Real-Time Person Identification in a Hospital Setting: A Systematic Review.

In the critical setting of a trauma team activation, team composition is crucial information that should be accessible at a glance. This calls for a technological solution, which are widely available, that allows access to the whereabouts of personnel. This diversity presents decision makers and users with many choices and considerations. The aim of this review is to give a comprehensive overview of available real-time person identification techniques and their respective characteristics. A systematic literature review was performed to create an overview of identification techniques that have been tested in medical settings or already have been implemented in clinical practice. These techniques have been investigated on a total of seven characteristics: costs, usability, accuracy, response time, hygiene, privacy, and user safety. The search was performed on 11 May 2020 in PubMed and the Web of Science Core Collection. PubMed and Web of Science yielded a total n = 265 and n = 228 records, respectively. The review process resulted in n = 23 included records. A total of seven techniques were identified: (a) active and (b) passive Radio-Frequency Identification (RFID) based systems, (c) fingerprint, (d) iris, and (e) facial identification systems and infrared (IR) (f) and ultrasound (US) (g) based systems. Active RFID was largely documented in the included literature. Only a few could be found about the passive systems. Biometric (c, d, and e) technologies were described in a variety of applications. IR and US techniques appeared to be a niche, as they were only spoken of in few (n = 3) studies.

Comparison of a flexible versus a rigid breast compression paddle: pain experience, projected breast area, radiation dose and technical image quality.

PURPOSE: To compare pain, projected breast area, radiation dose and image quality between flexible (FP) and rigid (RP) breast compression paddles. METHODS: The study was conducted in a Dutch mammographic screening unit (288 women). To compare both paddles one additional image with RP was made, consisting of either a mediolateral-oblique (MLO) or craniocaudal-view (CC). Pain experience was scored using the Numeric Rating Scale (NRS). Projected breast area was estimated using computer software. Radiation dose was estimated using the model by Dance. Image quality was reviewed by three radiologists and three radiographers. RESULTS: There was no difference in pain experience between both paddles (mean difference NRS: 0.08 ± 0.08, p = 0.32). Mean radiation dose was 4.5 % lower with FP (0.09 ± 0.01 p = 0.00). On MLO-images, the projected breast area was 0.79 % larger with FP. Paired evaluation of image quality indicated that FP removed fibroglandular tissue from the image area and reduced contrast in the clinically relevant retroglandular area at chest wall side. CONCLUSIONS: Although FP performed slightly better in the projected breast area, it moved breast tissue from the image area at chest wall side. RP showed better contrast, especially in the retroglandular area. We therefore recommend the use of RP for standard MLO and CC views.

Adaptive iterative dose reduction 3D versus filtered back projection in CT: evaluation of image quality.

OBJECTIVE: The purpose of this study was to evaluate image quality with filtered back projection (FBP) and adaptive iterative dose reduction 3D (AIDR 3D). MATERIALS AND METHODS: Phantom acquisitions were performed at six dose levels to assess spatial resolution, noise, and low-contrast detectability (LCD). Spatial resolution was assessed with the modulation transfer function at high and low contrast levels. Noise power spectrum and SD of attenuation were assessed. LCD was calculated with a mathematic model observer applied to phantom CT images. The subjective image quality of clinical CT scans was assessed by five radiologists. RESULTS: Compared with FBP, AIDR 3D resulted in substantial noise reduction at all frequencies with a similar shape of the noise power spectrum. Spatial resolution was similar for AIDR 3D and FBP. LCD improved with AIDR 3D, which was associated with a potential average dose reduction of 36% (range, 9-86%). The observer study showed that overall image quality improved and artifacts decreased with AIDR 3D. CONCLUSION: AIDR 3D performs better than FBP with regard to noise and LCD, resulting in better image quality, and performs similarly with respect to spatial resolution. The evaluation of image quality of clinical CT scans was consistent with the objective assessment of image quality with a phantom. The amount of dose reduction should be investigated for each clinical indication in studies with larger numbers of patients.

Lesion detection in digital breast tomosynthesis: human reader experiments indicate no benefit from the integration of information from multiple planes.

