IR Algorithms Research Articles

거동 불편한 환자의 전신 CT 검사 시 DLIR 기법의 유용성 평가

When whole-body computed tomography (WBCT) is performed after a single injection of contrast media, the arm's position is altered during each examination to minimize beam hardening artifacts caused by the arms. In this study, the image quality of neck CT images using the deep learning iterative reconstruction (DLIR) algorithm for patients whose arms were elevated during WBCT scans was compared with images reconstructed by FBP and IR algorithms in patients who were examined with their arms lowered. The Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) of the reconstructed images were quantitatively compared, while the image quality and artifacts were qualitatively evaluated. As a result, TF-H was evaluated higher in both SNR and CNR in all organs compared to FBP. Compared to the ASIR-V at 40%, the CNR was high, and the SNR was similar. In the qualitative evaluation, it was confirmed that the quality of the image was higher when compared to the image examined with the arm lowered, surpassing that of the existing reconstruction method. Therefore, it is believed that enhancing the quality of the neck CT image through reconstruction using deep learning after the examination with the arm raised, without altering the posture, during the WBCT examination of patients with mobility impairments.

RBP-DIP: Residual back projection with deep image prior for ill-posed CT reconstruction

The success of deep image prior (DIP) in a number of image processing tasks has motivated their application in image reconstruction problems in computed tomography (CT). In this paper, we introduce a residual back projection technique (RBP) that improves the performance of deep image prior framework in iterative CT reconstruction, especially when the reconstruction problem is highly ill-posed. The RBP-DIP framework uses an untrained U-net in conjunction with a novel residual back projection connection to minimize the objective function while improving reconstruction accuracy. In each iteration, the weights of the untrained U-net are optimized, and the output of the U-net in the current iteration is used to update the input of the U-net in the next iteration through the proposed RBP connection. The introduction of the RBP connection strengthens the regularization effects of the DIP framework in the context of iterative CT reconstruction leading to improvements in accuracy. Our experiments demonstrate that the RBP-DIP framework offers improvements over other state-of-the-art conventional IR methods, as well as pre-trained and untrained models with similar network structures under multiple conditions. These improvements are particularly significant in the few-view and limited-angle CT reconstructions, where the corresponding inverse problems are highly ill-posed and the training data is limited. Furthermore, RBP-DIP has the potential for further improvement. Most existing IR algorithms, pre-trained models, and enhancements applicable to the original DIP algorithm can also be integrated into the RBP-DIP framework.

OPTIMALISASI KUALITAS CITRA ANATOMI DENGAN VARIASI IDOSE DAN INTERATIVE MODEL RECONTRUCTION (IMR) PADA MSCT UROGRAPHY NON KONTRAS DENGAN TEKNIK TRACKING

