Simulation of hepatic arteries and synthesis of 2D fluoroscopic Images for interventional imaging studies

Whitehead, Joseph F.; Nikolau, Ethan P.; Periyasamy, Sarvesh; Torres, Luis A.; Laeseke, Paul F.; Speidel, Michael A.; Wagner, Martin

doi:10.1117/12.2549570

Cited by 10 publications

(9 citation statements)

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“…The vessel generation algorithm is built upon previous work [51][52][53] for the generation of anatomically and physiologically motivated hepatic arterial trees. This work extends the algorithm proposed in Whitehead et al 52 to provide more accurate bifurcation angles and improved computational efficiency, adds contrast dynamics and guarantees non-intersecting vasculature.…”

Section: Hepatic Vessel Generationmentioning

confidence: 99%

“…The x-ray angiographic image simulator used in this work was previously detailed by Whitehead et al 52 The simulator performed energy-dependent attenuation of rays traced through a volume of high-resolution voxels containing specified material types. The x-ray energy spectrum for a specified kV was obtained using SPEKTR 3.0.…”

Section: Angiographic Image Simulatormentioning

confidence: 99%

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In silico simulation of hepatic arteries: An open‐source algorithm for efficient synthetic data generation

et al. 2023

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BackgroundIn silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high‐resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random.PurposeThe purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation.MethodsThe vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization‐based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non‐intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented.Results: The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high‐resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law ), radii (median Murray deviation ), and smoothly curved, non‐intersecting vessels. Furthermore, the algorithm assures a main feeding artery to each Couinaud segment and is random (variability = 0.98 ± 0.01).ConclusionsThis method facilitates the generation of large datasets of high‐resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.

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Section: Hepatic Vessel Generationmentioning

confidence: 99%

Section: Angiographic Image Simulatormentioning

confidence: 99%

In silico simulation of hepatic arteries: An open‐source algorithm for efficient synthetic data generation

et al. 2023

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“…2). 28 synthetic vascular trees were generated per MDCT volume, following a variation of the method in 8 . Each vascular tree extended via random branching to a random number (15-30) of liver surface points from the porta hepatis, tapering from 3.5 mm to 1 mm diameter, with 1400 HU contrast.…”

Section: Training and Experimental Validationmentioning

confidence: 99%

“…The performance on the 3D masking task was assessed with 3D DICE scores against the ground truth 3D positions of the target structures. To obtain synthetic motion corrupted images, we introduced to each volume of the test set a smooth, deformable, timevarying MVF with [4,6,8] mm maximum motion amplitude, positioned randomly in the upper abdomen (resembling clinically observed motion 1 ). The temporal trajectory was set as a phase-shifted sinusoid with frequency ranging from 1 to 1.5 cycles/scan.…”

Section: Training and Experimental Validationmentioning

confidence: 99%

Deformable motion compensation for intraprocedural vascular cone-beam CT with sequential projection domain targeting and vessel-enhancing autofocus

Huang

et al. 2023

Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

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Cone-Beam CT (CBCT) is used in Interventional Radiology (IR) for identification of complex vascular anatomy, difficult to visualize in 2D fluoroscopy. However, long acquisition time makes CBCT susceptible to soft-tissue deformable motion that degrades visibility of fine vessels. We propose a targeted framework to compensate for deformable intra-scan motion via learned full-sequence models for identification of vascular anatomy coupled to an autofocus function specifically tailored to vascular imaging. The vessel-targeted autofocus acts in two stages: (i) identification of vascular and catheter targets in the projection domain; and (ii) autofocus optimization for a 4D vector field through an objective function that quantifies vascular visibility. Target identification is based on a deep learning model that operates on the complete sequence of projections, via a transformer encoder-decoder architecture that uses spatial-temporal self-attention modules to infer long-range feature correlations, enabling identification of vascular anatomy with highly variable conspicuity. The vascular autofocus function is derived through eigenvalues of the local image Hessian, which quantify the local image structure for identification of bright tubular structures. Motion compensation was achieved via spatial transformer operators that impart time dependent deformations to NPAR = 90 partial angle reconstructions, allowing for efficient minimization via gradient backpropagation. The framework was trained and evaluated in synthetic abdominal CBCTs obtained from liver MDCT volumes and including realistic models of contrast-enhanced vascularity with 15 to 30 end branches, 1 – 3.5 mm vessel diameter, and 1400 HU contrast. The targeted autofocus resulted in qualitative and quantitative improvement in vascular visibility in both simulated and clinical intra-procedural CBCT. The transformer-based target identification module resulted in superior detection of target vascularity and a lower number of false positives, compared to a baseline U-Net model acting on individual projection views, reflected as a 1.97x improvement in intersection-over-union values. Motion compensation in simulated data yielded improved conspicuity of vascular anatomy, and reduced streak artifacts and blurring around vessels, as well as recovery of shape distortion. These improvements amounted to an average 147% improvement in cross correlation computed against the motion-free ground truth, relative to the un-compensated reconstruction. Targeted autofocus yielded improved visibility of vascular anatomy in abdominal CBCT, providing better potential for intra-procedural tracking of fine vascular anatomy in 3D images. The proposed method poses an efficient solution to motion compensation in task-specific imaging, with future application to a wider range of imaging scenarios.

