جستجوی مقالات مرتبط با کلیدواژه « computer assisted » در نشریات گروه « پزشکی »

Electronic portal imaging devices (EPIDs) are applied to measure the dose and verify patients’ position.

The present study aims to evaluate the performance of EPID for measuring dosimetric parameters in small photon fields.

In this experimental study, the output factors and beam profiles were obtained using the amorphous silicon (a-Si) EPID for square field sizes ranging from 1×1 to 10×10 cm2 at energies 6 and 18 mega-voltage (MV). For comparison, the dosimetric parameters were measured with the pinpoint, diode, and Semiflex dosimeters. Additionally, the Monaco treatment planning system was selected to calculate the output factors and beam profiles.

There was a significant difference between the output factors measured using the EPID and that measured with the other dosimeters for field sizes lower than 8×8 cm2. In the energy of 6 MV, the gamma passing rates (3%/3 mm) between EPID and diode profile were 98%, 98%, 95%, 94%, 93%, and 94% for 1×1, 2×2, 3×3, 4×4, 5×5, and 10×10 cm2, respectively. The measured penumbra width with EPID was higher compared to that measured by the diode dosimeter for both energies. 

The EPID can measure the dosimetric parameters in small photon fields, especially for beam profiles and penumbra measurements.

In the present scenario, high precision-radiotherapy is delivered through Linear accelerators in which the dose delivery is achieved by delivering the proper monitor unit (MU). Treatment planning for the patients is carried out through treatment planning systems (TPS) in which the precise computation of MU is crucial. This TPS - calculated MU has to be verified using manual calculations for accurate dose delivery. In this study, we incorporated our in-house developed multi leaf collimator(MLC) shaper software and the well-known Clarkson method to compare the calculated MUs to the TPS-generated MUs. Conformal treatment plans of various sites of 30 patients were randomly selected containing different MLC-shaped field sizes. All the fields were shaped using MLC (leaf width of 1cm, 40 pairs) in the TPS. MLC log files were exported and fed into the in-house shaper software to get crucial inputs for the Clarkson-based calculation. The Tissue Maximum Ratio(TMR) & Scatter Maximum ratio(SMR) were utilized in our investigation. The Clarkson MU calculation was compared with the TPS calculation method. Paired t-test was performed for the statistical significance. The Clarkson method-based calculated had significant differences for all the esophageal cancers (p<0.05); however no significant difference was found in the other sites. The compared MUs were within the acceptable deviation with the TPS for Head & Neck, Prostrate and Cervical cancer. The estimated MUs had significant difference in non-homogenous medium. The shaper software can be further enhanced to receive MLC log files from the TPS.

To investigate the success rate and quality of automatic airway segmentation using ultra-low dose CT (ULD-CT) images of different reconstruction algorithms.

Fifty two children who underwent chest ULD-CT were divided into three groups for analysis based on age: group A (n=13, age, 1-2years), group B (n=19, age, 3-6years) and group C (n=20, age, 7-13years). CT images were reconstructed with filtered back-projection (FBP), 50% adaptive statistical iterative reconstruction-Veo (50%ASIR-V), 100%ASIR-V, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strengths. Subjective image quality was evaluated using a 5-point scale. CT value, noise, and sharpness of the trachea were measured. The VCAR software was used to automatically segment airways and reported the total volume. Segmentation success rates were recorded, and segmentation images were subjectively evaluated using a 6-point scale.

The average tracheal diameters were 8.53±1.88mm, 10.69±1.65mm, and 12.72±1.97mm, respectively for groups A, B, and C. The segmentation success rate depended on patient groups: group C reached 100%, while group A decreased significantly. In group A, 100%ASIR-V had the lowest rate at 7.69%, while DLIR-M and DLIR-H significantly improved the rate to 38.64% (P=0.03). For the segmented images, DLIR-H provided the lowest noise and highest subjective score while FBP images had the highest noise and 100%ASIR-V had the lowest overall score (P<0.05). There was no significant difference in the total airway volume among the six reconstructions.

