Optimization of contrast-enhanced breast imaging: Analysis using a cascaded linear system model.

Digital breast tomosynthesis (DBT) is a three-dimensional (3D) x-ray imaging modality that has been shown to decrease the obscuring effect of breast structural noise, thereby increasing lesion conspicuity. To further improve breast cancer detection, much recent work has been devoted to the development of contrast enhanced DBT (CEDBT). Taking advantage of angiogenesis in malignant tissue, CEDBT involves the injection of radio-opaque material (i.e. iodine) and measures the relative increase in uptake of contrast in breast cancer. Either temporal or dual energy subtraction techniques may be used to implement CEDBT. Our present work is to develop a cascaded linear system model for DBT with a CEDBT option to calculate the ideal observer signal to noise ratio (SNR) of lesions in the presence of structural noise, evaluate the efficacy of CEDBT in the removal of structural noise, and examine the associated increase in x-ray quantum noise. Our model will include the effects of dual energy subtraction on signal and noise transfer, and transfer of power-law form anatomical noise through a DBT system using a modified filtered backprojection (FBP) algorithm. This model will be used for the optimization of x-ray techniques and reconstruction filters in CEDBT.

We previously developed a dynamic 4D anthropomorphic breast phantom, which can be used to optimize contrast-based breast imaging systems, accounting for patient variability and contrast kinetics [1]. In this study we aim to compare the performance of contrast-enhanced mammographic and tomosynthesis imaging protocols followed by temporal subtraction and dual-energy subtraction, qualitatively and quantitatively across a couple of patient models. Signal-difference-to-noise ratio (SDNR) is measured for the six paradigms of contrast enhanced, temporally subtracted, and dual-energy subtracted mammography and tomosynthesis and compared. The results show how the performance is more dependent on the breast model in mammography than in tomosynthesis. Also, it is observed that dual-energy subtraction can be beneficial in mammography, whereas it is not advantageous in tomosynthesis. Lastly, the results suggest that temporal subtraction in general outperforms dual-energy subtraction.

To evaluate the effect of a temporal subtraction technique for digital chest radiography with regard to the accuracy of detection of lung nodules. Twenty solitary lung nodules smaller than 30 mm in diameter, including 10 lung cancers and 10 benign nodules, were used. The nodules were grouped subjectively according to their subtlety. For non-nodular cases, 20 nodules without perceptible interval changes were selected. All chest radiographs were obtained by using a computed radiographic system, and temporal subtraction images were produced by using a program developed at the University of Chicago. The effect of the temporal subtraction image was evaluated by using an observer performance study, with use of receiver operating characteristic analysis. Observer performance with temporal subtraction images was substantially improved (A(z) = 0.980 and 0.958), as compared with that without temporal subtraction images (A(z) = 0.920 and 0.825) for the certified radiologists and radiology residents, respectively. The temporal subtraction technique clearly improved diagnostic accuracy for detecting lung nodules, especially subtle cases. The temporal subtraction technique is useful for improving detection accuracy for peripheral lung nodules on digital chest radiographs.

Breast cancer remains a leading cause of cancer-related morbidity and mortality worldwide. Early detection of breast cancer using high-quality mammography is essential for improving prognosis and treatment outcomes. Digital breast tomosynthesis with synthesized two-dimensional mammography (SM) reduces dose and tissue overlap; however, its comprehensive physical evaluations remain limited. This study quantitatively compared the image quality of conventional digital mammography (DM) and SM acquired using the AMULET Innovality System. Polymethyl methacrylate, American College of Radiology, and contrast-detail (CD) mammography phantoms were imaged 10 times in both DM and SM modes. Image quality metrics, including signal-to-noise ratio (SNR) maps, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), and CD curves, were evaluated. DM demonstrated higher SNR uniformity and suppressed low- and high-frequency noise, and the CNR values were 1.3-2 times greater than those of SM across all object sizes. MTF analysis showed superior resolution in DM (MTF(f50), 4.0 cycles/mm) compared with that of the SM (2.5 cycles/mm). CD curve analysis confirmed the better detectability of fine structures in DM, with values of 134.9 for DM and 56.7 for SM. Quantitative phantom-based evaluation demonstrated that DM showed superior performance to SM in several physical image quality metrics, including SNR uniformity, noise characteristics, spatial resolution, and contrast-detail detectability. These findings indicate systematic differences in physical image quality between DM and SM; however, further observer and clinical studies are required to clarify how these differences translate to diagnostic performance in clinical practice.

