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Background A curve-shaped compression paddle could reduce the pain experienced by some women at breast cancer screening. Purpose To compare curved and standard compression systems in terms of pain experience and image quality in mammography screening. Materials and Methods In this randomized controlled trial conducted between October 2021 and February 2022, participants screened at three screening sites in the Netherlands were randomized to either a curved-paddle or sham-paddle group. The sham paddle was a standard paddle that was presented as a new paddle. At a standard screening examination, one additional image was acquired with a curved or sham paddle. Pain was measured on a numerical rating scale (range, 0-10). Participants provided a pain score after compression with the standard and test paddles, resulting in two scores per participant. Differences in pain scores were compared between groups using analysis of covariance, adjusting for pain score after standard-paddle compression. Two radiographers and two radiologists performed unblinded paired comparisons of curved-paddle vs standard-paddle images, using standard image quality criteria (radiographers evaluated 1246 image pairs using 12 criteria; radiologists evaluated 320 image pairs using six criteria). The one-sample Wilcoxon signed-rank test was used to determine if there was a significant preference for either paddle. Results In total, 2499 female participants (mean age, 61.6 years ± 7.1 [SD]) were studied; 1250 in the curved-paddle group and 1249 in the sham-paddle group. The mean pain score decreased by an additional 0.19 points in the curved-paddle group compared with the sham-paddle group (95% CI: 0.09, 0.28; P < .001). In terms of image quality, the observers showed no preference or a preference for the standard paddle. Decreased image contrast (range Bonferroni-corrected P values: P < .001 to P > .99) and visibility of structures were the main concerns for curved-paddle images. Conclusion The use of the curved paddle resulted in a minimal pain reduction during mammography breast compression but image quality was reduced. © RSNA, 2024 Supplemental material is available for this article.
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Neoplasias de la Mama , Mamografía , Humanos , Femenino , Mamografía/métodos , Neoplasias de la Mama/diagnóstico por imagen , Persona de Mediana Edad , Anciano , Dimensión del Dolor , Detección Precoz del Cáncer/métodos , Países Bajos , Dolor/etiología , Dolor/prevención & control , Mama/diagnóstico por imagenRESUMEN
PURPOSE: To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance. METHODS: Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients. The logistic regression models were subsequently used to simulate different screening programs with reader pairing based on individual true-positive rates (TPR) and/or false-positive rates (FPR). For this, retrospective results from breast cancer screening programs employing double reading in Sweden, England, and Norway were used. Outcomes of random pairing were compared against those composed of readers with similar and opposite TPRs/FPRs, with positive assessments defined by either reader flagging an examination as abnormal. RESULTS: The analysis data sets consisted of 936,621 (Sweden), 435,281 (England), and 1,820,053 (Norway) examinations. There was good agreement between the model-predicted and observed radiologists' TPR and FPR (r ≥ 0.969). Model-predicted negative-case disagreement rates showed high correlations (r ≥ 0.709), whereas positive-case disagreement rates had lower correlation levels due to sparse data (r ≥ 0.532). Pairing radiologists with similar FPR characteristics (Sweden: 4.50% [95% confidence interval: 4.46%-4.54%], England: 5.51% [5.47%-5.56%], Norway: 8.03% [7.99%-8.07%]) resulted in significantly lower FPR than with random pairing (Sweden: 4.74% [4.70%-4.78%], England: 5.76% [5.71%-5.80%], Norway: 8.30% [8.26%-8.34%]), reducing examinations sent to consensus/arbitration while the TPR did not change significantly. Other pairing strategies resulted in equal or worse performance than random pairing. CONCLUSIONS: Logistic regression models accurately predicted screening mammography assessments and helped explore different radiologist pairing strategies. Pairing readers with similar modeled FPR characteristics reduced the number of examinations unnecessarily sent to consensus/arbitration without significantly compromising the TPR. HIGHLIGHTS: A logistic-regression model can be derived that accurately predicts individual and paired reader performance during mammography screening reading.Pairing screening mammography radiologists with similar false-positive characteristics reduced false-positive rates with no significant loss in true positives and may reduce the number of examinations unnecessarily sent to consensus/arbitration.
