Motion-compensated and gated cone beam filtered back-projection for 3-D rotational X-ray angiography

The influence of motion-compensated reconstruction on coronary artery analysis for a dual-layer detector CT system: a dynamic phantom study

OBJECTIVES

Cardiac motion artifacts hinder the assessment of coronary arteries in coronary computed tomography angiography (CCTA). We investigated the impact of motion compensation reconstruction (MCR) on motion artifacts in CCTA at various heart rates (HR) using a dynamic phantom.

MATERIALS AND METHODS

An artificial hollow coronary artery (5-mm diameter lumen) filled with iodinated contrast agent (400 HU at 120 kVp), positioned centrally in an anthropomorphic chest phantom, was scanned using a dual-layer spectral detector CT. The artery was translated at constant horizontal velocities (0-80 mm/s, increment of 10 mm/s). For each velocity, five CCTA scans were repeated using a clinical protocol. Motion artifacts were quantified using the in-plane motion area. Regression analysis was performed to calculate the reduction in motion artifacts provided by MCR, by division of the slopes of non-MCR and MCR fitted lines.

RESULTS

Reference mean (95% confidence interval) motion artifact area was 24.9 mm2 (23.8, 26.0). Without MCR, motion artifact areas for velocities exceeding 20 mm/s were significantly larger (up to 57.2 mm2 (40.1, 74.2)) than the reference. With MCR, no significant differences compared to the reference were shown for all velocities, except for 70 mm/s (29.0 mm2 (27.0, 31.0)). The slopes of the fitted data were 0.44 and 0.04 for standard and MCR reconstructions, respectively, resulting in an 11-time motion artifact reduction.

CONCLUSION

MCR may improve CCTA assessment in patients by reducing coronary artery motion artifacts, especially in those with elevated HR who cannot receive beta blockers or do not attain the targeted HR.

CLINICAL RELEVANCE STATEMENT

This vendor-specific motion compensation reconstruction may improve coronary computed tomography angiography assessment in patients by reduction of coronary artery motion artifacts, especially in those with elevated various heart rates (HR) who cannot receive beta blockers or do not attain the targeted HR.

KEY POINTS

• Motion artifacts are known to hinder the assessment of coronary arteries on coronary CT angiography (CCTA), leading to more non-diagnostic scans. • This dynamic phantom study shows that motion compensation reconstruction (MCR) reduces motion artifacts at various velocities, which may help to decrease the number of non-diagnostic scans. • MCR in this study showed to reduce motion artifacts 11-fold.

Feasibility of a Prototype Image Reconstruction Algorithm for Motion Correction in Interventional Cone-Beam CT Scans

RATIONALE AND OBJECTIVES

Assess the feasibility of a prototype image reconstruction algorithm in correcting motion artifacts in cone-beam computed tomography (CBCT) scans of interventional instruments in the lung.

MATERIALS AND METHODS

First, phantom experiments were performed to assess the algorithm, using the Xsight lung phantom with custom inserts containing straight or curved catheters. During scanning, the inserts moved in a continuous sinusoidal or breath-hold mimicking pattern, with varying amplitudes and frequencies. Subsequently, the algorithm was applied to CBCT data from navigation bronchoscopy procedures. The algorithm's performance was assessed quantitatively via edge-sharpness measurements and qualitatively by three specialists.

RESULTS

In the phantom study, the algorithm improved sharpness in 13 out of 14 continuous sinusoidal motion and five out of seven breath-hold mimicking scans, with more significant effects at larger motion amplitudes. Analysis of 27 clinical scans showed that the motion corrected reconstructions had significantly sharper edges than standard reconstructions (2.81 (2.24-6.46) vs. 2.80 (2.16-4.75), p = 0.003). These results were consistent with the qualitative assessment, which showed higher scores in the sharpness of bronchoscope-tissue interface and catheter-tissue interface in the motion-corrected reconstructions. However, the tumor demarcation ratings were inconsistent between raters, and the overall image quality of the new reconstructions was rated lower.

CONCLUSION

Our findings suggest that applying the new prototype algorithm for motion correction in CBCT images is feasible. The algorithm improved the sharpness of medical instruments in CBCT scans obtained during diagnostic navigation bronchoscopy procedures, which was demonstrated both quantitatively and qualitatively.

AAPM Task Group Report 238: 3D C-arms with volumetric imaging capability

This report reviews the image acquisition and reconstruction characteristics of C-arm Cone Beam Computed Tomography (C-arm CBCT) systems and provides guidance on quality control of C-arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image-Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report. Section 1 provides an introduction to C-arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C-arm fluoroscopy quality control (QC) in relation to 3D C-arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C-arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C-arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free-in-air and in-phantom approaches to evaluating radiation dose indices. The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6.

Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction

BACKGROUND

Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling.

PURPOSE

This study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images.

METHODS

A 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC).

RESULTS

The trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average (± standard deviation) RMSE in mm^-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040 ± 0.0009, 0.0029 ± 0.0002, 0.0024 ± 0.0003 and 0.0021 ± 0.0003. Corresponding NCC were 0.84 ± 0.05, 0.91 ± 0.05, 0.91 ± 0.05 and 0.93 ± 0.04.

CONCLUSIONS

CNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.