Purpose: In digital breast tomosynthesis (DBT), radiologists need to review a stack of 20 to 80 tomosynthesis images, depending upon breast size. This causes a significant increase in reading time. However, it is currently unknown whether there is a perceptual benefit to viewing a mass in the 3D tomosynthesis volume. To answer this question, this study investigated whether adjacent lesion-containing planes provide additional information that aids lesion detection for DBT-like and breast CT-like (bCT) images. Method: Human reader detection performance was determined for low-contrast targets shown in a single tomosynthesis image at the center of the target (2D) or shown in the entire tomosynthesis image stack (3D). Using simulations, targets embedded in simulated breast backgrounds, and images were generated using a DBT-like (50 deg angular range) and a bCT-like (180 deg angular range) imaging geometry. Experiments were conducted with spherical and capsule-shaped targets. Eleven readers reviewed 1600 images in two-alternative forced-choice experiments. The area under the receiver operating characteristic curve (AUC) and reading time were computed for the 2D and 3D reading modes for the DBT and bCT imaging geometries and for both target shapes. Results: Spherical lesion detection was higher in 2D mode than in 3D, for both DBT- and bCT-like images (DBT: AUC2D=0.790, AUC3D=0.735, P=0.03; bCT: AUC2D=0.869, AUC3D=0.716, P<0.05), but equivalent for capsule-shaped signals (DBT: AUC2D=0.891, AUC3D=0.915, P=0.19; bCT: AUC2D=0.854, AUC3D=0.847, P=0.88). Average reading time was up to 134% higher for 3D viewing (P<0.05). Conclusions: For the detection of low-contrast lesions, there is no inherent visual perception benefit to reviewing the entire DBT or bCT stack. The findings of this study could have implications for the development of 2D synthetic mammograms: a single synthesized 2D image designed to include all lesions present in the volume might allow readers to maintain detection performance at a significantly reduced reading time.

Increase in perceived case suspiciousness due to local contrast optimisation in digital screening mammography.

OBJECTIVES: To determine the influence of local contrast optimisation on diagnostic accuracy and perceived suspiciousness of digital screening mammograms. METHODS: Data were collected from a screening region in the Netherlands and consisted of 263 digital screening cases (153 recalled,110 normal). Each case was available twice, once processed with a tissue equalisation (TE) algorithm and once with local contrast optimisation (PV). All cases had digitised previous mammograms. For both algorithms, the probability of malignancy of each finding was scored independently by six screening radiologists. Perceived case suspiciousness was defined as the highest probability of malignancy of all findings of a radiologist within a case. Differences in diagnostic accuracy of the processing algorithms were analysed by comparing the areas under the receiver operating characteristic curves (A(z)). Differences in perceived case suspiciousness were analysed using sign tests. RESULTS: There was no significant difference in A(z) (TE: 0.909, PV 0.917, P = 0.46). For all radiologists, perceived case suspiciousness using PV was higher than using TE more often than vice versa (ratio: 1.14-2.12). This was significant (P <0.0083) for four radiologists. CONCLUSIONS: Optimisation of local contrast by image processing may increase perceived case suspiciousness, while diagnostic accuracy may remain similar. KEY POINTS: Variations among different image processing algorithms for digital screening mammography are large. Current algorithms still aim for optimal local contrast with a low dynamic range. Although optimisation of contrast may increase sensitivity, diagnostic accuracy is probably unchanged. Increased local contrast may render both normal and abnormal structures more conspicuous.

Metal artifact reduction for CT: development, implementation, and clinical comparison of a generic and a scanner-specific technique.

PURPOSE: To develop, implement, and compare two metal artifact reduction methods for CT. METHODS: Two methods for metal artifact reduction were developed. The first is based on applying corrections in a Radon transformation of the CT images. The second method is based on a forward projection of the CT images and applying corrections in the scanner's original raw data. The first method is generic since it does not depend on the scanner specifications. For the second method, detailed information on the design of the CT scanner and the raw data of the study is required. Clinical implementation and evaluation were performed using pre- and post-operative CT scans of four patients with shoulder prosthesis. For comparison of these methods, the authors developed a quantitative technique that compares improvement in image quality for the two metal artifact reduction techniques with the image quality of the uncorrected images. RESULTS: Metal artifact reduction using either of the two methods yields a decrease of noise and artifacts in CT scans of patients with shoulder prostheses. Artifacts that appeared as bright and dark streaks were reduced or eliminated and as a result image quality improved. Quantitative assessment of clinical images showed improved image quality for both techniques of metal artifact reduction, but the method based on correction in original raw data performed better in all comparisons. CONCLUSION: Both methods are effective for metal artifact reduction, but better performance was observed for the method that is based on correcting the original raw data. The used evaluation technique provides an objective way of evaluating the metal artifacts in clinical CT images.

