Effect of dose reduction on the detection of mammographic lesions: a mathematical observer model analysis

Dose-efficiency quantification of computed tomography systems using a model-observer

BACKGROUND

Recent advances in computed tomography (CT) technology have considerably improved the quality of CT images and reduced radiation exposure in patients. At present, however, there is no generally accepted figure of merit (FOM) for comparing the dose efficiencies of CT systems.

PURPOSE

(i) To establish an FOM that characterizes the quality of CT images in relation to the radiation dose by means of a mathematical model observer and (ii) to evaluate the new FOM on different CT systems and image reconstruction algorithms.

METHODS

Images of a homogeneous phantom with four low-contrast inserts were acquired using three different CT systems at three dose levels and a representative protocol for CT imaging of low-contrast objects in the abdomen. The images were reconstructed using filtered-back projection and iterative algorithms. A channelized hotelling observer with difference-of-Gaussian channels was applied to compute the detectability (d ' $d^{\prime}$ ). This was done for each insert and each of the considered imaging conditions from square regions of interest (ROIs) that were (semi-)automatically centered on the inserts. The estimated detectabilities (d ' $d^{\prime}$ ) were averaged in the first step over the three dose levels (⟨ d ' ⟩ $\langle {d^{\prime}} \rangle $ ), and subsequently over the four contrast inserts (⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ ). All calculation steps included a dedicated assessment of the related uncertainties following accepted metrological guidelines.

RESULTS

The determined detectabilities (d ' $d^{\prime}$ ) varied considerably with the contrast and diameter of the four inserts, as well as with the radiation doses and reconstruction algorithms used for image generation (d ' $d^{\prime}\;$ = 1.3-5.5). Thus, the specification of a single detectability as an FOM is not well suited for comprehensively characterizing the dose efficiency of a CT system. A more comprehensive and robust characterization was provided by the averaged detectabilities⟨ d ' ⟩ $\langle {d^{\prime}} \rangle $ and, in particular,⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ . Our analysis reveals that the model observer analysis is very sensitive to the exact position of the ROIs.

CONCLUSIONS

The presented automatable software approach yielded with the weighted detectability⟨ d ' ⟩ w ${\langle {d^{\prime}} \rangle _{\rm{w}}}$ an objective FOM to benchmark different CT systems and reconstruction algorithms in a robust and reliable manner. An essential advantage of the proposed model-observer approach is that uncertainties in the FOM can be provided, which is an indispensable prerequisite for type testing.

Accurate and efficient measurement of channelized Hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom

BACKGROUND

Current CT quality control (QC) for low-contrast detectability relies on visual inspection and measurement of contrast-to-noise ratio (CNR). However, CNR numbers become unreliable when it comes to nonlinear methods, such as iterative reconstruction (IR) and deep-learning-based techniques. Image quality metrics using channelized Hotelling observer (CHO) have been validated to be well correlated with human observer performance on phantom-based and patient-based tasks, but it has not been widely used in routine CT QC mainly because the CHO calculation typically requires a large number of repeated scans in order to provide accurate and precise estimate of index of detectability (d').

PURPOSE

The main goal of this work is to optimize channel filters and other CHO parameters and accurately estimate the low-contrast detectability with minimum number of repeated scans for the widely used American College of Radiology (ACR) CT accreditation phantom so that it can become practically feasible for routine CT QC tests.

METHODS

To provide a converged d' value, an ACR phantom was repeatedly scanned 100 times at three dose levels (24, 12, and 6 mGy). Images were reconstructed with two kernels (FBP Br44 and IR Br44-3). d' as a function of number of repeated scans was determined for different number of background regions of interest (ROIs), different number of low-contrast objects, different number of slices per each object, and different channel filter options. A reference d' was established using the optimized CHO setting, and the bias of d' was quantified using the d' calculated from all 100 repeated scans. The variation of d' at each condition was estimated using a resampling method combining random subsampling among 100 repeated scans and bootstrapping of the ensembles of signal and background ROIs.

RESULTS

Optimized parameters in CHO calculation were determined: two background ROIs per object, four objects per low-contrast object size, nine non-overlapping slices per object, and a 4-channel Gabor filter. The bias and uncertainty were estimated at different numbers of repeated scans using these parameters. When only one single scan was used in the CHO calculation, the bias of d' was below 6.2% and the uncertainty 15.6-19.6% for the 6, 5, and 4 mm objects, while with three repeated scans the bias was below 2.0% and uncertainty 8.7-10.9% for the three object sizes.

CONCLUSION

With optimized parameter settings in CHO, efficient and accurate measurement of low-contrast detectability on the commonly used ACR phantom becomes feasible, which could potentially lead to adoption of CHO-based low-contrast evaluation in routine QC tests.

Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial

PURPOSE

The purpose of this study was to measure the threshold diameter of calcifications and masses for 2D imaging, digital breast tomosynthesis (DBT), and synthetic 2D images, for a range of breast glandularities. This study shows the limits of detection for each of the technologies and the strengths and weaknesses of each in terms of visualizing the radiological features of small cancers.

