Automatic Change Detection in Sparse Repeat CT Scanning

We describe a new method for the automatic detection of changes in repeat CT scanning with a reduced X-ray radiation dose. We present a theoretical formulation of the automatic change detection problem based on the on-line sparse-view repeat CT scanning dose optimization framework. We prove that the change detection problem is NP-hard and therefore cannot be efficiently solved exactly. We describe a new greedy change detection algorithm that is simple and robust and relies on only two key parameters. We demonstrate that the greedy algorithm accurately detects small, low contrast changes with only 12 scan angles. Our experimental results show that the new algorithm yields a mean changed region recall rate >89% and a mean precision rate >76%. It outperforms both our previous heuristic approach and a thresholding method using a low-dose prior image-constrained compressed sensing (PICCS) reconstruction of the repeat scan. The resulting changed region map may obviate the need for a high-quality repeat scan image when no major changes are detected and may streamline the radiologist's workflow by highlighting the regions of interest.

Radon Space Dose Optimization in Repeat CT Scanning

We present a new method for on-line radiation dose optimization in repeat computer tomography (CT) scanning. Our method uses the information of the baseline scan during the repeat scanning to significantly reduce the radiation dose without compromising the repeat scan quality. It automatically registers the patient to the baseline scan using fractional scanning and detects in sinogram space the patient regions where changes have occurred without having to reconstruct the repeat scan image. It scans only these regions in the patient, thereby considerably reducing the necessary radiation dose. It then completes the missing values of the sparsely sampled repeat scan sinogram with those of the fully sampled baseline sinogram in regions where no changes were detected and computes the repeat scan image by standard filtered backprojection reconstruction. Experiments on a patient scan with simulated changes yield a mean recall of 98% using <19% of a full dose. Experiments on real CT scans of an abdomen phantom produce similar results, with a mean recall of 94.5% and only 14.4% of a full dose more than the theoretical optimum. As hardly any changed rays are missed, the reconstructed images are practically indistinguishable from a full dose scan. Our method successfully detects small, low contrast changes and produces an accurate repeat scan reconstruction using three times less radiation than an image space baseline method.

Reduced-Dose Imageless Needle and Patient Tracking in Interventional CT Procedures

This paper describes a new method for imageless needle and patient tracking in interventional CT procedures based on fractional CT scanning. Our method accurately locates a needle with a spherical marker attached to it at a known distance from the tip with respect to the patient in the CT scanner coordinate frame with online sparse scan sampling and without reconstructing the CT image. The key principle of our method is to detect the needle and attached spherical marker in projection (sinogram) space based on the strongly attenuated X-ray signal due to the metallic composition of the needle and the needle's thin cylindrical geometry, and based on the marker's spherical geometry. A transformation from projection space to physical space uniquely determines the location and orientation of the needle and the needle tip position. Our method works directly in projection space and simultaneously performs patient registration and needle localization for every fractional CT scanning acquisition using the same sparse set of views. We performed registration and needle tip localization in five abdomen phantom scans using a rigid needle, and obtained a voxel-size tip localization error. Our experimental results indicate a voxel-sized deviation of the localization from a comparable method in 3-D image space, with the benefit of allowing X-ray dose reduction via fractional scanning at each localization. This benefit enables more frequent tip localizations during needle insertion for a similar total dose, or a reduced total dose for the same frequency of tip localization.

Sparse 3D Radon Space Rigid Registration of CT Scans: Method and Validation Study

We present a new method for rigid registration of CT datasets in 3D Radon space based on sparse sampling of scanning projections. The inputs are the two 3D Radon transforms of the CT scans, one densely sampled and the other sparsely sampled (limited number of scan angles/ranges). The output is the rigid transformation that best matches them. The method first finds the best matching between each projection direction vector in the sparse transform and the corresponding direction vector in the dense transform. It then solves a system of linear equations derived from the direction vector pairs (parallel-beam projections) or finds a solution by non-linear optimization (fan-beam and cone-beam projections). Experimental studies show that our method for 3D parallel beam registration outperforms image space registration in terms of convergence range with significantly reduced X-ray dose compared to a full conventional CT scan.

A new method for the automatic retrieval of medical cases based on the RadLex ontology

PURPOSE

The goal of medical case-based image retrieval (M-CBIR) is to assist radiologists in the clinical decision-making process by finding medical cases in large archives that most resemble a given case. Cases are described by radiology reports comprised of radiological images and textual information on the anatomy and pathology findings. The textual information, when available in standardized terminology, e.g., the RadLex ontology, and used in conjunction with the radiological images, provides a substantial advantage for M-CBIR systems.

METHODS

We present a new method for incorporating textual radiological findings from medical case reports in M-CBIR. The input is a database of medical cases, a query case, and the number of desired relevant cases. The output is an ordered list of the most relevant cases in the database. The method is based on a new case formulation, the Augmented RadLex Graph and an Anatomy-Pathology List. It uses a new case relatedness metric [Formula: see text] that prioritizes more specific medical terms in the RadLex tree over less specific ones and that incorporates the length of the query case.

