Robot-assisted medical imaging entails the use of a robot to acquire a medical image. Examples include robot-assisted endoscopic camera imaging, ultrasound imaging where the transducer is held by a robot, X-ray imaging where the source and detector are positioned by robots, and actuated capsule endoscopy, where the capsule is maneuvered by external magnetic actuation. Robot assistance enables the controlled trajectory of the imaging system with high precision and accuracy. This makes it possible to compound acquisitions for increased aperture and for volumetric or tomographic imaging, to track medical instrumentation, and to adjust imaging trajectory in a feedback loop as a function of the patient. Intraoperative robotic medical imaging can provide valuable information to the physician and facilitates the registration of preoperative imaging to the patient. In this review article, we describe some of the robotic imaging systems developed for diagnosis and intervention guidance. In particular, based on the surveyed research activity, we will describe our view of the state of the art in ultrasound, endoscopy, X-ray, optical coherence tomography, and nuclear medicine. We will discuss approaches to autonomous scanning and physics-driven approaches such as elastography and photoacoustic tomography, where the accurate placement and trajectory control of the imaging system using a robot are of paramount importance. We will map out the current state of the art and discuss potential avenues of research.

Robot-Assisted Medical Imaging: A Review

Current photon counting x-ray detector (PCD) technology faces limitations associated with spectral fidelity and photon starvation. One strategy for addressing these limitations is to supplement PCD data with high-resolution, low-noise data acquired with an energy-integrating detector (EID). In this work, we propose an iterative, hybrid reconstruction technique which combines the spectral properties of PCD data with the resolution and signal-to-noise characteristics of EID data. Our hybrid reconstruction technique is based on an algebraic model of data fidelity which substitutes the EID data into the data fidelity term associated with the PCD reconstruction, resulting in a joint reconstruction problem. Within the split Bregman framework, these data fidelity constraints are minimized subject to additional constraints on spectral rank and on joint intensity-gradient sparsity measured between the reconstructions of the EID and PCD data. Following a derivation of the proposed technique, we apply it to the reconstruction of a digital phantom which contains realistic concentrations of iodine, barium, and calcium encountered in small-animal micro-CT. The results of this experiment suggest reliable separation and detection of iodine at concentrations ≥ 5 mg/ml and barium at concentrations ≥ 10 mg/ml in 2-mm features for EID and PCD data reconstructed with inherent spatial resolutions of 176 μm and 254 μm, respectively (point spread function, FWHM). Furthermore, hybrid reconstruction is demonstrated to enhance spatial resolution within material decomposition results and to improve low-contrast detectability by as much as 2.6 times relative to reconstruction with PCD data only. The parameters of the simulation experiment are based on an in vivo micro-CT experiment conducted in a mouse model of soft-tissue sarcoma. Material decomposition results produced from this in vivo data demonstrate the feasibility of distinguishing two K-edge contrast agents with a spectral separation on the order of the energy resolution of the PCD hardware.

/pdf/hybrid-spectral-ct-reconstruction-dd9556c4sn.pdf

Hybrid spectral CT reconstruction.

Cone-beam computed tomography (CBCT) imaging is becoming increasingly important for a wide range of applications such as image-guided surgery, image-guided radiation therapy as well as diagnostic imaging such as breast and orthopaedic imaging. The potential benefits of non-circular source-detector trajectories was recognized in early work to improve the completeness of CBCT sampling and extend the field of view (FOV). Another important feature of interventional imaging is that prior knowledge of patient anatomy such as a preoperative CBCT or prior CT is commonly available. This provides the opportunity to integrate such prior information into the image acquisition process by customized CBCT source-detector trajectories. Such customized trajectories can be designed in order to optimize task-specific imaging performance, providing intervention or patient-specific imaging settings. The recently developed robotic CBCT C-arms as well as novel multi-source CBCT imaging systems with additional degrees of freedom provide the possibility to largely expand the scanning geometries beyond the conventional circular source-detector trajectory. This recent development has inspired the research community to innovate enhanced image quality by modifying image geometry, as opposed to hardware or algorithms. The recently proposed techniques in this field facilitate image quality improvement, FOV extension, radiation dose reduction, metal artifact reduction as well as 3D imaging under kinematic constraints. Because of the great practical value and the increasing importance of CBCT imaging in image-guided therapy for clinical and preclinical applications as well as in industry, this paper focuses on the review and discussion of the available literature in the CBCT trajectory optimization field. To the best of our knowledge, this paper is the first study that provides an exhaustive literature review regarding customized CBCT algorithms and tries to update the community with the clarification of in-depth information on the current progress and future trends.