Purpose: To investigate lesion enhancement with iodinated contrast material using dual energy breast computed tomography (CT). Method and Materials: The dual energy cone‐beam breast CT system consisted of a flat panel detector and an x‐ray tube installed on an optical bench. The x‐ray tube voltage and x‐ray beam filtration were switched at 30 Hz between 50 kVp (2.0‐mm Al filter) and 120 kVp (2.0‐mm Al + 0.8‐mm Ag filtration). A cylindrical breast phantom was constructed from polymethylmethacrylate (PMMA) and filled with oil and shredded PMMA. Five known concentrations of iodine (0.5, 1, 2, 4, 8 mgI/ml) were embedded in the phantom. The images were reconstructed with a Feldkamp filtered‐back‐projection algorithm. Dual energy subtraction was used to eliminate the contrast of oil and PMMA to enhance iodine signals. Results: The low energy scan could not distinguish PMMA and iodine concentrations of 0.5 and 1 mgI/ml. The high energy image could not distinguish PMMA and iodine concentration of 8 mgI/ml. After subtracting the PMMA, all five concentrations of iodine could be clearly visualized on the dual energy CT image. The dual energy iodine signal (CTDE) and the iodine concentration (CI) were related by (R = 0.998 and SEE=15.26) Conclusion: Dual energy contrast‐enhanced breast CT can potentially improve lesion.

Contrast-enhanced digital breast tomosynthesis (CE-DBT) is a novel modality for imaging breast lesion morphology and vascularity. The purpose of this study is to assess the feasibility of dual-energy subtraction as a technique for CE-DBT (a temporal subtraction CE-DBT technique has been described previously). As CE-DBT evolves, exploration of alternative image acquisition techniques will contribute to its optimisation. Evaluation of dual-energy CE-DBT was conducted with Institutional Review Board (IRB) approval from our institution and in compliance with federal Health Insurance Portability and Accountability Act (HIPAA) guidelines. A 55-year old patient with a known malignancy in the right breast underwent imaging with MRI and CE-DBT. CE-DBT was performed in the medial lateral oblique view with a DBT system, which was modified under IRB approval to allow high-energy image acquisition with a 0.25 mm Cu filter. Image acquisition occurred via both temporal and dual-energy subtraction CE-DBT. Between the pre- and post-contrast DBT image sets, a single bolus of iodinated contrast agent (1.0 ml kg(-1)) was administered, followed by a 60 ml saline flush. The contrast agent and saline were administrated manually at a rate of approximately 2 ml s(-1). Images were reconstructed using filtered-back projection and transmitted to a clinical PACS workstation. Dual-energy CE-DBT was shown to be clinically feasible. In our index case, the dual-energy technique was able to provide morphology and kinetic information about the known malignancy. This information was qualitatively concordant with that of CE-MRI. Compared with the temporal subtraction CE-DBT technique, dual-energy CE-DBT appears less susceptible to motion artefacts.

Dual energy (DE) imaging consists of obtaining kilovoltage (kV) x-ray images at two different diagnostic energies and performing a weighted subtraction of these images. A third image is then produced that highlights soft tissue. DE imaging has been used by radiologists to aid in the detection of lung malignancies. However, it has not been used clinically in radiotherapy. The goal of this study is to assess the feasibility of performing DE imaging using a commercial on-board imaging system. Both a simple and an anthropomorphic phantom were constructed for this analysis. Planar kV images of the phantoms were obtained using varied imaging energies and mAs. Software was written to perform DE subtraction using empirically determined weighting factors. Tumor detectability was assessed quantitatively using the signal-difference-to-noise ratio (SDNR). Overall DE subtraction suppressed high density objects in both phantoms. The optimal imaging technique, providing the largest SDNR with a dose less than our reference technique was 140 kVp, 1.0 mAs and 60 kVp, 3.2 mAs. Based on this analysis, DE subtraction imaging is feasible using a commercial on-board imaging system and may improve the visualization of tumors in lung cancer patients undergoing image-guided radiotherapy.

