A Review on Automated Sleep Study.

In recent years, research on automated sleep analysis has witnessed significant growth, reflecting advancements in understanding sleep patterns and their impact on overall health. This review synthesizes findings from an exhaustive analysis of 87 papers, systematically retrieved from prominent databases such as Google Scholar, PubMed, IEEE Xplore, and ScienceDirect. The selection criteria prioritized studies focusing on methods employed, signal modalities utilized, and machine learning algorithms applied in automated sleep analysis. The overarching goal was to critically evaluate the strengths and weaknesses of the proposed methods, shedding light on the current landscape and future directions in sleep research. An in-depth exploration of the reviewed literature revealed a diverse range of methodologies and machine learning approaches employed in automated sleep studies. Notably, K-Nearest Neighbors (KNN), Ensemble Learning Methods, and Support Vector Machine (SVM) emerged as versatile and potent classifiers, exhibiting high accuracies in various applications. However, challenges such as performance variability and computational demands were observed, necessitating judicious classifier selection based on dataset intricacies. In addition, the integration of traditional feature extraction methods with deep structures and the combination of different deep neural networks were identified as promising strategies to enhance diagnostic accuracy in sleep-related studies. The reviewed literature emphasized the need for adaptive classifiers, cross-modality integration, and collaborative efforts to drive the field toward more accurate, robust, and accessible sleep-related diagnostic solutions. This comprehensive review serves as a solid foundation for researchers and practitioners, providing an organized synthesis of the current state of knowledge in automated sleep analysis. By highlighting the strengths and challenges of various methodologies, this review aims to guide future research toward more effective and nuanced approaches to sleep diagnostics.

Impact of deep learning-based multiorgan segmentation methods on patient-specific internal dosimetry in PET/CT imaging: A comparative study.

PURPOSE: Accurate and fast multiorgan segmentation is essential in image-based internal dosimetry in nuclear medicine. While conventional manual PET image segmentation is widely used, it suffers from both being time-consuming as well as subject to human error. This study exploited 2D and 3D deep learning (DL) models. Key organs in the trunk of the body were segmented and then used as a reference for networks. METHODS: The pre-trained p2p-U-Net-GAN and HighRes3D architectures were fine-tuned with PET-only images as inputs. Additionally, the HighRes3D model was alternatively trained with PET/CT images. Evaluation metrics such as sensitivity (SEN), specificity (SPC), intersection over union (IoU), and Dice scores were considered to assess the performance of the networks. The impact of DL-assisted PET image segmentation methods was further assessed using the Monte Carlo (MC)-derived S-values to be used for internal dosimetry. RESULTS: A fair comparison with manual low-dose CT-aided segmentation of the PET images was also conducted. Although both 2D and 3D models performed well, the HighRes3D offers superior performance with Dice scores higher than 0.90. Key evaluation metrics such as SEN, SPC, and IoU vary between 0.89-0.93, 0.98-0.99, and 0.87-0.89 intervals, respectively, indicating the encouraging performance of the models. The percentage differences between the manual and DL segmentation methods in the calculated S-values varied between 0.1% and 6% with a maximum attributed to the stomach. CONCLUSION: The findings prove while the incorporation of anatomical information provided by the CT data offers superior performance in terms of Dice score, the performance of HighRes3D remains comparable without the extra CT channel. It is concluded that both proposed DL-based methods provide automated and fast segmentation of whole-body PET/CT images with promising evaluation metrics. Between them, the HighRes3D is more pronounced by providing better performance and can therefore be the method of choice for 18F-FDG-PET image segmentation.

Quantification of Artifacts and Image Distortions in 1.5 Tesla Magnetic Resonance Images of a Commercial Multi-Channel Vaginal Cylinder Brachytherapy Applicator Set.