https://iopscience.iop.org/article/10.1088/1361-6560/ac8590/pdf

Source-detector trajectory optimization in cone-beam computed tomography: a comprehensive review on today’s state-of-the-art

Objectives:To assess the impact of head motion artefacts and an automated artefact-correction system on cone beam CT (CBCT) image quality and interpretability for simulated diagnostic tasks.Methods...

/pdf/an-ex-vivo-study-of-automated-motion-artefact-correction-and-1l877fh3xw.pdf

An ex vivo study of automated motion artefact correction and the impact on cone beam CT image quality and interpretability

In cone-beam CT, involuntary patient motion and inaccurate or irreproducible scanner motion substantially degrades image quality. To avoid artifacts this motion needs to be estimated and compensated during image reconstruction. In previous work we showed that Fourier consistency conditions (FCC) can be used in fan-beam CT to estimate motion in the sinogram domain. This work extends the FCC to [Formula: see text] cone-beam CT. We derive an efficient cost function to compensate for [Formula: see text] motion using [Formula: see text] detector translations. The extended FCC method have been tested with five translational motion patterns, using a challenging numerical phantom. We evaluated the root-mean-square-error and the structural-similarity-index between motion corrected and motion-free reconstructions. Additionally, we computed the mean-absolute-difference (MAD) between the estimated and the ground-truth motion. The practical applicability of the method is demonstrated by application to respiratory motion estimation in rotational angiography, but also to motion correction for weight-bearing imaging of knees. Where the latter makes use of a specifically modified FCC version which is robust to axial truncation. The results show a great reduction of motion artifacts. Accurate estimation results were achieved with a maximum MAD value of 708 μm and 1184 μm for motion along the vertical and horizontal detector direction, respectively. The image quality of reconstructions obtained with the proposed method is close to that of motion corrected reconstructions based on the ground-truth motion. Simulations using noise-free and noisy data demonstrate that FCC are robust to noise. Even high-frequency motion was accurately estimated leading to a considerable reduction of streaking artifacts. The method is purely image-based and therefore independent of any auxiliary data.

/pdf/motion-compensation-for-cone-beam-ct-using-fourier-2i7ikt8d5f.pdf

Motion compensation for cone-beam CT using Fourier consistency conditions.

Purpose:
Intraoperative C-arm cone-beam CT (CBCT) is subject to artifacts arising from patient motion during the fairly long (∼5–20 s) scan times. We present a fiducial free method to mitigate motion artifacts using 3D-2D image registration that simultaneously corrects residual errors in geometric calibration.

Methods:
A 3D-2D registration process was used to register each projection to DRRs computed from the 3D image by maximizing gradient orientation (GO) using the CMA-ES optimizer. The resulting rigid 6 DOF transforms were applied to the system projection matrices, and a 3D image was reconstructed via model-based image reconstruction (MBIR, which accommodates the resulting noncircular orbit). Experiments were conducted using a Zeego robotic C-arm (20 s, 200°, 496 projections) to image a head phantom undergoing various types of motion: 1) 5° lateral motion; 2) 15° lateral motion; and 3) 5° lateral motion with 10 mm periodic inferior-superior motion. Images were reconstructed using a penalized likelihood (PL) objective function, and structural similarity (SSIM) was measured for axial slices of the reconstructed images. A motion-free image was acquired using the same protocol for comparison.

Results:
There was significant improvement (p 0.99, indicating near identity to the motion-free reference. The point spread function (PSF) measured from a wire in the phantom was restored to that of the reference in each case.

