Verification of the accuracy of a hybrid breast imaging simulation framework for virtual clinical trial applications

Vancoillie, Liesbeth; Marshall, Nicholas; Cockmartin, Lesley; Vignero, Janne; Zhang, Guozhi; Bosmans, Hilde

doi:10.1117/1.jmi.7.4.042804

Cited by 14 publications

(22 citation statements)

References 22 publications

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“…The bounding boxes of the four mass models have original dimensions of 35×35×35 mm. 3 The models were resized in all three dimensions to a range of 6-34 mm (median 14 mm) of non-calcified lesions reported in a previous reader study. 22 The fitting and measurements were obtained using the ImageJ package "3D Analysis."…”

Section: Anatomical Breast Modelsmentioning

confidence: 99%

“…Virtual clinical trials (VCTs) have been widely used as a rapid and cost‐effective alternative for evaluating and optimizing imaging technologies 1–5 . VCTs allow for the simulation of images with voxel‐wise ground‐truth, which is not available in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

“…Virtual clinical trials (VCTs) have been widely used as a rapid and cost-effective alternative for evaluating and optimizing imaging technologies. [1][2][3][4][5] VCTs allow for the simulation of images with voxel-wise ground-truth, which is not available in clinical practice.The images and ground truth can be used to evaluate the performance of imaging systems, modalities, and techniques. 1,6,7 The ground truth yields information regarding the strengths and weaknesses of imaging systems,providing valuable knowledge that can be used for myriad purposes including to support the regulatory approval of novel imaging systems.…”

Section: Introductionmentioning

confidence: 99%

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Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

et al. 2022

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Purpose Virtual clinical trials (VCTs) require computer simulations of representative patients and images to evaluate and compare changes in performance of imaging technologies. The simulated images are usually interpreted by model observers whose performance depends upon the selection of imaging cases used in training evaluation models. This work proposes an efficient method to simulate and calibrate soft tissue lesions, which matches the detectability threshold of virtual and human readings. Methods Anthropomorphic breast phantoms were used to evaluate the simulation of four mass models (I–IV) that vary in shape and composition of soft tissue. Ellipsoidal (I) and spiculated (II–IV) masses were simulated using composite voxels with partial volumes. Digital breast tomosynthesis projections and reconstructions of a clinical system were simulated. Channelized Hotelling observers (CHOs) were evaluated using reconstructed slices of masses that varied in shape, composition, and density of surrounded tissue. The detectability threshold of each mass model was evaluated using receiver operating characteristic (ROC) curves calculated with the CHO's scores. Results The area under the curve (AUC) of each calibrated mass model were within the 95% confidence interval (mean AUC [95% CI]) reported in a previous reader study (0.93 [0.89, 0.97]). The mean AUC [95% CI] obtained were 0.94 [0.93, 0.96], 0.92 [0.90, 0.93], 0.92 [0.90, 0.94], 0.93 [0.92, 0.95] for models I to IV, respectively. The mean AUC results varied substantially as a function of shape, composition, and density of surrounded tissue. Conclusions For successful VCTs, lesions composed of soft tissue should be calibrated to simulate imaging cases that match the case difficulty predicted by human readers. Lesion composition, shape, and size are parameters that should be carefully selected to calibrate VCTs.

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Section: Anatomical Breast Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

et al. 2022

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“…The outcome of AFC studies has been previously shown to correlate with measurements of image quality. 16,18 Few VCT studies have been made of synthetic 2D imaging. Ikejimba et al undertook a contrast detail test using a 3D physical phantom.…”