RESULTS

An experimental study on 8 CT queries from the 2015 VISCERAL 3D Case Retrieval Challenge database consisting of 1497 volumetric CT scans shows that our method has accuracy rates of 82 and 70% on the first 10 and 30 most relevant cases, respectively, thereby outperforming six other methods.

CONCLUSIONS

The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.

Tumor burden evaluation in NF1 patients with plexiform neurofibromas in daily clinical practice

BACKGROUND

Existing volumetric measurements of plexiform neurofibromas (PNs) are time consuming and error prone, as they require delineation of PN boundaries, a procedure that is not practical in the typical clinical setting. The aim of this study is to assess the Plexiform Neurofibroma Instant Segmentation Tool (PNist), a novel semi-automated segmentation program that we developed for PN delineation in a clinical context. PNist was designed to greatly simplify volumetric assessment of PNs through use of an intuitive user interface while providing objectively consistent results with minimal interobserver and intraobserver variabilities in reasonable time.

MATERIALS AND METHODS

PNs were measured in 30 magnetic resonance imaging (MRI) scans from 12 patients with neurofibromatosis 1. Volumetric measurements were performed using PNist and compared to a standard semi-automated volumetric method (Analyze 9.0).

RESULTS

High correlation was detected between PNist and the semi-automated method (R(2) = 0.996), with a mean volume overlap error of 9.54 % and low intraobserver and interobserver variabilities. The segmentation time required for PNist was 60 % of the time required for Analyze 9.0 (360 versus 900 s, respectively). PNist was also reliable when assessing changes in tumor size over time, compared to the existing commercial method.

CONCLUSIONS

Our study suggests that the new PNist method is accurate, intuitive, and less time consuming for PN segmentation compared to existing commercial volumetric methods. The workflow is simple and user-friendly, making it an important clinical tool to be used by radiologists, neurologists and neurosurgeons on a daily basis, helping them deal with the complex task of evaluating PN burden and progression.

Automatic lung tumor segmentation with leaks removal in follow-up CT studies

PURPOSE

In modern oncology, disease progression and response to treatment are routinely evaluated with a series of volumetric scans. The number of tumors and their volume (mass) over time provides a quantitative measure for the evaluation. Thus, many of the scans are follow-up scans. We present a new, fully automatic algorithm for lung tumors segmentation in follow-up CT studies that takes advantage of the baseline delineation.

METHODS

The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the output is the tumor delineations in the follow-up CT scan; the output is the tumor delineations in the follow-up CT scan. The algorithm consists of four steps: (1) deformable registration of the baseline scan and tumor's delineations to the follow-up CT scan; (2) segmentation of these tumors in the follow-up CT scan with the baseline CT and the tumor's delineations as priors; (3) detection and correction of follow-up tumors segmentation leaks based on the geometry of both the foreground and the background; and (4) tumor boundary regularization to account for the partial volume effects.

RESULTS

Our experimental results on 80 pairs of CT scans from 40 patients with ground-truth segmentations by a radiologist yield an average DICE overlap error of 14.5 % ([Formula: see text]), a significant improvement from the 30 % ([Formula: see text]) result of stand-alone level-set segmentation.

CONCLUSION

The key advantage of our method is that it automatically builds a patient-specific prior to the tumor. Using this prior in the segmentation process, we developed an algorithm that increases segmentation accuracy and robustness and reduces observer variability.

Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI

This paper presents an automatic method for the segmentation, internal classification and follow-up of optic pathway gliomas (OPGs) from multi-sequence MRI datasets. Our method starts with the automatic localization of the OPG and its core with an anatomical atlas followed by a binary voxel classification with a probabilistic tissue model whose parameters are estimated from the MR images. The method effectively incorporates prior location, tissue characteristics, and intensity information for the delineation of the OPG boundaries in a consistent and repeatable manner. Internal classification of the segmented OPG volume is then obtained with a robust method that overcomes grey-level differences between learning and testing datasets. Experimental results on 25 datasets yield a mean surface distance error of 0.73 mm as compared to manual segmentation by experienced radiologists. Our method exhibits reliable performance in OPG growth follow-up MR studies, which are crucial for monitoring disease progression. To the best of our knowledge, this is the first method that addresses automatic segmentation, internal classification, and follow-up of OPG.

Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading

This paper describes an evaluation framework that allows a standardized and objective quantitative comparison of carotid artery lumen segmentation and stenosis grading algorithms. We describe the data repository comprising 56 multi-center, multi-vendor CTA datasets, their acquisition, the creation of the reference standard and the evaluation measures. This framework has been introduced at the MICCAI 2009 workshop 3D Segmentation in the Clinic: A Grand Challenge III, and we compare the results of eight teams that participated. These results show that automated segmentation of the vessel lumen is possible with a precision that is comparable to manual annotation. The framework is open for new submissions through the website http://cls2009.bigr.nl.