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“…CBCT simulation: Two abdominal MDCT volumes from the CT-ORG dataset were used as the base anatomy for evaluation of motion compensation. To simulate TACE procedures, contrast enhanced vascularity was added to the volume, following a similar approach to ref [5]. CBCT projections were obtained with a high-fidelity CBCT projector with system geometry pertinent to interventional robotic C-arms with source-axis-distance of 785 mm and source-detectordistance of 1200 mm.…”

Section: Experimental Assessmentmentioning

confidence: 99%

Multi-stage Adaptive Sampling Autofocus (MASA) with a learned metric for deformable motion compensation in interventional cone-beam CT

Huang

Siewerdsen²,

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

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Cone-beam CT (CBCT) is widespread in abdominal interventional imaging, but its long acquisition time makes it susceptible to patient motion. Image-based autofocus has shown success in CBCT deformable motion compensation, via deep autofocus metrics and multi-region optimization, but it is challenged by the large parameter dimensionality required to capture intricate motion trajectories. This work leverages the differentiable nature of deep autofocus metrics to build a novel optimization strategy, Multi-Stage Adaptive Spine Autofocus (MASA), for compensation of complex deformable motion in abdominal CBCT. MASA poses the autofocus problem as a multi-stage adaptive sampling strategy of the motion trajectory, sampled with Hermite spline basis with variable amplitude and knot temporal positioning. The adaptive method permits simultaneous optimization of the sampling phase, local temporal sampling density, and time-dependent amplitude of the motion trajectory. The optimization is performed in a multi-stage schedule with increasing number of knots that progressively accommodates complex trajectories in late stages, preconditioned by coarser components from early stages, and with minimal increase in dimensionality. MASA was evaluated in controlled simulation experiments with two types of motion trajectories: i) combinations of slow drifts with sudden jerk (sigmoid) motion; and ii) combinations of periodic motion sources of varying frequency into multi-frequency trajectories. Further validation was obtained in clinical data from liver CBCT featuring motion of contrast-enhanced vessels, and soft-tissue structures. The adaptive sampling strategy provided successful motion compensation in sigmoid trajectories, compared to fixed sampling strategies (mean SSIM increase of 0.026 compared to 0.011). Inspection of the estimated motion showed the capability of MASA to automatically allocate larger sampling density to parts of the scan timeline featuring sudden motion, effectively accommodating complex motion without increasing the problem dimension. Experiments on multifrequency trajectories with 3-stage MASA (5, 10, and 15 knots) yielded a twofold SSIM increase compared to single-stage autofocus with 15 knots (0.076 vs 0.040, respectively). Application of MASA to clinical datasets resulted in simultaneous improvement on the delineation of both contrast-enhanced vessels and soft-tissue structures in the liver. A new autofocus framework, MASA, was developed including a novel multi-stage technique for adaptive temporal sampling of the motion trajectory in combination with fully differentiable deep autofocus metrics. This novel adaptive sampling approach is a crucial step for application of deformable motion compensation to complex temporal motion trajectories.

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Simulation of hepatic arteries and synthesis of 2D fluoroscopic Images for interventional imaging studies

Cited by 10 publications

References 0 publications

In silico simulation of hepatic arteries: An open‐source algorithm for efficient synthetic data generation

In silico simulation of hepatic arteries: An open‐source algorithm for efficient synthetic data generation

Deformable motion compensation for intraprocedural vascular cone-beam CT with sequential projection domain targeting and vessel-enhancing autofocus

Multi-stage Adaptive Sampling Autofocus (MASA) with a learned metric for deformable motion compensation in interventional cone-beam CT

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