Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks

Purpose: This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (Ki) from standardized uptake value (SUV) and/or dynamic PET image series. Methods: Whole-body 18F-FDG dynamic PET images of 19 subjects consisting of 13 frames or passes were employed for training a residual deep learning model with SUV and/or dynamic series as input and Ki-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme. Owing to the availability of SUV images acquired 60 min post-injection (20 min total acquisition time), the data sets used for the training of the models were split into two groups: “With SUV” and “Without SUV.” For “With SUV” group, the model was first trained using only SUV images and then the passes (starting from pass 13, the last pass, to pass 9) were added to the training of the model (one pass each time). For this group, 6 models were developed with input data consisting of SUV, SUV plus pass 13, SUV plus passes 13 and 12, SUV plus passes 13 to 11, SUV plus passes 13 to 10, and SUV plus passes 13 to 9. For the “Without SUV” group, the same trend was followed, but without using the SUV images (5 models were developed with input data of passes 13 to 9). For model performance evaluation, the mean absolute error (MAE), mean error (ME), mean relative absolute error (MRAE%), relative error (RE%), mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between the predicted Ki-Patlak images by the two groups and the reference Ki-Patlak images generated through Patlak analysis using the whole acquired data sets. For specific evaluation of the method, regions of interest (ROIs) were drawn on representative organs, including the lung, liver, brain, and heart and around the identified malignant lesions. Results: The MRAE%, RE%, PSNR, and SSIM indices across all patients were estimated as 7.45 ± 0.94%, 4.54 ± 2.93%, 46.89 ± 2.93, and 1.00 ± 6.7 × 10−7, respectively, for models predicted using SUV plus passes 13 to 9 as input. The predicted parameters using passes 13 to 11 as input exhibited almost similar results compared to the predicted models using SUV plus passes 13 to 9 as input. Yet, the bias was continuously reduced by adding passes until pass 11, after which the magnitude of error reduction was negligible. Hence, the predicted model with SUV plus passes 13 to 9 had the lowest quantification bias. Lesions invisible in one or both of SUV and Ki-Patlak images appeared similarly through visual inspection in the predicted images with tolerable bias. Conclusion: This study concluded the feasibility of direct deep learning-based approach to estimate Ki-Patlak parametric maps without requiring the input function and with a fewer number of passes. This would lead to shorter acquisition times for WB dynamic imaging with acceptable bias and comparable lesion detectability performance.</p

Predicting students' performance using machine learning algorithms and educational data mining (a case study of Shahed University)

The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA

Evaluation of alveolar basement membrane function in the diabetes mellitus patients

چکیده: زمینه و هدف: افزایش ضخامت غشاء پایه آلوئولی در مبتلایان دیابت تیپ یک و دو همراه با درگیری عروق کوچک ریه در آتوپسی‌ها گزارش شده است. برای ارزیابی فونکسیون غشاء پایه آلوئولی از اندازه گیری ظرفیت انتقال گاز منواکسید کربن در ریه استفاده می‌شود. هدف این مطالعه بررسی ظرفیت انتشار منواکسید کربن ریه در بیماران مبتلا به دیابت می باشد. روش بررسی: در یک مطالعه توصیفی - تحلیلی ظرفیت انتشار منواکسید کربن ریه در 70 (55 زن و 15 مرد) بیمار مبتلا به دیابت بدون سابقه بیماری قلبی، ریوی، کلیوی، کم خونی و بدون شکایت از علایم تنفسی اندازه گیری شد. 55 زن و 15 مرد سالم با خصوصیات مشابه بیماران برای گروه کنترل انتخاب شدند. در گروه بیماران و کنترل که اسپیرومتری طبیعی داشتند. ظرفیت انتشار منواکسید کربن ریه بصورت متد یکبار تنفس انجام شد. داده ها با استفاده از آزمون آماری t-student تجزیه و تحلیل شد. یافته ها: میزان متوسط ظرفیت انتشار منواکسید کربن ریه در بیماران مبتلا به دیابت بدون علایم تنفسی و گروه کنترل به ترتیب 4/2±65/9 و 79/1±10/9 میلی مول بر دقیقه بر کیلو پاسکال بود (05/0P>). همچنین میزان ظرفیت اصلاح شده حجمی تبادل منواکسید کربن در ریه نسبت به گروه کنترل تفاوت معنی‌دار نداشت. نتیجه گیری: این مطالعه نشان دهنده عدم کاهش ظرفیت انتشار منواکسید کربن در ریه در بیماران دیابتی بدون علایم تنفسی است که نشان دهنده سلامت بستر آلوئول و کاپیلرهای ریه ای می باشد. با استناد به این مطالعه به نظر می رسد انسولین استنشاقی در بیماران دیابتی بدون عوارض عروقی بخوبی جذب شود

Oguchi Disease Associated with Keratoconus

This is a Photo Essay and does not have an abstract. Please download the PDF or view the article in HTML

AUTOMATED BRAIN TUMOR SEGMENTATION IN MR IMAGES USING A HIDDEN MARKOV CLASSIFIER FRAMEWORK TRAINED BY SVD-DERIVED FEATURES