Background: The BEBIG Portio multi-channel applicator provides better target dose coverage and sparing organs-at-risk compared to a single-channel cylinder. However, artifacts and distortions of Portio in magnetic resonance images (MRI) have not yet been reported. Objective: We aimed to quantify the artifacts and distortions in its 1.5-Tesla MR images before clinical use. Material and Methods: In this experimental study, we employed a gelatin-filled phantom to conduct our measurements. T2-weighted (T2W) images were examined for artifacts and distortions. Computed tomography (CT) images were used as a reference to assess image distortions. Artifact severity was measured by recording the full-width-at-half-maximum (FWHM) image pixel values at various positions along the length of the applicator/channels. CT and MRI-based applicator reconstruction accuracy were then compared, and signal-to-noise ratio (SNR) and contrast were also determined for the applicator images. Results: The applicator distortion level for the Portio applicator was less than the image spatial resolution (0.5±0.5 pixels). The average FWHM for the tandem applicator images was 5.23±0.39 mm, while it was 3.21±0.37 mm for all channels (compared to their actual diameters of 5.0 mm and 3.0 mm, respectively). The average applicator reconstruction difference between CT and MR images was 0.75±0.30 mm overall source dwell positions. The image SNR and contrast were both acceptable. Conclusion: These findings indicate that the Portio applicator has a satisfactory low level of artifacts and image distortions in 1.5-Tesla, T2W images. It may, therefore, be a promising option for MRI-guided multi-channel vaginal brachytherapy.

The Effect of Angiotensin-Converting Enzyme Insertion/Deletion Polymorphism on the Severity and Death Rate of COVID-19 in Iranian Patients.

The study was designed to assess the association of ACE I/D polymorphism with the severity and prognosis of COVID-19 in the Iranian population. Hence, 186 adult patients were categorized into three clinical groups based on the severity of COVID-19: 1) Outpatients or mildly symptomatic patients as control (n = 71); 2) Hospitalized patients or severe symptomatic cases (n = 53); 3) Inpatients led to ICU/death or critically ill patients needed mechanical ventilation (n = 62). The possible association of ACE I/D polymorphism with the risk of comorbidities and serum level of C-reactive protein was evaluated in two severe cases. The results showed that the frequency of D and I alleles are 69.35% and 30.65%, respectively, in the total population. The analysis of allelic frequencies via Fisher's exact test confirmed significantly higher frequency of D allele in both severe groups than that in the mild one, 78.31% in Hospitalized patients (OR = 2.56; 95% CI 1.46 to 4.46; p-value = 0.0011) and 74.19% in Inpatients led to ICU/death (OR = 2.04; 95% CI = 1.22 to 3.43; p-value = 0.0094) compared to 58.45% in Outpatients. The results of genotype proportions displayed an association between COVID-19 severity and DD genotype. Overall, our findings in Iranian patients supported the undeniable role of the DD genotype in the intensity of the disease, comparable to other populations. Furthermore, there is no definite evidence regarding the protective effect of the I allele in our inquiry.

Improving SMAP soil moisture spatial resolution in different climatic conditions using remote sensing data.

Soil moisture (SM) at the interface between the land surface and atmosphere is one of the major environmental parameters which plays an important role in hydrological applications. In this article, the SM measured by Soil Moisture Active Passive (SMAP) is downscaled from 3- to 1-km spatial resolution. The main purpose is to evaluate the performance of two different downscaling methods over a variety of climatic conditions and land cover types. These two methods, based on regression and artificial neural network (ANN), are used for enabling us to cross-validate and reliably interpret the obtained results since the number of ground measurements is not sufficient for accuracy assessment. These methods are applied over four different case studies; one is located in the USA, i.e., state of Utah (semi-arid), and the remaining three are located in Iran, i.e., Fars (arid and semi-arid), Yazd (hyper-arid), and Golestan (humid). In both methods, different combinations of input features correlated with SM including land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperatures in horizontal and vertical polarizations (TBH and TBV), shortwave infrared (SWIR), and digital elevation model (DEM) are used. It is found that the DEM does not add extra information in downscaling. The reason is due to high correlation between topography and LST. Moreover, SWIR is most likely able to model only large-scale variations of SM. The downscaled SM products are then compared to 1-km resolution SMAP SM extracted from Sentinel-1 for the study areas in Iran and in situ measurements in Utah. Both methods produce results which are considerably consistent except that the regression method adds more spatial details in the downscaled SM. The achievements illustrate that the performance of both downscaling methods is higher in areas with more homogeneous climatic conditions, i.e., Yazd and Golestan. The best evaluation metrics including correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE) for Yazd and Golestan are R = 0.89, RMSE = 0.025 m3/m3, and MAPE = 21.13% and R = 0.93, RMSE = 0.044 m3/m3, and MAPE = 21.95%, respectively. Moreover, large model biases are associated with dense vegetated areas and high altitudes. The best downscaling accuracy in both methods over all study areas belongs to bare soil and flat regions.