Comparative analysis of setup margin calculation in cone beam CT, by van Herk formula, using two different image registration methods

Introduction:

This study aimed to quantify the difference in setup margin in cone beam computed tomography (CBCT) setup imaging, utilising the van Herk formula for two different image registration methods. Two alternative techniques of registration, bony landmark (BL) matching and soft tissue matching (ST) for head and neck cancer patients, were investigated.

Methods:

This study included 30 head and neck cancer patients who received a simultaneous integrated boost of 54–60–66 Gy in 30 fractions, using volumetric modulated arc treatment. A total of 867 CBCT images were acquired during patient setup and further analysed for setup margin calculation. A region of interest was described using a clip box between the reference and CBCT image to calculate the patient’s positional inaccuracy in three translational directions, X, Y and Z, where X was mediolateral, Y was the cranial-caudal, and Z was the anterior-posterior direction in the patient-based coordinate system, respectively. The shifts were captured by altering the BL and ST matching, and the setup margin was calculated using the van Herk formula (=2·5Σ + 0·7σ where Σ was the systematic and σ was the random error).

Results:

The difference between bony and ST matching in most cases was observed to be 1·4 mm in all translational directions at a 95% confidence interval and <1° in all rotational directions. The rotational error was found to be below the action level (±3°); hence, no corrections related to rotational error were made. The translational setup margin for bone and ST-based registration was X (BL) = 4·6 mm, X (ST) = 4·4 mm, Y (BL) = 6·3 mm, Y (ST) = 4·7 mm, Z (BL) = 3·0 mm, Z (ST) = 3·6mm.

Conclusion:

Two distinct registration approaches for head-neck patient setup did not yield any significant difference in the setup margin calculation. A suitable approach for CBCT and reference CT registration technique was required for the setup margin calculation. Confusion in selecting the correct image registration procedure can result in incorrect treatment execution. The compatibility of the two registration approaches was established in this study. Image fusion was neutralised before the second match (ST) to avoid hysteresis. For setup verification using CBCT for the head and neck region, both bone and ST registration were compatible for setup verification.

Four-dimensional computed tomography of the left ventricle, Part I: Motion artifact reduction

PURPOSE

Standard four-dimensional computed tomography (4DCT) cardiac reconstructions typically include spiraling artifacts that depend not only on the motion of the heart but also on the gantry angle range over which the data was acquired. We seek to reduce these motion artifacts and, thereby, improve the accuracy of left ventricular wall positions in 4DCT image series.

METHODS

We use a motion artifact reduction approach (ResyncCT) that is based largely on conjugate pairs of partial angle reconstruction (PAR) images. After identifying the key locations where motion artifacts exist in the uncorrected images, paired subvolumes within the PAR images are analyzed with a modified cross-correlation function in order to estimate 3D velocity and acceleration vectors at these locations. A subsequent motion compensation process (also based on PAR images) includes the creation of a dense motion field, followed by a backproject-and-warp style compensation. The algorithm was tested on a 3D printed phantom, which represents the left ventricle (LV) and on challenging clinical cases corrupted by severe artifacts.

RESULTS

The results from our preliminary phantom test as well as from clinical cardiac scans show crisp endocardial edges and resolved double-wall artifacts. When viewed as a temporal series, the corrected images exhibit a much smoother motion of the LV endocardial boundary as compared to the uncorrected images. In addition, quantitative results from our phantom studies show that ResyncCT processing reduces endocardial surface distance errors from 0.9 ± 0.8 to 0.2 ± 0.1 mm.

CONCLUSIONS

The ResyncCT algorithm was shown to be effective in reducing motion artifacts and restoring accurate wall positions. Some perspectives on the use of conjugate-PAR images and on techniques for CT motion artifact reduction more generally are also given.

Deep Learning-based Inaccuracy Compensation in Reconstruction of High Resolution XCT Data

While X-ray computed tomography (XCT) is pushed further into the micro- and nanoscale, the limitations of various tool components and object motion become more apparent. For high-resolution XCT, it is necessary but practically difficult to align these tool components with sub-micron precision. The aim is to develop a novel reconstruction methodology that considers unavoidable misalignment and object motion during the data acquisition in order to obtain high-quality three-dimensional images and that is applicable for data recovery from incomplete datasets. A reconstruction software empowered by sophisticated correction modules that autonomously estimates and compensates artefacts using gradient descent and deep learning algorithms has been developed and applied. For motion estimation, a novel computer vision methodology coupled with a deep convolutional neural network approach provides estimates for the object motion by tracking features throughout the adjacent projections. The model is trained using the forward projections of simulated phantoms that consist of several simple geometrical features such as sphere, triangle and rectangular. The feature maps extracted by a neural network are used to detect and to classify features done by a support vector machine. For missing data recovery, a novel deep convolutional neural network is used to infer high-quality reconstruction data from incomplete sets of projections. The forward and back projections of simulated geometric shapes from a range of angular ranges are used to train the model. The model is able to learn the angular dependency based on a limited angle coverage and to propose a new set of projections to suppress artefacts. High-quality three-dimensional images demonstrate that it is possible to effectively suppress artefacts caused by thermomechanical instability of tool components and objects resulting in motion, by center of rotation misalignment and by inaccuracy in the detector position without additional computational efforts. Data recovery from incomplete sets of projections result in directly corrected projections instead of suppressing artefacts in the final reconstructed images. The proposed methodology has been proven and is demonstrated for a ball bearing sample. The reconstruction results are compared to prior corrections and benchmarked with a commercially available reconstruction software. Compared to conventional approaches in XCT imaging and data analysis, the proposed methodology for the generation of high-quality three-dimensional X-ray images is fully autonomous. The methodology presented here has been proven for high-resolution micro-XCT and nano-XCT, however, is applicable for all length scales.

