Systematic approach to a channelized Hotelling model observer implementation for a physical phantom containing mass-like lesions: Application to digital breast tomosynthesis

Petrov, Dimitar; Marshall, Nicholas; Young, Kenneth C.; Bosmans, Hilde

doi:10.1016/j.ejmp.2018.12.033

Cited by 16 publications

(12 citation statements)

References 54 publications

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“…Since the L1 phantom exists only in the form of a single prototype, the reproducibility of the phantom manufacturing process has never been tested, but this would be essential for general use and incorporation into standards. Furthermore, the size of the phantom proved to be too small and would require a large number of acquisitions to provide a large number of lesion free background patches for automatic readout by model observers 15 …”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the size of the phantom proved to be too small and would require a large number of acquisitions to provide a large number of lesion free background patches for automatic readout by model observers. 15 In this work, we tried to overcome all these drawbacks. We replaced the spiculated masses with more non-spiculated masses of different sizes, resulting in a more differentiated gradation of mass sizes.…”

Section: Introductionmentioning

confidence: 99%

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Technical note: Realization and uncertainty analysis for an adjustable 3D structured breast phantom in digital breast tomosynthesis

et al. 2023

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BackgroundProjection imaging phantoms are often optimized for 2‐dimensional image characteristics in homogeneous backgrounds. Therefore, evaluation of image quality in tomosynthesis (DBT) lacks accepted and established phantoms.PurposeWe describe a 3D breast phantom with a structured, variable background. The phantom is an adaptable and advanced version of the L1 phantom by Cockmartin et al. Phantom design and its use for quality assurance measurements for DBT devices are described. Four phantoms were compared to assess the objectivity.MethodsThe container size was increased to a diameter of 24 cm and a total height of 53.5 mm. Spiculated masses were replaced by five additional non‐spiculated masses for higher granularity in threshold diameter resolution. These patterns are adjustable to the imaging device. The masses were printed in one session with a base layer using two‐component 3D printing. New materials compared to the L1 phantom improved the attenuation difference between the lesion models and the background. Four phantoms were built and intra‐human observer, inter‐human observer and inter‐phantom variations were determined. The latter assess the reproducibility of the phantom production. Coefficients of variance (V) were calculated for all three variations.ResultsThe difference of the attenuation coefficients between the lesion models and the background was 0.20 cm−1 (with W/Al at 32 kV, equivalent to 19–20 keV effective energy) compared to 0.21 cm−1 for 50/50 glandular/adipose breast tissue and cancerous lesions. PMMA equivalent thickness of the phantom was 47.0 mm for the Siemens Mammomat Revelation. For the masses, the for the intra‐observer variation was 0.248, the averaged inter‐observer variation, was 0.383. for phantom variance was 0.321. For the micro‐calcifications, was 0.0429, 0.0731 and 0.0759.ConclusionsPosition, orientation and shape of the masses are reproducible and attenuation differences appropriate. The phantom presented proved to be a candidate test object for quality control.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Technical note: Realization and uncertainty analysis for an adjustable 3D structured breast phantom in digital breast tomosynthesis

et al. 2023

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“…With the aim of improving its diagnostic performance and hence also helping to reduce the rate of recalls, in the last decades research efforts have been directed towards new pseudo-3D or 3D X-ray breast imaging technologies such as digital breast tomosynthesis (DBT) [3][4][5] and breast computed tomography [6], respectively. These techniques allow to overcomein part or totally, respectivelythe overlap of normal and pathological tissues in the direction of the incident beam, which can decrease the visibility of malignant abnormalities or simulate the appearance of a lesion [7][8][9]. DBT acquires many two-dimensional projections from various angular positions of the X-ray tube around the compressed breast, at a comparable level of radiation dose with respect to DM [5].…”