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BACKGROUND: Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commercial reconstruction methods may often not be available in medical research, making image-domain noise simulation methods useful. However, the introduction of non-linear reconstruction methods, such as iterative and deep learning-based reconstruction, makes noise insertion in the image domain intractable, as it is not possible to determine the noise textures analytically. PURPOSE: To develop a deep learning-based image-domain method to generate low-dose CT images from clinical-dose CT (CDCT) images for non-linear reconstruction methods. METHODS: We propose a fully image domain-based method, utilizing a series of three convolutional neural networks (CNNs), which, respectively, denoise CDCT images, predict the standard deviation map of the low-dose image, and generate the noise power spectra (NPS) of local patches throughout the low-dose image. All three models have U-net-based architectures and are partly or fully three-dimensional. As a use case for this study and with no loss of generality, we use paired low-dose and clinical-dose brain CT scans. A dataset of 326 $\hskip.001pt 326$ paired scans was retrospectively obtained. All images were acquired with a wide-area detector clinical system and reconstructed using its standard clinical iterative algorithm. Each pair was registered using rigid registration to correct for motion between acquisitions. The data was randomly partitioned into training ( 251 $\hskip.001pt 251$ samples), validation ( 25 $\hskip.001pt 25$ samples), and test ( 50 $\hskip.001pt 50$ samples) sets. The performance of each of these three CNNs was validated separately. For the denoising CNN, the local standard deviation decrease, and bias were determined. For the standard deviation map CNN, the real and estimated standard deviations were compared locally. Finally, for the NPS CNN, the NPS of the synthetic and real low-dose noise were compared inside and outside the skull. Two proof-of-concept denoising studies were performed to determine if the performance of a CNN- or a gradient-based denoising filter on the synthetic low-dose data versus real data differed. RESULTS: The denoising network had a median decrease in noise in the cerebrospinal fluid by a factor of 1.71 $1.71$ and introduced a median bias of + 0.7 $ + 0.7$ HU. The network for standard deviation map estimation had a median error of + 0.1 $ + 0.1$ HU. The noise power spectrum estimation network was able to capture the anisotropic and shift-variant nature of the noise structure by showing good agreement between the synthetic and real low-dose noise and their corresponding power spectra. The two proof of concept denoising studies showed only minimal difference in standard deviation improvement ratio between the synthetic and real low-dose CT images with the median difference between the two being 0.0 and +0.05 for the CNN- and gradient-based filter, respectively. CONCLUSION: The proposed method demonstrated good performance in generating synthetic low-dose brain CT scans without access to the projection data or to the reconstruction method. This method can generate multiple low-dose image realizations from one clinical-dose image, so it is useful for validation, optimization, and repeatability studies of image-processing algorithms.
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This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday.
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Purpose: The average (fav) or peak (fpeak) noise power spectrum (NPS) frequency is often used as a one-parameter descriptor of the CT noise texture. Our study develops a more complete two-parameter model of the CT NPS and investigates the sensitivity of human observers to changes in it. Approach: A model of CT NPS was created based on its fpeak and a half-Gaussian fit (σ) to the downslope. Two-alternative forced-choice staircase studies were used to determine perceptual thresholds for noise texture, defined as parameter differences with a predetermined level of discrimination performance (80% correct). Five imaging scientist observers performed the forced-choice studies for eight directions in the fpeak/σ-space, for two reference NPSs (corresponding to body and lung kernels). The experiment was repeated with 32 radiologists, each evaluating a single direction in the fpeak/σ-space. NPS differences were quantified by the noise texture contrast (Ctexture), the integral of the absolute NPS difference. Results: The two-parameter NPS model was found to be a good representation of various clinical CT reconstructions. Perception thresholds for fpeak alone are 0.2 lp/cm for body and 0.4 lp/cm for lung NPSs. For σ, these values are 0.15 and 2 lp/cm, respectively. Thresholds change if the other parameter also changes. Different NPSs with the same fpeak or fav can be discriminated. Nonradiologist observers did not need more Ctexture than radiologists. Conclusions: fpeak or fav is insufficient to describe noise texture completely. The discrimination of noise texture changes depending on its frequency content. Radiologists do not discriminate noise texture changes better than nonradiologists.