Three-dimensional rotational angiography in congenital heart disease: Present status and evolving future

Three-dimensional rotational angiography (3D-RA) enables volumetric imaging through rotation of the C-arm of an angiographic system and real-time 3D reconstruction during cardiac catheterization procedures. In the field of congenital heart disease (CHD), 3D-RA has gained considerable traction, owing to its capability for enhanced visualization of spatial relationships in complex cardiac morphologies and real time image guidance in an intricate interventional environment. This review provides an overview of the current applications, strengths, and limitations of 3D-RA acquisition in the management of CHD and potential future directions. In addition, issues of dosimetry, radiation exposure, and optimization strategies will be reviewed. Further implementation of 3D-RA will be driven by patient benefits relative to existing 3D imaging capabilities and fusion techniques balanced against radiation exposure.

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks

Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions more difficult. We propose deep-learning-based measures for coronary motion artifact recognition and quantification in order to assess the diagnostic reliability and image quality of coronary CT angiography images. More specifically, the application, steering and evaluation of motion compensation algorithms can be triggered by these measures. A Coronary Motion Forward Artifact model for CT data (CoMoFACT) is developed and applied to clinical cases with excellent image quality to introduce motion artifacts using simulated motion vector fields. The data required for supervised learning is generated by the CoMoFACT from 17 prospectively ECG-triggered clinical cases with controlled motion levels on a scale of 0-10. Convolutional neural networks achieve an accuracy of 93.3% ± 1.8% for the classification task of separating motion-free from motion-perturbed coronary cross-sectional image patches. The target motion level is predicted by a corresponding regression network with a mean absolute error of 1.12 ± 0.07. Transferability and generalization capabilities are demonstrated by motion artifact measurements on eight additional CCTA cases with real motion artifacts.

Cardiac Motion Correction for Helical CT Scan With an Ordinary Pitch

Cardiac X-ray computed tomography (CT) imaging is still challenging due to the cardiac motion during CT scanning, which leads to the presence of motion artifacts in the reconstructed image. In response, many cardiac X-ray CT imaging algorithms have been proposed, based on motion estimation (ME) and motion compensation (MC), to improve the image quality by alleviating the motion artifacts in the reconstructed image. However, these ME/MC algorithms are mainly based on an axial scan or a low-pitch helical scan. In this paper, we propose a ME/MC-based cardiac imaging algorithm for the data set acquired from a helical scan with an ordinary pitch of around 1.0 so as to obtain the whole cardiac image within a single scan of short time without ECG gating. In the proposed algorithm, a sequence of partial angle reconstructed (PAR) images is generated by using consecutive parts of the sinogram, each of which has a small angular span. Subsequently, an initial 4-D motion vector field (MVF) is obtained using multiple pairs of conjugate PAR images. The 4-D MVF is then refined based on an image quality metric so as to improve the quality of the motion-compensated image. Finally, a time-resolved cardiac image is obtained by performing motion-compensated image reconstruction by using the refined 4-D MVF. Using digital XCAT phantom data sets and a human data set commonly obtained via a helical scan with a pitch of 1.0, we demonstrate that the proposed algorithm significantly improves the image quality by alleviating motion artifacts.

Feasibility and Validity of Printing 3D Heart Models from Rotational Angiography

Rotational angiography (RA) has proven to be an excellent method for evaluating congenital disease (CHD) in the cardiac cath lab, permitting acquisition of 3D datasets with superior spatial resolution. This technique has not been routinely implemented for 3D printing in CHD. We describe our case series of models printed from RA and validate our technique. All patients with models printed from RA were selected. RA acquisitions from a Toshiba Infinix-I system were postprocessed and printed with a Stratasys Eden 260. Two independent observers measured 5-10 points of interest on both the RA and the 3D model. Bland Altman plot was used to compare the measurements on rotational angiography to the printed model. Models were printed from RA in 5 patients (age 2 months-1 year). Diagnoses included (a) coronary artery aneurysm, (b) Glenn shunt, (c) coarctation of the aorta, (d) tetralogy of Fallot with MAPCAs, and (e) pulmonary artery stenosis. There was no significant measurement difference between RA and the printed model (r = 0.990, p < 0.01, Bland Altman p = 0.987). There was also no significant inter-observer variability. The MAPCAs model was referenced by the surgeon intraoperatively and was accurate. Rotational angiography can generate highly accurate 3D models in congenital heart disease, including in small vascular structures. These models can be extremely useful in patient evaluation and management.

Motion compensation for cone-beam CT using Fourier consistency conditions

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.