Development of a multi-purpose quality control phantom for MRI-based treatment planning in high-dose-rate brachytherapy of cervical cancer.

Purpose: Suitable commissioning and quality control (QC) tests for high-dose-rate brachytherapy (HDR-BT) is necessary to ensure dosimetric and geometric accuracy of the treatment. This study aimed to present the methodology of developing a novel multi-purpose QC phantom (AQuA-BT) and examples of its' application in 3D image-based (particularly magnetic resonance imaging [MRI]-based) planning for cervix BT. Material and methods: Design criteria led to a phantom with sufficient size waterproof box for dosimetry and capability for inserting other components inside the phantom for: (A) Validating dose calculation algorithms in treatment planning systems (TPSs) using a small-volume ionization chamber; (B) Testing volume calculation accuracy in TPSs for bladder, rectum, and sigmoid organs at risk (OARs) constructed by 3D printing; (C) Quantification of MRI distortions using 17 semi-elliptical plates with 4,317 control points to mimic a realistic female's pelvis size; and (D) Quantification of image distortions and artifacts induced by MRI-compatible applicators using a specific radial fiducial marker. The utility of the phantom was tested in various QC procedures. Results: The phantom was successfully implemented for examples of intended QC procedures. The maximum deviation between the absorbed doses to water assessed with our phantom and those calculated by SagiPlan TPS was 1.7%. The mean discrepancy in volumes of TPS-calculated OARs was 1.1%. The differences between known distances within the phantom on MR imaging were within 0.7 mm compared with computed tomography. Conclusions: This phantom is a promising useful tool for dosimetric and geometric quality assurance (QA) in MRI-based cervix BT.

QCard-NM: Developing a semiautomatic segmentation method for quantitative analysis of the right ventricle in non-gated myocardial perfusion SPECT imaging.

BACKGROUND: Recent studies have shown that the right ventricular (RV) quantitative analysis in myocardial perfusion imaging (MPI) SPECT can be beneficial in the diagnosis of many cardiopulmonary diseases. This study proposes a new algorithm for right ventricular 3D segmentation and quantification. METHODS: The proposed Quantitative Cardiac analysis in Nuclear Medicine imaging (QCard-NM) algorithm provides RV myocardial surface estimation and creates myocardial contour using an iterative 3D model fitting method. The founded contour is then used for quantitative RV analysis. The proposed method was assessed using various patient datasets and digital phantoms. First, the physician's manually drawn contours were compared to the QCard-NM RV segmentation using the Dice similarity coefficient (DSC). Second, using repeated MPI scans, the QCard-NM's repeatability was evaluated and compared with the QPS (quantitative perfusion SPECT) algorithm. Third, the bias of the calculated RV cavity volume was analyzed using 31 digital phantoms using the QCard-NM and QPS algorithms. Fourth, the ability of QCard-NM analysis to diagnose coronary artery diseases was assessed in 60 patients referred for both MPI and coronary angiography. RESULTS: The average DSC value was 0.83 in the first dataset. In the second dataset, the coefficient of repeatability of the calculated RV volume between two repeated scans was 13.57 and 43.41 ml for the QCard-NM and QPS, respectively. In the phantom study, the mean absolute percentage errors for the calculated cavity volume were 22.6% and 42.2% for the QCard-NM and QPS, respectively. RV quantitative analysis using QCard-NM in detecting patients with severe left coronary system stenosis and/or three-vessel diseases achieved a fair performance with the area under the ROC curve of 0.77. CONCLUSION: A novel model-based iterative method for RV segmentation task in non-gated MPI SPECT is proposed. The precision, accuracy, and consistency of the proposed method are demonstrated by various validation techniques. We believe this preliminary study could lead to developing a framework for improving the diagnosis of cardiopulmonary diseases using RV quantitative analysis in MPI SPECT.