The airway segmentation success rate in ULD-CT for children depends on the tracheal size. DLIR improves airway segmentation success rate and image quality.

Cone beam computed tomography (CBCT) is an imaging modality that has recently gained increasing popularity for dental imaging. This study aimed to investigate the usage of CBCT imaging among Iranian Association of Endodontists members using an online survey.   Iranian endodontic practitioners were recruited to participate in the study. A web-based questionnaire was designed and sent to 328 endodontists. The questionnaire was available for a one-month long period during November 2019. The questionnaire included basic demographic details of the participants and questions related to CBCT application in endodontic treatment procedures. The validity and reliability of the questionnaire were assessed by expert endodontists. The chi-square test was used for data analysis, and a p-value less than 0.05 was considered statistically significant. A total of 101 participants completed the survey, giving an overall completed response rate of 30.8%. Ninety-four percent of participants (n=95) used CBCT imaging in their practice. There were significant differences in some variables between endodontists who frequently prescribed CBCT as compared to those who rarely prescribed it (P<0.05). CBCT was prescribed more frequently by endodontists who received training in CBCT usage, those performing periapical surgeries, and those using magnification in their practice. The survey indicated that CBCT technology is widely used among Iranian endodontists particularly if they have already received the required training. The most common indications for CBCT were detecting vertical root fracture, teeth with complex anatomy and additional canals, and root resorption.

The primary aim of this study was to assess the reliability of the ten -segment classification system proposed (TSC) by Krause et al. and see how it compares with the traditionally used Schatzker classification, AO classification system, and Luo’s “Three columns” classification (ThCC) system. The second aim of this study was to assess the inter-observer reliability of the above classifications based on professional experience by comparing the entry level of residents (1 year into postgraduation), senior residents (1 year after postgraduation completion), and faculty (>10 years after postgraduation completion). 50 TPFs were classified by a 10-segment classification system, and its intra-observer (at 1-month interval) and inter-observer reproducibility was checked using k values by three different groups with varying levels of experience (Group I, II, and III comprised of 2 juniors residents, senior residents and consultants each), and the same was compared for three other common classification systems (Schatzker, AO and 3 –column). 10-segment classification showed least k for both inter-observer (0.08) and intra-observer (0.03) reliability. Highest individual inter-observer (k= 0.52) and intra-observer reliability (k= 0.31) was for Schatzker classification in Group I. Lowest individual inter-observer and intra-observer reliability was seen for 10-segment classification (k= 0.07) and AO classification system (k= -0.03) respectively. 10-segment classification showed the lowest k for both inter-observer and intra-observer reliability. The inter-observer reliability for the Schatzker, AO, and 3- column classifications reduced with increasing experience of the observer (JR>SR>Consultant). A possible reason could be a more critical evaluation of the fractures with increasing seniority. Level of evidence: I

The health organisation has suffered from the lack of diagnosis support systems and physicians in India. Further, the physicians are struggling to treat many patients, and the hospitals also have the lack of a radiologist especially in rural areas; thus, almost all cases are handled by a single physician, leading to many misdiagnoses. Computer aided diagnostic systems are being developed to address this problem. The current study aimed to review the different methods to detect pneumonia using neural networks and compare their approach and results. For the best comparisons, only papers with the same data set ChestXray14 are studied.

