Implementation of a high-resolution, high-contrast magnetic resonance imaging protocol with extended delayed phases for peritoneal mesothelioma.

Background: Imaging of peritoneal malignancies using conventional cross-sectional imaging is challenging, but accurate assessment of peritoneal disease burden could guide better selection for definitive surgery. Here we demonstrate feasibility of high-resolution, high-contrast magnetic resonance imaging (MRI) of peritoneal mesothelioma and explore optimal timing for delayed post-contrast imaging. Methods: Prospective data from inpatients with malignant peritoneal mesothelioma (MPM), imaged with a novel MRI protocol, were analyzed. The new sequences augmenting the clinical protocol were (I) pre-contrast coronal high-resolution T2-weighted single-shot fast spin echo (COR hr T2w SSH FSE) of abdomen and pelvis; and (II) post-contrast coronal high-resolution three-dimensional (3D) T1-weighted modified Dixon (COR hr T1w mDIXON) of abdomen, acquired at five delay times, up to 20 min after administration of a double dose of contrast agent. Quantitative analysis of contrast enhancement was performed using linear regression applied to normalized signal in lesion regions of interest (ROIs). Qualitative analysis was performed by three blinded radiologists. Results: MRI exams from 14 participants (age: mean ± standard deviation, 60±12 years; 71% male) were analyzed. The rate of lesion contrast enhancement was strongly correlated with tumor grade (cumulative nuclear score) (r=-0.65, P<0.02), with 'early' delayed phase (12 min post-contrast) and 'late' delayed phase (19 min post-contrast) performing better for higher grade and lower grade tumors, respectively, in agreement with qualitative scoring of image contrast. Conclusions: High-resolution, high-contrast MRI with extended post-contrast imaging is a viable modality for imaging peritoneal mesothelioma. Multiple, extended (up to 20 min post-contrast) delayed phases are necessary for optimal imaging of peritoneal mesothelioma, depending on the grade of disease.

Optimizing dual-energy CT technique for iodine-based contrast-to-noise ratio, a theoretical study.

BACKGROUND: Dual-energy CT (DECT) systems provide valuable material-specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast-to-noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x-ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall image quality. PURPOSE: The goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiation dose. METHODS: The noise propagation in the process of projection based material estimation from DECT measurements is analyzed. The main components of the study are the mean pixel variances for the sinogram and monochromatic image and the CNR, which were shown to depend on the Jacobian matrix of the sinograms-to-DECT measurements map. Analytic estimates for the mean sinogram and monochromatic image pixel variances and the CNR as functions of tube potentials, fluence, and VMI energy are derived, and then used in a virtual phantom experiment as an objective function for optimizing the tube settings and VMI energy to minimize the image noise and maximize the CNR. RESULTS: It was shown that DECT measurements corresponding to kV settings that maximize the square of Jacobian determinant values over a domain of interest lead to improved stability of basis material reconstructions. Instances of non-uniqueness in DECT were addressed, focusing on scenarios where the Jacobian determinant becomes zero within the domain of interest despite significant spectral separation. The presence of non-uniqueness can lead to singular solutions during the inversion of sinograms-to-DECT measurements, underscoring the importance of considering uniqueness properties in parameter selection. Additionally, the optimal VMI energy and tube potentials for maximal CNR was determined. When the x-ray beam filter material was fixed at 2 mm of aluminum and the photon fluence for low and high kV scans were considered equal, the tube potential pair of 60/120 kV led to the maximal iodine CNR in the VMI at 53 keV. CONCLUSIONS: Optimizing DECT scan parameters to maximize the CNR can be done in a systematic way. Also, choosing the parameters that maximize the Jacobian determinant over the set of expected line integrals leads to more stable reconstructions due to the reduced amplification of the measurement noise. Since the values of the Jacobian determinant depend strongly on the imaging task, careful consideration of all of the relevant factors is needed when implementing the proposed framework.

Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function.

Deep neural networks used for reconstructing sparse-view CT data are typically trained by minimizing a pixel-wise mean-squared error or similar loss function over a set of training images. However, networks trained with such pixel-wise losses are prone to wipe out small, low-contrast features that are critical for screening and diagnosis. To remedy this issue, we introduce a novel training loss inspired by the model observer framework to enhance the detectability of weak signals in the reconstructions. We evaluate our approach on the reconstruction of synthetic sparse-view breast CT data, and demonstrate an improvement in signal detectability with the proposed loss.