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BACKGROUND: Dynamic myocardial computed tomography perfusion (CTP) is a novel imaging technique that increases the applicability of CT for cardiac imaging; however, the scanning requires a substantial radiation dose. PURPOSE: To investigate the feasibility of dose reduction in dynamic CTP by comparing all-heartbeat acquisitions to periodic skipping of heartbeats. MATERIAL AND METHODS: We retrieved imaging data of 38 dynamic CTP patients and created new datasets with every fourth, third or second beat (Skip1:4, Skip1:3, Skip1:2, respectively) removed. Seven observers evaluated the resulting images and perfusion maps for perfusion deficits. The mean blood flow (MBF) in each of the 16 myocardial segments was compared per skipped-beat level, normalized by the respective MBF for the full dose, and averaged across patients. The number of segments/cases whose MBF was <1.0â mL/g/min were counted. RESULTS: Out of 608 segments in 38 cases, the total additional number of false-negative (FN) segments over those present in the full-dose acquisitions and the number of additional false-positive cases were shown as acquisition (segment [%], case): Skip1:4: 7 (1.2%, 1); Skip1:3: 12 (2%, 3), and Skip1:2: 5 (0.8%, 2). The variability in quantitative MBF analysis in the repeated analysis for the reference condition resulted in 8 (1.3%) additional FN segments. The normalized results show a comparable MBF across all segments and patients, with relative mean MBFs as 1.02 ± 0.16, 1.03 ± 0.25, and 1.06 ± 0.30 for the Skip1:4, Skip1:3, and Skip1:2 protocols, respectively. CONCLUSION: Skipping every second beat acquisition during dynamic myocardial CTP appears feasible and may result in a radiation dose reduction of 50%. Diagnostic performance does not decrease after removing 50% of time points in dynamic sequence.
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Imagen de Perfusión Miocárdica , Dosis de Radiación , Humanos , Masculino , Femenino , Estudios Retrospectivos , Persona de Mediana Edad , Imagen de Perfusión Miocárdica/métodos , Anciano , Estudios de Factibilidad , Tomografía Computarizada por Rayos X/métodos , Adulto , Frecuencia Cardíaca/fisiología , Anciano de 80 o más AñosRESUMEN
BACKGROUND: The development of a new imaging modality, such as 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT), requires optimization of the acquisition technique, particularly within the 2D contrast-enhanced imaging modality. Given the extensive parameter space, cascade-systems analysis is commonly used for such optimization. PURPOSE: To implement and validate a parallel-cascaded model for bCT, focusing on optimizing and characterizing system performance in the projection domain to enhance the quality of input data for image reconstruction. METHODS: A parallel-cascaded system model of a state-of-the-art bCT system was developed and model predictions of the presampled modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) were compared with empirical data collected in the projection domain. Validation was performed using the default settings of 49 kV with 1.5 mm aluminum filter and at 65 kV and 0.257 mm copper filter. A 10 mm aluminum plate was added to replicate the breast attenuation. Air kerma at the isocenter was measured at different tube current levels. Discrepancies between the measured projection domain metrics and model-predicted values were quantified using percentage error and coefficient of variation (CoV) for MTF and NNPS, respectively. The optimal filtration was for a 5 mm iodine disk detection task at 49, 55, 60, and 65 kV. The detectability index was calculated for the default aluminum filtration and for copper thicknesses ranging from 0.05 to 0.4 mm. RESULTS: At 49 kV, MTF errors were +5.1% and -5.1% at 1 and 2 cycles/mm, respectively; NNPS CoV was 5.3% (min = 3.7%; max = 8.5%). At 65 kV, MTF errors were -0.8% and -3.2%; NNPS CoV was 13.1% (min = 11.4%; max = 16.9%). Air kerma output was linear, with 11.67 µGy/mA (R2 = 0.993) and 19.14 µGy/mA (R2 = 0.996) at 49 and 65 kV, respectively. For iodine detection, a 0.25 mm-thick copper filter at 65 kV was found optimal, outperforming the default technique by 90%. CONCLUSION: The model accurately predicts bCT system performance, specifically in the projection domain, under varied imaging conditions, potentially contributing to the enhancement of 2D contrast-enhanced imaging in 4D DCE-bCT.