METHODS

Mathematical voxel breast phantoms with glandularities by volume of 9%, 18%, and 30% with a thickness of 53 mm were created. Simulated ill-defined masses and calcification clusters with a range of diameters were inserted into some of these breast models. The imaging characteristics of a Siemens Inspiration X-ray system were measured for a 29 kV, tungsten/rhodium anode/filter combination. Ray tracing through the breast models was undertaken to create simulated 2D and DBT projection images. These were then modified to adjust the image sharpness, and to add scatter and noise. The mean glandular doses for the images were 1.43, 1.47, and 1.47 mGy for 2D and 1.92, 1.97, and 1.98 mGy for DBT for the three glandularities. The resultant images were processed to create 2D, DBT planes and synthetic 2D images. Patches of the images with or without a simulated lesion were extracted, and used in a four-alternative forced choice study to measure the threshold diameters for each imaging mode, lesion type, and glandularity. The study was undertaken by six physicists.

RESULTS

The threshold diameters of the lesions were 6.2, 4.9, and 6.7 mm (masses) and 225, 370, and 399 μm, (calcifications) for 2D, DBT, and synthetic 2D, respectively, for a breast glandularity of 18%. The threshold diameter of ill-defined masses is significantly smaller for DBT than for both 2D (p≤0.006) and synthetic 2D (p≤0.012) for all glandularities. Glandularity has a significant effect on the threshold diameter of masses, even for DBT where there is reduced background structure in the images. The calcification threshold diameters for 2D images were significantly smaller than for DBT and synthetic 2D for all glandularities. There were few significant differences for the threshold diameter of calcifications between glandularities, indicating that the background structure has little effect on the detection of calcifications. We measured larger but nonsignificant differences in the threshold diameters for synthetic 2D imaging than for 2D imaging for masses in the 9% (p = 0.059) and 18% (p = 0.19) glandularities. The threshold diameters for synthetic 2D imaging were larger than for 2D imaging for calcifications (p < 0.001) for all glandularities.

CONCLUSIONS

We have shown that glandularity has only a small effect on the detection of calcifications, but the threshold diameter of masses was significantly larger for higher glandularity for all of the modalities tested. We measured nonsignificantly larger threshold diameters for synthetic 2D imaging than for 2D imaging for masses at the 9% (p = 0.059) and 18% (p = 0.19) glandularities and significantly larger diameters for calcifications (p < 0.001) for all glandularities. The lesions simulated were very subtle and further work is required to examine the clinical effect of not seeing the smallest calcifications in clusters.

Medical image quality metrics for foveated model observers

Purpose: A recently proposed model observer mimics the foveated nature of the human visual system by processing the entire image with varying spatial detail, executing eye movements, and scrolling through slices. The model can predict how human search performance changes with signal type and modality (2D versus 3D), yet its implementation is computationally expensive and time-consuming. Here, we evaluate various image quality metrics using extensions of the classic index of detectability expression and assess foveated model observers for search tasks. Approach: We evaluated foveated extensions of a channelized Hotelling and nonprewhitening matched filter model with an eye filter. The proposed methods involve calculating a model index of detectability ( d ' ) for each retinal eccentricity and combining these with a weighting function into a single detectability metric. We assessed different versions of the weighting function that varied in the required measurements of the human observers' search (no measurements, eye movement patterns, size of the image, and median search times). Results: We show that the index of detectability across eccentricities weighted using the eye movement patterns of observers best predicted human performance in 2D versus 3D search performance for a small microcalcification-like signal and a larger mass-like. The metric with a weighting function based on median search times was the second best predicting human results. Conclusions: The findings provide a set of model observer tools to evaluate image quality in the early stages of imaging system evaluation or design without implementing the more computationally complex foveated search model.

Foveated Model Observers for Visual Search in 3D Medical Images

Model observers have a long history of success in predicting human observer performance in clinically-relevant detection tasks. New 3D image modalities provide more signal information but vastly increase the search space to be scrutinized. Here, we compared standard linear model observers (ideal observers, non-pre-whitening matched filter with eye filter, and various versions of Channelized Hotelling models) to human performance searching in 3D 1/f2.8 filtered noise images and assessed its relationship to the more traditional location known exactly detection tasks and 2D search. We investigated two different signal types that vary in their detectability away from the point of fixation (visual periphery). We show that the influence of 3D search on human performance interacts with the signal's detectability in the visual periphery. Detection performance for signals difficult to detect in the visual periphery deteriorates greatly in 3D search but not in 3D location known exactly and 2D search. Standard model observers do not predict the interaction between 3D search and signal type. A proposed extension of the Channelized Hotelling model (foveated search model) that processes the image with reduced spatial detail away from the point of fixation, explores the image through eye movements, and scrolls across slices can successfully predict the interaction observed in humans and also the types of errors in 3D search. Together, the findings highlight the need for foveated model observers for image quality evaluation with 3D search.

Image Quality and Radiation Dose for Fibrofatty Breast using Target/filter Combinations in Two Digital Mammography Systems

Objectives:

The aim of this study was to determine the optimum combinations of target and filter materials for various X-ray tube voltage settings, as well as their effects on image quality and radiation dose. This was done using different digital mammography (DM) imaging systems with a breast equivalent phantom.

Material and Methods:

Two DM units with a tungsten (W) target, silver (Ag), and rhodium (Rh) filters and dual molybdenum (Mo) and Rh targets/filters were used. A tissue-equivalent mammography phantom of 6 cm thickness equivalent to a fibrofatty breast was exposed 20 times to different target/filter material combinations (W/Rh, W/Ag, Rh/Rh, Mo/Rh, and Mo/Mo) and various kV settings (28–34 kV). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for each exposure.