Technical specifications of dose management systems: An international atomic energy agency survey.

PURPOSE: Dose management systems (DMS) have been introduced in radiological services to facilitate patient radiation dose management and optimization in medical imaging. The purpose of this study was to gather as much information as possible on the technical characteristics of DMS currently available, regarding features that may be considered essential for simply ensuring regulatory compliance or desirable to fully utilize the potential role of DMS in optimization of many aspects of radiological examinations. METHODS: A technical survey was carried out and all DMS developers currently available (both commercial and open source) were contacted and were asked to participate. An extensive questionnaire was prepared and uploaded in the IAEA International Research Integration System (IRIS) online platform which was used for data collection process. Most of the questions (93%) required a "Yes/No" answer, to facilitate an objective analysis of the survey results. Some free text questions and comments' slots were also included, to allow participants to give additional information and clarifications where necessary. Depending on the answer, they were considered either as "Yes" or "No." RESULTS: Given the way that the questions were posed, every positive response indicated that a feature was offered. Thus, the percentage of positive responses was used as a measure of adherence. The percentages of positive answers per section (and sub-section) are presented in graphs and limitations of this type of analysis are discussed in detail. CONCLUSIONS: The results of this survey clearly exhibit that large differences exist between the various DMS developers. Consequently, potential end users of a DMS should carefully determine which of the features available are essential for their needs, prioritize desirable features, but also consider their infrastructure, the level of support required and the budget available before selecting a DMS.

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.

AAPM task group report 305: Guidance for standardization of vendor-neutral reject analysis in radiography.

Reject rate analysis is considered an integral part of a diagnostic radiography quality control (QC) program. A rejected image is a patient radiograph that was not presented to a radiologist for diagnosis and that contributes unnecessary radiation dose to the patient. Reject rates that are either too high or too low may suggest systemic department shortcomings in QC mechanisms. Due to the lack of standardization, reject data often cannot be easily compared between radiography systems from different vendors. The purpose of this report is to provide guidance to help standardize data elements that are required for comprehensive reject analysis and to propose data reporting and workflows to enable an effective and comprehensive reject rate monitoring program. Essential data elements, a proposed schema for classifying reject reasons, and workflow implementation options are recommended in this task group report.

Technical note: Water-equivalent thickness of Superflab bolus material at diagnostic x-ray energies.

PURPOSE: The purpose of this work was to determine the water-equivalent thickness of Superflab bolus material for narrow and broad field-of-view (FOV) x-ray geometries at diagnostic x-ray energies. METHODS: Transmission measurements were performed for incremental thicknesses of Superflab bolus material and water in narrow and broad FOV x-ray geometries. The transmission data was fit to a non-linear model for x-ray transmission - the Archer model. Water-equivalent thickness of Superflab was calculated based upon fitting parameters to transmission curves for 75, 95, and 115 kV x-ray tube voltages. Measured x-ray transmission factors for water and Superflab were used to determine the water equivalence of Superflab. RESULTS: For all x-ray tube voltages and geometries, the water equivalence of Superflab was greater than one, indicating that Superflab is more attenuating than water. This effect was stronger for broad FOV geometries. At 95 kV, 30 cm of Superflab corresponded to 32.0 cm of water in the narrow FOV geometry, and 34.3 cm of water in the broad FOV geometry. The Archer model fitting parameters and Superflab water equivalence are reported for all x-ray beam conditions explored in this work. CONCLUSIONS: Superflab bolus material is more attenuating than water at diagnostic x-ray energies. The Archer model and its respective fitting parameters reported in this work may be used to estimate the water-equivalent thickness of Superflab for diagnostic x-ray spectra.

A Clinically Optimal Protocol for the Imaging of Enteric Tubes: On the Basis of Radiologist Interpreted Diagnostic Utility and Radiation Dose Reduction.

