Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors

• Published in: Journal of biomedical optics Vol. 27; no. 3; p. 036003
• Main Authors: Di Sciacca, Giuseppe, Maffeis, Giulia, Farina, Andrea, Dalla Mora, Alberto, Pifferi, Antonio, Taroni, Paola, Arridge, Simon
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.03.2022; SPIE; S P I E - International Society for
• Subjects: Acoustic properties; Acoustics; Algorithms; Breast - diagnostic imaging; Breast - pathology; Breast cancer; Breast Neoplasms - pathology; Classification; Cost analysis; Diagnosis; Female; Humans; Ill posed problems; Image reconstruction; Imaging; Learning algorithms; Lesions; Machine learning; Mammography; Medical imaging; Medical imaging equipment; Medical screening; Nonlinear optics; Optical properties; Optics; Phantoms, Imaging; Pipe lines; Sensors; Simulation; Software; Tomography; Tomography, Optical - methods; Ultrasonic imaging; Ultrasonography; Ultrasound; United States; Italy; breast cancer; breast digital phantom; diffuse optical tomography; lesion classification; ultrasound
• ISSN: 1083-3668; 1560-2281; 1560-2281
• DOI: 10.1117/1.JBO.27.3.036003

Significance: Diffuse optical tomography is an ill-posed problem. Combination with ultrasound can improve the results of diffuse optical tomography applied to the diagnosis of breast cancer and allow for classification of lesions. Aim: To provide a simulation pipeline for the assessment of reconstruction and classification methods for diffuse optical tomography with concurrent ultrasound information. Approach: A set of breast digital phantoms with benign and malignant lesions was simulated building on the software VICTRE. Acoustic and optical properties were assigned to the phantoms for the generation of B-mode images and optical data. A reconstruction algorithm based on a two-region nonlinear fitting and incorporating the ultrasound information was tested. Machine learning classification methods were applied to the reconstructed values to discriminate lesions into benign and malignant after reconstruction. Results: The approach allowed us to generate realistic US and optical data and to test a two-region reconstruction method for a large number of realistic simulations. When information is extracted from ultrasound images, at least 75% of lesions are correctly classified. With ideal two-region separation, the accuracy is higher than 80%. Conclusions: A pipeline for the generation of realistic ultrasound and diffuse optics data was implemented. Machine learning methods applied to a optical reconstruction with a nonlinear optical model and morphological information permit to discriminate malignant lesions from benign ones.