Results:

The W/Ag combination resulted in the lowest entrance skin dose and mean glandular dose (MGD). The MGD for the W/Rh combination was 60% less than that of the W/Ag combination at 34 kV (P < 0.05). There was a direct relationship in the SNR with the Mo/Mo, Mo/Rh, and Rh/Rh combinations and an inverse relationship with the CNR in the 34 kV range. There were statistically significant differences between all five target/filter combinations, and the best SNR and CNR were observed for the W/Rh combination with a reduced radiation dose in the range of 28–30 kV (P < 0.05).

Conclusion:

For a breast thicknesses of 6 cm with a fibrofatty nature, the W/Rh combination delivers high performance in terms of image quality at a lower dose.

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

Using aluminum for scatter control in mammography: preliminary work using measurements of CNR and FOM

Full-field digital mammography (FFDM) systems provide the current gold standard in mammographic examinations. Although FFDM provides the lowest mammographic doses, the radiation dose to the breast during mammographic examinations is still a concern. Thus, image quality optimization at the lowest dose is a major goal. In planar X-ray imaging, thin sheets of aluminum (Al) are used as filtration to reduce the number of low-energy X-ray photons reaching the patient. The goal of this work was to evaluate whether Al can be used in FFDM to remove scatter radiation from reaching the image detector, hence improving image quality. Doses were compared with the use of a grid. A Hologic Selenia mammographic unit was used to acquire images of two phantoms, namely, the ACR phantom and a Perspex phantom of 5 cm. Images were acquired using two tube voltages (kVp) and filter combinations under two exposure/dose conditions. Al sheets of various thicknesses were placed between the phantom and the image detector. Contrast-to-noise ratio (CNR) and figure of merit (FOM) values were measured and compared with images acquired using a grid. When a constant dose was delivered to the image detector, the highest CNR was achieved using a grid; however, the highest FOM values were achieved when using 0.05 mm thick Al sheets. This study successfully demonstrates that thin sheets of Al can be used in mammography examinations to reduce scattered radiation and improve image quality, as indicated by the measured CNR values. Given the limitations of this work, further kVp and target/filter combinations and various methods of image quality measurement need to be studied.

Investigation of Exposure Factors for Various Breast Composition and Thicknesses in Digital Screening Mammography Related to Breast Dose

OBJECTIVE

To investigate the effect of exposure factors used in digital screening mammography on image quality of different breast compositions.

MATERIAL AND METHODS

A digital mammography unit, with tungsten (W) as target, rhodium (Rh) and silver (Ag) as filters, and amorphous selenium detectors, was used to image Computerized Imaging Reference Systems (CIRS) Model 12A phantoms of thickness 4, 5, and 6 cm. Images of each phantom were obtained using target-filter combinations of W/Rh and W/Ag, at 28, 30, and 32 kVp. Images were evaluated by 5 senior technologists with experience in mammography. Image scores were assigned, for each type of feature present in the phantom. Statistical analysis was performed using nonparametric tests to compare sets of image scores at p = 0.05.

RESULTS

A small but statistically significant improvement was detected in the visibility of microcalcifications (8.8 ± 0.2; p = 0.031) for the W/Rh combination but this did not show any differences in the visibility of masses or fibers. The entrance skin dose (ESD) and mean glandular dose (MGD) were lower for the W/Ag (ESD = 1.30-3.70; MGD = 0.44-0.93 mGy) combination compared to W/Rh (ESD = 1.66-5.40; MGD = 0.52-1.12 mGy). The Mann-Whitney test revealed that 30-kV exposure with the W/Rh combination showed a significantly better visibility of specks in the 30/70 phantom compared to other exposures.

CONCLUSION

The use of an Rh filter showed a better image quality for all phantoms. 28 and 30 kVp with the W/Rh combination provided a slightly better image quality, and the MGD is less than 1.2 mGy.

Correlation between a 2D channelized Hotelling observer and human observers in a low-contrast detection task with multislice reading in CT

PURPOSE

Model observers have been successfully developed and used to assess the quality of static 2D CT images. However, radiologists typically read images by paging through multiple 2D slices (i.e., multislice reading). The purpose of this study was to correlate human and model observer performance in a low-contrast detection task performed using both 2D and multislice reading, and to determine if the 2D model observer still correlate well with human observer performance in multislice reading.

METHODS

A phantom containing 18 low-contrast spheres (6 sizes × 3 contrast levels) was scanned on a 192-slice CT scanner at five dose levels (CTDI_vol = 27, 13.5, 6.8, 3.4, and 1.7 mGy), each repeated 100 times. Images were reconstructed using both filtered-backprojection (FBP) and an iterative reconstruction (IR) method (ADMIRE, Siemens). A 3D volume of interest (VOI) around each sphere was extracted and placed side-by-side with a signal-absent VOI to create a 2-alternative forced choice (2AFC) trial. Sixteen 2AFC studies were generated, each with 100 trials, to evaluate the impact of radiation dose, lesion size and contrast, and reconstruction methods on object detection. In total, 1600 trials were presented to both model and human observers. Three medical physicists acted as human observers and were allowed to page through the 3D volumes to make a decision for each 2AFC trial. The human observer performance was compared with the performance of a multislice channelized Hotelling observer (CHO_MS), which integrates multislice image data, and with the performance of previously validated CHO, which operates on static 2D images (CHO_2D). For comparison, the same 16 2AFC studies were also performed in a 2D viewing mode by the human observers and compared with the multislice viewing performance and the two CHO models.