RATIONALE AND OBJECTIVES: The purpose of this study was to develop and evaluate a patient thickness-based protocol specifically for the confirmation of enteric tube placements in bedside abdominal radiographs. Protocol techniques were set to maintain image quality while minimizing patient dose. MATERIALS AND METHODS: A total of 226 pre-intervention radiographs were obtained to serve as a baseline cohort for comparison. After the implementation of a thickness-based protocol, a total of 229 radiographs were obtained as part of an intervention cohort. Radiographs were randomized and graded for diagnostic quality by seven expert radiologists based on a standardized conspicuity scale (grades: 0 non-diagnostic to 3+). Basic patient demographics, body mass index, ventilatory status, and enteric tube type were recorded and subgroup analyses were performed. Effective dose was estimated for both cohorts. RESULTS: The dedicated thickness-based protocol resulted in a significant reduction in effective dose of 80% (p-value < 0.01). There was no significant difference in diagnostic quality between the two cohorts with 209 (92.5%) diagnostic radiographs in the baseline and 221 (96.5%) diagnostic radiographs in the thickness-based protocol (p-value 0.06). CONCLUSION: A protocol optimized for the confirmation of enteric tube placements was developed. This protocol results in lower patient effective dose, without sacrificing diagnostic accuracy. The technique chart is provided for reference. The protocol development process outlined in this work could be readily generalized to other imaging clinical tasks.

High-Resolution Full-3D Specimen Imaging for Lumpectomy Margin Assessment in Breast Cancer.

BACKGROUND: Two-dimensional (2D) specimen radiography (SR) and tomosynthesis (DBT) for breast cancer yield data that lack high-depth resolution. A volumetric specimen imager (VSI) was developed to provide full-3D and thin-slice cross-sectional visualization at a 360° view angle. The purpose of this prospective trial was to compare VSI, 2D SR, and DBT interpretation of lumpectomy margin status with the final pathologic margin status of breast lumpectomy specimens. METHODS: The study enrolled 200 cases from two institutions. After standard imaging and interpretation was performed, the main lumpectomy specimen was imaged with the VSI device. Image interpretation was performed by three radiologists after surgery based on VSI, 2D SR, and DBT. A receiver operating characteristic (ROC) curve was created for each method. The area under the curve (AUC) was computed to characterize the performance of the imaging method interpreted by each user. RESULTS: From 200 lesions, 1200 margins were interpreted. The AUC values of VSI for the three radiologists were respectively 0.91, 0.90, and 0.94, showing relative improvement over the AUCs of 2D SR by 54%, 13%, and 40% and DBT by 32% and 11%, respectively. The VSI has sensitivity ranging from 91 to 94%, specificity ranging from 81 to 85%, a positive predictive value ranging from 25 to 30%, and a negative predicative value of 99%. CONCLUSIONS: The ROC curves of the VSI were higher than those of the other specimen imaging methods. Full-3D specimen imaging can improve the correlation between the main lumpectomy specimen margin status and surgical pathology. The findings from this study suggest that using the VSI device for intraoperative margin assessment could further reduce the re-excision rates for women with malignant disease.

Development of a neonate X-ray phantom for 2D imaging applications using single-tone inkjet printing.

PURPOSE: Inkjet printers can be used to fabricate anthropomorphic phantoms by the use of iodine-doped ink. However, challenges persist in implementing this technique. The calibration from grayscale to ink density is complex and time-consuming. The purpose of this work is to develop a printing methodology that requires a simpler calibration and is less dependent on printer characteristics to produce the desired range of x-ray attenuation values. METHODS: Conventional grayscale printing was substituted by single-tone printing; that is, the superposition of pure black layers of iodinated ink. Printing was performed with a consumer-grade inkjet printer using ink made of potassium-iodide (KI) dissolved in water at 1 g/ml. A calibration for the attenuation of ink was measured using a commercial x-ray system at 70 kVp. A neonate radiograph obtained at 70 kVp served as an anatomical model. The attenuation map of the neonate radiograph was processed into a series of single-tone images. Single-tone images were printed, stacked, and imaged at 70 kVp. The phantom was evaluated by comparing attenuation values between the printed phantom and the original radiograph; attenuation maps were compared using the structural similarity index measure (SSIM), while attenuation histograms were compared using the Kullback-Leibler (KL) divergence. A region of interest (ROI)-based analysis was also performed, where the attenuation distribution within given ROIs was compared between phantom and patient. The phantom sharpness was evaluated in terms of modulation transfer function (MTF) estimates and signal spread profiles of high spatial resolution features in the image. RESULTS: The printed phantom required 36 pages. The printing queue was automated and it took about 2 h to print the phantom. The radiograph of the printed phantom demonstrated a close resemblance to the original neonate radiograph. The SSIM of the phantom with respect to that of the patient was 0.53. Both patient and phantom attenuation histograms followed similar distributions, and the KL divergence between such histograms was 0.20. The ROI-based analysis showed that the largest deviations from patient attenuation values were observed at the higher and lower ends of the attenuation range. The limiting resolution of the proposed methodology was about 1 mm. CONCLUSION: A methodology to generate a neonate phantom for 2D imaging applications, using single-tone printing, was developed. This method only requires a single-value calibration and required less than 2 h to print a complete phantom.