Development and validation of segmentation and interpolation techniques in sinograms for metal artifact suppression in CT.

PURPOSE: Metal prostheses cause artifacts in computed tomography (CT) images. The purpose of this work was to design an efficient and accurate metal segmentation in raw data to achieve artifact suppression and to improve CT image quality for patients with metal hip or shoulder prostheses. METHODS: The artifact suppression technique incorporates two steps: metal object segmentation in raw data and replacement of the segmented region by new values using an interpolation scheme, followed by addition of the scaled metal signal intensity. Segmentation of metal is performed directly in sinograms, making it efficient and different from current methods that perform segmentation in reconstructed images in combination with Radon transformations. Metal signal segmentation is achieved by using a Markov random field model (MRF). Three interpolation methods are applied and investigated. To provide a proof of concept, CT data of five patients with metal implants were included in the study, as well as CT data of a PMMA phantom with Teflon, PVC, and titanium inserts. Accuracy was determined quantitatively by comparing mean Hounsfield (HU) values and standard deviation (SD) as a measure of distortion in phantom images with titanium (original and suppressed) and without titanium insert. Qualitative improvement was assessed by comparing uncorrected clinical images with artifact suppressed images. RESULTS: Artifacts in CT data of a phantom and five patients were automatically suppressed. The general visibility of structures clearly improved. In phantom images, the technique showed reduced SD close to the SD for the case where titanium was not inserted, indicating improved image quality. HU values in corrected images were different from expected values for all interpolation methods. Subtle differences between interpolation methods were found. CONCLUSIONS: The new artifact suppression design is efficient, for instance, in terms of preserving spatial resolution, as it is applied directly to original raw data. It successfully reduced artifacts in CT images of five patients and in phantom images. Sophisticated interpolation methods are needed to obtain reliable HU values close to the prosthesis.

A technique for simulating the effect of dose reduction on image quality in digital chest radiography.

PURPOSE: The purpose of this study is to provide a pragmatic tool for studying the relationship between dose and image quality in clinical chest images. To achieve this, we developed a technique for simulating the effect of dose reduction on image quality of digital chest images. MATERIALS AND METHODS: The technique was developed for a digital charge-coupled-device (CCD) chest unit with slot-scan acquisition. Raw pixel values were scaled to a lower dose level, and a random number representing noise to each specific pixel value was added. After adding noise, raw images were post processed in the standard way. Validation was performed by comparing pixel standard deviation, as a measure of noise, in simulated images with images acquired at actual lower doses. To achieve this, a uniform test object and an anthropomorphic phantom were used. Additionally, noise power spectra of simulated and actual images were compared. Also, detectability of simulated lesions was investigated using a model observer. RESULTS: The mean difference in noise values between simulated and real lower-dose phantom images was smaller than 5% for relevant clinical settings. Noise power spectra appeared to be comparable on average but simulated images showed slightly higher noise levels for higher spatial frequencies and slightly lower noise levels for lower spatial frequencies. Comparable detection performance was shown in simulated and actual images with slightly worse detectability for simulated lower dose images. CONCLUSION: We have developed and validated a method for simulating dose reduction. Our method seems an acceptable pragmatic tool for studying the relationship between dose and image quality.

Development of a 3D printed anthropomorphic lung phantom for image quality assessment in CT.