RESULTS

Human observer performance was well correlated with the CHO_2D performance in the 2D viewing mode [Pearson product-moment correlation coefficient R = 0.972, 95% confidence interval (CI): 0.919 to 0.990] and with the CHO_MS performance in the multislice viewing mode (R = 0.952, 95% CI: 0.865 to 0.984). The CHO_2D performance, calculated from the 2D viewing mode, also had a strong correlation with human observer performance in the multislice viewing mode (R = 0.957, 95% CI: 879 to 0.985). Human observer performance varied between the multislice and 2D modes. One reader performed better in the multislice mode (P = 0.013); whereas the other two readers showed no significant difference between the two viewing modes (P = 0.057 and P = 0.38).

CONCLUSIONS

A 2D CHO model is highly correlated with human observer performance in detecting spherical low contrast objects in multislice viewing of CT images. This finding provides some evidence for the use of a simpler, 2D CHO to assess image quality in clinically relevant CT tasks where multislice viewing is used.

The role of extra-foveal processing in 3D imaging

The field of medical image quality has relied on the assumption that metrics of image quality for simple visual detection tasks are a reliable proxy for the more clinically realistic visual search tasks. Rank order of signal detectability across conditions often generalizes from detection to search tasks. Here, we argue that search in 3D images represents a paradigm shift in medical imaging: radiologists typically cannot exhaustively scrutinize all regions of interest with the high acuity fovea requiring detection of signals with extra-foveal areas (visual periphery) of the human retina. We hypothesize that extra-foveal processing can alter the detectability of certain types of signals in medical images with important implications for search in 3D medical images. We compare visual search of two different types of signals in 2D vs. 3D images. We show that a small microcalcification-like signal is more highly detectable than a larger mass-like signal in 2D search, but its detectability largely decreases (relative to the larger signal) in the 3D search task. Utilizing measurements of observer detectability as a function retinal eccentricity and observer eye fixations we can predict the pattern of results in the 2D and 3D search studies. Our findings: 1) suggest that observer performance findings with 2D search might not always generalize to 3D search; 2) motivate the development of a new family of model observers that take into account the inhomogeneous visual processing across the retina (foveated model observers).

Impact of number of repeated scans on model observer performance for a low-contrast detection task in computed tomography

Channelized Hotelling observer (CHO) models have been shown to correlate well with human observers for several phantom-based detection/classification tasks in clinical computed tomography (CT). A large number of repeated scans were used to achieve an accurate estimate of the model's template. The purpose of this study is to investigate how the experimental and CHO model parameters affect the minimum required number of repeated scans. A phantom containing 21 low-contrast objects was scanned on a 128-slice CT scanner at three dose levels. Each scan was repeated 100 times. For each experimental configuration, the low-contrast detectability, quantified as the area under receiver operating characteristic curve, [Formula: see text], was calculated using a previously validated CHO with randomly selected subsets of scans, ranging from 10 to 100. Using [Formula: see text] from the 100 scans as the reference, the accuracy from a smaller number of scans was determined. Our results demonstrated that the minimum number of repeated scans increased when the radiation dose level decreased, object size and contrast level decreased, and the number of channels increased. As a general trend, it increased as the low-contrast detectability decreased. This study provides a basis for the experimental design of task-based image quality assessment in clinical CT using CHO.

A Case for Wide-Angle Breast Tomosynthesis

RATIONALES AND OBJECTIVES

Conventional mammography is largely limited by superimposed anatomy. Digital breast tomosynthesis (DBT) and computed tomography (CT) alleviate this limitation but with added out-of-plane artifacts or limited chest wall coverage. This article presents a wide-angle breast tomosynthesis (WBT), aimed to provide a practical solution to these limitations, and offers an initial study of its utility in comparison with DBT and CT using a singular evaluation platform.

MATERIALS AND METHODS

Using an anthropomorphic virtual breast phantom, a Monte Carlo code modeled a breast imaging system for three modalities of DBT, WBT, and breast CT (44°, 99°, and 198° total angle range, respectively) at four breast compression levels, all at a constant mean glandular dose level of 1.5 mGy. Reconstructed volumes were generated using iterative reconstruction methods. Lesion detectability was estimated using contrast-to-noise ratio and a channelized Hotelling observer model in terms of the area under the receiver operating characteristic (AUC).

RESULTS

Results showed improved detection with increased angular span and compression. The estimated AUCs for WBT were similar to that of CT. Comparative performance averaged over all thicknesses between CT and WBT was 4.3 ± 3.0%, whereas that between WBT and DBT was 5.6 ± 1.0%. At compression levels reflective of the modality (7-, 5-, and 4-cm thickness for CT, WBT, and DBT, respectively), WBT yielded an AUC comparable to CT (performance difference of 1.2%) but superior to DBT (performance difference of 5.5%).

CONCLUSIONS

The proposed imaging modality showed significant advantages over conventional DBT. WBT exhibited superior imaging performance over DBT at lower compression levels, highlighting further potential for reduced breast compression.

