Simulation of breast lesions based upon fractal Perlin noise

• Published in: Physica medica Vol. 114; p. 102681
• Main Authors: Tomic, Hanna, Costa, Arthur C., Bjerkén, Anna, Vieira, Marcelo A.C., Zackrisson, Sophia, Tingberg, Anders, Timberg, Pontus, Dustler, Magnus, Bakic, Predrag R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023
• Subjects: Clinical Medicine; Computational phantoms; Klinisk medicin; Lesion simulation; Mammography; Medical and Health Sciences; Medicin och hälsovetenskap; Radiologi och bildbehandling; Radiology, Nuclear Medicine and Medical Imaging; Virtual clinical trials; Mammography; Computational phantoms; Virtual clinical trials; Lesion simulation
• ISSN: 1120-1797; 1724-191X
• DOI: 10.1016/j.ejmp.2023.102681

•A novel algorithm for generating computer-simulated soft tissue breast lesions.•Computer-simulated breast lesions generated by using fractal Perlin noise.•Includes simulated lesions inserted in computational breast phantoms.•Simulated lesions evaluated and classified by observers according to BI-RADS.•A simulation method that provides almost real-time lesion generation. Steadily increasing use of computational/virtual phantoms in medical physics has motivated expanding development of new simulation methods and data representations for modelling human anatomy. This has emphasized the need for increased realism, user control, and availability. In breast cancer research, virtual phantoms have gained an important role in evaluating and optimizing imaging systems. For this paper, we have developed an algorithm to model breast abnormalities based on fractal Perlin noise. We demonstrate and characterize the extension of this approach to simulate breast lesions of various sizes, shapes, and complexity. Recently, we developed an algorithm for simulating the 3D arrangement of breast anatomy based on Perlin noise. In this paper, we have expanded the method to also model soft tissue breast lesions. We simulated lesions within the size range of clinically representative breast lesions (masses, 5–20 mm in size). Simulated lesions were blended into simulated breast tissue backgrounds and visualized as virtual digital mammography images. The lesions were evaluated by observers following the BI-RADS assessment criteria. Observers categorized the lesions as round, oval or irregular, with circumscribed, microlobulated, indistinct or obscured margins. The majority of the simulated lesions were considered by the observers to have a realism score of moderate to well. The simulation method provides almost real-time lesion generation (average time and standard deviation: 1.4 ± 1.0 s). We presented a novel algorithm for computer simulation of breast lesions using Perlin noise. The algorithm enables efficient simulation of lesions, with different sizes and appearances.