For practicing medical physicists, CT image quality assessment is, in general, limited to the ones required by ACR and based on the axial images, such as CT number uniformity, image noise, contrast‐to‐noise ratio (CNR), and high contrast resolution. However, with more advanced technologies introduced by CT vendors, just assessing these basic image quality metrics may not be enough. Noise power spectrum (NPS), characterizing the signal noise component at each spatial frequency, is a powerful tool to predict the CT performance related to lesion detectability and noise texture. The quality of coronal and sagittal reformatted images is, in a large part, determined by the slice profile. How to accurately assess the slice profile is important for clinical medical physicists but not well recognized.Automatic tube current modulation (ATCM) has been adopted by major CT manufacturers, to maintain image quality across the scan range. The way how ATMC works varies significantly among vendors. A “standard” method is needed to evaluate the performance of this critical dose‐saving and image quality improvement technique. As dual energy CT becomes more readily available for routine clinical use, the need of image quality assessment metrics unique to the dual energy CT mode is becoming urgent.The session will facilitate the discussion of NPS and its application in the evaluation of FBP and iterative reconstruction. This session will also present the methods to measure the slice profile and characterize the performance of ATCM. The last part of this session will focus on the physics of CT dual energy mode, as well as unique image quality metrics to evaluate the performance of the dual energy mode.Learning Objectives: Be familiar with procedures on how to correctly compute CT noise power spectrum Understand why basic image quality metrics used for FBP may not be sufficient to characterize the performance of advanced iterative reconstruction Understand CT slice profile and how to accurately measure it Be familiar with ATCM from major CT vendors and how to evaluate its performance Understand different hardware approaches for dual energy CT Be familiar with image quality metrics relevant to dual energy mode Schmidt: Employee of Siemens HealthcareFan: Employee of GE Healthcare

Digital radiography (DR) has recently emerged as an attractive alternative to computed radiography (CR) for the acquisition of general radiographic studies in a digital environment. It offers the possibility of improved spatial and contrast resolution, decreased radiation dose due to improved efficiency of detection of x-ray photons, and perhaps most importantly, holds out the promise of increased technologist productivity. To achieve maximum efficiency, DR must be completely integrated into existing information systems, including the hospital and radiology information systems (HIS/RIS) and, when present, the picture archival and communication system (PACS). The early experience with the integration of DR at the Baltimore Veterans Affairs Medical Center (VAMC) has identified several challenges that exist to the successful integration of DR. DR has only recently been defined as a separate Digital Imaging and Communications in Medicine (DICOM) modality and images obtained will, at first, be listed under the category of CR. Matrix sizes with some DR products on the market exceed the current size limitations of some PACS. The patient throughput may be substantially greater with DR than with CR, and this in combination with the larger size of image files may result in greater demands for network and computer performance in the process of communication with the HIS/RIS and PACS. Additionally, in a hybrid department using both CR and DR, new rules must be defined for prefetching and display of general radiographic studies to permit these examinations to be retrieved and compared together. Advanced features that are planned for DR systems, such as dual-energy subtraction, tomosynthesis, and temporal subtraction, will likely require additional workstation tools beyond those currently available for CR.