Impact of Number of Repeated Scans on Model Observer Performance for a Low-contrast Detection Task in CT

Channelized Hotelling observer (CHO) has been validated against human observers for detection/classification tasks in clinical CT and shows encouraging correlations. However, the goodness of correlations depends on the number of repeated scans used in CHO to estimate the template and covariance matrices. The purpose of this study is to investigate how the number of repeated scans affects the CHO performance in predicting human observers. A phantom containing 21 low-contrast objects (3 contrast levels and 7 sizes) was scanned on a 128-slice CT scanner at three dose levels. Each scan was repeated 100 times. Images were reconstructed using a filtered-backprojection kernel and a commercial iterative reconstruction method. For each dose level and reconstruction setting, the low-contrast detectability, quantified with the area under receiver operating characteristic curve (Az), was calculated using a previously validated CHO. To determine the dependency of CHO performance on the number of repeated scans, the Az value was calculated for each object and dose/reconstruction setting using all 100 repeated scans. The Az values were also calculated using randomly selected subsets of the scans (from 10 to 90 scans with an increment of 10 scans). Using the Az from the 100 scans as the reference, the accuracy of Az from a smaller number of scans was determined. The minimum necessary number of scans was subsequently derived. For the studied signal-known-exactly detection task, results demonstrated that, the minimal number of scans required to accurately predict human observer performance depends on dose level, object size and contrast level, and channel filters.

The potential use of ultra-low radiation dose images in digital mammography--a clinical proof-of-concept study in craniocaudal views

OBJECTIVE

To estimate the potential of low-dose images in digital mammography by analysing the effect of substantial dose reduction in craniocaudal (CC) views on clinical performance.

METHODS

At routine mammography, additional CC views were obtained with about 10% of the standard dose. Five radiologists retrospectively read the standard [mediolateral oblique (MLO) + CC] and combination low-dose mammograms (standard MLO + low-dose CC). If present, lesion type, conspicuity and suggested work-up were recorded. Final diagnoses were made by histology or follow up. A t-test or χ(2) test was used to compare results.

RESULTS

421 cases were included, presenting 5 malignancies, 66 benign lesions and multiple non-specific radiologic features. Using MLO with low-dose CC, all lesions were detected by at least one reader, but altogether less often than with standard mammography (sensitivity, 73.9% vs 81.5%). Missed lesions concerned all types. Lesions detected with both protocols were described similarly (p = 0.084) with comparable work-up recommendations (p = 0.658).

CONCLUSION

Mammography with ultra-low-dose CC images particularly influences detection. While sensitivity decreased, specificity was unaffected. In this proof-of-concept study a lower limit was to be determined that is not intended nor applicable for clinical practice. This should facilitate further research in optimization of a low-dose approach, which has potential in a relatively young and largely asymptomatic population.

ADVANCES IN KNOWLEDGE

Tungsten/silver-acquired mammography images might facilitate substantial dose reduction. Ultra-low-dose CC images reduce sensitivity, but not specificity. Low-dose images have potential in a largely young and asymptomatic population; a baseline is set for further research in optimization of a low-dose approach.

Implementation of a channelized Hotelling observer model to assess image quality of x-ray angiography systems

Evaluation of flat-panel angiography equipment through conventional image quality metrics is limited by the scope of standard spatial-domain image quality metric(s), such as contrast-to-noise ratio and spatial resolution, or by restricted access to appropriate data to calculate Fourier domain measurements, such as modulation transfer function, noise power spectrum, and detective quantum efficiency. Observer models have been shown capable of overcoming these limitations and are able to comprehensively evaluate medical-imaging systems. We present a spatial domain-based channelized Hotelling observer model to calculate the detectability index (DI) of our different sized disks and compare the performance of different imaging conditions and angiography systems. When appropriate, changes in DIs were compared to expectations based on the classical Rose model of signal detection to assess linearity of the model with quantum signal-to-noise ratio (SNR) theory. For these experiments, the estimated uncertainty of the DIs was less than 3%, allowing for precise comparison of imaging systems or conditions. For most experimental variables, DI changes were linear with expectations based on quantum SNR theory. DIs calculated for the smallest objects demonstrated nonlinearity with quantum SNR theory due to system blur. Two angiography systems with different detector element sizes were shown to perform similarly across the majority of the detection tasks.

Correlation between human and model observer performance for discrimination task in CT

Although physical metrics can objectively characterize computed tomography (CT) image quality, quantitative approaches to predict human observer performance are more accurate and clinically relevant. This study compared a modified channelized Hotelling model observer (CHO) with human observers in a shape discrimination task. Eight lesion-mimicking rods (two contrasts, two sizes and two shapes) were inserted into a 35 × 26 cm(2) torso-shaped water phantom and scanned 100 times on a 128-slice CT scanner at five dose levels. CT images were reconstructed using filtered backprojection (FBP) and iterative reconstruction (IR) techniques. Two-alternative forced choice studies were constructed with hexagonal and circular rod images put side-by-side in a randomized order. An edge mask was introduced to CHO to reflect the human observers' emphasis on lesion boundaries in discriminating shape. For small size lesions, the performance of three human observers and the modified CHO was highly correlated across lesion contrasts, CT doses and reconstruction algorithms; while for large size lesions, a ceiling effect was observed for both human and model observers' performance at high doses. Our result suggests the potential of CHO to predict human observer performance for both FBP and IR. For this shape discrimination task with uniform background, IR significantly improved human and model observer performance compared to FBP, with the amount of improvement depending on lesion size, contrast and dose.

