Low Contrast Detectability Research Articles

CHARACTERIZATION OF TWO CT SYSTEMS USING A CHANNELIZED HOTELLING OBSERVER AND NPS METRIC.

We investigated the performances of two computed tomography (CT) systems produced by the same manufacturers (Somatom Flash and Edge Siemens) with different detector technologies (Ultrafast Ceramic and Stellar) and different generation of iterative reconstruction (IR) algorithms (SAFIRE and ADMIRE). A homemade phantom was scanned and the images were reconstructed with filtered back-projection (FBP) and IR algorithms. In terms of image quality, the performances of the systems were checked using the low-contrast detectability, evaluated by a Channelized Hotelling Observer (CHO), and the noise power spectrum (NPS). The analysis with CHO showed the best performance of Edge respect to Flash system for both FBP and IR algorithms. This better behavior, which reaches 20%, has been ascribed to the Stellar detector. From the NPS analysis, the noise reduction due to Stellar detector was 57%, moreover ADMIRE algorithm preserves a more traditional CT image texture appearance versus SAFIRE due to a lower NPS peak shift.

Effect of Iterative Reconstruction Algorithm Associated with Low Contrast Detectability Performance from CT Pulmonary Angiography Examinations

Iterative reconstruction (IR) in Computed Tomography (CT) scan examinations are known for noise reduction to achieve a higher level of diagnostic. This study aims to evaluate the radiation dose and image quality performance in different iterative reconstruction levels on CT Pulmonary Angiography (CTPA) examination and to establish a figure of merit (FOM) between both parameters. Thirty adult patients who underwent CTPA examinations were retrospectively included in the present study. Scanning acquisition protocol and radiation dose information, together with patient habitus information, was collected and recorded. The vessel attenuation was measured in five consecutive areas surrounding the pulmonary artery for each iterative reconstruction image. Results of radiation dose were presented in terms of Computed Tomography Dose Index (CTDIvol) obtained from CT-console and SSDE that was calculated by the AAPM report 204 recommendation method. Meanwhile, image quality was estimated by contrast-to-noise ratio (CNR) on average from five consecutive areas of CNR. The mean values of CTDIvol and SSDE were 13.79 ± 7.72 mGy and 17.25 ± 8.92 mGy, respectively. The amount of CNR was significantly different within the iterative reconstruction level (p <0.001). There was a significant difference in the figure of merit for both CNR2/SSDE and CNR2/CTDIvol attained in different iterative reconstruction levels, (p <0.001) and (p <0.001), respectively. In conclusion, we successfully evaluate the value of radiation dose and image quality performance and establish a figure of merit for both parameters to aid further verification of scanning protocols by radiology staff.

Physical evaluation of an ultra-high-resolution CT scanner

ObjectivesTo evaluate the technical performance of an ultra-high-resolution CT (UHRCT) system.MethodsThe physico-technical capabilities of a novel commercial UHRCT system were assessed and compared with those of a current-generation multi-detector (MDCT) system. The super-high-resolution (SHR) mode of the system uses 0.25 mm (at isocentre) detector elements (dels) in the in-plane and longitudinal directions, while the high-resolution (HR) mode bins two dels in the longitudinal direction. The normal-resolution (NR) mode bins dels 2 × 2, resulting in a del-size equivalent to that of the MDCT system. In general, standard procedures and phantoms were used to perform these assessments.ResultsThe UHRCT MTF (10% MTF 4.1 lp/mm) is twice as high as that of the MDCT (10% MTF 1.9 lp/mm), which is comparable to the MTF in the NR mode (10% MTF 1.7 lp/mm). The width of the slice sensitivity profile in the SHR mode (FWHM 0.45 mm) is about 60% of that of the MDCT (FWHM 0.77 mm). Uniformity and CT numbers are within the expected range. Noise in the high-resolution modes has a higher magnitude and higher frequency components compared with MDCT. Low-contrast visibility is lower for the NR, HR and SHR modes compared with MDCT, but about a 14%, for NR, and 23%, for HR and SHR, dose increase gives the same results.ConclusionsHR and SHR mode scanning results in double the spatial resolution, with about a 23% increase in dose required to achieve the same low-contrast detectability.Key Points• Resolution on UHRCT is up to twice as high as for the tested MDCT.• With abdominal settings, UHRCT needs higher dose for the same low-contrast detectability as MDCT, but dose is still below achievable levels as defined by current diagnostic reference levels.• The UHRCT system used in normal-resolution mode yields comparable resolution and noise characteristics as the MDCT system.