Section: Introductionmentioning

confidence: 99%

“…There are many studies using AFC to examine the effect of different imaging technologies, but there are challenges to compare results as they depend on the system being tested, the phantom type, 12 the detector type, choice of threshold, material or attenuation coefficient of lesions, the tomosynthesis acquisition method, and the reconstruction and processing software. In addition, there are different reporting parameters: threshold diameter, [15][16][17][18] threshold contrast, 19,20 and percentage correct. 15,21,22 There are common threads demonstrated in the results of these studies: DBT is better for visualizing masses and 2D is better for calcifications; the detection of calcifications is dependent on dose but this is not true for masses; and the detection of cancers is dependent on the acquisition method and detector.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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The purpose of this study was to measure the threshold diameter of calcifications and masses for 2D imaging, digital breast tomosynthesis (DBT), and synthetic 2D images, for a range of breast glandularities. This study shows the limits of detection for each of the technologies and the strengths and weaknesses of each in terms of visualizing the radiological features of small cancers. Methods: Mathematical voxel breast phantoms with glandularities by volume of 9%,18%,and 30% with a thickness of 53 mm were created.Simulated ill-defined masses and calcification clusters with a range of diameters were inserted into some of these breast models. The imaging characteristics of a Siemens Inspiration X-ray system were measured for a 29 kV, tungsten/rhodium anode/filter combination. Ray tracing through the breast models was undertaken to create simulated 2D and DBT projection images. These were then modified to adjust the image sharpness, and to add scatter and noise. The mean glandular doses for the images were 1.43, 1.47, and 1.47 mGy for 2D and 1.92, 1.97, and 1.98 mGy for DBT for the three glandularities. The resultant images were processed to create 2D, DBT planes and synthetic 2D images. Patches of the images with or without a simulated lesion were extracted, and used in a four-alternative forced choice study to measure the threshold diameters for each imaging mode, lesion type, and glandularity. The study was undertaken by six physicists. Results: The threshold diameters of the lesions were 6.2, 4.9, and 6.7 mm (masses) and 225, 370, and 399 µm, (calcifications) for 2D, DBT, and synthetic 2D, respectively, for a breast glandularity of 18%. The threshold diameter of illdefined masses is significantly smaller for DBT than for both 2D (p≤0.006) and synthetic 2D (p≤0.012) for all glandularities. Glandularity has a significant effect on the threshold diameter of masses, even for DBT where there is reduced background structure in the images. The calcification threshold diameters for 2D images were significantly smaller than for DBT and synthetic 2D for all glandularities. There were few significant differences for the threshold diameter of calcifications between glandularities, indicating that the background structure has little effect on the detection of calcifications. We measured larger but nonsignificant differences in the threshold diameters for synthetic 2D imaging than for 2D imaging for masses in the 9% (p = 0.059) and 18% (p = 0.19) glandularities. The threshold diameters for synthetic 2D imaging were larger than for 2D imaging for calcifications (p < 0.001) for all glandularities.

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Microcalcification detectability in breast CT images using CNN observers

Lyu,

Abbey,

Hernandez

et al. 2023

Medical Physics

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BackgroundBreast computed tomography (CT) is an emerging breast imaging modality, and ongoing developments aim to improve breast CT's ability to detect microcalcifications. To understand the effects of different parameters on microcalcification detectability, a virtual clinical trial study was conducted using hybrid images and convolutional neural network (CNN)‐based model observers. Mathematically generated microcalcifications were embedded into breast CT data sets acquired at our institution, and parameters related to calcification size, calcification contrast, cluster diameter, cluster density, and image display method (i.e., single slices, slice averaging, and maximum‐intensity projections) were evaluated for their influence on microcalcification detectability.PurposeTo investigate the individual effects and the interplay of parameters affecting microcalcification detectability in breast CT.MethodsSpherical microcalcifications of varying diameters (0.04, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40 mm) and native intensities were computer simulated to portray the partial volume effects of the imaging system. Calcifications were mathematically embedded into 109 patient breast CT volume data sets as individual calcifications or as clusters of calcifications. Six numbers of calcifications (1, 3, 5, 7, 10, 15) distributed within six cluster diameters (1, 3, 5, 6, 8, 10 mm) were simulated to study the effect of cluster density. To study the role of image display method, 2D regions of interest (ROIs) and 3D volumes of interest (VOIs) were generated using single slice extraction, slice averaging, and maximum‐intensity projection (MIP). 2D and 3D CNNs were trained on the ROIs and VOIs, and receiver operating characteristic (ROC) curve analysis was used to evaluate detection performance. The area under the ROC curve (AUC) was used as the primary performance metric.ResultsDetection performance decreased with increasing section thickness, and peak detection performance occurred using the native section thickness (0.2 mm) and MIP display. The MIP display method, despite using a single slice, yielded comparable performance to the native section thickness, which employed 50 slices. Reduction in slices did not sacrifice detection accuracy and provided significant computational advantages over multi‐slice image volumes. Larger cluster diameters resulted in reduced overall detectability, while smaller cluster diameters led to increased detectability. Additionally, we observed that the presence of more calcifications within a cluster improved the overall detectability, while fewer calcifications decreased it.ConclusionsAs breast CT is still a relatively new breast imaging modality, there is an ongoing need to identify optimal imaging protocols. This work demonstrated the utility of MIP presentation for displaying image volumes containing microcalcification clusters. It is likely that human observers may also benefit from viewing MIPs compared to individual slices. The results of this investigation begin to elucidate how model observers interact with microcalcification clusters in a 3D volume, and will be useful for future studies investigating a broader set of parameters related to breast CT.

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Verification of the accuracy of a hybrid breast imaging simulation framework for virtual clinical trial applications

Cited by 14 publications

References 22 publications

Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

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

Microcalcification detectability in breast CT images using CNN observers

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