Automated image segmentation is an essential step in quantitative image analysis. This study assesses the performance of a deep learning-based model for lung segmentation from computed tomography (CT) images of normal and COVID-19 patients.  A descriptive-analytical study was conducted from December 2020 to April 2021 on the CT images of patients from various educational hospitals affiliated with Mashhad University of Medical Sciences (Mashhad, Iran). Of the selected images and corresponding lung masks of 1,200 confirmed COVID-19 patients, 1,080 were used to train a residual neural network. The performance of the residual network (ResNet) model was evaluated on two distinct external test datasets, namely the remaining 120 COVID-19 and 120 normal patients. Different evaluation metrics such as Dice similarity coefficient (DSC), mean absolute error (MAE), relative mean Hounsfield unit (HU) difference, and relative volume difference were calculated to assess the accuracy of the predicted lung masks. The Mann-Whitney U test was used to assess the difference between the corresponding values in the normal and COVID-19 patients. P<0.05 was considered statistically significant. The ResNet model achieved a DSC of 0.980 and 0.971 and a relative mean HU difference of -2.679% and -4.403% for the normal and COVID-19 patients, respectively. Comparable performance in lung segmentation of normal and COVID-19 patients indicated the model’s accuracy for identifying lung tissue in the presence of COVID-19-associated infections. Although a slightly better performance was observed in normal patients. The ResNet model provides an accurate and reliable automated lung segmentation of COVID-19 infected lung tissue.A preprint version of this article was published on arXiv before formal peer review (https://arxiv.org/abs/2104.02042).

Ideal implant placement decreases the postoperative surgical, prosthetic, and functional complications. This study aimed to design and fabricate a surgical guide for accurate positioning and angulation of dental implants in edentulous mandibular models and assess its efficacy.

After initial designing and fabrication of resin model of the surgical guide and eliminating its shortcomings, the final model was fabricated using 6061t6 aluminum alloy by a computer numerical control machine. The efficacy of the designed surgical guide was tested by placing 16 implants with the help of the surgical guide in two completely edentulousmandibular models. Next, cone-beam computed tomography DICOM images were obtained from the inserted dental implants, and analyzed by NNT Viewer software. One-sample t-test was applied to compare deviations of implant angle and distance from the planned angulation/position at P<0.05 level of significance.

The mean angular deviation between the planned and placed implants was 3.31±1.2° and 0.97±0.56° for 0° and 15° implants, respectively. The mean linear deviation between the planned and placed implants was 1.00±0.75 mm. Although the linear and angular differences between the planned and placed implants were statistically significant (P<0.05), they were clinically acceptable.

The designed surgical guide showed the expected efficacy with maximum mesiodistal angular deviation < 5° and linear deviation < 1 mm in 56% and < 1.5 mm in 75% of the placed implants, compared with the planned angulations/positions.

The aim of this study was to evaluate the validity and reliability of a newly designed cephalometric analysis program (Hexagon software) in comparison with manual and digital (Dolphin software) tracings.

Pre-treatment lateral cephalometric radiographs of 32 adult patients between 18 to 41 years (10 males and 22 females, mean age of 22.78 ± 5.17 years) were randomly chosen. For each radiograph, 10 angular and 6 linear measurements were calculated using three different methods (manual and digital using two different software programs). The cephalograms were manually traced using acetate paper, x-ray light box, 0.3 mm HB pencil, ruler, and protractor. For digital tracing, cephalograms were traced with Dolphin vertion-10 (USA) and Hexagon (Iran) software programs. All the analyses were performed by one operator 2 times with at least a four-week interval between the two tracings. The intra-class correlation coefficient (ICC) was used to evaluate the intra-examiner agreement, while the differences between the methods were analyzed using paired t-test, and ANOVA.

The intra-examiner repeatability of all measurements in all three tracing methods showed high agreement. Differences in measurements between the two software programs and hand tracing were not statistically significant for any of the cephalometric parameters (P>0.05).

The results demonstrated that the accuracy of cephalometric tracing by the Hexagon software was similar to the Dolphin software, and the manual tracing technique.

Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher’s attention, it still has some challenging problems in computer-aided diagnosis. This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm. In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features. The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms. Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.