Four dimensional material movies: High speed phase-contrast tomography by backprojection along dynamically curved paths

We present an approach towards four dimensional (4d) movies of materials, showing dynamic processes within the entire 3d structure. The method is based on tomographic reconstruction on dynamically curved paths using a motion model estimated by optical flow techniques, considerably reducing the typical motion artefacts of dynamic tomography. At the same time we exploit x-ray phase contrast based on free propagation to enhance the signal from micron scale structure recorded with illumination times down to a millisecond (ms). The concept is demonstrated by observing the burning process of a match stick in 4d, using high speed synchrotron phase contrast x-ray tomography recordings. The resulting movies reveal the structural changes of the wood cells during the combustion.

Cardiac Image Reconstruction via Nonlinear Motion Correction Based on Partial Angle Reconstructed Images

Even though the X-ray Computed Tomography (CT) scan is considered suitable for fast imaging, motion-artifact-free cardiac imaging is still an important issue, because the gantry rotation speed is not fast enough compared with the heart motion. To obtain a heart image with less motion artifacts, a motion estimation (ME) and motion compensation (MC) approach is usually adopted. In this paper, we propose an ME/MC algorithm that can estimate a nonlinear heart motion model from a sinogram with a rotation angle of less than 360°. In this algorithm, we first assume the heart motion to be nonrigid but linear, and thereby estimate an initial 4-D motion vector field (MVF) during a half rotation by using conjugate partial angle reconstructed images, as in our previous ME/MC algorithm. We then refine the MVF to determine a more accurate nonlinear MVF by maximizing the information potential of a motion-compensated image. Finally, MC is performed by incorporating the determined MVF into the image reconstruction process, and a time-resolved heart image is obtained. By using a numerical phantom, a physical cardiac phantom, and an animal data set, we demonstrate that the proposed algorithm can noticeably improve the image quality by reducing motion artifacts throughout the image.

Assessing cardiac function from total-variation-regularized 4D C-arm CT in the presence of angular undersampling

Time-resolved tomographic cardiac imaging using an angiographic C-arm device may support clinicians during minimally invasive therapy by enabling a thorough analysis of the heart function directly in the catheter laboratory. However, clinically feasible acquisition protocols entail a highly challenging reconstruction problem which suffers from sparse angular sampling of the trajectory. Compressed sensing theory promises that useful images can be recovered despite massive undersampling by means of sparsity-based regularization. For a multitude of reasons-most notably the desired reduction of scan time, dose and contrast agent required-it is of great interest to know just how little data is actually sufficient for a certain task. In this work, we apply a convex optimization approach based on primal-dual splitting to 4D cardiac C-arm computed tomography. We examine how the quality of spatially and temporally total-variation-regularized reconstruction degrades when using as few as [Formula: see text] projection views per heart phase. First, feasible regularization weights are determined in a numerical phantom study, demonstrating the individual benefits of both regularizers. Secondly, a task-based evaluation is performed in eight clinical patients. Semi-automatic segmentation-based volume measurements of the left ventricular blood pool performed on strongly undersampled images show a correlation of close to 99% with measurements obtained from less sparsely sampled data.

Marker-free motion correction in weight-bearing cone-beam CT of the knee joint

PURPOSE

To allow for a purely image-based motion estimation and compensation in weight-bearing cone-beam computed tomography of the knee joint.

METHODS

Weight-bearing imaging of the knee joint in a standing position poses additional requirements for the image reconstruction algorithm. In contrast to supine scans, patient motion needs to be estimated and compensated. The authors propose a method that is based on 2D/3D registration of left and right femur and tibia segmented from a prior, motion-free reconstruction acquired in supine position. Each segmented bone is first roughly aligned to the motion-corrupted reconstruction of a scan in standing or squatting position. Subsequently, a rigid 2D/3D registration is performed for each bone to each of K projection images, estimating 6 × 4 × K motion parameters. The motion of individual bones is combined into global motion fields using thin-plate-spline extrapolation. These can be incorporated into a motion-compensated reconstruction in the backprojection step. The authors performed visual and quantitative comparisons between a state-of-the-art marker-based (MB) method and two variants of the proposed method using gradient correlation (GC) and normalized gradient information (NGI) as similarity measure for the 2D/3D registration.

RESULTS

The authors evaluated their method on four acquisitions under different squatting positions of the same patient. All methods showed substantial improvement in image quality compared to the uncorrected reconstructions. Compared to NGI and MB, the GC method showed increased streaking artifacts due to misregistrations in lateral projection images. NGI and MB showed comparable image quality at the bone regions. Because the markers are attached to the skin, the MB method performed better at the surface of the legs where the authors observed slight streaking of the NGI and GC methods. For a quantitative evaluation, the authors computed the universal quality index (UQI) for all bone regions with respect to the motion-free reconstruction. The authors quantitative evaluation over regions around the bones yielded a mean UQI of 18.4 for no correction, 53.3 and 56.1 for the proposed method using GC and NGI, respectively, and 53.7 for the MB reference approach. In contrast to the authors registration-based corrections, the MB reference method caused slight nonrigid deformations at bone outlines when compared to a motion-free reference scan.

CONCLUSIONS

The authors showed that their method based on the NGI similarity measure yields reconstruction quality close to the MB reference method. In contrast to the MB method, the proposed method does not require any preparation prior to the examination which will improve the clinical workflow and patient comfort. Further, the authors found that the MB method causes small, nonrigid deformations at the bone outline which indicates that markers may not accurately reflect the internal motion close to the knee joint. Therefore, the authors believe that the proposed method is a promising alternative to MB motion management.