Metal artifact reduction in cervix brachytherapy with titanium applicators using dual-energy CT through virtual monoenergetic images and an iterative algorithm: A phantom study.

PURPOSE: To evaluate an iterative metal-artifact reduction (iMAR) algorithm, dual-energy CT (DECT) through virtual monoenergetic images (VMI), and a combination of iMAR and DECT for reducing metal artifact severity (AS) induced by Fletcher titanium applicators used in cervix brachytherapy, the efficacy of which are hitherto unreported. METHODS AND MATERIALS: 120 kVp single-energy CT (SECT) (Siemens) of BEBIG tandem applicators, varying in shape (straight or curved) and diameter (3.5 mm or 5 mm) in a custom-made water-filled phantom, and their DECT images obtained from extrapolation of 80 kVp and 140 kVp, were reconstructed using four methods: DECT through VMI±iMAR, and SECT±iMAR. The DECT images were reconstructed monoenergetically at 70, 150, and 190 keV. AS was evaluated using measured values and statistical analysis. RESULTS: iMAR, DECT, and combined DECT and iMAR reduced AS (p < 0.05). DECT had a lower AS than SECT, even without iMAR (p < 0.025). SECT+iMAR was more effective than DECT-iMAR with VMI at 70 and 190 keV (p < 0.05), whereas showing no statistically significant difference at 150 keV. With DECT and iMAR combined, AS was reduced more effectively compared to the SECT+iMAR or DECT alone. It also reduced the mean interobserver uncertainty by 0.2 mm. CONCLUSIONS: These findings indicate that iMAR reduces the AS caused by Fletcher titanium applicators for both SECT and DECT, a combination of iMAR and DECT is superior to either strategy alone, and at low energies, DECT+iMAR also produces similar artifact reduction. These practical strategies promise more accurate source-position and structure definitions in CT-based gynecological brachytherapy treatment planning.

Performance Improvement in Brain Tumor Detection in MRI Images Using a Combination of Evolutionary Algorithms and Active Contour Method.

The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain tumor diagnosis. The aim of the present study is to automatically detect the location of the tumor in a brain MRI image with high accuracy. For this end, in the proposed algorithm, first, the skull is separated from the brain using morphological operators. The image is then segmented by six evolutionary algorithms, i.e., Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), and Gray Wolf Optimization (GWO), as well as two other frequently-used techniques in the literature, i.e., K-means and Otsu thresholding algorithms. Afterwards, the tumor area is isolated from the brain using the four features extracted from the main tumor. Evaluation of the segmented area revealed that the PSO has the best performance compared with the other approaches. The segmented results of the PSO are then used as the initial curve for the Active contour to precisely specify the tumor boundaries. The proposed algorithm is applied on fifty images with two different types of tumors. Experimental results on T1-weighted brain MRI images show a better performance of the proposed algorithm compared to other evolutionary algorithms, K-means, and Otsu thresholding methods.

Instantaneous fMRI based cerebral parameters for automatic Alzheimer, mild cognitive impairment and healthy subject classification.