Robotic assisted spinal surgery–from concept to clinical practice

After several years of product development, animal trials and human cadaver testing, the SpineAssist--a miniature bone-mounted robotic system--has recently entered clinical use. To the best of the authors' knowledge, this is the only available image-based mechanical guidance system that enables pedicle screw insertion with an overall accuracy in the range of 1 mm in both open and minimally invasive procedures. In this paper, we describe the development and clinical trial process that has brought the SpineAssist to its current state, with an emphasis on the various difficulties encountered along the way and the corresponding solutions. All aspects of product development are discussed, including mechanical design, CT-to-fluoroscopy image registration, and surgical techniques. Finally, we describe a series of preclinical trials with human cadavers, as well as clinical use, which verify the system's accuracy and efficacy.

Image-guided system with miniature robot for precise positioning and targeting in keyhole neurosurgery

This paper describes a novel image-guided system for precise automatic targeting in minimally invasive keyhole neurosurgery. The system consists of the MARS miniature robot fitted with a mechanical guide for needle, probe or catheter insertion. Intraoperatively, the robot is directly affixed to a head clamp or to the patient's skull. It automatically positions itself with respect to predefined targets in a preoperative CT/MRI image following an anatomical registration with an intraoperative 3D surface scan of the patient's facial features and registration jig. We present the system architecture, surgical protocol, custom hardware (targeting and registration jig), and software modules (preoperative planning, intraoperative execution, 3D surface scan processing, and three-way registration). We also describe a prototype implementation of the system and in vitro registration experiments. Our results indicate a system-wide target registration error of 1.7 mm (standard deviation = 0.7 mm), which is close to the required 1.0-1.5 mm clinical accuracy in many keyhole neurosurgical procedures.

Robot-assisted image-guided targeting for minimally invasive neurosurgery: planning, registration, and in-vitro experiment

This paper present a novel image-guided system for precise automatic targeting in keyhole minimally invasive neurosurgery. The system consists of a miniature robot fitted with a mechanical guide for needle/probe insertion. Intraoperatively, the robot is directly affixed to a head clamp or to the patient skull. It automatically positions itself with respect to predefined targets in a preoperative CT/MRI image following an anatomical registration with a intraoperative 3D surface scan of the patient facial features. We describe the preoperative planning and registration modules, and an in-vitro registration experiment of the entire system which yields a target registration error of 1.7 mm (std = 0.7 mm).

FRACAS: a system for computer-aided image-guided long bone fracture surgery

This article describes FRACAS, a computer-integrated orthopedic system for assisting surgeons in performing closed medullary nailing of long bone fractures. FRACAS's goal is to reduce the surgeon's cumulative exposure to radiation and surgical complications associated with alignment and positioning errors of bone fragments, nail insertion, and distal screw locking. It replaces uncorrelated, static fluoroscopic images with a virtual reality display of three-dimensional bone models created from preoperative computed tomography and tracked intraoperatively in real time. Fluoroscopic images are used to register the bone models to the intraoperative situation and to verify that the registration is maintained. This article describes the system concept, software prototypes of preoperative modules (modeling, nail selection, and visualization), intraoperative modules (fluoroscopic image processing and tracking), and preliminary in vitro experimental results to date. Our experiments suggest that the modeling, nail selection, and visualization modules yield adequate results and that fluoroscopic image processing with submillimetric accuracy is practically feasible on clinical images.

Computer-integrated revision total hip replacement surgery: concept and preliminary results

This paper describes an ongoing project to develop a computer-integrated system to assist surgeons in revision total hip replacement (RTHR) surgery. In RTHR surgery, a failing orthopedic hip implant, typically cemented, is replaced with a new one by removing the old implant, removing the cement and fitting a new implant into an enlarged canal broached in the femur. RTHR surgery is a difficult procedure fraught with technical challenges and a high incidence of complications. The goals of the computer-based system are the significant reduction of cement removal labor and time, the elimination of cortical wall penetration and femur fracture, the improved positioning and fit of the new implant resulting from precise, high-quality canal milling and the reduction of bone sacrificed to fit the new implant. Our starting points are the ROBODOC system for primary hip replacement surgery and the manual RTHR surgical protocol. We first discuss the main difficulties of computer-integrated RTHR surgery and identify key issues and possible solutions. We then describe possible system architectures and protocols for preoperative planning and intraoperative execution. We present a summary of methods and preliminary results in CT image metal artifact removal, interactive cement cut-volume definition and cement machining, anatomy-based registration using fluoroscopic X-ray images and clinical trials using an extended RTHR version of ROBODOC. We conclude with a summary of lessons learned and a discussion of current and future work.