Local motion-compensated method for high-quality 3D coronary artery reconstruction

The 3D reconstruction of coronary artery from X-ray angiograms rotationally acquired on C-arm has great clinical value. While cardiac-gated reconstruction has shown promising results, it suffers from the problem of residual motion. This work proposed a new local motion-compensated reconstruction method to handle this issue. An initial image was firstly reconstructed using a regularized iterative reconstruction method. Then a 3D/2D registration method was proposed to estimate the residual vessel motion. Finally, the residual motion was compensated in the final reconstruction using the extended iterative reconstruction method. Through quantitative evaluation, it was found that high-quality 3D reconstruction could be obtained and the result was comparable to state-of-the-art method.

Coping with real world data: Artifact reduction and denoising for motion-compensated cardiac C-arm CT

PURPOSE

Detailed analysis of cardiac motion would be helpful for supporting clinical workflow in the interventional suite. With an angiographic C-arm system, multiple heart phases can be reconstructed using electrocardiogram gating. However, the resulting angular undersampling is highly detrimental to the quality of the reconstructed images, especially in nonideal intraprocedural imaging conditions. Motion-compensated reconstruction has previously been shown to alleviate this problem, but it heavily relies on a preliminary reconstruction suitable for motion estimation. In this work, the authors propose a processing pipeline tailored to augment these initial images for the purpose of motion estimation and assess how it affects the final images after motion compensation.

METHODS

The following combination of simple, direct methods inspired by the core ideas of existing approaches proved beneficial: (a) Streak reduction by masking high-intensity components in projection domain after filtering. (b) Streak reduction by subtraction of estimated artifact volumes in reconstruction domain. (c) Denoising in spatial domain using a joint bilateral filter guided by an uncompensated reconstruction. (d) Denoising in temporal domain using an adaptive Gaussian smoothing based on a novel motion detection scheme.

RESULTS

Experiments on a numerical heart phantom yield a reduction of the relative root-mean-square error from 89.9% to 3.6% and an increase of correlation with the ground truth from 95.763% to 99.995% for the motion-compensated reconstruction when the authors' processing is applied to the initial images. In three clinical patient data sets, the signal-to-noise ratio measured in an ideally homogeneous region is increased by 37.7% on average. Overall visual appearance is improved notably and some anatomical features are more readily discernible.

CONCLUSIONS

The authors' findings suggest that the proposed sequence of steps provides a clear advantage over an arbitrary sequence of individual image enhancement methods and is fit to overcome the issue of lacking image quality in motion-compensated C-arm imaging of the heart. As for future work, the obtained results pave the way for investigating how accurately cardiac functional motion parameters can be determined with this modality.

Cardiac motion correction based on partial angle reconstructed images in x-ray CT

PURPOSE

Cardiac x-ray CT imaging is still challenging due to heart motion, which cannot be ignored even with the current rotation speed of the equipment. In response, many algorithms have been developed to compensate remaining motion artifacts by estimating the motion using projection data or reconstructed images. In these algorithms, accurate motion estimation is critical to the compensated image quality. In addition, since the scan range is directly related to the radiation dose, it is preferable to minimize the scan range in motion estimation. In this paper, the authors propose a novel motion estimation and compensation algorithm using a sinogram with a rotation angle of less than 360°. The algorithm estimates the motion of the whole heart area using two opposite 3D partial angle reconstructed (PAR) images and compensates the motion in the reconstruction process.

METHODS

A CT system scans the thoracic area including the heart over an angular range of 180° + α + β, where α and β denote the detector fan angle and an additional partial angle, respectively. The obtained cone-beam projection data are converted into cone-parallel geometry via row-wise fan-to-parallel rebinning. Two conjugate 3D PAR images, whose center projection angles are separated by 180°, are then reconstructed with an angular range of β, which is considerably smaller than a short scan range of 180° + α. Although these images include limited view angle artifacts that disturb accurate motion estimation, they have considerably better temporal resolution than a short scan image. Hence, after preprocessing these artifacts, the authors estimate a motion model during a half rotation for a whole field of view via nonrigid registration between the images. Finally, motion-compensated image reconstruction is performed at a target phase by incorporating the estimated motion model. The target phase is selected as that corresponding to a view angle that is orthogonal to the center view angles of two conjugate PAR images. To evaluate the proposed algorithm, digital XCAT and physical dynamic cardiac phantom datasets are used. The XCAT phantom datasets were generated with heart rates of 70 and 100 bpm, respectively, by assuming a system rotation time of 300 ms. A physical dynamic cardiac phantom was scanned using a slowly rotating XCT system so that the effective heart rate will be 70 bpm for a system rotation speed of 300 ms.

RESULTS

In the XCAT phantom experiment, motion-compensated 3D images obtained from the proposed algorithm show coronary arteries with fewer motion artifacts for all phases. Moreover, object boundaries contaminated by motion are well restored. Even though object positions and boundary shapes are still somewhat different from the ground truth in some cases, the authors see that visibilities of coronary arteries are improved noticeably and motion artifacts are reduced considerably. The physical phantom study also shows that the visual quality of motion-compensated images is greatly improved.