PURPOSE: To design a 3D printed anthropomorphic lung vessel phantom for CT image quality assessment and to evaluate the phantom image and dose characteristics. METHODS: An in-house algorithm generated a vessel tree model, based on human lungs anatomy, which was 3D printed using a multi jet modeling printer (0.25 mm ≤ vessel diameters ≤ 8.25 mm) and inserted in an elliptical holder (thorax surrogate). The phantom was scanned (Toshiba Aquilion Genesis CT) and compared in terms of attenuation (Hounsfield units, HU) and dose characteristics with studies of five patients (normal BMI) and a commercial torso phantom, performed with the same thorax protocol. The pixel value distribution in the lung area was assessed with histograms. To investigate the adjustment of tube current modulation, tube load and CTDI were compared. RESULTS: The histogram peaks for respectively vessels and surrounding tissue were at 105 HU and -985 HU (3D printed phantom), at -25 HU and -1000 HU (torso phantom) and at 25 HU and -875 HU (average patient). The contrast between vessels and surrounding was -1090 HU (3D printed), -975 HU (torso phantom), and -900 HU (average patient). The measured HU values (soft tissue and vertebra) were (32 ±â€¯15) HU and (210 ±â€¯71) HU (average patient); (4 ±â€¯4) HU, (390 ±â€¯39) HU (torso phantom) and (119 ±â€¯5) HU, (951 ±â€¯31) HU (3D printed phantom and holder). CTDIvol was (1.9 ±â€¯4.7 mGy) for patients, 1.9 mGy for the torso phantom and 2.1 mGy for the 3D printed lung phantom. CONCLUSIONS: An anthropomorphic 3D printed lung phantom was developed and its CT image and dose characteristics evaluated. The phantom has the potential to provide clinically relevant and reproducible measures of CT image quality.

Automatic quantitative analysis of pulmonary vascular morphology in CT images.

PURPOSE: Vascular remodeling is a significant pathological feature of various pulmonary diseases, which may be assessed by quantitative computed tomography (CT) imaging. The purpose of this study was therefore to develop and validate an automatic method for quantifying pulmonary vascular morphology in CT images. METHODS: The proposed method consists of pulmonary vessel extraction and quantification. For extracting pulmonary vessels, a graph-cuts-based method is proposed which considers appearance (CT intensity) and shape (vesselness from a Hessian-based filter) features, and incorporates distance to the airways into the cost function to prevent false detection of airway walls. For quantifying the extracted pulmonary vessels, a radius histogram is generated by counting the occurrence of vessel radii, calculated from a distance transform-based method. Subsequently, two biomarkers, slope α and intercept ß, are calculated by linear regression on the radius histogram. A public data set from the VESSEL12 challenge was used to independently evaluate the vessel extraction. The quantitative analysis method was validated using images of a three-dimensional (3D) printed vessel phantom, scanned by a clinical CT scanner and a micro-CT scanner (to obtain a gold standard). To confirm the association between imaging biomarkers and pulmonary function, 77 scleroderma patients were investigated with the proposed method. RESULTS: In the independent evaluation with the public data set, our vessel segmentation method obtained an area under the receiver operating characteristic (ROC) curve of 0.976. The median radius difference between clinical and micro-CT scans of a 3D printed vessel phantom was 0.062 ± 0.020 mm, with interquartile range of 0.199 ± 0.050 mm. In the studied patient group, a significant correlation between diffusion capacity for carbon monoxide and the biomarkers, α (R = -0.27, P = 0.018) and ß (R = 0.321, P = 0.004), was obtained. CONCLUSION: In conclusion, the proposed method was validated independently using a public data set resulting in an area under the ROC curve of 0.976 and using a 3D printed vessel phantom data set, showing a vessel sizing error of 0.062 mm (0.16 in-plane pixel units). The correlation between imaging biomarkers and diffusion capacity in a clinical data set confirmed an association between lung structure and function. This quantification of pulmonary vascular morphology may be helpful in understanding the pathophysiology of pulmonary vascular diseases.

Optimization of the difference-of-Gaussian channel sets for the channelized Hotelling observer.