Improving Low-contrast Detectability and Noise Texture Pattern for Computed Tomography Using Iterative Reconstruction Accelerated with Machine Learning Method: A Phantom Study

To evaluate the performance of iterative reconstruction (IR) and filtered back projection (FBP) images in terms of low-contrast detectability at different radiation doses, IR levels, and slice thickness using the mathematical model observer with a focus on low-contrast detectability. The CCT189 MITA CT IQ Low-Contrast Phantom was used and helical scans were performed using a 64-detector CT scanner. Tube voltage was set at 120 kVp and tube current was adjusted from 45 to 600 mA. Images were reconstructed at slice thicknesses of 0.625 and 5.0 mm with FBP and five types of iterative progressive reconstruction with visual modeling (IPV) algorithms. The noise power spectrum (NPS) and normalized NPS were calculated. To evaluate low-contrast detectability, the model observer with the channelized Hotelling observer model was applied using low-contrast modules in the phantom. The NPS and normalized NPS for IPV images had similar curves as that for FBP images. At a slice thickness of 0.625 mm and equivalent radiation dose level, the mean improvement of low-contrast detectability for IPV images was 1.19-2.15-fold greater than FBP images with corresponding noise reduction levels. At equivalent noise levels of 5.0-8.0 HU, low-contrast detectability of the IPVstd2 to IPVstr2 images as almost the same or better than that of the FBP images. However, the detectability of the IPVstr4 image was lower than that of the FBP image (p = 0.02). Low-contrast detectability of the IPV images was improved with a similar normalized NPS as with FBP images. Furthermore, a radiation reduction of >50% was achieved for the IPV images, while maintaining similar low-contrast detectability.

Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low-contrast detectability in abdomen CT.

PurposeThe utilization of iterative reconstruction makes it difficult to identify the dose‐noise relationship, resulting in empirical design of scan protocols and inconsistent conclusions on dose reduction for consistent image quality. This study was to quantitatively determine the dose and the blending fraction of adaptive statistical iterative reconstruction (ASIR) based on the specified low‐contrast detectability (LCD).MethodsA tissue equivalent abdomen phantom and a GE discovery 750 HD computed tomography (CT) were utilized. The normality of the noise distribution was tested at various spatial scales (2.1–9.8 mm) in the presence of ASIR (10–100%) with a wide range of doses (2.24–38 mGy). The statically defined minimum detectable contrast (MDC) was used as the image quality metric. The parametric model decomposed the MDC into two terms: one with and the other without ASIR, each was related to the dose in the form of power law with factors and indices dependent on spatial scales. The parameters were identified by least‐square fitting to the experimental data. By considering the constraint of the blending fraction in the range of [0, 1], the dose and ASIR blending fraction were determined for any specified low‐contrast detectability (LCD), quantified by the MDC at the concerned lesion size.ResultsIt was verified that noise distribution is normal in the presence of ASIR. It was also found that the noises obtained from the subtractions of adjacent slices had an underestimate of 20% as compared to the ground truth noises, regardless of the spatial scale, pitch, or ASIR blending fraction. The least‐square fitting for the parametric model resulted in correlation coefficients from 0.905 to 0.996. The root‐mean‐square errors ranged from 1.27% to 7.15%.ConclusionThe parametric model can be used to form a look‐up‐table for dose and ASIR blending fraction. The dose choices may be substantially limited in some cases depending on the required LCD.

Evaluation of low-contrast detectability for iterative reconstruction in pediatric abdominal computed tomography: a phantom study.