Interpreting brain MR images are becoming automated, to such extent that in some cases “all” the diagnostic procedure is done by computers. Therefore, diagnosing the patients is done by a comparably higher accuracy. Computer models that have been trained by a priori knowledge act as the decision makers. They make decisions about each new image, based on the training data fed to them previously. In case of cancerous images, the model picks that image up, and isolates the malignant tissue in the image as neatly as possible. In this paper we have developed an unsupervised learning system for automatic tumor segmentation and detection that can be applied to low contrast images

Evaluating Occupational Exposure of Workers for Metallurgy with Alkanol Amines

Liquids being used in metallurgy are a composition of dangerous chemicals including Alkanol amines. Alkanol amines include Mono-, Di- and 3- ethanol amine. Alkanol amines are used as lubricant in metallurgy. Dermal absorption of these chemical substances is so important and some studies are being done about carcinogenesis of these chemical substances. Meanwhile, ethanol amine has been recognized as a factor causing occupational asthma. The present study was done on 29 turnery and rolling workers in Cupper Industrial Complex of Sarcheshmeh in descriptive- sectional manner. Data related to concentration of Alkanol amines in the atmosphere were gathered with the method proposed by NIOSH and data for pulmonary function were extracted from spirometry experiments. Demographic data were obtained from medical files of the workers. Statistical tests were carried out using software SPSS. In this study, workers' Time Weighted Average (TWA) individual exposure to Mono-ethanol amine (MEA) with density scope 0.03- 1.16, exposure to Di-ethanol amine (DEA) with density scope 0.36-1.35 and exposure to TEA with density scope 0.49-1.28 equal 0.54, 0.87 and 0.85 mg/m3 respectively without occupational group separation for each. Also, FVC reduction in studied individuals without occupational group separation was 3.17% (SD= 6.55%). The results indicated that workers' Time Weighted Average exposure to Mono-Di-Tri- ethanol amine was lower than occupational legal limit. In rolling process, exposure to Alkanol amines is lower compared to other processes of metallurgy because of semi- enclosure of this process. Having done Pearson correlation test to determine relation between individuals' work experience and FVC reduction, it was observed that there is no meaningful relation between these two variables

The Effect of Breast Phantom Material on the Dose Distribution in AccuBoost Brachytherapy

Introduction: Long-term teletherapy program is not suitable for old and working patients and those living in areas where little access to primary health care is available. Accelerated partial breast irradiation (APBI) using high dose rate (HDR) brachytherapy is an appropriate alternative for these patients due to its limited number of fractions. The AccuBoost is a system for delivering APBI. The brachytherapy dose is delivered from parallel-opposed beams from 192Ir sources in circle applicators. This study was conducted to investigate the effects of breast phantom material on the dose distribution in AccuBoost brachytherapy using Monte Carlo N-Particle method. Material and Methods: In this study,different inhomogeneous breast phantoms composed of various materials were simulated. Dosimetric evaluations including a comparison of dose distribution between different breast phantom materials and water phantom was performed. Results: There was no significant difference between the breast and water phantoms in terms of mean dose values in different positions of each phantom. The most significant differences between the doses of different compositions and water were found to be about 6% near the skin. Conclusion: No significant differences were observed between the breast phantoms composed of diverse materials and water phantoms considering the dose distributions.  Therefore, it is not necessary to replace the current treatment planning systems using Task Group No. 43 formalism with combined model-based and patient-specific dosimetry methods

Evaluating Occupational Exposure of Workers for Metallurgy with Alkanol Amines

Liquids being used in metallurgy are a composition of dangerous chemicals including Alkanol amines. Alkanol amines include Mono-, Di- and 3- ethanol amine. Alkanol amines are used as lubricant in metallurgy. Dermal absorption of these chemical substances is so important and some studies are being done about carcinogenesis of these chemical substances. Meanwhile, ethanol amine has been recognized as a factor causing occupational asthma. The present study was done on 29 turnery and rolling workers in Cupper Industrial Complex of Sarcheshmeh in descriptive- sectional manner. Data related to concentration of Alkanol amines in the atmosphere were gathered with the method proposed by NIOSH and data for pulmonary function were extracted from spirometry experiments. Demographic data were obtained from medical files of the workers. Statistical tests were carried out using software SPSS. In this study, workers' Time Weighted Average (TWA) individual exposure to Mono-ethanol amine (MEA) with density scope 0.03- 1.16, exposure to Di-ethanol amine (DEA) with density scope 0.36-1.35 and exposure to TEA with density scope 0.49-1.28 equal 0.54, 0.87 and 0.85 mg/m3 respectively without occupational group separation for each. Also, FVC reduction in studied individuals without occupational group separation was 3.17% (SD= 6.55%). The results indicated that workers' Time Weighted Average exposure to Mono-Di-Tri- ethanol amine was lower than occupational legal limit. In rolling process, exposure to Alkanol amines is lower compared to other processes of metallurgy because of semi- enclosure of this process. Having done Pearson correlation test to determine relation between individuals' work experience and FVC reduction, it was observed that there is no meaningful relation between these two variables