CONCLUSIONS

The authors propose a novel PAR image-based cardiac motion estimation and compensation algorithm. The algorithm requires an angular scan range of less than 360°. The excellent performance of the proposed algorithm is illustrated by using digital XCAT and physical dynamic cardiac phantom datasets.

Reconstruction of coronary arteries from X-ray angiography: A review

Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research.

Dose reduction in cone-beam CT scanning for intracranial stent deployment before coil embolization of intracranial wide-neck aneurysms

PURPOSE

Flat panel detector (FD)-equipped angiography machines are increasingly used for neuro-angiographic imaging. During intracranial stent-assisted coil embolization procedures, it is very important to clearly and quickly visualize stent shape after deployment in the vessel. It is necessary to quickly visualize stents by cone-beam computed tomography (CBCT). The aim of this study was to compare CBCTs at 10 and 20 s, and to confirm that this method is useful for neuro-endovascular treatment procedures.

MATERIALS AND METHODS

We treated 30 patients with wide-necked intracranial aneurysms with a flexible, self-expanding neurovascular stent and subsequent aneurysm embolization with platinum micro-coils. We performed the CBCT after stent deployment. We compared the 10 s and 20 s CBCTs, using the full width one-half maximum (FWHM) visualization.

RESULTS

Accurate stent placement with subsequent coil occlusion of the aneurysms was feasible in all patients. Stent struts were clearly visualized on both 10 s and 20 s CBCTs. Importantly, 10 s CBCT can reduce the radiation dose by about 42%, compared with 20 s CBCT. Performing 10 s CBCT with a 14% dilution of the contrast medium may significantly improve image acquisition during stent-assisted coil embolization.

CONCLUSIONS

Reduced-dose, 10 s CBCT can visualize stents in clinical cases, while significantly reducing radiation exposure.

How I do it: Cone-beam CT during transarterial chemoembolization for liver cancer

Cone-beam computed tomography (CBCT) is an imaging technique that provides computed tomographic (CT) images from a rotational scan acquired with a C-arm equipped with a flat panel detector. Utilizing CBCT images during interventional procedures bridges the gap between the world of diagnostic imaging (typically three-dimensional imaging but performed separately from the procedure) and that of interventional radiology (typically two-dimensional imaging). CBCT is capable of providing more information than standard two-dimensional angiography in localizing and/or visualizing liver tumors ("seeing" the tumor) and targeting tumors though precise microcatheter placement in close proximity to the tumors ("reaching" the tumor). It can also be useful in evaluating treatment success at the time of procedure ("assessing" treatment success). CBCT technology is rapidly evolving along with the development of various contrast material injection protocols and multiphasic CBCT techniques. The purpose of this article is to provide a review of the principles of CBCT imaging, including purpose and clinical evidence of the different techniques, and to introduce a decision-making algorithm as a guide for the routine utilization of CBCT during transarterial chemoembolization of liver cancer.

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/.

Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT

The acquisition of data for cardiac imaging using a C-arm computed tomography system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable three-dimensional (3D)/3D registration performed on initial 3D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3D images. In this paper, the sensitivity of the 3D/3D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing combined with the improved total variation algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical phantom data set with and without a catheter and on two porcine models using qualitative and quantitative measures. The quantitative results of the phantom experiments show that if no dense object is present within the scan field of view, the quality of the FDK initial images is sufficient for motion estimation via 3D/3D registration. When a catheter or pacing electrode is present, the shadow of these objects needs to be removed before the initial image reconstruction. An additional bilateral filter shows no major improvement with respect to the final motion-compensated reconstruction quality. The results with respect to image quality of the cathFDK, cathFFDK and FV images are comparable. In conclusion, in terms of computational complexity, the algorithm of choice is the cathFDK algorithm.

Dual-phase cone-beam computed tomography to see, reach, and treat hepatocellular carcinoma during drug-eluting beads transarterial chemo-embolization

The advent of cone-beam computed tomography (CBCT) in the angiography suite has been revolutionary in interventional radiology. CBCT offers 3 dimensional (3D) diagnostic imaging in the interventional suite and can enhance minimally-invasive therapy beyond the limitations of 2D angiography alone. The role of CBCT has been recognized in transarterial chemo-embolization (TACE) treatment of hepatocellular carcinoma (HCC). The recent introduction of a CBCT technique: dual-phase CBCT (DP-CBCT) improves intra-arterial HCC treatment with drug-eluting beads (DEB-TACE). DP-CBCT can be used to localize liver tumors with the diagnostic accuracy of multi-phasic multidetector computed tomography (M-MDCT) and contrast enhanced magnetic resonance imaging (CE-MRI) (See the tumor), to guide intra-arterially guidewire and microcatheter to the desired location for selective therapy (Reach the tumor), and to evaluate treatment success during the procedure (Treat the tumor). The purpose of this manuscript is to illustrate how DP-CBCT is used in DEB-TACE to see, reach, and treat HCC.