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Mama , Medios de Contraste , Medios de Contraste/química , Mama/diagnóstico por imagen , Tomografía Computarizada por Rayos X/instrumentación , Fantasmas de Imagen , Humanos , Mamografía/métodos , Mamografía/instrumentación , Procesamiento de Imagen Asistido por Computador/métodos , Relación Señal-RuidoRESUMEN
PURPOSE: This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening. METHODS: The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems. It discusses data pre-processing, learning paradigms, and the need for independent validation approaches. RESULTS: DL-based AI systems have shown significant improvements in breast cancer detection. They have the potential to enhance screening outcomes, reduce false negatives and positives, and detect subtle abnormalities missed by human observers. However, challenges like the lack of standardised datasets, potential bias in training data, and regulatory approval hinder their widespread adoption. CONCLUSIONS: AI technology has the potential to improve breast cancer screening by increasing accuracy and reducing radiologist workload. DL-based AI systems show promise in enhancing detection performance and eliminating variability among observers. Standardised guidelines and trustworthy AI practices are necessary to ensure fairness, traceability, and robustness. Further research and validation are needed to establish clinical trust in AI. Collaboration between researchers, clinicians, and regulatory bodies is crucial to address challenges and promote AI implementation in breast cancer screening.
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Inteligencia Artificial , Neoplasias de la Mama , Mamografía , Neoplasias de la Mama/diagnóstico por imagen , Humanos , Femenino , Mamografía/métodos , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Detección Precoz del Cáncer/métodosRESUMEN
Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views. Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women. Three experiments were done: first training on all SFM and processed images, second also including all raw images in training, and finally testing vendor generalization by leaving one dataset out at a time. Results: The model trained on SFM and processed mammograms achieved a good overall performance regardless of projection and vendor, with a mean (±std. dev.) dice score of 0.96±0.06 for all datasets combined. When raw images were included in training, the mean (±std. dev.) dice score for the raw images was 0.95±0.05 and for the processed images was 0.96±0.04. Testing on a dataset with processed DMs from a vendor that was excluded from training resulted in a difference in mean dice varying between -0.23 to +0.02 from that of the fully trained model. Conclusions: The proposed segmentation method yields accurate overall segmentation results for both raw and processed mammograms independent of view and vendor. The code and model weights are made available.