Background: The urinary tract (urinary system) consists of the two kidneys and ureters, the urinary bladder, and the ureters. There are several ways to examine abnormalities in the urinary tract radiologically. including the current CT scan, CT Scan (MSCT) is one of the modalities to evaluate and diagnose urinary tract disorders. MSCT of the urinary tract without using positive contrast media is able to identify images of the urinary tract in cases of urolithiasis. iDose and Interative Model Reconstruction (IMR) are the second generation of IR algorithms from the previous generation, namely iDose. This technology can also lower the dose to the patient and also improve the quality of the resulting image.
 Method: This type of research is an experiment with an observational approach with comparisons. This type of research is an experiment with data obtained from a comparison of noise values and the quality of anatomical images from tracking images with iDose and IMR variations. And an assessment was carried out by 3 radiologists. Place of data collection in radiology RSUD RAA Soewondo Pati. Time for data collection From April to November 2022. in assessing anatomical images using statistical tests. Data analysis was carried out using the Wilcoxon statistical test method because the data were ordinal with paired groups. The Wilcoxon statistical test was used to test the hypothesis and see the level of difference in anatomical image information. The level of significance (level of significance) is 95% or α> 0.05 and is done by assessing the p-value. For a significant level of assessment p <0.05 then Ho is rejected and p> 0.05 then Ho is accepted.
 Results: Friedman test on each anatomical part of the urinary tract system on Non-Contrast Urography MSCT images with Tracking Techniques with iDose 1-6 and IMR 1-3 variations, there are differences in anatomical image information with a p value of .000 (p <0.05). In the anatomy of the renal parenchyma, the highest mean rank is iDose 3 with a value of 8.15. In pelvic anatomy and kidney calices, the highest mean rank is at iDose 6 with a value of 8.05. In ureteral anatomy, the highest mean rank is iDose 3 with a value of 8.05. In the anatomy of the perirenal space, the highest mean rank is iDose 3 with a value of 8.20. Based on the results of the mean rank Friedman test to find out the optimal I Dose and IMR values, it was obtained iDose 1 with a mean rank value of 4.65, iDose 2 with a mean rank value of 4.95, iDose 3 with a mean rank value of 8.3, iDose 4 with a mean rank value of mean rank 6.4, iDose 5 with a mean rank value of 6.55, iDose 6 with a mean rank value of 7.75, IMR 1 with a mean rank value of 1.65, IMR 2 with a mean rank value of 2.05, and IMR 3 with a mean value rating 2.7.
 Conclusion: Changes in the values of iDose and IMR variations affect the anatomical image information of Non-Contrast MSCT Urography with Tracking Techniques, this is based on the results of the mean rank Friedman test performed on each anatomy of the renal parenchyma, pelvic calices, ureters and perirenal space which shows a difference. Based on the results of Friedman's mean rank test conducted on the entire anatomy of the renal parenchyma, ureters, and perirenal space, it showed that the optimal iDose and IMR variation values were iDose 3 with a mean rank value of 8.3.

Fair Top-k Ranking with multiple protected groups

Ranking items or people is a fundamental operation at the basis of several processes and services, not all of them happening online. Ranking is required for different tasks, including search, personalization, recommendation, and filtering. While traditionally ranking has been aimed solely at maximizing some global utility function, recently the awareness of potential discrimination for some of the elements to rank, has captured the attention of researchers, which have thus started devising ranking systems which are non-discriminatory or fair for the items being ranked. So far, researchers have mostly focused on group fairness, which is usually expressed in the form of constraints on the fraction of elements from some protected groups that should be included in the top-k positions, for any relevant k. These constraints are needed in order to correct implicit societal biases existing in the input data and reflected in the relevance or fitness score computed.In this article, we tackle the problem of selecting a subset of k individuals from a pool of n≫k candidates, maximizing global utility (i.e., selecting the “best” candidates) while respecting given group-fairness criteria. In particular, to tackle this Fair Top-kRanking problem, we adopt a ranked group-fairness definition which extends the standard notion of group fairness based on protected groups, by ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above, or indistinguishable from, a given minimum threshold. Our notion of utility requires, intuitively, that every individual included in the top-k should be more qualified than every candidate not included; and that for every pair of candidates in the top-k, the more qualified candidate should be ranked above.The main contribution of this paper is an algorithm for producing a fair top-k ranking that can be used when more than one protected group is present, which means that a statistical test based on a multinomial distribution needs to be used instead of one for a binomial distribution, as the original FA*IR algorithms does. This poses important technical challenges and increases both the space and time complexity of the re-ranking algorithm. Our experimental assessment on real-world datasets shows that our approach yields small distortions with respect to rankings that maximize utility without considering our fairness criteria.

Impact of iterative reconstruction algorithms on the applicability of Fourier-based detectability index for x-ray CT imaging.

