Whole-body voxel-based internal dosimetry using deep learning

In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed the gold standard technique for risk-benefit analysis of radiation hazards and correlation with patient outcome. Hence, we propose a novel method to perform whole-body personalized organ-level dosimetry taking into account the heterogeneity of activity distribution, non-uniformity of surrounding medium, and patient-specific anatomy using deep learning algorithms

DeepTOFSino:A deep learning model for synthesizing full-dose time-of-flight bin sinograms from their corresponding low-dose sinograms

Purpose: Reducing the injected activity and/or the scanning time is a desirable goal to minimize radiation exposure and maximize patients’ comfort. To achieve this goal, we developed a deep neural network (DNN) model for synthesizing full-dose (FD) time-of-flight (TOF) bin sinograms from their corresponding fast/low-dose (LD) TOF bin sinograms.Methods: Clinical brain PET/CT raw data of 140 normal and abnormal patients were employed to create LD and FD TOF bin sinograms. The LD TOF sinograms were created through 5% undersampling of FD list-mode PET data. The TOF sinograms were split into seven time bins (0, ±1, ±2, ±3). Residual network (ResNet) algorithms were trained separately to generate FD bins from LD bins. An extra ResNet model was trained to synthesize FD images from LD images to compare the performance of DNN in sinogram space (SS) vs implementation in image space (IS). Comprehensive quantitative and statistical analysis was performed to assess the performance of the proposed model using established quantitative metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM) region-wise standardized uptake value (SUV) bias and statistical analysis for 83 brain regions.Results: SSIM and PSNR values of 0.97 ± 0.01, 0.98 ± 0.01 and 33.70 ± 0.32, 39.36 ± 0.21 were obtained for IS and SS, respectively, compared to 0.86 ± 0.02and 31.12 ± 0.22 for reference LD images. The absolute average SUV bias was 0.96 ± 0.95% and 1.40 ± 0.72% for SS and IS implementations, respectively. The joint histogram analysis revealed the lowest mean square error (MSE) and highest correlation (R2 = 0.99, MSE = 0.019) was achieved by SS compared to IS (R2 = 0.97, MSE= 0.028). The Bland &amp; Altman analysis showed that the lowest SUV bias (-0.4%) and minimum variance (95% CI: -2.6%, +1.9%) were achieved by SS images. The voxel-wise t-test analysis revealed the presence of voxels with statistically significantly lower values in LD, IS, and SS images compared to FD images respectively.Conclusion: The results demonstrated that images reconstructed from the predicted TOF FD sinograms using the SS approach led to higher image quality and lower bias compared to images predicted from LD images.</p

Traumatic Endothelial Corneal Rings

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An assessment of static recrystallization in L-605 Cobalt-based superalloy

In this research, the effect of cold rolling, annealing time and temperature on microstructure and hardness were studied in L-605 superalloy. A cast bar of L-605 alloy was hot rolled at 1200 ºC. As the following, it was solutionized at 1230 ºC for 1 hour and finally was cold rolled by different amounts (i.e. 5-35 percent thickness reduction). The cold-rolled samples were heat treated for different times (i.e. 2-120 min.) at temperature range of 1068-1230 ºC in order to study their recrystallization behavior. The results of microstructural analysis indicated that static recrystallization is responsible for microstructural refinement and coarsening, so that an increase in the amounts of cold rolling resulted in a fully recrystallized microstructure at lower temperature. This analysis also indicated that annealing temperature is more effective than annealing time in grain growth. Microstructural evaluation as well as showed that carbides such as M7C3 and M23C6 which have been reported in some literature were not observed during rolling or annealing in this research.  It is perhaps due to usage of high annealing temperatures  or possibly due to their very low contents which was not possible for us to evaluate their formation with conventional methods. Hardness results revealed that higher annealing temperature lead to lower hardness values as expected

Anterior Chamber Intraocular Lens Exposure through Prelimbal Sclera: a Case Report

Purpose: To report a case of spontaneous exposure of anterior chamber intraocular lens in a patient with history of congenital cataract surgery ten years prior to presentation. Report of the case: A 27-year-old man presented with a two-year history of blurred vision, photophobia, mild ocular pain and redness in his left eye, with no history of prolonged eye rubbing, ocular surface disease or any evidence of trauma. On exam there was redness and swelling of the eyelids in the involved side; however, there was no evidence of any long standing ocular surface condition. Slit-lamp biomicroscopy disclosed an injected eye with haptic exposure of an angle-supported anterior chamber lens approximately 1 mm posterior to the limbus through the sclera. Anterior chamber was mildly inflamed and pupil was peaked towards the area of the exposed haptic. Ultrasound biomicroscopy revealed edematous and thickened sclera near the exposed tip. The dislocated lens was extracted and an iris-fixated anterior chamber lens was implanted instead. According to the size of the IOL and the white to white distance of the patient, it seemed that an incorrect selection of IOL size was the reason for the haptic exposure. Conclusion: We presented a rare case of spontaneous anterior chamber lens exposure and its surgical management in a patient with an otherwise healthy ocular surface. It was concluded that an error in IOL size selection might have been the cause of spontaneous haptic exposure.Key words:  Lenses; Intraocular; Anterior chamber; Sclera