The channelized-Hotelling observer (CHO) was investigated as a surrogate of human observers in task-based image quality assessment. The CHO with difference-of-Gaussian (DoG) channels has shown potential for the prediction of human detection performance in digital mammography (DM) images. However, the DoG channels employ parameters that describe the shape of each channel. The selection of these parameters influences the performance of the DoG CHO and needs further investigation. The detection performance of the DoG CHO was calculated and correlated with the detection performance of three humans who evaluated DM images in 2-alternative forced-choice experiments. A set of DM images of an anthropomorphic breast phantom with and without calcification-like signals was acquired at four different dose levels. For each dose level, 200 square regions-of-interest (ROIs) with and without signal were extracted. Signal detectability was assessed on ROI basis using the CHO with various DoG channel parameters and it was compared to that of the human observers. It was found that varying these DoG parameter values affects the correlation ( r 2 ) of the CHO with human observers for the detection task investigated. In conclusion, it appears that the the optimal DoG channel sets that maximize the prediction ability of the CHO might be dependent on the type of background and signal of ROIs investigated.

Automated analysis of secretory vesicle distribution at the ultrastructural level.

Neuroendocrine cells like chromaffin cells and PC-12 cells are established models for transport, docking and secretion of secretory vesicles. In micrographs, these vesicles are recognized by their electron dense core. The analysis of secretory vesicle distribution is usually performed manually, which is labour-intensive and subject to human bias and error. We have developed an algorithm to analyze secretory vesicle distribution and docking in electron micrographs. Our algorithm automatically detects the vesicles and calculates their distance to the plasma membrane on basis of the pixel coordinates, ensuring that all vesicles are counted and the shortest distance is measured. We validated the algorithm on a several preparations of endocrine cells. The algorithm was highly accurate in recognizing secretory vesicles and calculating their distribution including vesicle-docking analysis. Furthermore, the algorithm enabled the extraction of parameters that cannot be measured manually like vesicle clustering. Taking together, the algorithm facilitates and expands the unbiased and efficient analysis of secretory vesicle distribution and docking.

Automated cardiac phase selection with 64-MDCT coronary angiography.

OBJECTIVE: The aim of this study was to assess three different phase-selection methods for obtaining optimal CT coronary artery image quality. MATERIALS AND METHODS: ECG-gated CT coronary angiography scans of 40 patients (23 men, 17 women; mean age, 56 years) were retrieved. The patient group was composed of 20 consecutive patients with heart rates < or = 65 beats per minute (bpm) and 20 consecutive patients with heart rates > 65 bpm. Three phase-selection methods were evaluated: fixed phase selection, manual phase selection, and automated phase selection. Two scoring systems were used to evaluate diagnostic quality: scoring of axial images on a 5-point scale and scoring of multiplanar reconstructions (MPRs) on a forced-choice 3-point preference scale. Differences were tested by Wilcoxon's signed rank test for the entire patient group and the two subgroups including patients with heart rates < or = 65 bpm and those with heart rates > 65 bpm. RESULTS: Axial image evaluation of the entire patient group showed statistically significant superior image quality for the manual phase-selection method compared with the predefined phase-selection method and no statistically significant differences were found for the other comparisons. Analysis at heart rates < or = 65 bpm showed no significant differences between phase-selection methods. Analysis at heart rates > 65 bpm showed the best results for the automated phase-selection method, and image quality was significantly better for the automated and manual phase-selection methods than for the predefined phase-selection method. CONCLUSION: The automated phase-selection method accurately detects the optimal diagnostic phase for CT coronary artery evaluation and has the potential to reduce operator time needed for image reconstruction.

Signal template generation from acquired images for model observer-based image quality analysis in mammography.

Mammography images undergo vendor-specific processing, which may be nonlinear, before radiologist interpretation. Therefore, to test the entire imaging chain, the effect of image processing should be included in the assessment of image quality, which is not current practice. For this purpose, model observers (MOs), in combination with anthropomorphic breast phantoms, are proposed to evaluate image quality in mammography. In this study, the nonprewhitening MO with eye filter and the channelized Hotelling observer were investigated. The goal of this study was to optimize the efficiency of the procedure to obtain the expected signal template from acquired images for the detection of a 0.25-mm diameter disk. Two approaches were followed: using acquired images with homogeneous backgrounds (approach 1) and images from an anthropomorphic breast phantom (approach 2). For quality control purposes, a straightforward procedure using a single exposure of a single disk was found adequate for both approaches. However, only approach 2 can yield templates from processed images since, due to its nonlinearity, image postprocessing cannot be evaluated using images of homogeneous phantoms. Based on the results of the current study, a phantom should be designed, which can be used for the objective assessment of image quality.