The increasing application of iterative reconstruction algorithms in clinical computed tomography to improve image quality and reduce radiation dose, elicits strong interest, and needs model observers to optimize CT scanning protocols objectively and efficiently. The current paradigm for evaluating imaging system performance relies on Fourier methods, which presuppose a linear, wide-sense stationary system. Long-range correlations introduced by iterative reconstruction algorithms may narrow the applicability of Fourier techniques. Differences in the implementation of reconstruction algorithms between manufacturers add further complexity. The present work set out to quantify the errors entailed by the use of Fourier methods, which can lead to design decisions that do not correlate with detectability. To address this question, we evaluated the noise properties and the detectability index of the ideal linear observer using the spatial approach and the Fourier-based approach. For this purpose, a homogeneous phantom was imaged on two scanners: the Revolution CT (GE Healthcare) and the Somatom Definition AS+ (Siemens Healthcare) at different exposure levels. Images were reconstructed using different strength levels of IR algorithms available on the systems considered: Adaptative Statistical Iterative Reconstruction (ASIR-V) and Sinogram Affirmed Iterative Reconstruction (SAFIRE). Our findings highlight that the spatial domain estimate of the detectability index is higher than the Fourier domain estimate. This trend is found to be dependent on the specific regularization used by IR algorithms as well as the signal to be detected. The eigenanalysis of the noise covariance matrix and of its circulant approximation yields explanation about the evoked trends. In particular, this analysis suggests that the predictive power of the Fourier-based ideal linear observer depends on the ability of each basis analyzed to be relevant to the signal to be detected. The applicability of Fourier techniques is dependent on the specific regularization used by IR algorithms. These results argue for verifying the assumptions made when using Fourier methods since Fourier-task-based detectability index does not always correlate with signal detectability.

The Influence of Iterative Reconstruction Technique on the Diagnosis of Coronary Artery Calcification

This study was performed in a single center and written informed consent was obtained from all patients. Thirty-one consecutive patients (26 men/5 women) underwent CT calcium score to rule out CAC. Image data were reconstructed with both; filtered back projection (FBP) and different levels of IR algorithms. Both the qualitative and quantitative image quality and subjective diagnostic performance were compared; Agatston scores and calcium density were measured for a total of 100 coronary arteries.

Cone-beam breast computed tomography using ultra-fast image reconstruction with constrained, total-variation minimization for suppression of artifacts

Compressed sensing based iterative reconstruction algorithms for computed tomography such as adaptive steepest descent-projection on convex sets (ASD-POCS) are attractive due to their applicability in incomplete datasets such as sparse-view data and can reduce radiation dose to the patients while preserving image quality. Although IR algorithms reduce image noise compared to analytical Feldkamp-Davis-Kress (FDK) algorithm, they may generate artifacts, particularly along the periphery of the object. One popular solution is to use finer image-grid followed by down-sampling. This approach is computationally intensive but may be compensated by reducing the field of view. Our proposed solution is to replace the algebraic reconstruction technique within the original ASD-POCS by ordered subsets-simultaneous algebraic reconstruction technique (OS-SART) and with initialization using FDK image. We refer to this method as Fast, Iterative, TV-Regularized, Statistical reconstruction Technique (FIRST). In this study, we investigate FIRST for cone-beam dedicated breast CT with large image matrix. The signal-difference to noise ratio (SDNR), the difference of the mean value and the variance of adipose and fibroglandular tissues for both FDK and FIRST reconstructions were determined. With FDK serving as the reference, the root-mean-square error (RMSE), bias, and the full-width at half-maximum (FWHM) of microcalcifications in two orthogonal directions were also computed. Our results suggest that FIRST is competitive to the finer image-grid method with shorter reconstruction time. Images reconstructed using the FIRST do not exhibit artifacts and outperformed FDK in terms of image noise. This suggests the potential of this approach for radiation dose reduction in cone-beam breast CT.

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

Breakthrough Empirical Approach to Determining Deamidation Sites and Assessing Protein Stability for an Array of Therapeutic Proteins in Solution

Proteins are complex macromolecules that require comprehensive analysis. High resolution techniques are used to determine structure of proteins, yet further understanding of protein dynamics and behavior is needed for understanding protein stability. Deamidation is a phenomenon that occurs spontaneously in proteins, while in solution when subject to heat stress, low or high pH. The mechanism of deamidation is known, involving an asparagine residue undergoing a succinimide intermediate resulting in the generation of an aspartate residue. Implications of charge in the protein of interest due to a deamidation can lead to events such as aggregation, loss of affinity towards its target, and truncation. We have developed an innovative Best-in-Class label free high throughput platform technology specifically designed for comparative characterization of an array of proteins in solution. This platform technology takes advantage of: (1) Quantum Cascade Laser (QCL) microscope with enhanced signal-to-noise ratio (SNR) within the MID IR spectral region 1780 - 1000 cm−1; (2) innovative cells, (3) Hyperspectral Images (HSI) that allow for the visual inspection of the sample to determine the size and identity of any aggregates in the 4.3 µm - 2 mm size range and (4) QCL IR spectral analysis providing amino acid level resolution that is used to define the molecular behavior of proteins in solution. Our Correlation Dynamics software with 2D IR and Co-distribution correlation algorithms are used to map glutamine and asparagine deamidation sites or assess domain stability have a direct impact on drug design and development. Furthermore, ProteinMentor is well suited to be implemented orthogonally with other well-established analytical techniques. Two case studies will be presented as proof-of-concept and validation: (1) NIST mAb standard, subject to Hurricane Maria and (2) NCI mAb fragments.

An Infrared and Visible Image Fusion Algorithm Based on LSWT-NSST

Regarding the problems of image distortion, edge blurring, Gibbs phenomena in the traditional wavelet transform algorithm and the loss of subtle features in the Non-Subsampled Shearlet Transform (NSST), and considering the physical characteristics of infrared and visible images, an infrared and visible image fusion algorithm based on the Lifting Stationary Wavelet Transform (LSWT) and Non-Subsampled Shearlet Transform is proposed in this paper. First, since LSWT can quickly calculate and has all advantages of traditional WT, it is utilized to decompose infrared and visible images to obtain low-frequency coefficients and multi-scale and multi-directional high-frequency coefficients, respectively. Second, NSST multi-scale decomposition is used to extract the target features and detailed features of the image from the high and low-frequency sub-bands to obtain new high and low-frequency sub-bands. Third, according to the physical characteristics that low and high-frequency coefficients represent, different fusion rules are designed. Discrete Cosine Transform (DCT) and Local Spatial Frequency (LSF) are introduced in the low-frequency sub-band, and LSF adaptive weighted fusion rules are used in the DCT domain. The fusion strategy improves the regional contrast in the high-frequency sub-band with the spectral characteristics of human vision. Finally, the Inverse Lifting Stationary Wavelet Transform (ILSWT) is used to reconstruct the fusion coefficients to obtain the final fused images. To verify the advantages of the proposed algorithm in this paper, the classic and advanced 9 IR and VI fusion algorithms are selected for subjective and objective comparison. In the objective evaluation, a comprehensive ranking index is designed based on 9 classical indicators. Simulation experiments with 10 IR and VI fusion algorithms prove that the proposed algorithm has better performance and flexibility. The results show that the proposed algorithm in this paper fuses the images with clear edges, prominent targets, and good visual perception, and it outperforms state-of-the-art image fusion algorithms.

Comparison and Evaluation of Information Retrieval Models

Recently data is growing day by day in the internet . Data is in the form of Structured , unstructured and semi structured in nature. Information Retrieval is the field which is regarding the study of retrieval of unstructured or semi structured documents. For every aspect IR is being used in there are different models being used in them. There are so many models in so many applications,each having some relation to one another.In this paper we will evaluate and compare various IR model techniques and algorithms and see which model excels in which field of application.