A signal detection model for quantifying overregularization in nonlinear image reconstruction.

Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for nonlinear image reconstruction. The vast majority of metrics employed for evaluating nonlinear image reconstruction are based on some form of global image fidelity, such as image root mean square error (RMSE). Use of such metrics can lead to overregularization in the sense that they can favor removal of subtle details in the image. To address this shortcoming, we develop an image quality metric based on signal detection that serves as a surrogate to the qualitative loss of fine image details. The metric is demonstrated in the context of a breast CT simulation, where different equal-dose configurations are considered. The configurations differ in the number of projections acquired. Image reconstruction is performed with a nonlinear algorithm based on total variation constrained least-squares (TV-LSQ). The resulting images are studied as a function of three parameters: number of views acquired, total variation constraint value, and number of iterations. The images are evaluated visually, with image RMSE, and with the proposed signal-detection-based metric. The latter uses a small signal, and computes detectability in the sinogram and in the reconstructed image. Loss of signal detectability through the image reconstruction process is taken as a quantitative measure of loss of fine details in the image. Loss of signal detectability is seen to correlate well with the blocky or patchy appearance due to overregularization with TV-LSQ, and this trend runs counter to the image RMSE metric, which tends to favor the over-regularized images. The proposed signal detection-based metric provides an image quality assessment that is complimentary to that of image RMSE. Using the two metrics in concert may yield a useful prescription for determining CT algorithm and configuration parameters when nonlinear image reconstruction is used.

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation.

The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density <±3%; dose <±20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.

Iodine quantification in limited angle tomography.

PURPOSE: To develop and test the feasibility of a two-pass iterative reconstruction algorithm with material decomposition designed to obtain quantitative iodine measurements in digital breast tomosynthesis. METHODS: Contrast-enhanced mammography has shown promise as a cost-effective alternative to magnetic resonance imaging for imaging breast cancer, especially in dense breasts. However, one limitation is the poor quantification of iodine contrast since the true three-dimensional lesion shape cannot be inferred from the two-dimensional (2D) projection. Use of limited angle tomography can potentially overcome this limitation by segmenting the iodine map generated by the first-pass reconstruction using a convolutional neural network, and using this segmentation to restrict the iodine distribution in the second pass of the reconstruction. To evaluate the performance of the algorithms, a set of 2D digital breast phantoms containing targets with varying iodine concentration was used. In each breast phantom, a single simulated lesion with a random size (4 to 8 mm) was placed in a random location within each phantom, with the iodine distribution defined as either homogeneous or rim-enhanced and blood iodine concentration set between 1.4 and 5.6 mg/mL. Limited angle projection data of these phantoms were simulated for wide and narrow angle geometries, and the proposed reconstruction and segmentation algorithms were applied. RESULTS: The median Dice similarity coefficient of the segmented masks was 0.975 for the wide angle data and 0.926 for the narrow angle data. Using these segmentations during the second reconstruction pass resulted in an improvement in the concentration estimates (mean estimated-to-true concentration ratio, before and after second pass: 48% to 73% for wide angle; 30% to 73% for narrow angle), and a reduction in the coefficient of variation of the estimates (55% to 27% for wide angle; 54% to 35% for narrow angle). CONCLUSION: We demonstrate that the proposed two-pass reconstruction can potentially improve accuracy and precision of iodine quantification in contrast-enhanced tomosynthesis.