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

PURPOSE

Digital breast tomosynthesis (DBT) is a promising breast cancer screening tool that has already begun making inroads into clinical practice. However, there is ongoing debate over how to quantitatively evaluate and optimize these systems, because different definitions of image quality can lead to different optimal design strategies. Powerful and accurate tools are desired to extend our understanding of DBT system optimization and validate published design principles.

METHODS

The authors developed a virtual trial framework for task-specific DBT assessment that uses digital phantoms, open-source x-ray transport codes, and a projection-space, spatial-domain observer model for quantitative system evaluation. The authors considered evaluation of reconstruction algorithms as a separate problem and focused on the information content in the raw, unfiltered projection images. Specifically, the authors investigated the effects of scan angle and number of angular projections on detectability of a small (3 mm diameter) signal embedded in randomly-varying anatomical backgrounds. Detectability was measured by the area under the receiver-operating characteristic curve (AUC). Experiments were repeated for three test cases where the detectability-limiting factor was anatomical variability, quantum noise, or electronic noise. The authors also juxtaposed the virtual trial framework with other published studies to illustrate its advantages and disadvantages.

RESULTS

The large number of variables in a virtual DBT study make it difficult to directly compare different authors' results, so each result must be interpreted within the context of the specific virtual trial framework. The following results apply to 25% density phantoms with 5.15 cm compressed thickness and 500 μm(3) voxels (larger 500 μm(2) detector pixels were used to avoid voxel-edge artifacts): 1. For raw, unfiltered projection images in the anatomical-variability-limited regime, AUC appeared to remain constant or increase slightly with scan angle. 2. In the same regime, when the authors fixed the scan angle, AUC increased asymptotically with the number of projections. The threshold number of projections for asymptotic AUC performance depended on the scan angle. In the quantum- and electronic-noise dominant regimes, AUC behaviors as a function of scan angle and number of projections sometimes differed from the anatomy-limited regime. For example, with a fixed scan angle, AUC generally decreased with the number of projections in the electronic-noise dominant regime. These results are intended to demonstrate the capabilities of the virtual trial framework, not to be used as optimization rules for DBT.

CONCLUSIONS

The authors have demonstrated a novel simulation framework and tools for evaluating DBT systems in an objective, task-specific manner. This framework facilitates further investigation of image quality tradeoffs in DBT.

Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain

PURPOSE

The purpose of this study was to investigate the correlation between model observer and human observer performance in CT imaging for the task of lesion detection and localization when the lesion location is uncertain.

METHODS

Two cylindrical rods (3-mm and 5-mm diameters) were placed in a 35×26 cm torso-shaped water phantom to simulate lesions with -15 HU contrast at 120 kV. The phantom was scanned 100 times on a 128-slice CT scanner at each of four dose levels (CTDIvol=5.7, 11.4, 17.1, and 22.8 mGy). Regions of interest (ROIs) around each lesion were extracted to generate images with signal-present, with each ROI containing 128×128 pixels. Corresponding ROIs of signal-absent images were generated from images without lesion mimicking rods. The location of the lesion (rod) in each ROI was randomly distributed by moving the ROIs around each lesion. Human observer studies were performed by having three trained observers identify the presence or absence of lesions, indicating the lesion location in each image and scoring confidence for the detection task on a 6-point scale. The same image data were analyzed using a channelized Hotelling model observer (CHO) with Gabor channels. Internal noise was added to the decision variables for the model observer study. Area under the curve (AUC) of ROC and localization ROC (LROC) curves were calculated using a nonparametric approach. The Spearman's rank order correlation between the average performance of the human observers and the model observer performance was calculated for the AUC of both ROC and LROC curves for both the 3- and 5-mm diameter lesions.

RESULTS

In both ROC and LROC analyses, AUC values for the model observer agreed well with the average values across the three human observers. The Spearman's rank order correlation values for both ROC and LROC analyses for both the 3- and 5-mm diameter lesions were all 1.0, indicating perfect rank ordering agreement of the figures of merit (AUC) between the average performance of the human observers and the model observer performance.

CONCLUSIONS

In CT imaging of different sizes of low-contrast lesions (-15 HU), the performance of CHO with Gabor channels was highly correlated with human observer performance for the detection and localization tasks with uncertain lesion location in CT imaging at four clinically relevant dose levels. This suggests the ability of Gabor CHO model observers to meaningfully assess CT image quality for the purpose of optimizing scan protocols and radiation dose levels in detection and localization tasks for low-contrast lesions.

Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms

PURPOSE

Efficient optimization of CT protocols demands a quantitative approach to predicting human observer performance on specific tasks at various scan and reconstruction settings. The goal of this work was to investigate how well a channelized Hotelling observer (CHO) can predict human observer performance on 2-alternative forced choice (2AFC) lesion-detection tasks at various dose levels and two different reconstruction algorithms: a filtered-backprojection (FBP) and an iterative reconstruction (IR) method.

METHODS

A 35 × 26 cm(2) torso-shaped phantom filled with water was used to simulate an average-sized patient. Three rods with different diameters (small: 3 mm; medium: 5 mm; large: 9 mm) were placed in the center region of the phantom to simulate small, medium, and large lesions. The contrast relative to background was -15 HU at 120 kV. The phantom was scanned 100 times using automatic exposure control each at 60, 120, 240, 360, and 480 quality reference mAs on a 128-slice scanner. After removing the three rods, the water phantom was again scanned 100 times to provide signal-absent background images at the exact same locations. By extracting regions of interest around the three rods and on the signal-absent images, the authors generated 21 2AFC studies. Each 2AFC study had 100 trials, with each trial consisting of a signal-present image and a signal-absent image side-by-side in randomized order. In total, 2100 trials were presented to both the model and human observers. Four medical physicists acted as human observers. For the model observer, the authors used a CHO with Gabor channels, which involves six channel passbands, five orientations, and two phases, leading to a total of 60 channels. The performance predicted by the CHO was compared with that obtained by four medical physicists at each 2AFC study.

