In patients with suspected coronary artery disease (CAD), dynamic myocardial computed tomography perfusion (CTP) imaging combined with coronary CT angiography (CTA) has become a comprehensive ...diagnostic examination technique resulting in both anatomical and quantitative functional information on myocardial blood flow, and the presence and grading of stenosis. Recently, CTP imaging has been proven to have good diagnostic accuracy for detecting myocardial ischemia, comparable to stress magnetic resonance imaging and positron emission tomography perfusion, while being superior to single photon emission computed tomography. Dynamic CTP accompanied by coronary CTA can serve as a gatekeeper for invasive workup, as it reduces unnecessary diagnostic invasive coronary angiography. Dynamic CTP also has good prognostic value for the prediction of major adverse cardiovascular events. In this article, we will provide an overview of dynamic CTP, including the basics of coronary blood flow physiology, applications and technical aspects including protocols, image acquisition and reconstruction, future perspectives, and scientific challenges.
Key Points
• Stress dynamic myocardial CT perfusion combined with coronary CTA is a comprehensive diagnostic examination technique resulting in both anatomical and quantitative functional information.
• Dynamic CTP imaging has good diagnostic accuracy for detecting myocardial ischemia comparable to stress MRI and PET perfusion.
• Dynamic CTP accompanied by coronary CTA may serve as a gatekeeper for invasive workup and can guide treatment in obstructive coronary artery disease.
Background
Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo‐3D reconstruction and improved accuracy compared to digital mammography. However, DBT ...faces challenges in image quality and quantitative accuracy due to scatter radiation. Recent advancements in deep learning (DL) have shown promise in using fast convolutional neural networks for scatter correction, achieving comparable results to Monte Carlo (MC) simulations.
Purpose
To predict the scatter radiation signal in DBT projections within clinically‐acceptable times and using only clinically‐available data, such as compressed breast thickness and acquisition angle.
Methods
MC simulations to obtain scatter estimates were generated from two types of digital breast phantoms. One set consisted of 600 realistically‐shaped homogeneous breast phantoms for initial DL training. The other set was composed of 80 anthropomorphic phantoms, containing realistic internal tissue texture, aimed at fine tuning the DL model for clinical applications. The MC simulations generated scatter and primary maps per projection angle for a wide‐angle DBT system. Both datasets were used to train (using 7680 projections from homogeneous phantoms), validate (using 960 and 192 projections from the homogeneous and anthropomorphic phantoms, respectively), and test (using 960 and 48 projections from the homogeneous and anthropomorphic phantoms, respectively) the DL model. The DL output was compared to the corresponding MC ground truth using both quantitative and qualitative metrics, such as mean relative and mean absolute relative differences (MRD and MARD), and to previously‐published scatter‐to‐primary (SPR) ratios for similar breast phantoms. The scatter corrected DBT reconstructions were evaluated by analyzing the obtained linear attenuation values and by visual assessment of corrected projections in a clinical dataset. The time required for training and prediction per projection, as well as the time it takes to produce scatter‐corrected projection images, were also tracked.
Results
The quantitative comparison between DL scatter predictions and MC simulations showed a median MRD of 0.05% (interquartile range (IQR), −0.04% to 0.13%) and a median MARD of 1.32% (IQR, 0.98% to 1.85%) for homogeneous phantom projections and a median MRD of −0.21% (IQR, −0.35% to −0.07%) and a median MARD of 1.43% (IQR, 1.32% to 1.66%) for the anthropomorphic phantoms. The SPRs for different breast thicknesses and at different projection angles were within ± 15% of the previously‐published ranges. The visual assessment showed good prediction capabilities of the DL model with a close match between MC and DL scatter estimates, as well as between DL‐based scatter corrected and anti‐scatter grid corrected cases. The scatter correction improved the accuracy of the reconstructed linear attenuation of adipose tissue, reducing the error from −16% and −11% to −2.3% and 4.4% for an anthropomorphic digital phantom and clinical case with similar breast thickness, respectively. The DL model training took 40 min and prediction of a single projection took less than 0.01 s. Generating scatter corrected images took 0.03 s per projection for clinical exams and 0.16 s for one entire projection set.
Conclusions
This DL‐based method for estimating the scatter signal in DBT projections is fast and accurate, paving the way for future quantitative applications.