Imaging of fiber-like structures in digital breast tomosynthesis.

Fiber-like features are an important aspect of breast imaging. Vessels and ducts are present in all breast images, and spiculations radiating from a mass can indicate malignancy. Accordingly, fiber objects are one of the three types of signals used in the American College of Radiology digital mammography (ACR-DM) accreditation phantom. Our work focuses on the image properties of fiber-like structures in digital breast tomosynthesis (DBT) and how image reconstruction can affect their appearance. The impact of DBT image reconstruction algorithm and regularization strength on the conspicuity of fiber-like signals of various orientations is investigated in simulation. A metric is developed to characterize this orientation dependence and allow for quantitative comparison of algorithms and associated parameters in the context of imaging fiber signals. The imaging properties of fibers, characterized in simulation, are then demonstrated in detail with physical DBT data of the ACR-DM phantom. The characterization of imaging of fiber signals is used to explain features of an actual clinical DBT case. For the algorithms investigated, at low regularization setting, the results show a striking variation in conspicuity as a function of orientation in the viewing plane. In particular, the conspicuity of fibers nearly aligned with the plane of the x-ray source trajectory is decreased relative to more obliquely oriented fibers. Increasing regularization strength mitigates this orientation dependence at the cost of increasing depth blur of these structures.

Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification.

PURPOSE: Many computer aided diagnosis (CADx) tools for breast cancer begin by fully or semiautomatically segmenting a given breast lesion and then classifying the lesion's likelihood of malignancy using quantitative features extracted from the image. It is often assumed that better segmentation will result in better classification. However, this has not been thoroughly evaluated. The purpose of this study is to evaluate the relationship between computer segmentation performance and computer classification performance. METHOD: We used 85 breast lesions (32 benign, 56 malignant) from breast computed tomography (CT) cases of 82 women. We prepared one smooth and one sharp iterative image reconstructions (IIR) and a clinical reconstruction for each of the 82 breast CT scans. For each reconstruction, we created 15 segmentation outcomes by applying 15 different segmentation algorithms. Specifically, we simulated 15 segmentation algorithms by changing parameters in a single segmentation algorithm. We then created 15 classification outcomes by conducting quantitative image feature analysis on the segmented image results. Using a 10-fold cross-validation, we evaluated the relationship between segmentation and classification performances. RESULT: We found a low positive correlation between segmentation and classification performances for the smooth IIR (median Pearson's rho = 0.18), while a moderate positive correlation (median Pearson's rho = 0.4-0.43) was found between the two performances for the sharp IIR and clinical reconstruction. However, we found large variations in both segmentation and classification performances for the sharp IIR and clinical reconstruction. There were cases where segmentation algorithms resulted in similar segmentation performances, but the corresponding classification performances were different. These results indicate that an improvement in segmentation performance does not guarantee an improvement in the corresponding classification performance. CONCLUSION: Computer segmentation is an indirect variable affecting the computer classification. As better segmentation does not guarantee better classification, we should report both segmentation and classification performances when comparing segmentation algorithms.

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.

Neutrosophic segmentation of breast lesions for dedicated breast computed tomography.

We proposed the neutrosophic approach for segmenting breast lesions in breast computed tomography (bCT) images. The neutrosophic set considers the nature and properties of neutrality (or indeterminacy). We considered the image noise as an indeterminate component while treating the breast lesion and other breast areas as true and false components. We iteratively smoothed and contrast-enhanced the image to reduce the noise level of the true set. We then applied one existing algorithm for bCT images, the RGI segmentation, on the resulting noise-reduced image to segment the breast lesions. We compared the segmentation performance of the proposed method (named as NS-RGI) to that of the regular RGI segmentation. We used 122 breast lesions (44 benign and 78 malignant) of 111 noncontrast enhanced bCT cases. We measured the segmentation performances of the NS-RGI and the RGI using the Dice coefficient. The average Dice values of the NS-RGI and RGI were 0.82 and 0.80, respectively, and their difference was statistically significant ([Formula: see text]). We conducted a subsequent feature analysis on the resulting segmentations. The classifier performance for the NS-RGI ([Formula: see text]) improved over that of the RGI ([Formula: see text], [Formula: see text]).