A model observer study using acquired mammographic images of an anthropomorphic breast phantom.

PURPOSE: To study the feasibility of a task-based framework composed of an anthropomorphic breast phantom and mathematical model observers (MOs) for the evaluation of system-processed mammographic images. METHODS: A prototype anthropomorphic breast phantom with inserted gold discs of 0.1 mm and 0.25 mm diameter was imaged with two digital mammography systems (system A and B) at four different dose levels. From the acquired processed and unprocessed images, signal-present and signal-absent regions of interest (ROIs) were extracted. The ROIs were evaluated by a non-pre-whitening MO with eye filter (NPWE) and by three human observers in a two-alternative forced-choice experiment. We compared the human and the MO performance on a simple detection task of the calcification-like discs in ROIs with and without postprocessing. Proportion of correct responses of the human (PCH ) and NPWE (PCNPWE ) experiments was calculated and the correlation between the two was analyzed using a mixed-effect regression model. Correlation results including the goodness of fit (r2 ) of PCH and PCNPWE for all different parameters investigated were evaluated to determine whether NPWE MO can be used to predict human observer performance. RESULTS: PCH and PCNPWE increased with dose for all conditions investigated (signal size, processing status, and different system). In case of the 0.1 mm discs, for system A, r2 between PCH with PCNPWE was 0.81. For system B, r2 was 0.93. In case of the 0.25 mm discs, r2 in system A was 0.79 and for system B, r2 was 0.82. For the combined parameters investigated, and after excluding the 0.1 mm discs on system A because the results were influenced by aliasing, the overall r2 was 0.81. Image processing did not affect the detectability of calcification-like signals. No significant difference (P > 0.05) was found between the predicted PCH(pred) by the MO and the PCH for all different conditions. CONCLUSIONS: The framework seems promising to be used in objective image quality assessment. It was found to be relatively robust for the range of parameters investigated. However, further optimization of the anthropomorphic breast phantom and investigation of other MOs for a broader range of image quality assessment tasks is needed.

Inter-laboratory comparison of channelized hotelling observer computation.

PURPOSE: The task-based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well-established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise. MATERIALS AND METHODS: Image samples to estimate model observer performance for detection tasks were generated from two-dimensional CT image slices of a uniform water phantom. A common set of images was sent to participating laboratories to perform and document the following tasks: (a) estimate the detectability index of a well-defined CHO and its uncertainty in three conditions involving different sized targets all at the same dose, and (b) apply this CHO to an image set where ground truth was unknown to participants (lower image dose). In addition, and on an optional basis, we asked the participating laboratories to (c) estimate the performance of real human observers from a psychophysical experiment of their choice. Each of the 13 participating laboratories was confidentially assigned a participant number and image sets could be downloaded through a secure server. Results were distributed with each participant recognizable by its number and then each laboratory was able to modify their results with justification as model observer calculation are not yet a routine and potentially error prone. RESULTS: Detectability index increased with signal size for all participants and was very consistent for 6 mm sized target while showing higher variability for 8 and 10 mm sized target. There was one order of magnitude between the lowest and the largest uncertainty estimation. CONCLUSIONS: This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting.

Toward image quality assessment in mammography using model observers: Detection of a calcification-like object.