Iterative reconstruction is offered by all vendors to achieve similar or better CT image quality at lower doses than images reconstructed with filtered back-projection. The purpose of this study was to investigate the dose-reduction potential for pediatric abdominal CT imaging when using either a commercially available hybrid or a commercially available model-based iterative reconstruction algorithm from a single manufacturer. A phantom containing four low-contrast inserts and a uniform background with total attenuation equivalent to the abdomen of an average 8-year-old child was imaged on a CT scanner (IQon; Philips Healthcare, Cleveland, OH). We reconstructed images using both hybrid iterative reconstruction (iDose4) and model-based iterative reconstruction (Iterative Model Reconstruction). The four low-contrast inserts had circular cross-section with diameters of 3mm, 5mm, 7mm and 10mm and contrasts of 14 Hounsfield units (HU), 7 HU, 5 HU and 3 HU, respectively. Helical scans with identical kilovoltage (kV), pitch, rotation time, and collimation were repeated on the phantom at volume CT dose index (CTDIvol) of 2.0 milligrays (mGy), 3.0mGy, 4.5mGy and 6.0mGy. We measured the contrast-to-noise ratio (CNR) in each rod across scans. Additionally, we collected sub-images containing each rod and sub-images containing the background and used them in two-alternative forced choice observer experiments with four observers (two radiologists and two physicists). We calculated the dose-reduction potential of both iterative reconstruction algorithms relative to a scan performed at 6mGy and reconstructed with filtered back-projection. We calculated dose-reduction potential by either matching average equal observer performance in the two-alternative forced choice experiments or matching CNR. When matching CNR, the dose-reduction potential was 34% to 45% for hybrid iterative reconstruction and 89% to 95% for model-based iterative reconstruction. When matching average observer performance, the dose-reduction potential was 9% to 30% for hybrid iterative reconstruction and 57% to 74% for model-based iterative reconstruction. The range in dose-reduction potential depended on the rod size and contrast level. Observer performance in this phantom study indicates that the dose-reduction potential indicated by an observer study is less than that of CNR; extrapolating the results to clinical studies suggests that the dose-reduction potential would also be less.

State of the Art in Abdominal CT: The Limits of Iterative Reconstruction Algorithms.

The development and widespread adoption of iterative reconstruction (IR) algorithms for CT have greatly facilitated the contemporary practice of radiation dose reduction during abdominal CT examinations. IR mitigates the increased image noise typically associated with reduced radiation dose levels, thereby maintaining subjective image quality and diagnostic confidence for a variety of clinical tasks. Mounting evidence, however, points to important limitations of this method involving radiologists' ability to perform low-contrast diagnostic tasks, such as the detection of liver metastases or pancreatic masses. Radiologists need to be aware that use of IR can result in a decline of spatial resolution for low-contrast structures and degradation of low-contrast detectability when radiation dose reductions exceed approximately 25%. This article will review the principles of IR algorithm technology, describe the various commercial implementations of IR in CT, and review published studies that have evaluated the ability of IR to preserve diagnostic performance for low-contrast diagnostic tasks. In addition, future developments in CT noise reduction techniques and methods to rigorously evaluate their diagnostic performance will be discussed.

Compressed Sensing MR Imaging (CS-MRI) of the Knee: Assessment of Quality, Inter-reader Agreement, and Acquisition Time.

We compared 3 Tesla (3T) compressed sensing (CS)-MRI of different pulse sequences with various acceleration factors to standard fast spin-echo (FSE) sequences in terms of time, quality, and inter-reader agreement. Each sequence was qualitatively ranked and then qualitatively scored for blurring, artifact, low contrast detection, noise pattern, signal-to-noise ratio, and overall quality. The CS-MRI sequences demonstrated very good overall quality compared with routine FSE sequences with overall good inter-reader agreement.

Model observers for Low Contrast Detectability evaluation in dynamic angiography: A feasible approach

PurposeTo evaluate the feasibility of spatio-temporal generalisation of mathematical methods for protocol optimisation in interventional radiology. Materials and methodsTwo model observers were considered:•Channelized Hotelling Observer with spatio-temporal Gabor channels;•Non-Prewhitening Model Observer with spatio-temporal contrast sensitivity function.Furthermore, Low Contrast Detectability (LCD) was evaluated with a generalised statistical method by taking into account the noise integration capability of the human eye.A series of two alternative force choices (2AFC) experiments performed by four observers were used to evaluate the reliability of the proposed models.The evaluation of the mathematical methods was performed by comparing their results to the human observer performances in two steps:1. Firstly, a series of simulated images were used to tune the models2. In the second phase, tuned models were applied both to simulated images and actual images obtained with a commercial phantom to evaluate detectability scores. ResultsEvaluation with simulated images shows a good agreement with 2AFC results (RMSE < 10%). Phantom-based evaluations show a general decrease of such agreement, characterized by an RMSE lower than 16%. ConclusionsThe agreement with human observer experiments supports the feasibility of the proposed generalisations. Thus, they could be introduced in quality control programmes for a deeper protocol-characterisation or for clinical protocol-optimization when dynamic images are involved.

Evaluation of image quality and radiation dose in kilovoltage cone beam computed tomography

This study was to evaluate the image quality and radiation dose of kV-CBCT when varying mAs and reconstruction filter. The radiation dosimetry (CBDIw) and quality control of kV-CBCT image were done. Routine protocols in head, thorax, and pelvis protocols were performed as a reference image. Then the images were acquired with varied mAs, the other parameters were fixed. Each technique was reconstructed with different filter. The image quality in terms of low contrast and high contrast resolution including CBDIw were compared. The result found that the variation of mAs with auto filter affected low contrast resolution, but slightly effect high contrast resolution. When mAs increased, low contrast detectability increased. Decreasing one step of mAs (16.67%) kept in acceptable image quality and reduced about 15% of CBDIw. The smooth filter improved the low contrast resolution, but it decreased the high contrast resolution. While the sharp or ultra-sharp filter improved the high contrast resolution. In conclusion, the changing of mAs affects the low contrast resolution and CBDIW. While the reconstruction filter changed the both of low and high contrast resolution, but no effect to CBDIw. To optimize the image quality and radiation dose of routine protocols, mAs can reduce one step-down.

Channelized Hotelling observer correlation with human observers for low-contrast detection in liver CT images.

Task-based image quality procedures in CT that substitute a human observer with a model observer usually use single-slice images with uniform backgrounds from homogeneous phantoms. However, anatomical structures and inhomogeneities in organs generate noise that can affect the detection performance of human observers. The purpose of this work was to assess the impact of background type, uniform or liver, and the viewing modality, single- or multislice, on the detection performance of human and model observers. We collected abdominal CT scans from patients and homogeneous phantom scans in which we digitally inserted low-contrast signals that mimicked a liver lesion. We ran a rating experiment with the two background conditions with three signal sizes and three human observers presenting images in two reading modalities: single- and multislice. In addition, channelized Hotelling observers (CHO) for single- and multislice detection were implemented and evaluated according to their degree of correlation with the human observer performance. For human observers, there was a small but significant improvement in performance with multislice compared to the single-slice viewing mode. Our data did not reveal a significant difference between uniform and anatomical backgrounds. Model observers demonstrated a good correlation with human observers for both viewing modalities. Human observers have very similar performances in both multi- and single-slice viewing mode. It is therefore preferable to use single-slice CHO as this model is computationally more tractable than multislice CHO. However, using images from a homogeneous phantom can result in overestimating image quality as CHO performance tends to be higher in uniform than anatomical backgrounds, while human observers have similar detection performances.

Radiation Dose Reduction at Pediatric CT: Use of Low Tube Voltage and Iterative Reconstruction-Erratum.

Given the growing awareness of and concern for potential carcinogenic effects of exposure of children to ionizing radiation at CT, optimizing acquisition parameters is crucial to achieve diagnostically acceptable image quality at the lowest possible radiation dose. Among currently available dose reduction techniques, recent technical innovations have allowed the implementation of low tube voltage scans and iterative reconstruction (IR) techniques into daily clinical practice for pediatric CT. The benefits of lowering tube voltage include a considerable reduction in radiation dose and improved contrast on images, especially when an iodinated contrast medium is used. The increase in noise, which is attributed to decreased photon penetration, is a major drawback but is not as severe as that at adult CT because of the small body size of children. In addition, use of IR algorithms can suppress increased noise, yielding wider applicability for low tube voltage scans. However, a careful implementation strategy and methodologic approach are necessary to maximize the potential for dose reduction while preserving diagnostic image quality under each clinical condition. The potential pitfalls of and topics related to these techniques include (a) the effect of tube voltage on the surface radiation dose, (b) the effect of window settings, (c) accentuation of metallic artifacts, (d) deterioration of low contrast detectability at low-dose settings, (e) interscanner variation of x-ray spectra, and (f) a comparison with the use of a spectral shaping technique. Appropriate use of low tube voltage and IR techniques is helpful for radiation dose reduction in most applications of pediatric CT. Online DICOM image stacks are available for this article . ©RSNA, 2018.

A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.