Automatic identification and categorization of Alzheimer's patients and the ability to distinguish between different levels of this disease would be very helpful to the research community studying this disease since non-automatic approaches are both very time-consuming and highly dependent upon the experience of experts. Here, it is proposed that instantaneous cerebral phase and envelope information from functional magnetic resonance imaging data is of use to discriminate between Alzheimer's patients, mild cognitively impaired subjects and healthy individuals. Following a region-of-interest analysis of functional magnetic resonance imaging data, different features including power, entropy, and coherency features are derived from the instantaneous phase and envelope signal sequences. Various sets of features are calculated and fed to a sequential forward floating feature selection algorithm to identify the most discriminative and informative feature sets. A Student's t-test was employed to select the most relevant features from the sets. Finally, a K-nearest neighbor classifier is used to distinguish between classes in a three-class categorization problem. The reported performance in overall accuracy using functional magnetic resonance imaging data of 111 combined participants is 80.1% with 80.0% sensitivity for the distinction of both Alzheimer's and healthy categories. This is comparable to the state-of-the-art approaches recently proposed for this task. The significance of obtained results was statistically confirmed by the evaluation of standard classification performance indicators. Results illustrate that the analytic phase and envelope feature indices derived from the region of interest signals described here are significant discriminators suited to distinguish between Alzheimer patients and healthy subjects.

Enhanced Ultra-Sensitive Metamaterial Resonance Sensor based on Double Corrugated Metal stripe for Terahertz Sensing.

In this paper, an ultra-sensitive metamaterial terahertz sensor is proposed. The resonance sensor is designed based on a novel double corrugation form to enhance the ability of the sensor in the terms of sensitivity, Q-factor and the maximum sensible thickness of an analyte. The introduced structure can support the spoof surface plasmon and can resonate strongly at the tuned frequencies. Moreover, the structure of the terahertz sensor is investigated thoroughly from different points of view including frequency shifts due to variations in the thickness or refractive index of the analyte. In addition, the sensitivity of the sensor is approximated with a biharmonic fitting function for different combinations of refractive index and analyte thickness as "sensitivity surface". The sensor shows the maximum sensitivity of 1.75 THz/RIU for refractive index between 1-1.2 with a maximum thickness of 80 µm. Moreover, the simulation results approved that the double corrugation on the metal stripe improves the electromagnetic field interaction in the metal part greatly in comparison with the previously reported works. According to this work, the proposed structure can be applied for terahertz sensing with more abilities to sense even thicker biologic tissues.

A New Optimized Thresholding Method Using Ant Colony Algorithm for MR Brain Image Segmentation.

Image segmentation is considered as one of the most fundamental tasks in image processing applications. Segmentation of magnetic resonance (MR) brain images is also an important pre-processing step, since many neural disorders are associated with brain's volume changes. As a result, brain image segmentation can be considered as an essential measure toward automated diagnosis or interpretation of regions of interest, which can help surgical planning, analyzing changes of brain's volume in different tissue types, and identifying neural disorders. In many neural disorders such as Alzheimer and epilepsy, determining the volume of different brain tissues (i.e., white matter, gray matter, and cerebrospinal fluids) has been proven to be effective in quantifying diseases. A traditional way for segmenting brain images involves the use of a medical expert's experience in manually determining the boundary of different regions of interest in brain images. It may seem that manual segmentation of MR brain images by an expert is the first and the best choice. However, this method is proved to be time-consuming and challenging. Hence, numerous MR brain image segmentation methods with different degrees of complexity and accuracy have been introduced recently. Our work proposes an optimized thresholding method for segmentation of MR brain images using biologically inspired ant colony algorithm. In this proposed algorithm, textural features are adopted as heuristic information. Besides, post-processing image enhancement based on homogeneity is also performed to achieve a better performance. The empirical results on axial T1-weighted MR brain images have demonstrated competitive accuracy to traditional meta-heuristic methods, K-means, and expectation maximization.

Metal Artifact Reduction in Dental Computed Tomography Images Based on Sinogram Segmentation Using Curvelet Transform Followed by Hough Transform.

In X-ray computed tomography (CT), the presence of metal objects in a patient's body produces streak artifacts in the reconstructed images. During the past decades, many different methods were proposed for the reduction or elimination of the streaking artifacts. When scanning a patient, the projection data affected by metal objects (missing projections) appear as regions with high intensities in the sinogram. In spiral fan beam CT, these regions are sinusoid-like curves on sinogram. During the first time, if the metal curves are detected carefully, then, they can be replaced by corresponding unaffected projections using other slices or opposite views; therefore, the CT slices regenerated by the modified sonogram will be imaged with high quality. In this paper, a new method of the segmentation of metal traces in spiral fan-beam CT sinogram is proposed. This method is based on a sinogram curve detection using a curvelet transform followed by 2D Hough transform. The initial enhancement of the sinogram using modified curvelet transform coefficients is performed by suppressing all the coefficients of one band and applying 2D Hough transform to detect more precisely metal curves. To evaluate the performance of the proposed method for the detection of metal curves in a sinogram, precision and recall metrics are calculated. Compared with other methods, the results show that the proposed method is capable of detecting metal curves, with better precision and good recovery.

Metal artifact reduction in spiral fan-beam CT using a new sinogram segmentation scheme.

Objective of this study is to present and test a new method for metal artifact reduction (MAR) by segmenting raw CT data (sinogram). The artifact suppression technique incorporates two steps namely, metal projection segmentation in the sinogram and replacement of segmented regions by new values using an interpolation method. The proposed segmentation algorithm uses the sinogram instead of reconstructed CT slices. First, one of the best and newest region-based geometric active contour models is used to detect projection data affected by metal objects (missing projections). Then, the Hough-transform method is applied to detect all sinusoidal-like curves belonging to metal objects. Finally, a post image processing technique is used aiming to increase accuracy of the segmentation process. To provide a proof of performance, CT data of two patients with metallic teeth filling and pelvis prosthesis were included in the study as well as CT data of a phantom with metallic teeth inserts. Accuracy was determined by comparing mean, variance, mean squared error (MSE) and, peak signal to noise ratio (PSNR) as evaluation measurements of distortion in phantom images with respect to metallic teeth (original and suppressed) and without metallic teeth inserts. Quantitative results showed an average improvement of 12 dB in terms of PSNR and 517 in terms of MSE when the new MAR method was applied to remove metal artifacts. Qualitative improvement was also assessed by comparing uncorrected clinical images with artifact suppressed images. Moreover, qualitative comparison of the results of the proposed new method with the existing methods of MAR showed the superiority of the new method tested in this study.

Configuration of the inferior alveolar canal as detected by cone beam computed tomography.

AIMS: The aim of this study is to evaluate the course of the inferior alveolar canal (IAC) including its frequently seen variations in relation to root apices and the cortices of the mandible at fixed pre-determined anatomic reference points using cone beam volumetric computed tomography (CBVCT). MATERIAL AND METHODS: This retrospective study utilized CBVCT images from 44 patients to obtain quantifiable data to localize the IAC. Measurements to the IAC were made from the buccal and lingual cortical plates (BCP/LCP), inferior border of the mandible and the root apices of the mandibular posterior teeth and canine. Descriptive analysis was used to map out the course of the IAC. RESULTS: IACs were noted to course superiorly toward the root apices from the second molar to the first premolar and closer to the buccal cortical plate anteriorly. The canal was closest to the LCP at the level of the second molar. In 32.95% of the cases, the canal was seen at the level of the canine. CONCLUSIONS: This study indicates that caution needs to be exercised during endodontic surgical procedures in the mandible even at the level of the canine. CBVCT seems to provide an optimal, low-dose, 3D imaging modality to help address the complexities in canal configuration.

Detection of epileptic seizure using wireless sensor networks.

The monitoring of epileptic seizures is mainly done by means of electroencephalogram (EEG) monitoring. Although this method is accurate, it is not comfortable for the patient as the EEG-electrodes have to be attached to the scalp which hampers the patient's movement. This makes long-term home monitoring not feasible. In this paper, the aim is to propose a seizure detection system based on accelerometry for the detection of epileptic seizure. The used sensors are wireless, which can improve quality of life for the patients. In this system, three 2D accelerometer sensors are positioned on the right arm, left arm, and left thigh of an epileptic patient. Datasets from three patients suffering from severe epilepsy are used in this paper for the development of an automatic detection algorithm. This monitoring system is based on Wireless Sensor Networks and can determine the location of the patient when a seizure is detected and then send an alarm to hospital staff or the patient's relatives. Our wireless sensor nodes are MICAz Motes developed by Crossbow Technology. The proposed system can be used for patients living in a clinical environment or at their home, where they do only their daily routines. The analysis of the recorded data is done by an Artificial Neural Network and K Nearest-Neighbor to recognize seizure movements from normal movements. The results show that K Nearest Neighbor performs better than Artificial Neural Network for detecting these seizures. The results also show that if at least 50% of the signal consists of seizure samples, we can detect the seizure accurately. In addition, there is no need for training the algorithm for each new patient.

An effective method for segmentation of MR brain images using the ant colony optimization algorithm.

Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations; however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area.

An opposite view data replacement approach for reducing artifacts due to metallic dental objects.

PURPOSE: To present a conceptually new method for metal artifact reduction (MAR) that can be used on patients with multiple objects within the scan plane that are also of small sized along the longitudinal (scanning) direction, such as dental fillings. METHODS: The proposed algorithm, named opposite view replacement, achieves MAR by first detecting the projection data affected by metal objects and then replacing the affected projections by the corresponding opposite view projections, which are not affected by metal objects. The authors also applied a fading process to avoid producing any discontinuities in the boundary of the affected projection areas in the sinogram. A skull phantom with and without a variety of dental metal inserts was made to extract the performance metric of the algorithm. A head and neck case, typical of IMRT planning, was also tested. RESULTS: The reconstructed CT images based on this new replacement scheme show a significant improvement in image quality for patients with metallic dental objects compared to the MAR algorithms based on the interpolation scheme. For the phantom, the authors showed that the artifact reduction algorithm can efficiently recover the CT numbers in the area next to the metallic objects. CONCLUSIONS: The authors presented a new and efficient method for artifact reduction due to multiple small metallic objects. The obtained results from phantoms and clinical cases fully validate the proposed approach.

An adaptive approach to metal artifact reduction in helical computed tomography for radiation therapy treatment planning: experimental and clinical studies.

PURPOSE: In this article, an approach to metal artifact reduction is proposed that is practical for clinical use in radiation therapy. It is based on a new interpolation scheme of the projections associated with metal implants in helical computed tomography (CT) scanners. METHODS AND MATERIALS: A three-step approach was developed consisting of an automatic algorithm for metal implant detection, a correction algorithm for helical projections, and a new, efficient algorithm for projection interpolation. The modified raw projection data are transferred back to the CT scanner device where CT slices are regenerated using the built-in reconstruction operator. The algorithm was tested on a CT calibration phantom in which the density of inserted objects are known and on clinical prostate cases with two hip prostheses. The results are evaluated using the CT number and shape of the objects. RESULTS: The validations on a CT calibration phantom with various inserts of known densities show that the algorithm improved the overall image quality by restoring the shape and the representative CT number of the objects in the image. For the clinical hip replacement cases, a large fraction of the bladder, rectum, and prostate that were not visible on the original CT slices were recovered using the algorithm. Precise contouring of the target volume was thus feasible. Without this enhancement, physicians would have drawn bigger margins to be sure to include the target and, at the same time, could have prescribed a lower dose to keep the same level of normal tissue toxicity. CONCLUSIONS: In both phantom experiment and patient studies, the algorithm resulted in significant artifact reduction with increases in the reliability of planning procedure for the case of metallic hip prostheses. This algorithm is now clinically used as a preprocessing before treatment planning for metal artifact reduction.