Cardiac motion-corrected iterative cone-beam CT reconstruction using a semi-automatic minimum cost path-based coronary centerline extraction

Spatial and temporal motion characterization for x-ray CT

BACKGROUND

Motion induced image artifacts have been the focus of many investigations for x-ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post-processing algorithms to minimize motion artifacts.

PURPOSE

Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image-space spatial- and temporal-consistency metric (CM) to detect and characterize the local motion.

METHOD

In a non-rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region-of-interests for consistency-metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion.

RESULTS

Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in-plane motion, cross-plane motion, cone-beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images.

CONCLUSIONS

In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view-by-view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact.

Motion compensation for aortic valves using partial angle CT reconstructions motion compensation of cardiac valve CT

PURPOSE

A motion compensation method that is aimed at correcting motion artifacts of cardiac valves is proposed. The primary focus is the aortic valve.

METHODS

The method is based around partial angle reconstructions and a cost function including the image entropy. A motion model is applied to approximate the cardiac motion in the temporal and spatial domain. Based on characteristic values for velocities and strain during cardiac motion, penalties for the velocity and spatial derivatives are introduced to maintain anatomically realistic motion vector fields and avoid distortions. The model addresses global elastic deformation, but not the finer and more complicated motion of the valve leaflets.

RESULTS

The method is verified based on clinical data. Image quality was improved for most artifact impaired reconstructions. An image quality study with Likert scoring of the motion artifact severity on a scale from 1 (highest image quality) to 5 (lowest image quality/extreme artifact presence) was performed. The biggest improvements after applying motion compensation were achieved for strongly artifact impaired initial images scoring 4 and 5, resulting in an average change of the scores by -0.59 ± 0.06 and -1.33 ± 0.03, respectively. In case of artifact free images, a chance to introduce blurring was observed and their average score was raised by 0.42 ± 0.03.

CONCLUSION

Motion artifacts were consistently removed and image quality improved.

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Stack transition artifact removal (STAR) for cardiac CT

INTRODUCTION

In cardiac computed tomography (CT), irregular motion may lead to unique artifacts for scanners with a longitudinal collimation that does not cover the entire heart. Given partial coverage, subvolumes, or stacks, may be reconstructed and used to assemble a final CT volume. Irregular motion, for example, due to cardiac arrhythmia or breathing, may cause mismatch between neighboring stacks and therefore discontinuities within the final CT volume. The aim of this work is the removal of the discontinuities that are hereafter referred to as stack transition artifacts.

METHOD AND MATERIALS

A stack transition artifact removal (STAR) is achieved using a symmetric deformable image registration. A symmetric Demons algorithm was implemented and applied to stacks to remove mismatch and therefore the stack transition artifacts. The registration can be controlled with one parameter that affects the smoothness of the deformation vector field (DVF). The latter is crucial for realistically transforming the stacks. Different smoothness settings as well as an entirely automatic parameter selection that considers the required deformation magnitude for each registration were tested with patient data. Thirteen datasets were evaluated. Simulations were performed on two additional datasets.

RESULTS AND CONCLUSION

STAR considerably improved image quality while computing realistic DVFs. Discontinuities, for example, appearing as breaks or cuts in coronary arteries or cardiac valves, were removed or considerably reduced. A constant smoothing parameter that ensured satisfactory results for all datasets was found. The automatic parameter selection was able to find a proper setting for each individual dataset. Consequently, no over regularization of the DVF occurred that would unnecessarily limit the registration accuracy for cases with small deformations. The automatic parameter selection yielded the best overall results and provided a registration method for cardiac data that does not require user input.

CoroEval: a multi-platform, multi-modality tool for the evaluation of 3D coronary vessel reconstructions

We present a software, called CoroEval, for the evaluation of 3D coronary vessel reconstructions from clinical data. It runs on multiple operating systems and is designed to be independent of the imaging modality used. At this point, its purpose is the comparison of reconstruction algorithms or acquisition protocols, not the clinical diagnosis. Implemented metrics are vessel sharpness and diameter. All measurements are taken from the raw intensity data to be independent of display windowing functions. The user can either import a vessel centreline segmentation from other software, or perform a manual segmentation in CoroEval. An automated segmentation correction algorithm is provided to improve non-perfect centrelines. With default settings, measurements are taken at 1 mm intervals along the vessel centreline and from 10 different angles at each measurement point. This allows for outlier detection and noise-robust measurements without the burden and subjectivity a manual measurement process would incur. Graphical measurement results can be directly exported to vector or bitmap graphics for integration into scientific publications. Centreline and lumen segmentations can be exported as point clouds and in various mesh formats. We evaluated the diameter measurement process using three phantom datasets. An average deviation of 0.03 ± 0.03 mm was found. The software is available in binary and source code form at http://www5.cs.fau.de/CoroEval/.

Intraoperative Cone-Beam Computed Tomography and Multi-Slice Computed Tomography in Temporal Bone Imaging for Surgical Treatment

Objective

Conventional computed tomography (CT) imaging is the standard imaging technique for temporal bone diseases, whereas cone-beam CT (CBCT) imaging is a very fast imaging tool with a significant less radiation dose compared with conventional CT. We hypothesize that a system for intraoperative cone-beam CT provides comparable image quality to diagnostic CT for identifying temporal bone anatomical landmarks in cadaveric specimens.