This work aims to develop a new framework of image quality assessment using deep learning-based model observer (DL-MO) and to validate it in a low-contrast lesion detection task that involves CT images with patient anatomical background. The DL-MO was developed using the transfer learning strategy to incorporate a pretrained deep convolutional neural network (CNN), a partial least square regression discriminant analysis (PLS-DA) model and an internal noise component. The CNN was previously trained to achieve the state-of-the-art classification accuracy over a natural image database. The earlier layers of the CNN were used as a deep feature extractor, with the assumption that similarity exists between the CNN and the human visual system. The PLSR model was used to further engineer the deep feature for the lesion detection task in CT images. The internal noise component was incorporated to model the inefficiency and variability of human observer (HO) performance, and to generate the ultimate DL-MO test statistics. Seven abdominal CT exams were retrospectively collected from the same type of CT scanners. To compare DL-MO with HO, 12 experimental conditions with varying lesion size, lesion contrast, radiation dose, and reconstruction types were generated, each condition with 154 trials. CT images of a real liver metastatic lesion were numerically modified to generate lesion models with four lesion sizes (5, 7, 9, and 11mm) and three contrast levels (15, 20, and 25HU). The lesions were inserted into patient liver images using a projection-based method. A validated noise insertion tool was used to synthesize CT exams with 50% and 25% of routine radiation dose level. CT images were reconstructed using the weighted filtered back projection algorithm and an iterative reconstruction algorithm. Four medical physicists performed a two-alternative forced choice (2AFC) detection task (with multislice scrolling viewing mode) on patient images across the 12 experimental conditions. DL-MO was operated on the same datasets. Statistical analyses were performed to evaluate the correlation and agreement between DL-MO and HO. A statistically significant positive correlation was observed between DL-MO and HO for the 2AFC low-contrast detection task that involves patient liver background. The corresponding Pearson product moment correlation coefficient was 0.986 [95% confidence interval (0.950, 0.996)]. Bland-Altman agreement analysis did not indicate statistically significant differences. The proposed DL-MO is highly correlated with HO in a low-contrast detection task that involves realistic patient liver background. This study demonstrated the potential of the proposed DL-MO to assess image quality directly based on patient images in realistic, clinically relevant CT tasks.

Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance.

Evaluate and compare the image quality and acceptance of a full MBIR algorithm to that of an earlier full IR hybrid algorithm and filtered back projection (FBP). Acquisitions were performed with a 320 detector-row CT scanner with seven different dose levels. Images were reconstructed with three algorithms: FBP, full hybrid iterative reconstruction (HIR), and a full model-based iterative reconstruction algorithm (full MBIR). The sensitometry, spatial resolution, image texture, and low-contrast detectability of these algorithms were compared. Subjective analysis of low-contrast detectability was performed. Ten radiologists answered a questionnaire on image quality and confidence in full MBIR images in clinical practice. The contrast-to-noise ratio of full MBIR was significantly higher than in the other algorithms (p < 0.0015). The spatial resolution was also higher with full MBIR at high frequencies (> 0.3lp/mm). Full MBIR at low dose levels led to better low-contrast detectability and more inserts being identified with a higher confidence (p < 0.0001). Full MBIR was associated with a change in image texture compared to HIR and FBP. Eighty percent of radiologists judged general appearance and texture of full MBIR images worse than HIR. Moreover, compared with HIR, for 50% of radiologists, the diagnostic confidence on full MBIR images was worse. Questionnaire reliability was considered acceptable (Cronbach alpha 0.7). Compared to conventional iterative reconstruction algorithms, full MBMIR presented a higher image quality and low-contrast detectability and a worse acceptance among radiologists. • Full MBIR used led to an overall improvement in image quality compared with FBP and HIR. • Full MBIR leads to image texture change which reduces the confidence in these images among radiologists. • Awareness of the image texture change and improved quality of full MBIR reconstructed images could improve the acceptance of this technique in clinical practice.

Image Quality Assessment of the Digital Radiography Units in Tabriz, Iran: A Phantom Study.

Creating a high-quality image with the low patient dose is one of the most important goals in medical X-ray imaging. In this study, the image quality parameters of the digital radiographic units in Tabriz city were considered and compared with the international protocols. The image quality parameters were measured at 11 high workload digital radiography (DR) imaging centers in Tabriz city, and the results were compared to DINN 6868/58 standards. All centers equipped with the direct DR units passed the spatial resolution, low contrast detectability, contrast dynamic range, and noise tests, while the computed radiography (CR) units only could pass the two last tests. The highest spatial resolution was observed 3.2 lp/mm in the DR unit while the lowest one was 1.8 lp/mm in the CR unit. The highest noise was measured to be 0.03 OD that was observed in the DR unit. The most difference between the nominal and measured peak kilovoltage and mAs was 3.1% and 6.8%, respectively. The entrance surface air kerma in all units was obtained <0.63 mGy. The measured half-value layer range was between 2.4 and 3.54 mmAl. The physical parameters of image quality such as spatial resolution, contrast, and noise are robustness quantitative parameters for the assessment of the image quality performance of the units. Therefore, measurement and control of these parameters using two-dimensional phantoms are very critical.

Application of Vendor-Neutral Iterative Reconstruction Technique to Pediatric Abdominal Computed Tomography.

ObjectiveTo compare image qualities between vendor-neutral and vendor-specific hybrid iterative reconstruction (IR) techniques for abdominopelvic computed tomography (CT) in young patients.Materials and MethodsIn phantom study, we used an anthropomorphic pediatric phantom, age-equivalent to 5-year-old, and reconstructed CT data using traditional filtered back projection (FBP), vendor-specific and vendor-neutral IR techniques (ClariCT; ClariPI) in various radiation doses. Noise, low-contrast detectability and subjective spatial resolution were compared between FBP, vendor-specific (i.e., iDose1 to 5; Philips Healthcare), and vendor-neutral (i.e., ClariCT1 to 5) IR techniques in phantom. In 43 patients (median, 14 years; age range 1–19 years), noise, contrast-to-noise ratio (CNR), and qualitative image quality scores of abdominopelvic CT were compared between FBP, iDose level 4 (iDose4), and ClariCT level 2 (ClariCT2), which showed most similar image quality to clinically used vendor-specific IR images (i.e., iDose4) in phantom study. Noise, CNR, and qualitative imaging scores were compared using one-way repeated measure analysis of variance.ResultsIn phantom study, ClariCT2 showed noise level similar to iDose4 (14.68–7.66 Hounsfield unit [HU] vs. 14.78–6.99 HU at CT dose index volume range of 0.8–3.8 mGy). Subjective low-contrast detectability and spatial resolution were similar between ClariCT2 and iDose4. In clinical study, ClariCT2 was equivalent to iDose4 for noise (14.26–17.33 vs. 16.01–18.90) and CNR (3.55–5.24 vs. 3.20–4.60) (p > 0.05). For qualitative imaging scores, the overall image quality ([reader 1, reader 2]; 2.74 vs. 2.07, 3.02 vs. 2.28) and noise (2.88 vs. 2.23, 2.93 vs. 2.33) of ClariCT2 were superior to those of FBP (p < 0.05), and not different from those of iDose4 (2.74 vs. 2.72, 3.02 vs. 2.98; 2.88 vs. 2.77, 2.93 vs. 2.86) (p > 0.05).ConclusionVendor-neutral IR technique shows image quality similar to that of clinically used vendor-specific hybrid IR technique for abdominopelvic CT in young patients.

The investigation of dose and image quality of chest computed tomography using different combinations of noise index and adaptive statistic iterative reconstruction level.

Background:Computed tomography (CT) automatic tube current modulation (ATCM) systems and iterative reconstruction (IR) play an important role in CT radiation dose optimization. How the two can best be used together is one of the challenges faced by radiology professionals.Aim:To determine optimum settings of ATCM noise index (NI) together with adaptive statistic iterative reconstruction (ASIR) for a general electric (GE) scanner that aims to achieve similar image quality to the standard protocol used in the hospital (Smart mA technique with NI of 11.57 and 30% ASIR reconstruction) with a lower dose.Methods:Different NI and ASIR levels were set for scans of a phantom. Objective image quality assessments in terms of noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), low-contrast detectability (LCD), and modulation transfer function (MTF) were carried out in an anthropomorphic chest and a Catphan 700 phantom. Subjective image quality assessment was also performed with five readers to confirm whether the image quality of the new protocols was adequate.Result and Conclusion:SNR and CNR increased with the strength of ASIR, and decreased with higher NI settings. The MTF improved slightly for higher dose levels and from filtered back projection (FBP) to higher strength of ASIR. LCD improved with ASIR compared to FBP and with higher strengths of ASIR. Qualitative scoring ranged between 3.0 and 4.6. A moderate degree of reliability was found between scoring. Use of NI 15.04 with 70% ASIR can reduce dose by 41% compared to the standard protocol of NI 11.57 with 30% ASIR without degradation of image quality.