PURPOSE: Model observers (MOs) are of interest in the field of medical imaging to assess image quality. However, before procedures using MOs can be proposed in quality control guidelines for mammography systems, we need to know whether MOs are sensitive to changes in image quality and correlations in background structure. Therefore, as a proof of principle, in this study human and model observer (MO) performance are compared for the detection of calcification-like objects using different background structures and image quality levels of unprocessed mammography images. METHOD: Three different phantoms, homogeneous polymethyl methacrylate, BR3D slabs with swirled patterns (CIRS, Norfolk, VA, USA), and a prototype anthropomorphic breast phantom (Institute of Medical Physics and Radiation Protection, Technische Hochschule Mittelhessen, Germany) were imaged on an Amulet Innovality (FujiFilm, Tokyo, Japan) mammographic X-ray unit. Because the complexities of the structures of these three phantoms were different and not optimized to match the characteristics of real mammographic images, image processing was not applied in this study. In addition, real mammograms were acquired on the same system. Regions of interest (ROIs) were extracted from each image. In half of the ROIs, a 0.25-mm diameter disk was inserted at four different contrast levels to represent a calcification-like object. Each ROI was then modified, so four image qualities relevant for mammography were simulated. The signal-present and signal-absent ROIs were evaluated by a non-pre-whitening model observer with eye filter (NPWE) and a channelized Hotelling observer (CHO) using dense difference of Gaussian channels. The ROIs were also evaluated by human observers in a two alternative forced choice experiment. Detectability results for the human and model observer experiments were correlated using a mixed-effect regression model. Threshold disk contrasts for human and predicted human observer performance based on the NPWE MO and CHO were estimated. RESULTS: Global trends in threshold contrast were similar for the different background structures, but absolute contrast threshold levels differed. Contrast thresholds tended to be lower in ROIs from simple phantoms compared with ROIs from real mammographic images. The correlation between human and model observer performance was not affected by the range of image quality levels studied. CONCLUSIONS: The correlation between human and model observer performance does not depend on image quality. This is a promising outcome for the use of model observers in image quality analysis and allows for subsequent research toward the development of MO-based quality control procedures and guidelines.

The value of scatter removal by a grid in full field digital mammography.

Our objective in this study was to investigate the usefulness of an anti-scatter grid in digital mammography using a contrast detail phantom. The mammography system we investigated was a GE Senographe 2000D. We carried out phantom measurements under various conditions with and without using the anti-scatter grid. A new version of the CDMAM phantom (version 3.4) was used. This phantom consists of a matrix of square cells with disks of varying size and contrast. For given exposure conditions detectability of these disks can be determined and used for construction of contrast detail curves. Previously, a computer program was developed at our institute that performs a fully automatic analysis of the phantom recordings using the ideal observer model. Breast thickness was simulated by a homogeneous layer of PMMA in the range of 1 to 7 cm. Series of images were recorded for different KeV and target-filter combinations depending on the simulated thickness. The dose was kept constant for each thickness with and without using a grid. It appeared that image quality improved for simulated breast thickness below 5 cm when the grid was removed. In the range from 5 to 7 cm contrast detail curves obtained with or without a grid were similar. Results suggest that for compressed breast thickness in the range of 1 to 7 cm a grid might not be needed in the digital mammography system we investigated. Below 5 cm, omitting the grid may allow lower dose to the patient without losing image quality.

Effect of dose reduction on image quality and diagnostic performance in coronary computed tomography angiography.

To evaluate the effect of radiation dose reduction on image quality and diagnostic accuracy of coronary computed tomography (CT) angiography. Coronary CT angiography studies of 40 patients with (n = 20) and without (n = 20) significant (≥50 %) stenosis were included (26 male, 14 female, 57 ± 11 years). In addition to the original clinical reconstruction (100 % dose), simulated images were created that correspond to 50, 25 and 12.5 % of the original dose. Image quality and diagnostic performance in identifying significant stenosis were determined. Receiver-operator-characteristics analysis was used to assess diagnostic accuracy at different dose levels. The identification of patients with significant stenosis decreased consistently at doses of 50, 25 and 12.5 of the regular clinical acquisition (100 %). The effect was relatively weak at 50 % dose, and was strong at dose levels of 25 and 12.5 %. At lower doses a steady increase was observed for false negative findings. The number of coronary artery segments that were rated as diagnostic decreased gradually with dose, this was most prominent for smaller segments. The area-under-the-curve (AUC) was 0.90 (p = 0.4) at 50 % dose; accuracy decreased significantly with 25 % (AUC 0.70) and 12.5 % dose (AUC 0.60) (p < 0.0001), with underestimation of patients having significant stenosis. The clinical acquisition protocol for evaluation of coronary artery stenosis with CT angiography represents a good balance between image quality and patient dose. A potential for a modest (<50 %) reduction of tube current might exist. However, more substantial reduction of tube current will reduce diagnostic performance of coronary CT angiography substantially.

Automated analysis of neuronal morphology, synapse number and synaptic recruitment.

The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error. We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis.