3-D contrast enhanced ultrasound imaging of an in vivo chicken embryo with a sparse array and deep learning based adaptive beamforming

Abstract

3-D contrast enhanced ultrasound enables better visualization of inherently 3-D vascular geometries compared to an intersecting plane. Additionally, it would allow the application of motion correction techniques for all directions. Both contrast detection and motion correction work better on high-frame rate data. However high-frame rate 3-D ultrasound imaging with dense matrix arrays is challenging to realize. Sparse arrays alleviate some of the limitations in cable count and data rate that fully populated arrays encounter, but their increased level of secondary lobes negatively impacts image contrast. Meanwhile the use of unfocused transmit beams needed to achieve highframe rates negatively impacts resolution. Here we propose to use adaptive beamforming by deep learning (ABLE) to improve the image quality of contrast enhanced ultrasound images acquired with a sparse spiral array. We train the neural network on simulated data and evaluate simulated images and in vivo images of an ex ovo chicken embryo. ABLE improved resolution compared to delay-and-sum (DAS) and spatial coherence (SC) beamforming on the simulated and in vivo data. The qualitative improvements persist after histogram matching, indicating that the image quality improvement of the ABLE images was not purely due to dynamic range stretching.

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

“…Following the review of the title and abstract and, upon necessity, the full text further 187 records were rejected. After the reviewing process, a total of 36 original papers [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ] and 20 conference proceedings [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ,…”

“…This last paragraph collects 13 papers [ 28 , 32 , 33 , 36 , 46 , 57 , 58 , 71 , 74 , 78 , 79 , 80 , 82 ] that proposed heterogeneous US-based applications adopting DL systems. In particular, papers focused on embryo segmentation [ 28 , 57 , 79 , 80 , 82 ] and embryo reconstruction [ 78 ] or on the vascularisation of breast cancer tissue [ 32 , 46 ], lymph node [ 58 ], hind limb [ 33 ], chorioallantoic membrane [ 36 ] and lung [ 71 , 74 ].…”

“…Nine papers [ 28 , 32 , 33 , 46 , 57 , 58 , 79 , 80 , 82 ] adopted murine models, two [ 36 , 78 ] were working with chicken US images and two [ 71 , 74 ] with pig models.…”

“…Following the review of the title and abstract and, upon necessity, the full text further 187 records were rejected. After the reviewing process, a total of 36 original papers and 20 conference proceedings [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83] met the inclusion criteria. Figure 1 illustrates the flow chart of identification screening and selection processes.…”

“…The majority of the selected papers [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 36 , 37 , 38 , 41 , 42 , 45 , 47 , 48 , 49 , 50 , 57 , 58 , 61 , 71 , 72 , 73 , 74 , 77 , 78 , 79 , 80 , 82 ] used TL techniques to overcome data limitations rather than developing models from scratch. Data augmentation was performed in twenty-eight of the analysed papers, dealing with the cardiovascular system [ 31 , 34 , 48 , 56 , 73 , 75 , 77 ], abdominal organs [ 29 , 44 , 49 , 50 , 60 , 72 , 81 ], musculoskeletal system [ 55 , 61 ], embryo [ 28 , 57 , 78 , 79 , 80 , 82 ], breast tumour vascularisation [ 32 , 46 ], brain [ 59 , 63 ] and lung [ 71 ...…”