Conclusion:
The 3D-2D registration method provides a robust framework for mitigation of motion artifacts and is expected to hold for applications in the head, pelvis, and extremities with reasonably constrained operative setup. Further improvement can be achieved by incorporating multiple rigid components and non-rigid deformation within the framework. The method is highly parallelizable and could in principle be run with every acquisition.

Research supported by National Institutes of Health Grant No. R01-EB-017226 and academic-industry partnership with Siemens Healthcare (AX Division, Forcheim, Germany).

WE-AB-BRA-08: Correction of Patient Motion in C-Arm Cone-Beam CT Using 3D-2D Registration

We propose an easily computed estimator of marginal likelihoods from posterior simulation output, via reciprocal importance sampling, combining earlier proposals of DiCiccio et al (1997) and Robert and Wraith (2009). This involves only the unnormalized posterior densities from the sampled parameter values, and does not involve additional simulations beyond the main posterior simulation, or additional complicated calculations. It is unbiased for the reciprocal of the marginal likelihood, consistent, has finite variance, and is asymptotically normal. It involves one user-specified control parameter, and we derive an optimal way of specifying this. We illustrate it with several numerical examples.

pdf/easily-computed-marginal-likelihoods-from-posterior-2f6tre9h.pdf

Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator

In this work, we consider an estimation method in sparse Poisson models inspired by [1] and provide novel sign consistency results under mild conditions.

pdf/sign-consistent-estimation-in-a-sparse-poisson-model-34vkr3pm.pdf

Sign-consistent estimation in a sparse Poisson model

Purpose: Conventional workflow in interventional imaging often ignores a wealth of prior information of the patient anatomy and the imaging task. This work introduces a task-driven imaging framework that utilizes such information to prospectively design acquisition and reconstruction techniques for cone-beam CT (CBCT) in a manner that maximizes task-based performance in subsequent imaging procedures. Methods: The framework is employed in jointly optimizing tube current modulation, orbital tilt, and reconstruction parameters in filtered back-projection reconstruction for interventional imaging. Theoretical predictors of noise and resolution relates acquisition and reconstruction parameters to task-based detectability. Given a patient-specific prior image and specification of the imaging task, an optimization algorithm prospectively identifies the combination of imaging parameters that maximizes task-based detectability. Initial investigations were performed for a variety of imaging tasks in an elliptical phantom and an anthropomorphic head phantom. Results: Optimization of tube current modulation and view-dependent reconstruction kernel was shown to have greatest benefits for a directional task (e.g., identification of device or tissue orientation). The task-driven approach yielded techniques in which the dose and sharp kernels were concentrated in views contributing the most to the signal power associated with the imaging task. For example, detectability of a line pair detection task was improved by at least three fold compared to conventional approaches. For radially symmetric tasks, the task-driven strategy yielded results similar to a minimum variance strategy in the absence of kernel modulation. Optimization of the orbital tilt successfully avoided highly attenuating structures that can confound the imaging task by introducing noise correlations masquerading at spatial frequencies of interest. Conclusions: This work demonstrated the potential of a task-driven imaging framework to improve image quality and reduce dose beyond that achievable with conventional imaging approaches.

/pdf/task-driven-imaging-in-cone-beam-computed-tomography-3tramuqybu.pdf

Task-driven imaging in cone-beam computed tomography.

. We propose a novel and eﬃcient iterative two-stage variable selection approach for multivariate sparse GLARMA models, which can be used for modelling multivariate discrete-valued time series. Our approach consists in iteratively combining two steps: the estimation of the autoregressive moving average (ARMA) coeﬃcients of multivariate GLARMA models and the variable selection in the coeﬃcients of the Generalized Linear Model (GLM) part of the model performed by regularized methods. We explain how to implement our approach eﬃciently. Then we assess the performance of our methodology using synthetic data and compare it with alternative methods. Finally, we illustrate it on RNA-Seq data resulting from polyribosome proﬁling to determine translational status for all mRNAs in germinating seeds. Our approach, which is implemented in the MultiGlarmaVarSel R package and available on the CRAN, is very attractive since it beneﬁts from a low computational load and is able to outperform the other methods for recovering the null and non-null coeﬃcients.

Variable selection in sparse multivariate GLARMA models: Application to germination control by environment

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