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BACKGROUND: Simulated computed tomography (CT) images allow for knowledge of the underlying ground truth and for easy variation of imaging conditions, making them ideal for testing and optimization of new applications or algorithms. However, simulating all processes that affect CT images can result in simulations that are demanding in terms of processing time and computer memory. Therefore, it is of interest to determine how much the simulation can be simplified while still achieving realistic results. PURPOSE: To develop a scanner-specific CT simulation using physics-based simulations for the position-dependent effects and shift-invariant image corruption methods for the detector effects. And to investigate the impact on image realism of introducing simplifications in the simulation process that lead to faster and less memory-demanding simulations. METHODS: To make the simulator realistic and scanner-specific, the spatial resolution and noise characteristics, and the exposure-to-detector output relationship of a clinical CT system were determined. The simulator includes a finite focal spot size, raytracing of the digital phantom, gantry rotation during projection acquisition, and finite detector element size. Previously published spectral models were used to model the spectrum for the given tube voltage. The integrated energy at each element of the detector was calculated using the Beer-Lambert law. The resulting angular projections were subsequently corrupted by the detector modulation transfer function (MTF), and by addition of noise according to the noise power spectrum (NPS) and signal mean-variance relationship, which were measured for different scanner settings. The simulated sinograms were reconstructed on the clinical CT system and compared to real CT images in terms of CT numbers, noise magnitude using the standard deviation, noise frequency content using the NPS, and spatial resolution using the MTF throughout the field of view (FOV). The CT numbers were validated using a multi-energy CT phantom, the noise magnitude and frequency were validated with a water phantom, and the spatial resolution was validated with a tungsten wire. These metrics were compared at multiple scanner settings, and locations in the FOV. Once validated, the simulation was simplified by reducing the level of subsampling of the focal spot area, rotation and of detector pixel size, and the changes in MTFs were analyzed. RESULTS: The average relative errors for spatial resolution within and across image slices, noise magnitude, and noise frequency content within and across slices were 3.4%, 3.3%, 4.9%, 3.9%, and 6.2%, respectively. The average absolute difference in CT numbers was 10.2 HU and the maximum was 22.5 HU. The simulation simplification showed that all subsampling can be avoided, except for angular, while the error in frequency at 10% MTF would be maximum 16.3%. CONCLUSION: The simulation of a scanner-specific CT allows for the generation of realistic CT images by combining physics-based simulations for the position-dependent effects and image-corruption methods for the shift-invariant ones. Together with the available ground truth of the digital phantom, it results in a useful tool to perform quantitative analysis of reconstruction or post-processing algorithms. Some simulation simplifications allow for reduced time and computer power requirements with minimal loss of realism.
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Algoritmos , Tomografía Computarizada por Rayos X , Tomografía Computarizada por Rayos X/métodos , Simulación por Computador , Fantasmas de ImagenRESUMEN
OBJECTIVES: To develop a radiomics model in contrast-enhanced cone-beam breast CT (CE-CBBCT) for preoperative prediction of axillary lymph node (ALN) status and metastatic burden of breast cancer. METHODS: Two hundred and seventy-four patients who underwent CE-CBBCT examination with two scanners between 2012 and 2021 from two institutions were enrolled. The primary tumor was annotated in each patient image, from which 1781 radiomics features were extracted with PyRadiomics. After feature selection, support vector machine models were developed to predict ALN status and metastatic burden. To avoid overfitting on a specific patient subset, 100 randomly stratified splits were made to assign the patients to either training/fine-tuning or test set. Area under the receiver operating characteristic curve (AUC) of these radiomics models was compared to those obtained when training the models only with clinical features and combined clinical-radiomics descriptors. Ground truth was established by histopathology. RESULTS: One hundred and eighteen patients had ALN metastasis (N + (≥ 1)). Of these, 74 had low burden (N + (1~2)) and 44 high burden (N + (≥ 3)). The remaining 156 patients had none (N0). AUC values across the 100 test repeats in predicting ALN status (N0/N + (≥ 1)) were 0.75 ± 0.05 (0.67~0.93, radiomics model), 0.68 ± 0.07 (0.53~0.85, clinical model), and 0.74 ± 0.05 (0.67~0.88, combined model). For metastatic burden prediction (N + (1~2)/N + (≥ 3)), AUC values were 0.65 ± 0.10 (0.50~0.88, radiomics model), 0.55 ± 0.10 (0.40~0.80, clinical model), and 0.64 ± 0.09 (0.50~0.90, combined model), with all the ranges spanning 0.5. In both cases, the radiomics model was significantly better than the clinical model (both p < 0.01) and comparable with the combined model (p = 0.56 and 0.64). CONCLUSIONS: Radiomics features of primary tumors could have potential in predicting ALN metastasis in CE-CBBCT imaging. CLINICAL RELEVANCE STATEMENT: The findings support potential clinical use of radiomics for predicting axillary lymph node metastasis in breast cancer patients and addressing the limited axilla coverage of cone-beam breast CT. KEY POINTS: ⢠Contrast-enhanced cone-beam breast CT-based radiomics could have potential to predict N0 vs. N + (≥ 1) and, to a limited extent, N + (1~2) vs. N + (≥ 3) from primary tumor, and this could help address the limited axilla coverage, pending future verifications on larger cohorts. ⢠The average AUC of radiomics and combined models was significantly higher than that of clinical models but showed no significant difference between themselves. ⢠Radiomics features descriptive of tumor texture were found informative on axillary lymph node status, highlighting a higher heterogeneity for tumor with positive axillary lymph node.
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Neoplasias de la Mama , Humanos , Femenino , Neoplasias de la Mama/patología , Metástasis Linfática/patología , Axila/patología , Radiómica , Estudios Retrospectivos , Ganglios Linfáticos/diagnóstico por imagen , Ganglios Linfáticos/patología , Tomografía Computarizada de Haz CónicoRESUMEN
Currently, there are multiple breast dosimetry estimation methods for mammography and its variants in use throughout the world. This fact alone introduces uncertainty, since it is often impossible to distinguish which model is internally used by a specific imaging system. In addition, all current models are hampered by various limitations, in terms of overly simplified models of the breast and its composition, as well as simplistic models of the imaging system. Many of these simplifications were necessary, for the most part, due to the need to limit the computational cost of obtaining the required dose conversion coefficients decades ago, when these models were first implemented. With the advancements in computational power, and to address most of the known limitations of previous breast dosimetry methods, a new breast dosimetry method, based on new breast models, has been developed, implemented, and tested. This model, developed jointly by the American Association of Physicists in Medicine and the European Federation for Organizations of Medical Physics, is applicable to standard mammography, digital breast tomosynthesis, and their contrast-enhanced variants. In addition, it includes models of the breast in both the cranio-caudal and the medio-lateral oblique views. Special emphasis was placed on the breast and system models used being based on evidence, either by analysis of large sets of patient data or by performing measurements on imaging devices from a range of manufacturers. Due to the vast number of dose conversion coefficients resulting from the developed model, and the relative complexity of the calculations needed to apply it, a software program has been made available for download or online use, free of charge, to apply the developed breast dosimetry method. The program is available for download or it can be used directly online. A separate User's Guide is provided with the software.
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Neoplasias de la Mama , Mama , Humanos , Femenino , Mama/diagnóstico por imagen , Mamografía/métodos , Radiometría/métodos , Método de Montecarlo , Neoplasias de la Mama/diagnóstico por imagenRESUMEN
PURPOSE: To investigate diagnostic performance of stress-only dynamic myocardial computed tomography perfusion (CTP) without computed tomography coronary angiography (CCTA) to diagnose ischemia with invasive fractional flow reserve (FFR) as a reference standard. METHOD: 135 datasets (68 positive for ischemia with invasive FFR < 0.8) acquired with a 256-slice CT system (Revolution, GE Healthcare, Chicago, IL, USA) were retrieved, postprocessed with a deep learning-based algorithm (Advanced intelligent Clear-IQ Engine (AiCE), Canon Medical Systems, Otawara, Japan) (FC03/cardiac kernel, 8 mm slice thickness), analyzed using a dedicated workstation (Vitrea research 7.11.0. Vital Images, Minnetonka, MN, USA), and loaded into a clinical workstation (CardIQ, GE Healthcare, Chicago, IL, USA) for review. Ten observers with various experience from two research sites evaluated the post-processed images, perfusion slices and maps to indicate presence vs absence of perfusion defect and its probability (five-point Likert scale). Binary decisions and probability scores were used to calculate sensitivity and specificity for each reader, and to create receiver operating characteristics (ROC) curves, respectively. Furthermore, the correlation coefficient (ICC) was computed. ROC AUC of a purely quantitative analysis was obtained thanks to a color-coded map with a fixed scale superimposed on myocardial walls displaying myocardial blood flow (MBF) values. RESULTS: The overall case-based sensitivity and specificity for the detection of perfusion deficit were 0.79 and 0.30, respectively. No significant differences were detected in the AUC across readers (p value = 0.66). The AUC values were 0.50, 0.58, 0.63, 0.59, 0.45, 0.60, 0.56, 0.61, 0.52, 0.61. Absolute reader agreement ICC was 0.60 (good agreement) for an average case. CONCLUSION: Dynamic CTP alone has good sensitivity, but low specificity when analyzed without CCTA. These findings reinforce the need to guide the interpretation functional test with the knowledge of coronary artery anatomy.