Study Design

Cross-sectional study.

Setting

University tertiary care facility.

Subjects and Methods

Twenty cadaveric temporal bones were affixed into a head phantom and scanned with both a prototype cone-beam CT C-arm and multislice helical CT. Imaging performance was evaluated by 3 otologic surgeons and 1 head and neck radiologist. Participants were presented images in a randomized order and completed landmark identification questionnaires covering 21 structures.

Results

CBCT and multislice CT have comparable performance in identifying temporal structures. Three otologic surgeons indicated that CBCT provided statistically equivalent performance for 19 of 21 landmarks, with CBCT superior to CT for the chorda tympani and inferior for the crura of the stapes. Subgroup analysis showed that CBCT performed superiorly for temporal bone structures compared with CT. The radiologist rated CBCT and CT as statistically equivalent for 18 of 21 landmarks, with CT superior to CBCT for the crura of stapes, chorda tympani, and sigmoid sinus.

Conclusion

CBCT provides comparable image quality to conventional CT for temporal bone anatomical sites in cadaveric specimens. Clinical applications of low-dose CBCT imaging in surgical planning, intraoperative guidance, and postoperative assessment are promising but require further investigation.

Improving best-phase image quality in cardiac CT by motion correction with MAM optimization

PURPOSE

Research in image reconstruction for cardiac CT aims at using motion correction algorithms to improve the image quality of the coronary arteries. The key to those algorithms is motion estimation, which is currently based on 3-D/3-D registration to align the structures of interest in images acquired in multiple heart phases. The need for an extended scan data range covering several heart phases is critical in terms of radiation dose to the patient and limits the clinical potential of the method. Furthermore, literature reports only slight quality improvements of the motion corrected images when compared to the most quiet phase (best-phase) that was actually used for motion estimation. In this paper a motion estimation algorithm is proposed which does not require an extended scan range but works with a short scan data interval, and which markedly improves the best-phase image quality.

METHODS

Motion estimation is based on the definition of motion artifact metrics (MAM) to quantify motion artifacts in a 3-D reconstructed image volume. The authors use two different MAMs, entropy, and positivity. By adjusting the motion field parameters, the MAM of the resulting motion-compensated reconstruction is optimized using a gradient descent procedure. In this way motion artifacts are minimized. For a fast and practical implementation, only analytical methods are used for motion estimation and compensation. Both the MAM-optimization and a 3-D/3-D registration-based motion estimation algorithm were investigated by means of a computer-simulated vessel with a cardiac motion profile. Image quality was evaluated using normalized cross-correlation (NCC) with the ground truth template and root-mean-square deviation (RMSD). Four coronary CT angiography patient cases were reconstructed to evaluate the clinical performance of the proposed method.

RESULTS

For the MAM-approach, the best-phase image quality could be improved for all investigated heart phases, with a maximum improvement of the NCC value by 100% and of the RMSD value by 81%. The corresponding maximum improvements for the registration-based approach were 20% and 40%. In phases with very rapid motion the registration-based algorithm obtained better image quality, while the image quality of the MAM algorithm was superior in phases with less motion. The image quality improvement of the MAM optimization was visually confirmed for the different clinical cases.

CONCLUSIONS

The proposed method allows a software-based best-phase image quality improvement in coronary CT angiography. A short scan data interval at the target heart phase is sufficient, no additional scan data in other cardiac phases are required. The algorithm is therefore directly applicable to any standard cardiac CT acquisition protocol.

Four-dimensional image processing of myocardial CT perfusion for improved image quality and noise reduction

BACKGROUND

Image noise and multiple sources of artifact may affect the accurate interpretation of myocardial CT perfusion (CTP) studies. Although artifact within the image is often time dependent, tissue characteristics remain unchanged irrespective of cardiac phase.

OBJECTIVE

We assessed a new technique of 4-dimensional, spatiotemporal analysis, using redundant time domain information within additional phase acquisitions to reduce CTP image noise.

METHODS

Four-dimensional analysis was assessed in a static phantom and in 10 CTP studies with invasive fractional flow reserve (FFR) correlation. For each voxel within the CTP study the distribution of local Hounsfield values was measured in both time and space with the use of a customized program within MATLAB software. These values were filtered to eliminate those likely to represent noise or rapidly changing beam hardening artifact. All CTP images were acquired within a single heartbeat with 320 detector-row CT. Image noise was quantified as the SD of voxel values within myocardial segments. Contrast was measured between normal and abnormal vascular territories as assessed by FFR.

RESULTS

The mean image noise within the unprocessed CTP images was 30 HU (range, 23-42 HU). After 4-dimensional filtering the mean image noise was 22 HU (range, 15-29 HU). The mean reduction in image noise was 28% (P < 0.001). The mean contrast between normally perfused and ischemic segments was not significantly changed. The mean increase in contrast-to-noise ratio between ischemic territories and the myocardial average was 52% (P < 0.001).

CONCLUSION

Four-dimensional analysis of CTP significantly reduces image noise and may assist in the assessment of myocardial perfusion studies.