Denoising can be used as a tool to enhance image quality and enforce low radiation doses in X-ray medical imaging. The effectiveness of denoising techniques relies on the validity of the underlying noise model. In full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT), calibration steps like the detector offset and flat-fielding can affect some assumptions made by most denoising techniques. Furthermore, quantum noise found in X-ray images is signal-dependent and can only be treated by specific filters. In this work we propose a pipeline for FFDM and DBT image denoising that considers the calibration steps and simplifies the modeling of the noise statistics through variance-stabilizing transformations (VST). The performance of a state-of-the-art denoising method was tested with and without the proposed pipeline. To evaluate the method, objective metrics such as the normalized root mean square error (N-RMSE), noise power spectrum, modulation transfer function (MTF) and the frequency signal-to-noise ratio (SNR) were analyzed. Preliminary tests show that the pipeline improves denoising. When the pipeline is not used, bright pixels of the denoised image are under-filtered and dark pixels are over-smoothed due to the assumption of a signal-independent Gaussian model. The pipeline improved denoising up to 20% in terms of spatial N-RMSE and up to 15% in terms of frequency SNR. Besides improving the denoising, the pipeline does not increase signal smoothing significantly, as shown by the MTF. Thus, the proposed pipeline can be used with state-of-the-art denoising techniques to improve the quality of DBT and FFDM images.

Subtle lung nodules (SLNs) are lesions that are commonly missed in radiologic examinations of the chest. This thesis explores three factors that influence the detection of these lesions: contrast‐diameter characteristics of SLNs, physical performance of the imaging system, and structured noise in the chest radiographs. The contrast‐diameter characteristics of SLNs were determined from radiographs containing confirmed lesions. The nodules were found to have similar subject contrast profiles with diverse variability in peak contrast (0.02–0.13) and diameter (3–16 mm). The physical performance of a digital chest radiographic system was assessed by measuring its Modulation Transfer Function (MTF), Noise Power Spectrum (NPS), and Detective Quantum Efficiency (DQE) responses. The results showed that the system only minimally blurs the appearance of SLNs, and that for a typical chest exposure, the quantum mottle is low enough not to obscure the visibility of even the smallest SLNs. The relative influence of the structured and quantum noise was explored in a Receiver Operating Characteristic (ROC) study in which simulated tissue‐equivalent nodules were superimposed on images of either structured or quantum noise backgrounds. The detection threshold =0.8) was achieved for nodules 4.5 mm in diameter against structured backgrounds, and for nodules 1.0 mm in diameter against quantum noise backgrounds. The results indicate that anatomical structured noise is the main factor limiting the detection of SLNs in chest radiographs.

Purpose: To provide a quantitative and clinically relevant measure of the instrumentation noise of digital x-ray imagers, using the Instrumentation Noise Equivalent Exposure (INEE). Method and Materials: The NPS was “decomposed” into constituent elements of primary quantum noise, Poisson excess noise (Swank noise), secondary quantum noise, structure noise and additive electronic noise using a plot of the total measured NPS versus exposure and using known relationships based on a generalized parallel cascade model analysis. The INEE was determined by measuring the exposure at which the total instrumentation noise and primary quantum noise were equivalent. Exposures at the INEE correspond to a drop in DQE of one-half. To demonstrate the usefulness of this new metric, the spatial-frequency-dependent INEE was measured on a clinical (indirect) dynamic flat panel detector (FPD) with a pixel pitch of 194 μm and a 600 μm thick CsI:Tl phosphor. Results: The INEE was measured to be 2.0, 7.1 and 75 μR at 0, 1 and 1.2 cycles/mm, respectively. Above 1.2 cycles/mm the detector was instrumentation-noise-limited at all exposures, resulting in a degradation in image quality beyond that dictated by the noise in the incident x-ray quanta alone. Hence, above 1.2 cycles/mm the DQE was always less than half the maximum value. Conclusions: In this work, all sources of instrumentation noise were incorporated into the INEE formalism, consisting not only of the additive electronic noise, but also including the Poisson excess noise (i.e. noise resulting from the phosphor), secondary quantum noise (i.e. noise resulting from the conversion and transmission of quanta) and structure noise (i.e. noise resulting from variations in the detector sensitivity). The INEE can be used to assess quantum-noise-limited operation at all spatial frequencies of interest by characterizing the total instrumentation noise in terms of equivalent detector entrance exposure. (Partial support: NIH grants R01EB008425, R01EB002873, R01NS43924)

: Digital breast tomosynthesis (DBT) strives to overcome the obstacles presented in conventional 2D mammography by taking multiple projections over a fixed angle and reconstructing volumetric data isolates overlying anatomy from in-plane structures and amplify the conspicuity of lesions. Temporal subtraction automates the process of comparative analysis by using two images taken sequentially and subtracting them in order to find temporal discrepancies. The purpose of this project is to determine the feasibility of using temporal subtraction on DBT phantom images to allow for easier and earlier detection of breast cancer than with either technique alone. The investigator acquired initial tomosynthesis images with the compressible and deformable breast phantom using materials to simulate the breast parenchyma. This was a first step to see if the physical breast phantom originally conceived in theory would work in practice. Unfortunately, the materials used for the breast tissues did not provide a realistic enough breast simulation. Further work must be done to find different materials to use for the physical breast phantom so that realistic images can be used for the observer study. The investigator found that a 3D computer simulated breast phantom needs to provide a realistic and accurate representation of the breast parenchyma in order to offer a compelling argument for the technique. This can be accomplished through either mathematical methods using geometrical primitives or voxelizations of real patient data. The investigator has decided to use an approach combining empirical breast CT data with subdivision surfaces (SD)and non-uniform rational b-splines (NURBS) in the future for the computer breast simulation.

Two main spatial frequency and quantitative calculations and measurements am, used to study the imaging system of three industrial digital radiography (IDR) modules for non-destructive testing (NDT), namely complementary metal oxide semiconductor (CMOS) flat-panel digital detector with 50 mu m pixel pitch, computed radiography (CR) with phosphor imaging plate with 25 mu m pixel pitch and laser-type film digitiser with 50 mu m pixel pitch. The modulation transfer function (MTF) and noise power spectrum (NPS) measurement and calculations were adapted on order to evaluate the image quality of IDR images. The MTF has been used to characterise the resolution properties of analogue and digital X-ray imaging systems and measure image deterioration due to optical factors, such as diffusion of image forming radiation. The NPS is used in determining noise in images produced and the choices of normalisation of the NPS vary slightly from researcher to researcher, but the underlying methodology is consistent. In order to easily compare these two measurements and calculations, the integral of up to the system Nyquist frequency was used as the final image quality quantitative evaluation. For the MTF measurement and calculation, four regions of interest (ROI) were, defined to portray the randomness in performing real testing. The four ROI are then compared with each other. For the NPS measurement and calculation, ten ROI were defined, scattered and averaged to have overall noise estimation. From the measurements and calculations which were done, the averaged MTF at 20% modulation for the laser-type film digitiser system is 6.55 cycles/mm, CR modulated 4.48 and CMOS modulated 2.83 cycles/mm. For NPS measurement and calculation, the result obtained in decreasing order is laser-type film digitiser, CMOS and CR. The laser-type film digitiser system modulates the best transfer function at 20% but does not have the lowest NPS. The lowest and stable NPS is the CR system but it has the lowest modulating capability at 20%. The study shows that in order to perform NDT by using the evaluated modules, the user must know the true capability, of the system and how, it is designed for specific application and discontinuity detection.

Dual-energy chest radiography has the potential to provide better diagnosis of lung disease by removing the bone signal from the image. Dynamic dual-energy radiography is now possible with the introduction of digital flat-panel detectors. The purpose of this study is to evaluate the feasibility of using dynamic dual-energy chest radiography for functional lung imaging and tumor motion assessment. The dual-energy system used in this study can acquire up to 15 frames of dual-energy images per second. A swine animal model was mechanically ventilated and imaged using the dual-energy system. Sequences of soft-tissue images were obtained using dual-energy subtraction. Time subtracted soft-tissue images were shown to be able to provide information on regional ventilation. Motion tracking of a lung anatomic feature (a branch of pulmonary artery) was performed based on an image cross-correlation algorithm. The tracking precision was found to be better than 1 mm. An adaptive correlation model was established between the above tracked motion and an external surrogate signal (temperature within the tracheal tube). This model is used to predict lung feature motion using the continuous surrogate signal and low frame rate dual-energy images (0.1–3.0 frames per second). The average RMS error of the prediction was (1.1 ± 0.3) mm. The dynamic dual energy was shown to be potentially useful for lung functional imaging such as regional ventilation and kinetic studies. It can also be used for lung tumor motion assessment and prediction during radiation therapy.