Background
Modelling of the 3D breast shape under compression is of interest when optimizing image processing and reconstruction algorithms for mammography and digital breast tomosynthesis (DBT). ...Since these imaging techniques require the mechanical compression of the breast to obtain appropriate image quality, many such algorithms make use of breast‐like phantoms. However, if phantoms do not have a realistic breast shape, this can impact the validity of such algorithms.
Purpose
To develop a point distribution model of the breast shape obtained through principal component analysis (PCA) of structured light (SL) scans from patient compressed breasts.
Methods
SL scans were acquired at our institution during routine craniocaudal‐view DBT imaging of 236 patients, creating a dataset containing DBT and SL scans with matching information. Thereafter, the SL scans were cleaned, merged, simplified, and set to a regular grid across all cases. A comparison between the initial SL scans after cleaning and the gridded SL scans was performed to determine the absolute difference between them. The scans with points in a regular grid were then used for PCA. Additionally, the correspondence between SL scans and DBT scans was assessed by comparing features such as the chest‐to‐nipple distance (CND), the projected breast area (PBA) and the length along the chest‐wall (LCW). These features were compared using a paired t‐test or the Wilcoxon signed rank sum test. Thereafter, the PCA shape prediction and SL scans were evaluated by calculating the mean absolute error to determine whether the model had adequately captured the information in the dataset. The coefficients obtained from the PCA could then parameterize a given breast shape as an offset from the sample means. We also explored correlations of the PCA breast shape model parameters with certain patient characteristics: age, glandular volume, glandular density by mass, total breast volume, compressed breast thickness, compression force, nipple location, and centre of the chest‐wall.
Results
The median value across cases for the 90th and 99th percentiles of the interpolation error between the initial SL scans after cleaning and the gridded SL scans was 0.50 and 1.16 mm, respectively. The comparison between SL and DBT scans resulted in small, but statistically significant, mean differences of 1.6 mm, 1.6 mm, and 2.2 cm2 for the LCW, CND, and PBA, respectively. The final model achieved a median mean absolute error of 0.68 mm compared to the scanned breast shapes and a perfect correlation between the first PCA coefficient and the patient breast compressed thickness, making it possible to use it to generate new model‐based breast shapes with a specific breast thickness.
Conclusion
There is a good agreement between the breast shape coverage obtained with SL scans used to construct our model and the DBT projection images, and we could therefore create a generative model based on this data that is available for download on Github.
Purpose
To investigate the performance, such as energy dependence and sensitivity, of thermoluminescent dosimeters (TLD), metal oxide semiconductor field‐effect transistor dosimeters (MOSFET), and ...GafChromic™ films, and to validate the estimates of local dose deposition of a Monte Carlo (MC) simulation for breast dosimetry applications.
Methods
Experimental measurements were performed using a monoenergetic beam at the ELETTRA synchrotron radiation light source (Trieste, Italy). The three types of dosimeters were irradiated in a plane transversal to the beam axis and calibrated in terms of air kerma. The sensitivity of MOSFET dosimeters and GafChromic™ films was evaluated in the range of 18–28 keV. Three different calibration curves for the GafChromic™ films were tested (logarithmic, rational, and exponential functions) to evaluate the best‐fit curve in the dose range of 1–20 mGy. Internal phantom dose measurements were performed at 20 keV for four different depths (range 0–3 cm, with 1 cm steps) using a homogeneous 50% glandular breast phantom. A GEANT4 MC simulation was modified to match the experimental setup. Thirty sensitive volumes, on the axial‐phantom plane were included at each depth in the simulation to characterize the internal dose variation and compare it to the experimental TLD and MOSFET measurements. Experimental 2D dose maps were obtained with the GafChromic™ films and compared to the simulated 2D dose distributions estimated with the MC simulations.
Results
The sensitivity of the MOSFET dosimeters and GafChromic™ films increased with x‐ray energy, by up to 37% and 48%, respectively. Dose–response curves for the GafChromic™ film result in an uncertainty lower than 5% above 6 mGy, when a logarithmic relationship is used in the dose range of 1–10 mGy. All experimental values fall within the experimental uncertainty and a good agreement (within 5%) is found against the MC simulation. The dose decreased with increasing phantom depth, with the reduction being ~80% after 3 cm. The uncertainty of the empirical measurements makes the experimental values compatible with a flat behavior across the phantom slab for all the investigated depths, while the MC points to a dose profile with a maximum toward the center of the phantom.
