A longitudinal study on both 3D and 2D photoacoustic and Doppler ultrasound imaging of human hand rheumatoid arthritis progression has been performed using an automatic imaging system based on GEHC VividTM E95 with L8-18i-D probe and OPOTEK tunable laser system. Bi-weekly imaging has been performed starting from baseline (before patients start medication). Both photoacoustic and Doppler ultrasound can confirm the disease development, however, photoacoustic has higher correlation coefficients (with a median of 78.9%, p = 0.039) with patients’ PGA score.
A longitudinal study on both 3D and 2D photoacoustic and Doppler ultrasound images of rat leg rheumatoid arthritis development has been performed using an automatic imaging system based on a GE HealthCare VividTM E95 unit with a L8-18i-D probe, an OPOTEK tunable laser system, and a Universal Robots UR3 robotic arm. Daily imaging of ankle bones was performed starting from day 0 when the lyophilized Mycobacterium butyricum was injected to induce the disease. Although both photoacoustic and Doppler ultrasound can confirm the disease development, photoacoustic imaging is more sensitive to microvasculature and enables earlier detection of inflammation than Doppler ultrasound.
Image guidance aids neurosurgeons in making critical clinical decisions of safe maximal resection of diseased tissue. The brain however undergoes significant non-linear structural deformation on account of dura opening and tumor resection. Deformable registration of pre-operative ultrasound to intra-operative ultrasound may be used in mapping of pre-operative planning MRI to intraoperative ultrasound. Such mapping may aid in determining tumor resection margins during surgery. In this work, brain structures visible in pre- and intra-operative 3D ultrasound were used for automatic deformable registration. A Gaussian mixture model was used to automatically segment structures of interest in pre- and intra-operative ultrasound and patch-based normalized cross-correlation was used to establish correspondences between segmented structures. An affine registration based on correspondences was followed by B-spline based deformable registration to register pre- and intra-operative ultrasound. Manually labelled landmarks in pre- and intra-operative ultrasound were used to quantify the mean target registration error. We achieve a mean target registration error of 1.43±0.8 mm when validated with 17 pre- and intra-operative ultrasound image volumes of a public dataset.
KEYWORDS: Image registration, 3D modeling, Ultrasonography, Liver, Motion models, Magnetic resonance imaging, 3D image processing, Computer programming, Data acquisition, 3D acquisition
Image fusion-guided interventions often require planning MR/CT and interventional U/S images to be registered in realtime. Organ motion, patient breathing and inconsistent ultrasound probe positioning during intervention, all contribute to the challenges of real-time 3D deformable registration, where alignment accuracy and computation time are often mutual trade-offs. In this work, we propose a novel framework to align planning and interventional 3D U/S by training patientspecific deep-deformation models (PsDDM) at the planning stage. During intervention, planning 3D U/S volumes are efficiently warped onto the interventional 3D U/S volumes using the trained deep-deformation model, thus enabling the transfer of other modality (planning MR/CT) information in real-time on interventional images. The alignment of planning MR/CT to planning U/S is not time-critical as these can be aligned before the intervention with desired accuracy using any known multimodal deformable registration method. The feasibility of training PsDDM is shown on liver U/S data acquired with a custom-built MR-compatible, hands-free 3D ultrasound probe that allows simultaneous acquisition of planning MR and U/S. Liver U/S volumes exhibit large motion in time due to respiration and therefore serve as a good anatomy to quantify the accuracy of the PsDDM. For quantitative evaluation of the PsDDM, a large vessel bifurcation was manually annotated on 9 U/S volumes that were not used for training the PsDDM but from the same subject. Mean target registration error (TRE) between the centroids was 0.84mm ± 0.39mm, mean Hausdorff distance (HD) was 1.80mm ± 0.29mm and mean surface distance (MSD) was 0.44mm ± 0.06mm for all volumes. In another experiment, the PsDDM was trained using liver volumes from one scanning session, while the model was tested on data from a separate scanning session of the same patient, for which qualitative alignment results were presented.
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