Assessment of low contrast detection in CT using model observers: Developing a clinically-relevant tool for characterising adaptive statistical and model-based iterative reconstruction

PurposeThis study aims to assess CT image quality in a way that would meet specific requirements of clinical practice. Physics metrics like Fourier transform derived metrics were traditionally employed for that. However, assessment methods through a detection task have also developed quite extensively lately, and we chose here to rely on this modality for image quality assessment. Our goal was to develop a tool adapted for a fast and reliable CT image quality assessment in order to pave the way for new CT benchmarking techniques in a clinical context. Additionally, we also used this method to estimate the benefits brought by some IR algorithms. Materials and methodsA modified QRM chest phantom containing spheres of 5 and 8mm at contrast levels of 10 and 20HU at 120kVp was used. Images of the phantom were acquired at CTDIvol of 0.8, 3.6, 8.2 and 14.5mGy, before being reconstructed using FBP, ASIR 40 and MBIR on a GE HD 750 CT scanner. They were then assessed by eight human observers undergoing a 4-AFC test. After that, these data were compared with the results obtained from two different model observers (NPWE and CHO with DDoG channels). The study investigated the effects of the acquisition conditions as well as reconstruction methods. ResultsNPWE and CHO models both gave coherent results and approximated human observer results well. Moreover, the reconstruction technique used to retrieve the images had a clear impact on the PC values. Both models suggest that switching from FBP to ASIR 40 and particularly to MBIR produces an increase of the low contrast detection, provided a minimum level of exposure is reached. ConclusionOur work shows that both CHO with DDoG channels and NPWE models both approximate the trend of humans performing a detection task. Both models also suggest that the use of MBIR goes along with an increase of the PCs, indicating that further dose reduction is still possible when using those techniques. Eventually, the CHO model associated to the protocol we described in this study happened to be an efficient way to assess CT images in a clinical environment. In the future, this simple method could represent a sound basis to benchmark clinical practice and CT devices.

One-mSv CT colonography: Effect of different iterative reconstruction algorithms on radiologists’ performance

PurposeTo analyze the effect of different reconstruction algorithms on image noise and radiologists’ performance at ultra-low dose CT colonography (CTC) in human subjects. Materials and methodsThis retrospective study had institutional review board approval, with waiver of the need to obtain informed consent. CTC and subsequent colonoscopy were performed at the same day in 28 patients. CTC was scanned at the supine/prone positions using 120/100kVp and fixed 10mAs, and reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based IR (Veo) algorithms. Size-specific dose estimates (SSDE) and effective radiation doses were recorded. Image noise was compared among the three datasets using repeated measures analysis of variance (ANOVA). Per-polyp sensitivity and figure-of-merits were compared among the datasets using the McNemar test and jackknife alternative free-response receiver operating characteristic (JAFROC) analysis, respectively, by one novice and one expert reviewer in CTC. ResultsMean SSDE and effective radiation dose of CTC were 1.732mGy and 1.002mSv, respectively. Mean image noise at supine/prone position datasets was significantly lowest with Veo (17.2/13.3), followed by ASIR (52.4/38.9) and FBP (69.9/50.8) (P<0.0001). Forty-two polyps in 25 patients were reference polyps. For both readers, per-polyp sensitivity of all 42 polyps was highest with Veo reconstruction (81.0%, 64.3%), followed by ASIR (73.8%, 54.8%) and FBP (57.1%, 50.0%) with statistical significance between Veo and FBP for reader 1 (P=0.002). JAFROC analysis revealed that the figure-of-merit for the detection of polyps was highest with Veo (0.917, 0.786), followed by ASIR (0.881, 0.750) and FBP (0.750, 0.746) with statistical significances between Veo or ASIR and FBP for reader 1 (P<0.05). ConclusionOne-mSv CTC was not feasible using the standard FBP algorithm. However, diagnostic performance expressed as per-polyp sensitivity and figures-of-merit can be improved with the application of IR algorithms, particularly Veo.