Although radiation is recognized as the most effective nonsurgical treatment, the outcomes and control rates are generally poor. However, a combination of radiation therapy with hyperthermia and chemotherapy can improve the efficacy of treatment. The aim was to explore the potential of morphological and gradient-based features on microscopic images in improving the identification accuracy of subtle differences in cell structure during different treatments. Fifty single-cell images were used for each group and treatment regimen. The groups were individually subjected to: 1) hyperthermia at 43°C; 2) temozolomide (TMZ) chemotherapy at 10% inhibitory concentration; 3) radiotherapy at 2Gy; 4) combination of TMZ chemotherapy and hyperthermia; 5) combination of radiotherapy and hyperthermia; 6) combination of TMZ chemotherapy and radiotherapy; and 7) combination of TMZ chemotherapy, radiotherapy, and hyperthermia. Morphological and gradient-based features were extracted from each cell. The area under the receiver operating characteristic curve (AUC) was calculated for each significant feature to evaluate the performance of cell change detection. According to AUCs, gradient-based features showed superior performance to morphological features in identifying cell changes during all treatment regimens in all groups. In this regard, the AUC of the gradient-mean feature exceeded 0.599 for all groups. The ratio of maximum to minimum cell diameter was the best morphological feature, with an AUC above 0.588 for all groups. Quantitative analysis of features is a reliable indicator of damage, with the potential to characterize cell changes during treatment regimens.

Brain source imaging based on electroencephalogram (EEG) data aims to recover the neuron populations’ activity producing the scalp potentials. This procedure is known as the EEG inverse problem. Recently, beamformers have gained a lot of consideration in the EEG inverse problem.

Beamformers lack acceptable performance in the case of correlated brain sources. These sources happen when some regions of the brain have simultaneous or correlated activities such as auditory stimulation or moving left and right extremities of the body at the same time. In this paper, we have developed a multichannel beamformer robust to correlated sources.

In this simulation study, we have looked at the problem of brain source imaging and beamforming from a blind source separation point of view. We focused on the spatially constraint independent component analysis (scICA) algorithm, which generally benefits from the pre-known partial information of mixing matrix, and modified the steps of the algorithm in a way that makes it more robust to correlated sources. We called the modified scICA algorithm Multichannel ICA based EEG Beamformer (MIEB).

We evaluated the proposed algorithm on simulated EEG data and compared its performance quantitatively with three algorithms: scICA, linearly-constrained minimum-variance (LCMV) and Dual-Core beamformers; it is considered that the latter is specially designed to reconstruct correlated sources.

The MIEB algorithm has much better performance in terms of normalized mean squared error in recovering the correlated/uncorrelated sources both in noise free and noisy synthetic EEG signals. Therefore, it could be used as a robust beamformer in recovering correlated brain sources.

It is necessary to have an automated noise measurement system working accurately to optimize dose in computerized tomography (CT) examinations. This study aims to develop an algorithm to automate noise measurement that can be implemented in CT images of all body regions. In this retrospective study, our automated noise measurement method consists of three steps as follows: the first is segmenting the image of the patient. The second is developing a standard deviation (SD) map by calculating the SD value for each pixel with a sliding window operation. The third step is estimating the noise as the smallest SD from the SD map. The proposed method was applied to the images of a homogenous phantom and a full body adult anthropomorphic phantom, and retrospectively applied to 27 abdominal images of patients. For a homogeneous phantom, the noises calculated using our proposed and previous algorithms have a linear correlation with R2 = 0.997. It is found that the noise magnitude closely follows the magnitude of the water equivalent diameter (Dw) in all body regions. The proposed algorithm is able to distinguish the noise magnitude due to variations in tube currents and different noise suppression techniques such as strong, standard, mild, and weak ones in a reconstructed image using the AIDR 3D algorithm. An automated noise calculation has been proposed and successfully implemented in all body regions. It is not only accurate and easy to implement but also not influenced by the subjectivity of user.