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Enfermedad de la Arteria Coronaria , Estenosis Coronaria , Reserva del Flujo Fraccional Miocárdico , Imagen de Perfusión Miocárdica , Humanos , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Angiografía Coronaria/métodos , Estudios Retrospectivos , Reserva del Flujo Fraccional Miocárdico/fisiología , Imagen de Perfusión Miocárdica/métodos , Tomografía Computarizada por Rayos X/métodos , Angiografía por Tomografía Computarizada/métodos , Isquemia , Perfusión , Valor Predictivo de las PruebasRESUMEN
Background Despite variation in performance characteristics among radiologists, the pairing of radiologists for the double reading of screening mammograms is performed randomly. It is unknown how to optimize pairing to improve screening performance. Purpose To investigate whether radiologist performance characteristics can be used to determine the optimal set of pairs of radiologists to double read screening mammograms for improved accuracy. Materials and Methods This retrospective study was performed with reading outcomes from breast cancer screening programs in Sweden (2008-2015), England (2012-2014), and Norway (2004-2018). Cancer detection rates (CDRs) and abnormal interpretation rates (AIRs) were calculated, with AIR defined as either reader flagging an examination as abnormal. Individual readers were divided into performance categories based on their high and low CDR and AIR. The performance of individuals determined the classification of pairs. Random pair performance, for which any type of pair was equally represented, was compared with the performance of specific pairing strategies, which consisted of pairs of readers who were either opposite or similar in AIR and/or CDR. Results Based on a minimum number of examinations per reader and per pair, the final study sample consisted of 3 592 414 examinations (Sweden, n = 965 263; England, n = 837 048; Norway, n = 1 790 103). The overall AIRs and CDRs for all specific pairing strategies (Sweden AIR range, 45.5-56.9 per 1000 examinations and CDR range, 3.1-3.6 per 1000; England AIR range, 68.2-70.5 per 1000 and CDR range, 8.9-9.4 per 1000; Norway AIR range, 81.6-88.1 per 1000 and CDR range, 6.1-6.8 per 1000) were not significantly different from the random pairing strategy (Sweden AIR, 54.1 per 1000 examinations and CDR, 3.3 per 1000; England AIR, 69.3 per 1000 and CDR, 9.1 per 1000; Norway AIR, 84.1 per 1000 and CDR, 6.3 per 1000). Conclusion Pairing a set of readers based on different pairing strategies did not show a significant difference in screening performance when compared with random pairing. © RSNA, 2023.
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Mamografía , Examen Físico , Humanos , Estudios Retrospectivos , Inglaterra , RadiólogosRESUMEN
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.
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Mama , Aprendizaje Profundo , Mamografía , Intensificación de Imagen Radiográfica , Rayos X , Mama/diagnóstico por imagen , Método de Montecarlo , Mamografía/métodos , Neoplasias de la Mama/diagnóstico por imagen , Fantasmas de Imagen , Redes Neurales de la Computación , Intensificación de Imagen Radiográfica/métodos , Humanos , Femenino , Conjuntos de Datos como AsuntoRESUMEN
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.
RESUMEN
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.