Dataset Information

Development of 3D patient-based super-resolution digital breast phantoms using machine learning.

ABSTRACT: Digital phantoms are important tools for optimization and evaluation of x-ray imaging systems, and should ideally reflect the 3D structure of human anatomy and its potential variability. In addition, they need to include a good level of detail at a high enough spatial resolution to accurately model the continuous nature of the human anatomy. A pipeline to increase the spatial resolution of patient-based digital breast phantoms that can be used for computer simulations of breast imaging is proposed. Given a tomographic breast image of finite resolution, the proposed methods can generate a phantom and increase its resolution at will, not only simply through super-sampling, but also by generating additional random glandular details to account for glandular edges and strands to compensate for those that may have not been detected in the original image due to the limited spatial resolution of the imaging system used. The proposed algorithms use supervised learning to predict the loss in glandularity due to limited resolution, and then to realistically recover this loss by learning the mapping between low and high resolution images. They were trained on high-resolution synchrotron images (detector pixel size 60 ?m) reconstructed at seven voxel dimensions (60 ?m-480 ?m), and applied to patient images acquired with a clinical breast CT system (detector pixel size 194 ?m) to generate super-resolution phantoms (voxel sizes 68 ?m). Several evaluations were made to assess the appropriateness of the developed methods, both with the synchrotron (relative prediction error 0.010??±??0.004, recovering accuracy 0.95??±??0.04), and with the clinical images (average glandularity error at 194 ?m: 0.15%??±??0.12%). Finally, a breast radiologist assessed the realism of the developed phantoms by blindly comparing original and phantom images, resulting in not being able to distinguish the real from the phantom images. In conclusion, the proposed method can generate super-resolution phantoms from tomographic breast patient images that can be used for future computer simulations for optimization of new breast imaging technologies.

SUBMITTER: Caballo M

PROVIDER: S-EPMC6419955 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Development of 3D patient-based super-resolution digital breast phantoms using machine learning.

Caballo Marco M Fedon Christian C Brombal Luca L Mann Ritse R Longo Renata R Sechopoulos Ioannis I

Physics in medicine and biology 20181112 22

Digital phantoms are important tools for optimization and evaluation of x-ray imaging systems, and should ideally reflect the 3D structure of human anatomy and its potential variability. In addition, they need to include a good level of detail at a high enough spatial resolution to accurately model the continuous nature of the human anatomy. A pipeline to increase the spatial resolution of patient-based digital breast phantoms that can be used for computer simulations of breast imaging is propos ...[more]

PMID: 30418943

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Project description:Digital breast tomosynthesis (DBT) is a 3D x-ray imaging modality in which tomographic sections of the breast are generated from a limited range of tube angles. Because oblique x-ray incidence shifts the image of an object in subpixel detector element increments with each increasing projection angle, it is demonstrated that DBT is capable of super-resolution (i.e., subpixel resolution).By convention, DBT reconstructions are performed on planes parallel to the breast support at various depths of the breast volume. In order for resolution in each reconstructed slice to be comparable to the detector, the pixel size should match that of the detector elements; hence, the highest frequency that can be resolved in the plane of reconstruction is the alias frequency of the detector. This study considers reconstruction grids with much smaller pixelation to visualize higher frequencies. For analytical proof of super-resolution, a theoretical framework is developed in which the reconstruction of a high frequency sinusoidal input is calculated using both simple backprojection (SBP) and filtered backprojection. To study the frequency spectrum of the reconstruction, its Fourier transform is also determined. The experimental feasibility of super-resolution was investigated by acquiring images of a bar pattern phantom with frequencies higher than the detector alias frequency.Using analytical modeling, it is shown that the central projection cannot resolve frequencies exceeding the detector alias frequency. The Fourier transform of the central projection is maximized at a lower frequency than the input as evidence of aliasing. By contrast, SBP reconstruction can resolve the input, and its Fourier transform is correctly maximized at the input frequency. Incorporating filters into the reconstruction smoothens pixelation artifacts in the spatial domain and reduces spectral leakage in the Fourier domain. It is also demonstrated that the existence of super-resolution is dependent on position in the reconstruction and on the directionality of the input frequency. Consistent with the analytical results, experimental reconstructions of bar patterns showed visibility of frequencies greater than the detector alias frequency. Super-resolution was present at positions predicted from analytical modeling.This work demonstrates the existence of super-resolution in DBT. Super-resolution has the potential to impact the visualization of fine structural details in the breast, such as microcalcifications and other subtle signs of cancer.

Project description:Abstract End?to?end testing of a new breast radiotherapy technique preferably requires realistic phantom geometries, which is challenging to achieve using currently commercially available solutions. We have developed a series of three?dimensional (3D)?printed breast phantoms, with ionization chamber and radiochromic film inserts, which can be attached to a commercial anthropomorphic thorax phantom. A contoured left breast from a patient’s planning CT was mapped onto a CT of the CIRS E2E thorax phantom (CIRS Inc.) and cropped to fit the surface. Four versions of the breast were 3D printed, containing a cavity for an ionization chamber and slits for radiochromic film insertion in the three cardinal planes, respectively. The phantoms were fully compatible with surface scanning technology used for setup. The phantoms were validated using a whole?breast volumetric modulated arc therapy protocol with a simultaneous integrated boost to the tumor bed (VMAT?SIB). Six patient plans and one original plan on the breast phantom were verified with planar portal imaging, point dose, and film measurements in the MultiCube phantom and planar ??analysis using ArcCHECK diode array. Six patient plans were recalculated on the breast phantom (hybrid plans) and delivered with point dose and film measurements with 3% (local)/2 mm ??analysis. One complete end?to?end test on the breast phantom was performed. All plan quality verifications had point dose differences below 2.4% from the calculated dose and ??agreement scores (?AS) > 87.3% for film measurements in the MultiCube, portal dosimetry, and ArcCHECK. Point dose differences in the 3D?printed phantoms were below 2.6% (median ?1.4%, range ?2.6%; 0.3%). Median ?AS was 96.4% (range 80.1%–99.7%) for all film inserts. The proposed 3D?printed attachable breast dosimetry phantoms have been shown to be a valuable tool for end?to?end testing of a new radiotherapy protocol. The workflow described in this report can aid users to create their own phantom?specific breast 3D?printed phantoms.

Dataset Information

Development of 3D patient-based super-resolution digital breast phantoms using machine learning.

Publications

Development of 3D patient-based super-resolution digital breast phantoms using machine learning.

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