach for the tracking of respiratory motion and the correction of unwanted respiratory motion by the use of respiratory-cardiac gated-SPECT (RC-GSPECT). In addition, we have evaluated the use of RC-GSPECT for quantitative and visual assessment of myocardial perfusion and function. Materials and Methods: Twenty-six patients with known or suspected coronary artery disease (CAD)-underwent two-day stress and rest 99mTc-Tetrofosmin myocardial scintigraphy using both conventional GSPECT and RC-GSPECT methods. The respiratory signals were induced by use of a CT real-time position management (RPM) respiratory gating interface. A PIO-D144 card, which is transistor-transistor logic (TTL) compatible, was used as the input interface for simultaneous detection of both ECG and respiration signals. Results: A total of 26 patients with known or suspected CAD were examined in this study. Stress and rest myocardial respiratory motion in the vertical direction was 8.8-16.6 mm (mean, 12.4 ± 2.9 mm) and 7.8-11.8 mm (mean, 9.5 ± 1.6 mm), respectively. The percentages of tracer intensity in the inferior, inferoseptal and septal walls as well as the inferior to lateral (I/L) uptake ratio was significantly higher with the use of RC-GSPECT as compared to the use of GSPECT (p &lt; 0.01). In a left ventricular ejection fraction (LVEF) correlation analysis between the use of rest GSPECT and RC-GSPECT with echocardiography, better correlation was noted between RC-GSPECT and echocardiography as compared with the use of GSPECT (y = 0.9654x + 1.6514; r = 0.93, p &lt; 0.001 versus y = 0.8046x + 5.1704; r = 0.89, p &lt; 0.001). Nineteen (19/26) patients (73.1) showed abnormal myocardial perfusion scans with reversible regional myocardial defects; of the 19 patients, 14 (14/26) patients underwent coronary angiography. Conclusion: Respiratory induced motion can be successfully corrected simultaneously with the use of ECG-gated SPECT in MPI studies using this proposed technique. Moreover, the use of ECG-gated SPECT improved image quality, especially in the inferior and septal regions that are mostly affected by diaphragmatic attenuation. However, the effect of respiratory correction depends mainly on the patient respiratory pattern and may be clinically relevant in certain cases

Compensation of Cross-Contamination in Simultaneous 201Tl/99mTc Myocardial Perfusion SPECT Imaging

Introduction: It is a common protocol to use 201Tl for the rest and 99mTc for the stress cardiac SPECT imaging. Theoretically, both types of imaging may be performed simultaneously using different energy windows for each radionuclide. However, a potential limitation is the cross-contamination of scattered photons from 99mTc and collimator X-rays into the 201Tl energy window. We used a middle energy window method to correct this cross-contamination. Material and Methods: Using NCAT, a typical software torso phantom was generated. An extremely thin line source of 99mTc activity was placed inside the cardiac region of the phantom and no activity in the other parts.  The SimSET Monte Carlo simulator was used to image the phantom in different energy windows. To find the relationship between projections in different energy windows, deconvolution theory was used. We investigated the ability of the suggested functions in three steps: Monte Carlo simulation, phantom experiment and clinical study. In the last step, SPECT images of eleven patients who had angiographic data were acquired in different energy windows. All of these images were compared by determining the contrast between a defect or left ventricle cavity and the myocardium. Results: We found a new 2D kernel which had an exponential pattern with a much higher center. This function was used for modeling 99mTc down scatter distribution from the middle window image. X-ray distribution in the 201Tl window was also modeled as the 99mTc photopeak image convolved by a Gaussian function. Significant improvements in the contrasts of the simultaneous dual 201Tl images were found in each step before and after reconstruction. In comparison with other similar methods, better results were acquired using our suggested functions. Conclusion: Our results showed contrast improvement in thallium images after correction, however, many other parameters should be evaluated for clinical approaches. There are many advantages in simultaneous dual isotope imaging. It halves imaging time and reduces patient waiting time and discomfort. Identical rest/stress registration of images also facilitates physicists’ motion or attenuation corrections and physicians’ image interpretation