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

Projection Space Implementation of Deep Learning-Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space

Purpose: To assess the performance of full dose (FD) positron emission tomography (PET) image synthesis in both image and projection space from low-dose (LD) PET images/sinograms without sacrificing diagnostic quality using deep learning techniques. Methods: Clinical brain PET/CT studies of 140 patients were retrospectively employed for LD to FD PET conversion. 5% of the events were randomly selected from the FD list-mode PET data to simulate a realistic LD acquisition. A modified 3D U-Net model was implemented to predict FD sinograms in the projection-space (PSS) and FD images in image-space (PIS) from their corresponding LD sinograms/images, respectively. The quality of the predicted PET images was assessed by two nuclear medicine specialists using a five-point grading scheme. Quantitative analysis using established metrics including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), region-wise standardized uptake value (SUV) bias, as well as first-, second- and high-order texture radiomic features in 83 brain regions for the test and evaluation dataset was also performed. Results: All PSS images were scored 4 or higher (good to excellent) by the nuclear medicine specialists. PSNR and SSIM values of 0.96 ± 0.03, 0.97 ± 0.02 and 31.70 ± 0.75, 37.30 ± 0.71 were obtained for PIS and PSS, respectively. The average SUV bias calculated over all brain regions was 0.24 ± 0.96% and 1.05 ± 1.44% for PSS and PIS, respectively. The Bland-Altman plots reported the lowest SUV bias (0.02) and variance (95% CI: -0.92, +0.84) for PSS compared with the reference FD images. The relative error of the homogeneity radiomic feature belonging to the Grey Level Co-occurrence Matrix category was -1.07 ± 1.77 and 0.28 ± 1.4 for PIS and PSS, respectively Conclusion: The qualitative assessment and quantitative analysis demonstrated that the FD PET prediction in projection space led to superior performance, resulting in higher image quality and lower SUV bias and variance compared to FD PET prediction in the image domain

Does Ergonomics Improve Product Quality and Reduce Costs? A Review Article

Competition is an ongoing challenge confronting industrial corporations, particularly automobile manufacturing. Striving to improve product quality and productivity, automotive industries have used different quality management approaches, such as reduced variability, total quality management, and lean management, over recent years. Furthermore, incorporating proactive ergonomics such as physical and organizational ergonomics and psychosocial factors into the structure of a company is considered to be a support for productivity and quality. Several studies have shown the effects of ergonomics on better quality. Application of both quality management approaches and ergonomics in an integrated manner in the manufacturing production system is emphasized because they are similar concepts with the same objectives, that is, to improve efficiency. In this study, a comprehensive review was undertaken and 25 studies were reviewed in order to define how integration of an ergonomic approach in the manufacturing production system can reduce defects and improve quality in the production process

Automated Brain Tumor Segmentation on Multi-MR Sequences to Determine the Most Efficient Sequence using a Deep Learning Method

Brain tumor segmentation is an important step in the diagnosis and treatment planning of cancer patients. The procedure of manual brain tumor segmentation suffers from a long processing time. In this light, automatic brain tumor segmentation is highly appealing in the clinical routine. This study sets out to segment the tumors from brain MR images and to investigate the effectiveness/usefulness of the different MRI sequences for this purpose. Here, the MR images from the BRATS challenge were utilized. 310 patients with four different MRI sequences, including T1, T1ce, T2, and FLAIR were employed to train a ResNet deep CNN. Four separate models were trained with each of the input MR sequences to identify the best sequence for brain tumor segmentation. To assess the performance of these models, 60 patients (external dataset) were quantitatively evaluated. The quantitative results indicated that the FLAIR sequence is more reliable for automatic brain tumor segmentation than other sequences with an accuracy of 0.77±0.10 in terms of Dice compared to Dice indices of 0.73±0.12, 0.73±0.15, and 0.62±0.17 obtained from T1, T2, and T1ce sequences, respectively. Based on the results of this study, FLAIR is a more reliable sequence than other sequences for brain tumor segmentation