RESULTS

The human and model observers were highly correlated at each dose level for each lesion size for both FBP and IR. The Pearson's product-moment correlation coefficients were 0.986 [95% confidence interval (CI): 0.958-0.996] for FBP and 0.985 (95% CI: 0.863-0.998) for IR. Bland-Altman plots showed excellent agreement for all dose levels and lesions sizes with a mean absolute difference of 1.0% ± 1.1% for FBP and 2.1% ± 3.3% for IR.

CONCLUSIONS

Human observer performance on a 2AFC lesion detection task in CT with a uniform background can be accurately predicted by a CHO model observer at different radiation dose levels and for both FBP and IR methods.

Comparison of signal to noise ratios from spatial and frequency domain formulations of nonprewhitening model observers in digital mammography

PURPOSE

Image quality indices based upon model observers are promising alternatives to laborious human readings of contrast-detail images. This is especially appealing in digital mammography as limiting values for contrast thresholds determine, according to some international protocols, the acceptability of these systems in the radiological practice. The objective of the present study was to compare the signal to noise ratios (SNR) obtained with two nonprewhitening matched filter model observer approaches, one in the spatial domain and the other in the frequency domain, and with both of them worked out for disks as present in the CDMAM phantom.

METHODS

The analysis was performed using images acquired with the Siemens Novation and Inspiration digital mammography systems. The spatial domain formulation uses a series of high dose CDMAM images as the signal and a routine exposure of two flood images to calculate the covariance matrix. The frequency domain approach uses the mathematical description of a disk and modulation transfer function (MTF) and noise power spectrum (NPS) calculated from images.

RESULTS

For both systems most of the SNR values calculated in the frequency domain were in very good agreement with the SNR values calculated in the spatial domain. Both the formulations in the frequency domain and in the spatial domain show a linear relationship between SNR and the diameter of the CDMAM discs.

CONCLUSIONS

The results suggest that both formulations of the model observer lead to very similar figures of merit. This is a step forward in the adoption of figures of merit based on NPS and MTF for the acceptance testing of mammography systems.

Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT

This Special Report presents the consensus of the Summit on Management of Radiation Dose in Computed Tomography (CT) (held in February 2011), which brought together participants from academia, clinical practice, industry, and regulatory and funding agencies to identify the steps required to reduce the effective dose from routine CT examinations to less than 1 mSv. The most promising technologies and methods discussed at the summit include innovations and developments in x-ray sources; detectors; and image reconstruction, noise reduction, and postprocessing algorithms. Access to raw projection data and standard data sets for algorithm validation and optimization is a clear need, as is the need for new, clinically relevant metrics of image quality and diagnostic performance. Current commercially available techniques such as automatic exposure control, optimization of tube potential, beam-shaping filters, and dynamic z-axis collimators are important, and education to successfully implement these methods routinely is critically needed. Other methods that are just becoming widely available, such as iterative reconstruction, noise reduction, and postprocessing algorithms, will also have an important role. Together, these existing techniques can reduce dose by a factor of two to four. Technical advances that show considerable promise for additional dose reduction but are several years or more from commercial availability include compressed sensing, volume of interest and interior tomography techniques, and photon-counting detectors. This report offers a strategic roadmap for the CT user and research and manufacturer communities toward routinely achieving effective doses of less than 1 mSv, which is well below the average annual dose from naturally occurring sources of radiation.

Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection

PURPOSE

Mammography is known to be one of the most difficult radiographic exams to interpret. Mammography has important limitations, including the superposition of normal tissue that can obscure a mass, chance alignment of normal tissue to mimic a true lesion and the inability to derive volumetric information. It has been shown that stereomammography can overcome these deficiencies by showing that layers of normal tissue lay at different depths. If standard stereomammography (i.e., a single stereoscopic pair consisting of two projection images) can significantly improve lesion detection, how will multiview stereoscopy (MVS), where many projection images are used, compare to mammography? The aim of this study was to assess the relative performance of MVS compared to mammography for breast mass detection.

METHODS

The MVS image sets consisted of the 25 raw projection images acquired over an arc of approximately 45 degrees using a Siemens prototype breast tomosynthesis system. The mammograms were acquired using a commercial Siemens FFDM system. The raw data were taken from both of these systems for 27 cases and realistic simulated mass lesions were added to duplicates of the 27 images at the same local contrast. The images with lesions (27 mammography and 27 MVS) and the images without lesions (27 mammography and 27 MVS) were then postprocessed to provide comparable and representative image appearance across the two modalities. All 108 image sets were shown to five full-time breast imaging radiologists in random order on a state-of-the-art stereoscopic display. The observers were asked to give a confidence rating for each image (0 for lesion definitely not present, 100 for lesion definitely present). The ratings were then compiled and processed using ROC and variance analysis.

RESULTS

The mean AUC for the five observers was 0.614 +/- 0.055 for mammography and 0.778 +/- 0.052 for multiview stereoscopy. The difference of 0.164 +/- 0.065 was statistically significant with a p-value of 0.0148.