Thin Vessel Detection and Thick Vessel Edge Enhancement to Boost Performance of Retinal Vessel Extraction Methods

Abnormal changes in the geometry of retinal blood vessels is often a key indicator of certain ocular patholo- gies. Currently, the detection of such pathological changes is largely a manual labor-intensive process that must be performed by expert clinicians to ensure a correct diagnosis. Although there have been previous efforts to automate such diagnostic procedures using computer-aided diagnostic (CAD) applications, these computerized methods seem to lack the necessary sensitivity to detect blood vessels which are of low con- trast, i.e. they are not easily distinguished from their surrounding background. In order to improve CAD detection of low contrast blood vessels, a specialized filter construction is proposed with functionality which can be implemented as an add-on module to enhance the performance of existing CAD blood vessel detector applications. The improved low-contrast detection capability of the specialized filters has been validated by a series of tests in which the proposed technique outperforms other state-of-the-art CAD applications when processing retinal fundus images extracted from publicly available image databases.

Evaluation of Low-contrast Detectability of Iterative Reconstruction Algorithm in High-speed CT Technology for Head

The purpose of this study was to assess the feasibility of high-speed CT technology for head without deterioration of low-contrast detectability using the brain LCD (Canon Medical Systems) of iterative reconstruction. System performance (SP) function analysis, low-contrast object specific contrast-to-noise ratio (CNRLO) analysis, and visual evaluation using Scheffe's paired comparison were performed. Additionally, analysis of the correlation of CNRLO and visual scores was performed. SP was performed with the self-made phantom. CNRLO was calculated with the catphan 504 phantom (CTP 515). Visual evaluation was performed using the brain phantom which simulated such as cerebral infarction and investigated on a fivepoint scale. All images were acquired with pitch factor of 0.61 (low pitch) and 1.40 (high pitch). All images were reconstructed with filtered back projection (FBP), brain LCD standard (LCD STD) and strong (LCD STR). SP of brain LCD improved compared with FBP. CNRLO of FBP decreased in high pitch compared with low pitch. CNRLO of brain LCD images acquired by low- and high pitch were improved compared with FBP. Visual scores denoted similar trends to that of CNRLO and there was high correlation with CNRLO. It was suggested that using brain LCD can achieve the high speed CT technology for head without deterioration of low-contrast detectability.

Applicability and repeatability in angiography systems of statistical method for the low contrast detectability

Purpose The aim of this study is to evaluate the applicability and repeatability of the Low Contrast Detectability (LCD) statistical method in angiography systems [1] , [2] . Methods A phantom consisting of a square aluminum target and a 5-step-wedge (0.2–1 mmAl) was built according to Radice et al. [2] . Different acquisition protocols were used (low, normal and high dose fluoroscopy; abdomen and cerebtal fluorography), and measurements were repeated for several thicknesses of PolyMethylMethAcrylate (PMMA). A Matlab software automatically calculates LCD values in terms of Pixel Value (PV) and contrast threshold (Ct); it also converts PV in mmAl (Fig. 1), providing the LCD(mmAl) [2] . The short- and long-term repeatability were evaluated by repeating acquisitions five times for every session and monthly (for six months) respectively. Results The Coefficients of Variation CV(%) reported in Table 1 are always higher for LCD(mmAl) than for LCD(PV) and LCD(Ct), especially in case of long-term measurements which consequently are not repeatable. This difference is related to unstable conversion from PV to mmAl; indeed, the image noise makes difficult to select the steps of the wedge. To overcome the problem, it would be possible to extend the step-wedge up to 2 mmAl, to introduce the point (0,0) in the conversion curve and to use only the curves with R2 > 0.8. Conclusions The LCD statistical method is easy to implement and provides consistent results for LCD(PV) and LCD(Ct). However, the LCD(mmAl) values seem to be not repeatable because of the instability of PV-mmAl conversion; the suggested solutions can improve the conversion, leading to more robust LCD (mmAl) values.