Portable abdomen radiography: moving to thickness-based protocols.

BACKGROUND: Default pediatric protocols on many digital radiography systems are configured based on patient age. However, age does not adequately characterize patient size, which is the principal determinant of proper imaging technique. Use of default pediatric protocols by inexperienced technologists can result in patient overexposure, inadequate image quality, or repeated examinations. OBJECTIVE: To ensure diagnostic image quality at a well-managed patient radiation exposure by transitioning to thickness-based protocols for pediatric portable abdomen radiography. MATERIALS AND METHODS: We aggregated patient thickness data, milliamperes (mAs), kilovoltage peak (kVp), exposure index (EI), source-to-detector distance, and grid use for all portable abdomen radiographs performed in our pediatric hospital in a database with a combination of automated and manual data collection techniques. We then analyzed the database and used it as the basis to construct thickness-based protocols with consistent image quality across varying patient thicknesses, as determined by the EI. RESULTS: Retrospective analysis of pediatric portable exams performed at our adult-focused hospitals demonstrated substantial variability in EI relative to our pediatric hospital. Data collection at our pediatric hospital over 4 months accumulated roughly 800 portable abdomen exams, which we used to develop a thickness-based technique chart. CONCLUSION: Through automated retrieval of data in our systems' digital radiography exposure logs and recording of patient abdomen thickness, we successfully developed thickness-based techniques for portable abdomen radiography.

Analysis of Volume Overestimation Artifacts in the Breast Outline Segmentation in Tomosynthesis.

In digital breast tomosynthesis (DBT), the reconstruction is calculated from x-ray projection images acquired over a small range of angles. One step in the reconstruction process is to identify the pixels that fall outside the shadow of the breast, to segment the breast from the background (air). In each projection, rays are back-projected from these pixels to the focal spot. All voxels along these rays are identified as air. By combining these results over all projections, a breast outline can be determined for the reconstruction. This paper quantifies the accuracy of this breast segmentation strategy in DBT. In this study, a physical phantom modeling a breast under compression was analyzed with a prototype next-generation tomosynthesis (NGT) system described in previous work. Multiple wires were wrapped around the phantom. Since the wires are thin and high contrast, their exact location can be determined from the reconstruction. Breast parenchyma was portrayed outside the outline defined by the wires. Specifically, the size of the phantom was overestimated along the posteroanterior (PA) direction; i.e., perpendicular to the plane of conventional source motion. To analyze how the acquisition geometry affects the accuracy of the breast outline segmentation, a computational phantom was also simulated. The simulation identified two ways to improve the segmentation accuracy; either by increasing the angular range of source motion laterally or by increasing the range in the PA direction. The latter approach is a unique feature of the NGT design; the advantage of this approach was validated with our prototype system.

Clinical Significance of US Artifacts.

Artifacts are frequently encountered at clinical US, and while some are unwanted, others may reveal valuable information related to the structure and composition of the underlying tissue. They are essential in making ultrasonography (US) a clinically useful imaging modality but also can lead to errors in image interpretation and can obscure diagnoses. Many of these artifacts can be understood as deviations from the assumptions made in generating the image. Therefore, understanding the physical basis of US image formation is critical to understanding US artifacts and thus proper image interpretation. This review is limited to gray-scale artifacts and is organized into discussions of beam- and resolution-related, location-related (ie, path and speed), and attenuation-related artifacts. Specifically, artifacts discussed include those related to physical mechanisms of spatial resolution, speckle, secondary lobes, reflection and reverberation, refraction, speed of sound, and attenuation. The underlying physical mechanisms and appearances are discussed, followed by real-world strategies to mitigate or accentuate these artifacts, depending on the clinical application. Relatively new US modes, such as spatial compounding, tissue harmonic imaging, and speckle reduction imaging, are now often standard in many imaging protocols; the effects of these modes on US artifacts are discussed. The ability of a radiologist to understand the fundamental physics of ultrasound, recognize common US artifacts, and provide recommendations for altering the imaging technique is essential for proper image interpretation, troubleshooting, and utilization of the full potential of this modality. ©RSNA, 2017.