Conclusions
The calibration procedures and the experimental methodologies proposed lead to good accuracy for internal breast dose estimation. In addition, these procedures can be successfully applied to validate MC codes for breast dosimetry at the local dose level. The agreement among the experimental and MC results not only shows the correctness of the empirical procedures used but also of the simulation parameters.
Purpose
To develop and evaluate a new automatic classification algorithm to identify voxels containing skin, vasculature, adipose, and fibroglandular tissue in dedicated breast CT images.
Methods
The ...proposed algorithm combines intensity‐ and region‐based segmentation methods with energy minimizing splines and unsupervised data mining approaches for classifying and segmenting the different tissue types. Breast skin segmentation is achieved by a region‐growing method which uses constraints from the previously extracted skin centerline to add robustness to the model and to reduce the false positive rate. An energy minimizing active contour model is then used to classify adipose tissue voxels by including gradient flow and region‐based features. Finally, blood vessels are separated from fibroglandular tissue by a k‐means clustering algorithm based on automatically extracted shape‐based features. To evaluate the accuracy of the algorithm, two sets of 15 different patient breast CT scans, each acquired with different breast CT systems and acquisition settings were obtained. Three slices from each scan were manually segmented under the supervision of an experienced breast radiologist and considered the gold standard. Comparisons with manual segmentation were quantified using five similarity metrics: Dice similarity coefficient (DSC), sensitivity, conformity coefficient, and two Hausdorff distance measures. To evaluate the robustness to image noise, the segmentation was repeated after separately adding Gaussian noise with increasing standard deviation (in four steps, from 0.01 to 0.04) to an additional 15 slices from the first dataset. In addition, to evaluate vasculature classification, three different pre‐ and postcontrast injection patient breast CT images were classified and compared. Finally, DSC was also used for quantitative comparisons with previously proposed approaches for breast CT tissue classification using 10 images from the first dataset.
Results
The algorithm showed a high accuracy in classifying the different tissue types for both breast CT systems, with an average DSC of 95% and 90% for the first and second image dataset, respectively. Furthermore, it demonstrated to be robust to image noise with a robustness to image noise of 85%, 83%, 79%, and 71% for the images corrupted with the four increasing noise levels. Previous methods for breast tissues classification resulted, for the tested dataset, in an average global DSC of 87%, while our approach resulted in a global average DSC of 94.5%.
Conclusions
The proposed algorithm resulted in accurate and robust breast tissue classification, with no prior training or threshold setting. Potential applications include breast density quantification and tissue pattern characterization (both biomarkers of cancer development), simulation‐based radiation dose analysis, and patient data‐based phantom design, which could be used for further breast imaging research.
Purpose
To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload.
Methods ...and materials
A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gathered from nine previously performed multi-reader multi-case receiver operating characteristic (MRMC ROC) studies. An AI system was used to obtain a score between 1 and 10 for each exam, representing the likelihood of cancer present. Using all AI scores between 1 and 9 as possible thresholds, the exams were divided into groups of low- and high likelihood of cancer present. It was assumed that, under the pre-selection scenario, only the high-likelihood group would be read by radiologists, while all low-likelihood exams would be reported as normal. The area under the reader-averaged ROC curve (AUC) was calculated for the original evaluations and for the pre-selection scenarios and compared using a non-inferiority hypothesis.
Results
Setting the low/high-likelihood threshold at an AI score of 5 (high likelihood > 5) results in a trade-off of approximately halving (− 47%) the workload to be read by radiologists while excluding 7% of true-positive exams. Using an AI score of 2 as threshold yields a workload reduction of 17% while only excluding 1% of true-positive exams. Pre-selection did not change the average AUC of radiologists (inferior 95% CI > − 0.05) for any threshold except at the extreme AI score of 9.
Conclusion
It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload.
Key Points
• There is potential to use artificial intelligence to automatically reduce the breast cancer screening reading workload by excluding exams with a low likelihood of cancer.
• The exclusion of exams with the lowest likelihood of cancer in screening might not change radiologists’ breast cancer detection performance.
• When excluding exams with the lowest likelihood of cancer, the decrease in true-positive recalls would be balanced by a simultaneous reduction in false-positive recalls.
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