CONCLUSIONS

The differences in the AUCs and the p-value suggest that multiview stereoscopy has a statistically significant advantage over mammography in the detection of simulated breast masses. This highlights the dominance of anatomical noise compared to quantum noise for breast mass detection. It also shows that significant lesion detection can be achieved with MVS without any of the artifacts associated with tomosynthesis.

Impact of JPEG 2000 compression on lesion detection in MR imaging

PURPOSE

Lossy compression algorithms enable the efficient transmission of large medical image datasets over bandwidth-limited digital networks or facilitate the long-term storage of the daily image production. Although JPEG 2000 has been adopted by DICOM as the standard for lossy-to-lossless compression, still a set of guidelines needs to be derived that allows for the usage of the lossy mode, potentially jeopardizing the accuracy of lesion detection and characterization, and so, of the resulting diagnosis task effectiveness in the medical diagnosis process. In this article the authors present and evaluate a generic methodology for the determination of the minimal bit rate that still ensures an accurate detection in magnetic resonance images of specific lesions. In this article, they demonstrate the methodology for two particular pathologies, i.e., multiple sclerosis and Virchow-Robin space enlargements.

METHODS

Involving qualified personnel, the minimal bit rate is estimated from ROC experiments initially simulated through mathematical observers that are designed with several objective metrics. The mathematical observer models included three variants of the Hotelling observer plus the non-prewhitening matched filter with eye model, while the objective measures are based on distance, correlation, singular value decomposition, and structural similarity.

RESULTS

The results indicate that the highest compression without seriously affecting the detection of the studied lesions is achieved at a bit rate of 0.125 bpp. At this value, the detection effectiveness exceeded 95% with less than 5% standard deviation, while only 4.4% of the outcomes were classified as false negatives by the experts and 11.6% as false positives.

CONCLUSIONS

The optimal bit rate found assures that important information on the small investigated structures is still preserved for their accurate detection and their a posteriori characterization.

Effect of dose reduction on the ability of digital mammography to detect simulated microcalcifications

The purpose of this article was to report the relationship between radiation dose and the ability of sentence digital mammography to detect microcalcifications. All images were acquired by computed radiography and an anthropomorphic breast phantom. The tube voltage and anode/filter combination used were 28 kVp and Mo/Mo. Simulated microcalcifications with an approximate diameter of 250-350 μm were positioned on the phantom. Groups of six microcalcifications were arranged in one of two patterns, a line cluster 1 cm long or a hexagonal cluster 4 mm wide. One of the six microcalcifications was removed to create a negative control. Each cluster was placed on 25 different points. Four levels of milliampere-second (mAs) values were applied: 100%, 50%, 25%, and 12.5%. Five staff radiologists participated in an observer performance test. All observers used a workstation with a 3-megapixel monochrome LCD monitor. The areas under the receiver-operating characteristics curves (AUC) were used to compare diagnostic performance among the four doses. The overall AUC scores were 0.97 with 100% mAs, 0.93 (n.s.) with 50%, 0.90 (p < 0.05) with 25%, and 0.81 (p < 0.01) with 12.5% mAs. Among the negative series, the percentage of images on which observers were able to identify the removed microcalcification point decreased from 88.8% with 100% mAs to 83.6% (n.s.) with 50%, 74.8% (p < 0.001) with 25%, and 67.2% (p < 0.001) with 12.5% mAs. A certain level of dose reduction in digital mammography may be an option.

A mathematical model platform for optimizing a multiprojection breast imaging system

Multiprojection imaging is a technique in which a plurality of digital radiographic images of the same patient are acquired within a short interval of time from slightly different angles. Information from each image is combined to determine the final diagnosis. Projection data are either reconstructed into slices as in the case of tomosynthesis or analyzed directly as in the case of multiprojection correlation imaging technique, thereby avoiding reconstruction artifacts. In this study, the authors investigated the optimum geometry of acquisitions of a multiprojection breast correlation imaging system in terms of the number of projections and their total angular span that yield maximum performance in a task that models clinical decision. Twenty-five angular projections of each breast from 82 human subjects in our breast tomosynthesis database were each supplemented with a simulated 3 mm mass. An approach based on Laguerre-Gauss channelized Hotelling observer was developed to assess the detectability of the mass in terms of receiver operating characteristic (ROC) curves. Two methodologies were developed to integrate results from individual projections into one combined ROC curve as the overall figure of merit. To optimize the acquisition geometry, different components of acquisitions were changed to investigate which one of the many possible configurations maximized the area under the combined ROC curve. Optimization was investigated under two acquisition dose conditions corresponding to a fixed total dose delivered to the patient and a variable dose condition, based on the number of projections used. In either case, the detectability was dependent on the number of projections used, the total angular span of those projections, and the acquisition dose level. In the first case, the detectability approximately followed a bell curve as a function of the number of projections with the maximum between 8 and 16 projections spanning angular arcs of about 23 degrees-45 degrees, respectively. In the second case, the detectability increased with the number of projections approaching an asymptote at 11-17 projections for an angular span of about 45 degrees. These results indicate the inherent information content of the multi-projection image data reflecting the relative role of quantum and anatomical noise in multiprojection breast imaging. The optimization scheme presented here may be applied to any multiprojection imaging modalities and may be extended by including reconstruction in the case of digital breast tomosynthesis and breast computed tomography.