SU‐E‐I‐19: Optimization of Low Contrast Detectability Across Two CT Manufacturers

Purpose:Our goal was to evaluate low contrast detectability (LCD) for abdominal CT protocols across two CT scanner manufacturers, while producing a similar noise texture and CTDIvol for acquired images.Methods:A CIRS tissue equivalent LCD phantom containing three columns of 7 spherical targets, ranging from 10 mm to 2.4 mm, that are 5, 10, and 20 HU below the background matrix (HUBB) was scanned using two a GE HD750 64 slice scanner and a Siemens Somatom Definition AS 64 slice scanner. Protocols were designed to deliver a CTDIvol of 12.26 mGy and images were reconstructed with FBP, ASIR and Sapphire. Comparisons were made with those algorithms that had matching noise power spectrum peaks (NPS). NPS information was extracted from a previously published article that matched NPS peak frequencies across manufacturers by calculating the NPS from uniform phantom images reconstructed with several IR algorithms.Results:The minimum detectable lesion size in the 20 HUBB and 10 HUBB column was 6.3 mm, and 10 mm in the 5 HUBB column for the GE HD 750 scanner. The minimum detectable lesion size in the 20 HUBB column was 4.8 mm, in the 10 HUBB column, 9.5 mm, and the 5 HUBB column, 10 mm for the Siemens Somatom Definition AS.Conclusion:Reducing radiation dose while improving or maintaining LCD is possible with application of IR. However, there are several different IR algorithms, with each generating a different resolution and noise texture. In multi‐manufacturer settings, matching only the CTDIvol between manufacturers may Result in a loss of clinically relevant information.

A Technique of Data Fusion for Effective Text Retrieval

The goal of Information retrieval systems is to provide useful information for user’s information need. For a collection of documents and a given query, an IR system returns a ranked list of documents. Different IR systems based on IR models such as Vector Space, Smart Vector Space, Extended Boolean, Latent Semantic Indexing etc. produce different text documents for the same query. They rarely return the same documents in response to the same queries. This has led to the field of “data fusion”, which seeks to improve the quality of results being presented to user, by combining the outputs of multiple IR algorithms or systems into a single result set. CombMNZ is a score-based fusion algorithm which adds all the reported scores for a document and multiplies the sum value to the number of retrieval models that have returned that document. This paper focuses on Norm_CombMNZ algorithm which normalizes the result obtained from CombMNZ, so that scores lie in 0 to 1 common range and better ranking judgment can be made. The performance of individual IR system is compared with the performance of data fusion system using performance measures such as recall and precision. The graphical result shows that Norm_CombMNZ provides fused resulting text documents to the user, in the form of effective text retrieval.

Effect of different reconstruction algorithms on computer-aided diagnosis (CAD) performance in ultra-low dose CT colonography

PurposeTo assess the effect of different reconstruction algorithms on computer-aided diagnosis (CAD) performance in ultra-low-dose CT colonography (ULD CTC). Materials and methodsIRB approval and informed consents were obtained. Thirty prospectively enrolled patients underwent non-contrast CTC at 120kVp/10mAs in supine and 100kVp/10mAs in prone positions, followed by same-day colonoscopy. Images were reconstructed with filtered back projection (FBP), 80% adaptive statistical iterative reconstruction (ASIR80), and model-based iterative reconstruction (MBIR). A commercial CAD system was applied and per-polyp sensitivities and numbers of false-positives (FPs) were compared among algorithms. ResultsMean effective radiation dose of CTC was 1.02mSv. Of 101 polyps detected and removed by colonoscopy, 61 polyps were detected on supine and on prone CTC datasets on consensus unblinded review, resulting in 122 visible polyps (32 polyps<6mm, 52 6–9.9mm, and 38≥10mm). Per-polyp sensitivity of CAD for all polyps was highest with MBIR (56/122, 45.9%), followed by ASIR80 (54/122, 44.3%) and FBP (43/122, 35.2%), with significant differences between FBP and IR algorithms (P<0.017). Per-polyp sensitivity for polyps≥10mm was also higher with MBIR (25/38, 65.8%) and ASIR80 (24/38, 63.2%) than with FBP (20/38, 58.8%), albeit without statistical significance (P>0.017). Mean number of FPs was significantly different among algorithms (FBP, 1.4; ASIR, 2.1; MBIR, 2.4) (P=0.011). ConclusionAlthough the performance of stand-alone CAD for ULD CTC can be improved, IR algorithms, particularly MBIR, were shown to significantly increase the per-polyp sensitivity of CAD compared to FBP according to this study. Therefore, as ULD CTC only requires 1.02mSv, specific optimization of CAD for ULD CTC and IR algorithms is strongly warranted to make ULD CTC with CAD clinically viable.

High fidelity system modeling for high quality image reconstruction in clinical CT.

Today, while many researchers focus on the improvement of the regularization term in IR algorithms, they pay less concern to the improvement of the fidelity term. In this paper, we hypothesize that improving the fidelity term will further improve IR image quality in low-dose scanning, which typically causes more noise. The purpose of this paper is to systematically test and examine the role of high-fidelity system models using raw data in the performance of iterative image reconstruction approach minimizing energy functional. We first isolated the fidelity term and analyzed the importance of using focal spot area modeling, flying focal spot location modeling, and active detector area modeling as opposed to just flying focal spot motion. We then compared images using different permutations of all three factors. Next, we tested the ability of the fidelity terms to retain signals upon application of the regularization term with all three factors. We then compared the differences between images generated by the proposed method and Filtered-Back-Projection. Lastly, we compared images of low-dose in vivo data using Filtered-Back-Projection, Iterative Reconstruction in Image Space, and the proposed method using raw data. The initial comparison of difference maps of images constructed showed that the focal spot area model and the active detector area model also have significant impacts on the quality of images produced. Upon application of the regularization term, images generated using all three factors were able to substantially decrease model mismatch error, artifacts, and noise. When the images generated by the proposed method were tested, conspicuity greatly increased, noise standard deviation decreased by 90% in homogeneous regions, and resolution also greatly improved. In conclusion, the improvement of the fidelity term to model clinical scanners is essential to generating higher quality images in low-dose imaging.

Half-dose non-contrast CT in the investigation of urolithiasis: image quality improvement with third-generation integrated circuit CT detectors.

The objective of this study is to establish the effect of third-generation integrated circuit (IC) CT detector on objective image quality in full- and half-dose non-contrast CT of the urinary tract. 51 consecutive patients with acute renal colic underwent non-contrast CT of the urinary tract using a 128-slice dual-source CT before (n = 24) and after (n = 27) the installation of third-generation IC detectors. Half-dose images were generated using projections from detector A using the dual-source RAW data. Objective image noise in the liver, spleen, right renal cortex, and right psoas muscle was compared between DC and IC cohorts for full-dose and half-dose images reconstructed with FBP and IR algorithms using 1 cm(2) regions of interest. Presence and size of obstructing ureteric calculi were also compared for full-dose and half-dose reconstructions using DC and IC detectors. No statistical difference in age and lateral body size was found between patients in the IC and DC cohorts. Radiation dose, as measured by size-specific dose estimates, did not differ significantly either between the two cohorts (10.02 ± 4.54 mGy IC vs. 12.28 ± 7.03 mGy DC). At full dose, objective image noise was not significantly lower in the IC cohort as compared to the DC cohort for the liver, spleen, and right psoas muscle. At half dose, objective image noise was lower in the IC cohort as compared to DC cohort at the liver (21.32 IC vs. 24.99 DC, 14.7% decrease, p < 0.001), spleen (19.33 IC vs. 20.83 DC, 7.20% decrease, p = 0.02), and right renal cortex (20.28 IC vs. 22.98 DC, 11.7% decrease, p = 0.005). Mean obstructing ureteric calculi size was not significantly different when comparison was made between full-dose and half-dose images, regardless of detector type (p > 0.05 for all comparisons). Third-generation IC detectors result in lower objective image noise at full- and half-radiation dose levels as compared with traditional DC detectors. The magnitude of noise reduction was greater at half-radiation dose indicating that the benefits of using novel IC detectors are greater in low and ultra-low-dose CT imaging.

Can Iterative Reconstruction Improve Imaging Quality for Lower Radiation CT Perfusion? Initial Experience

Initial results using IR for CT of the head showed satisfactory subjective and objective imaging quality with a 20-40% radiation dose reduction. The aim of our study was to compare the influence of IR and FBP algorithms on perfusion parameters at standard and lowered doses of CTP. Forty patients with unilateral carotid stenosis post-carotid stent placement referred for follow-up CTP were divided into 2 groups (tube currents were 100 mAs in group A and 80 mAs in group B). Datasets were reconstructed with IR and FBP algorithms; and SNRs of gray matter, white matter, and arterial and venous ROIs were compared. CBF, CBV, and MTT means and SNRs were evaluated by using linear regression, and qualitative imaging scores were compared across the 2 algorithms. The mean effective radiation dose of group B (2.06 mSv) was approximately 20% lower than that of group A (2.56 mSv). SNRs for ROIs in the dynamic contrast-enhanced images were significantly higher than those for the FBP images. Correlations of the SNRs for CBF, CBV, and MTT across the 2 algorithms were moderate (R² = 0.46, 0.23, and 0.44, respectively). ROIs in gray matter rather than the IR algorithm predicted increasing SNRs in all CBF, CBV, and MTT maps. Two cases of significant restenosis were confirmed in both algorithms. CBV, CBF, and MTT imaging scores did not differ significantly across algorithms or groups. Lower dose CTP (20% below normal dose) without IR can effectively identify oligemic tissue in poststenting follow-up. IR does not alter the absolute values or increase the SNRs of perfusion parameters. Other methods should be attempted to improve SNRs in settings with low tube currents.

The Diurnal Cycle of Clouds and Precipitation along the Sierra Madre Occidental Observed during NAME-2004: Implications for Warm Season Precipitation Estimation in Complex Terrain

AbstractThis study examines the spatial and temporal variability in the diurnal cycle of clouds and precipitation tied to topography within the North American Monsoon Experiment (NAME) tier-I domain during the 2004 NAME enhanced observing period (EOP, July–August), with a focus on the implications for high-resolution precipitation estimation within the core of the monsoon. Ground-based precipitation retrievals from the NAME Event Rain Gauge Network (NERN) and Colorado State University–National Center for Atmospheric Research (CSU–NCAR) version 2 radar composites over the southern NAME tier-I domain are compared with satellite rainfall estimates from the NOAA Climate Prediction Center Morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) operational and Tropical Rainfall Measuring Mission (TRMM) 3B42 research satellite estimates along the western slopes of the Sierra Madre Occidental (SMO). The rainfall estimates are examined alongside hourly images of high-resolution Geostationary Operational Environmental Satellite (GOES) 11-μm brightness temperatures.An abrupt shallow to deep convective transition is found over the SMO, with the development of shallow convective systems just before noon on average over the SMO high peaks, with deep convection not developing until after 1500 local time on the SMO western slopes. This transition is shown to be contemporaneous with a relative underestimation (overestimation) of precipitation during the period of shallow (deep) convection from both IR and microwave precipitation algorithms due to changes in the depth and vigor of shallow clouds and mixed-phase cloud depths. This characteristic life cycle in cloud structure and microphysics has important implications for ice-scattering microwave and infrared precipitation estimates, and thus hydrological applications using high-resolution precipitation data, as well as the study of the dynamics of convective systems in complex terrain.