Computer simulations provide virtual hands-on experience when actual hands-on experience is not possible. To use these simulations in medical science, they need to be able to predict the behavior of actual processes with actual patient-specific geometries. Many uncertainties enter in the process of developing these simulations, starting with creating the geometry. The actual patient-specific geometry is often complex and hard to process. Usually, simplifications to the geometry are introduced in exchange for faster results. However, when simplified, these simulations can no longer be considered patient-specific as they do not represent the actual patient they come from. The ultimate goal is to keep the geometries truly patient-specific without any simplification. However, even without simplifications, the patient-specific geometries are based on medical imaging modalities and consequent use of numerical algorithms to create and process the 3D surface. Multiple users are asked to process medical images of a complex geometry. Their resulting geometries are used to assess how the user’s choices determine the resulting dimensions of the 3D model. It is shown that the resulting geometry heavily depends on user’s choices.

 Repeatability and accuracy of measurements made on cone-beam computed tomography (CBCT) images are critical in dental practice especially in implantology. The aim of this study was to investigate the reproducibility of linear measurements made on reconstructed CBCT images.

Methods:

 In this in vitro, experimental study, 5 radiopaque markers were attached to the molars (left and right side), premolars (left and right side) and midline areas of 10 human cadaver dry mandibles. The distance between the markers and the lower border of the mandible was measured by a digital caliper and considered as the gold standard. CBCT images were taken, and the distance between the markers and the lower mandibular border was measured on cross-sectional images by three maxillofacial radiologists using Romexis software. The same measurements were made 1 month later to assess the reproducibility of measurements. The intra-class correlation coefficient was calculated to assess the repeatability and agreement between the observers.

 Compared with the gold standard, the mean error percentage in linear measurements was calculated to be 3.25%. The overall reproducibility of CBCT linear measurements was 0.865. The inter-observer agreement was calculated to be 0.972.

CBCT showed acceptable accuracy, repeatability and reliability for linear measurements, and can be used as an accurate tool for this purpose.

Chronic alcohol consumption carries a high risk for oral and pharyngeal cancers among persons who have never smoked. Excessive alcohol consumption displays cytogenetic changes in oral mucosa cells. Cytomorphometric analysis of oral mucosal cells helps in the early detection of cytomorphological transformations in alcoholics before and after the onset of carcinoma.

A prospective, hospital-based, comparative study was done after written informed consent. Smears were obtained from the clinically normal buccal mucosa of 102 randomly selected alcoholic patients attending the medicine outpatient department aged above 25 years who consumed a minimum of 45 ml alcohol per day for at least 10 years and of 102 nonalcoholics as control. The slides were immediately fixed in absolute methanol and stained by the Papanicolaou (Pap) staining technique. PAP-stained smears were examined under the light microscope. Using the image J 1.47 image analysis software, a morphometric analysis of around 50 cells/case was done.

A statistically significant increase in mean cytoplasmic area (P < 0.001), mean nuclear area (P < 0.01), and cell-to-nuclear parameter ratio (P < 0.001) was seen in the alcohol group in comparison with the control group.

Prolonged consumption of alcohol produces cytomorphometric changes in buccal mucosal cells before the onset of premalignant lesions.

The ability to monitor kidney function after transplantation is one of the major factors to improve care of patients.

Authors recommend a computerized texture analysis using run-length matrix features for detection of changes in kidney tissue after allograft in ultrasound imaging.

A total of 40 kidney allograft recipients (28 male, 12 female) were used in this longitudinal study. Of the 40 patients, 23 and 17 patients showed increased serum creatinine (sCr) (increased group) and decreased sCr (decreased group), respectively. Twenty run-length matrix features were used for texture analysis in three normalizations. Correlations of texture features with serum creatinine (sCr) level and differences between before and after follow-up for each group were analyzed. An area under the receiver operating characteristic curve (Az) was measured to evaluate potential of proposed method.

The features under default and 3sigma normalization schemes via linear discriminant analysis (LDA) showed high performance in classifying decreased group with an Az of 1. In classification of the increased group, the best performance gains were determined in the 3sigma normalization schemes via LDA with an Az of 0.974 corresponding to 95.65% sensitivity, 91.30% specificity, 93.47% accuracy, 91.67% PPV, and 95.45% NPV.

Run-length matrix features not only have high potential for characterization but also can help physicians to diagnose kidney failure after transplantation.