Section: Introductionmentioning

confidence: 99%

A deep learning classifier for digital breast tomosynthesis

et al. 2021

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To develop a computerized detection system for the automatic classification of the presence/absence of mass lesions in digital breast tomosynthesis (DBT) annotated exams, based on a deep convolutional neural network (DCNN). Materials and Methods: Three DCNN architectures working at image-level (DBT slice) were compared: two stateof-the-art pre-trained DCNN architectures (AlexNet and VGG19) customized through transfer learning, and one developed from scratch (DBT-DCNN). To evaluate these DCNN-based architectures we analysed their classification performance on two different datasets provided by two hospital radiology departments. DBT slice images were processed following normalization, background correction and data augmentation procedures. The accuracy, sensitivity, and area-under-the-curve (AUC) values were evaluated on both datasets, using receiver operating characteristic curves. A Grad-CAM technique was also implemented providing an indication of the lesion position in the DBT slice. Results: Accuracy, sensitivity and AUC for the investigated DCNN are in-line with the best performance reported in the field. The DBT-DCNN network developed in this work showed an accuracy and a sensitivity of (90% ± 4%) and (96% ± 3%), respectively, with an AUC as good as 0.89 ± 0.04. A k-fold cross validation test (with k = 4) showed an accuracy of 94.0% ± 0.2%, and a F1-score test provided a value as good as 0.93 ± 0.03. Grad-CAM maps show high activation in correspondence of pixels within the tumour regions. Conclusions: We developed a deep learning-based framework (DBT-DCNN) to classify DBT images from clinical exams. We investigated also a possible application of the Grad-CAM technique to identify the lesion position.

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“…Thus, we examined signal detectability using three different reconstruction filter schemes and two different data acquisition angles. The human observer study was conducted for the given tasks, and then, human observer performance was compared with that of channelized Hotelling observers (CHO) with Laguerre-Gauss (LG) channels [16-18, 28, 31] and dense difference-of-Gaussian (D-DOG) channels [26,31,32]. We measured CNR, SPS, and β values to find the relationship between human observer performance and the traditional metrics.…”

Section: Introductionmentioning

confidence: 99%

Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection

2020

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For digital breast tomosynthesis (DBT) systems, we investigate the effects of the reconstruction filters for different data acquisition angles on signal detection. We simulated a breast phantom with a 30% volume glandular fraction (VGF) of breast anatomy using the power law spectrum and modeled the breast mass as a spherical object with a 1 mm diameter. Projection data were acquired using two different data acquisition angles and numbers of projection view pairs, and in-plane breast images were reconstructed using the Feldkamp-Davis-Kress (FDK) algorithm with three different reconstruction filter schemes. To measure the ability to detect a signal, we conducted the human observer study with a binary detection task and compared the signal detectability of human to that of channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) channels and dense difference-of-Gaussian (D-DOG) channels. We also measured the contrast-to-noise ratio (CNR), signal power spectrum (SPS), and β values of the anatomical noise power spectrum (NPS) to show the association between human observer performance and these traditional metrics. Our results show that using a slice thickness (ST) filter degraded the signal detection performance of human observers at the same data acquisition angle. This could be predicted by D-DOG CHO with internal noise, but the correlation between the traditional metrics and signal detectability was not observed in this work. OPEN ACCESSCitation: Lee C, Han M, Baek J (2020) Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection. PLoS ONE 15(3): e0229915. https://doi.

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Systematic approach to a channelized Hotelling model observer implementation for a physical phantom containing mass-like lesions: Application to digital breast tomosynthesis

Cited by 16 publications

References 54 publications

Technical note: Realization and uncertainty analysis for an adjustable 3D structured breast phantom in digital breast tomosynthesis

Technical note: Realization and uncertainty analysis for an adjustable 3D structured breast phantom in digital breast tomosynthesis

A deep learning classifier for digital breast tomosynthesis

Human observer performance on in-plane digital breast tomosynthesis images: Effects of reconstruction filters and data acquisition angles on signal detection

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