Automatic vasculature identification in coronary angiograms by adaptive geometrical tracking

As the uneven distribution of contrast agents and the perspective projection principle of X-ray, the vasculatures in angiographic image are with low contrast and are generally superposed with other organic tissues; therefore, it is very difficult to identify the vasculature and quantitatively estimate the blood flow directly from angiographic images. In this paper, we propose a fully automatic algorithm named adaptive geometrical vessel tracking (AGVT) for coronary artery identification in X-ray angiograms. Initially, the ridge enhancement (RE) image is obtained utilizing multiscale Hessian information. Then, automatic initialization procedures including seed points detection, and initial directions determination are performed on the RE image. The extracted ridge points can be adjusted to the geometrical centerline points adaptively through diameter estimation. Bifurcations are identified by discriminating connecting relationship of the tracked ridge points. Finally, all the tracked centerlines are merged and smoothed by classifying the connecting components on the vascular structures. Synthetic angiographic images and clinical angiograms are used to evaluate the performance of the proposed algorithm. The proposed algorithm is compared with other two vascular tracking techniques in terms of the efficiency and accuracy, which demonstrate successful applications of the proposed segmentation and extraction scheme in vasculature identification.

A simple framework to generate 3D patient-specific model of coronary artery bifurcation from single-plane angiographic images

Although computer-based simulations, such as structural finite element analysis, have proven their usefulness to support procedural planning of coronary stenting, the link between the clinical practice and these engineering techniques is still limited to research test-cases. A key point to further promote such an interaction is to generate in a fast and effective manner the computational grids from the medical images. Hence, the present study proposes a simple framework to generate 3D meshes of coronary bifurcations from a pair of planar angiographic images obtained by X-ray angiography, which is the gold standard technique for the diagnosis of coronary artery stenosis.

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.

Evaluation of interpolation methods for surface-based motion compensated tomographic reconstruction for cardiac angiographic C-arm data

PURPOSE

For interventional cardiac procedures, anatomical and functional information about the cardiac chambers is of major interest. With the technology of angiographic C-arm systems it is possible to reconstruct intraprocedural three-dimensional (3D) images from 2D rotational angiographic projection data (C-arm CT). However, 3D reconstruction of a dynamic object is a fundamental problem in C-arm CT reconstruction. The 2D projections are acquired over a scan time of several seconds, thus the projection data show different states of the heart. A standard FDK reconstruction algorithm would use all acquired data for a filtered backprojection and result in a motion-blurred image. In this approach, a motion compensated reconstruction algorithm requiring knowledge of the 3D heart motion is used. The motion is estimated from a previously presented 3D dynamic surface model. This dynamic surface model results in a sparse motion vector field (MVF) defined at control points. In order to perform a motion compensated reconstruction, a dense motion vector field is required. The dense MVF is generated by interpolation of the sparse MVF. Therefore, the influence of different motion interpolation methods on the reconstructed image quality is evaluated.

METHODS

Four different interpolation methods, thin-plate splines (TPS), Shepard's method, a smoothed weighting function, and a simple averaging, were evaluated. The reconstruction quality was measured on phantom data, a porcine model as well as on in vivo clinical data sets. As a quality index, the 2D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2D ventricle blood pool was quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. For the phantom data set, the normalized root mean square error (nRMSE) and the universal quality index (UQI) were also evaluated in 3D image space.

RESULTS

The quantitative evaluation of all experiments showed that TPS interpolation provided the best results. The quantitative results in the phantom experiments showed comparable nRMSE of ≈0.047 ± 0.004 for the TPS and Shepard's method. Only slightly inferior results for the smoothed weighting function and the linear approach were achieved. The UQI resulted in a value of ≈ 99% for all four interpolation methods. On clinical human data sets, the best results were clearly obtained with the TPS interpolation. The mean contour deviation between the TPS reconstruction and the standard FDK reconstruction improved in the three human cases by 1.52, 1.34, and 1.55 mm. The Dice coefficient showed less sensitivity with respect to variations in the ventricle boundary.

CONCLUSIONS

In this work, the influence of different motion interpolation methods on left ventricle motion compensated tomographic reconstructions was investigated. The best quantitative reconstruction results of a phantom, a porcine, and human clinical data sets were achieved with the TPS approach. In general, the framework of motion estimation using a surface model and motion interpolation to a dense MVF provides the ability for tomographic reconstruction using a motion compensation technique.

Motion-compensated mega-voltage cone beam CT using the deformation derived directly from 2D projection images

This paper presents a novel method for respiratory motion compensated reconstruction for cone beam computed tomography (CBCT). The reconstruction is based on a time sequence of motion vector fields, which is generated by a dynamic geometrical object shape model. The dynamic model is extracted from the 2D projection images of the CBCT. The process of the motion extraction is converted into an optimal 3D multiple interrelated surface detection problem, which can be solved by computing a maximum flow in a 4D directed graph. The method was tested on 12 mega-voltage (MV) CBCT scans from three patients. Two sets of motion-artifact-free 3D volumes, full exhale (FE) and full inhale (FI) phases, were reconstructed for each daily scan. The reconstruction was compared with three other motion-compensated approaches based on quantification accuracy of motion and size. Contrast-to-noise ratio (CNR) was also quantified for image quality. The proposed approach has the best overall performance, with a relative tumor volume quantification error of 3.39 ± 3.64% and 8.57 ± 8.31% for FE and FI phases, respectively. The CNR near the tumor area is 3.85 ± 0.42 (FE) and 3.58 ± 3.33 (FI). These results show the clinical feasibility to use the proposed method to reconstruct motion-artifact-free MVCBCT volumes.

Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: A Simulation and a Phantom Study

Long acquisition times lead to image artifacts in thoracic C-arm CT. Motion blur caused by respiratory motion leads to decreased image quality in many clinical applications. We introduce an image-based method to estimate and compensate respiratory motion in C-arm CT based on diaphragm motion. In order to estimate respiratory motion, we track the contour of the diaphragm in the projection image sequence. Using a motion corrected triangulation approach on the diaphragm vertex, we are able to estimate a motion signal. The estimated motion signal is used to compensate for respiratory motion in the target region, for example, heart or lungs. First, we evaluated our approach in a simulation study using XCAT. As ground truth data was available, a quantitative evaluation was performed. We observed an improvement of about 14% using the structural similarity index. In a real phantom study, using the artiCHEST phantom, we investigated the visibility of bronchial tubes in a porcine lung. Compared to an uncompensated scan, the visibility of bronchial structures is improved drastically. Preliminary results indicate that this kind of motion compensation can deliver a first step in reconstruction image quality improvement. Compared to ground truth data, image quality is still considerably reduced.

Image quality improvements in C-Arm CT (CACT) for liver oncology applications: preliminary study in rabbits

INTRODUCTION

C-Arm CT (CACT) is a new imaging modality in liver oncology therapy that allows for the acquisition of 3D images intra-procedurally. CACT has been used to enhance intra-arterial therapies for the liver by improving lesion detection, avoiding non-target embolization, and allowing for more selective delivery of agents. However, one of the limitations of this technology is image artifacts created by respiratory motion.

PURPOSE

To determine in this preliminary study improvements in image acquisition, motion compensation, and high resolution 3D reconstruction that can improve CACT image quality (IQ).

MATERIAL AND METHODS

Three adult male New Zealand white rabbits were used for this study. First, a control rabbit was used to select the best x-ray acquisition imaging protocol and then two rabbits were implanted with liver tumor to further develop 3D image reconstruction and motion compensation algorithms.

RESULTS

The best IQ was obtained using the low 80 kVp protocol with motion compensated reconstruction with high resolution and fast acquisition speed (60 fps, 5 s/scan, and 312 images).

CONCLUSION

IQ improved by: (1) decreasing acquisition time, (2) applying motion-compensated reconstruction, and (3) high resolution 3D reconstruction. The findings of this study can be applied to future animal studies and eventually could be translated into the clinical environment.

Nonrigid registration-based coronary artery motion correction for cardiac computed tomography

PURPOSE

X-ray computed tomography angiography (CTA) is the modality of choice to noninvasively monitor and diagnose heart disease with coronary artery health and stenosis detection being of particular interest. Reliable, clinically relevant coronary artery imaging mandates high spatiotemporal resolution. However, advances in intrinsic scanner spatial resolution (CT scanners are available which combine nearly 900 detector columns with focal spot oversampling) can be tempered by motion blurring, particularly in patients with unstable heartbeats. As a result, recently numerous methods have been devised to improve coronary CTA imaging. Solutions involving hardware, multisector algorithms, or β-blockers are limited by cost, oversimplifying assumptions about cardiac motion, and populations showing contraindications to drugs, respectively. This work introduces an inexpensive algorithmic solution that retrospectively improves the temporal resolution of coronary CTA without significantly affecting spatial resolution.

METHODS

Given the goal of ruling out coronary stenosis, the method focuses on "deblurring" the coronary arteries. The approach makes no assumptions about cardiac motion, can be used on exams acquired at high heart rates (even over 75 beats/min), and draws on a fast and accurate three-dimensional (3D) nonrigid bidirectional labeled point matching approach to estimate the trajectories of the coronary arteries during image acquisition. Motion compensation is achieved by employing a 3D warping of a series of partial reconstructions based on the estimated motion fields. Each of these partial reconstructions is created from data acquired over a short time interval. For brevity, the algorithm "Subphasic Warp and Add" (SWA) reconstruction.

RESULTS

The performance of the new motion estimation-compensation approach was evaluated by a systematic observer study conducted using nine human cardiac CTA exams acquired over a range of average heart rates between 68 and 86 beats/min. Algorithm performance was based-lined against exams reconstructed using standard filtered-backprojection (FBP). The study was performed by three experienced reviewers using the American Heart Association's 15-segment model. All vessel segments were evaluated to quantify their viability to allow a clinical diagnosis before and after motion estimation-compensation using SWA. To the best of the authors' knowledge this is the first such observer study to show that an image processing-based software approach can improve the clinical diagnostic value of CTA for coronary artery evaluation.

CONCLUSIONS

Results from the observer study show that the SWA method described here can dramatically reduce coronary artery motion and preserve real pathology, without affecting spatial resolution. In particular, the method successfully mitigated motion artifacts in 75% of all initially nondiagnostic coronary artery segments, and in over 45% of the cases this improvement was enough to make a previously nondiagnostic vessel segment clinically diagnostic.

A fully four-dimensional, iterative motion estimation and compensation method for cardiac CT

PURPOSE

To develop a new fully four-dimensional (4D), iterative image reconstruction algorithm for cardiac CT that alternates the following two methods: estimation of a time-dependent motion vector field (MVF) of the heart from image data and reconstruction of images using the estimated MVF and projection data.

METHODS

Volumetric image data at different cardiac phase points were obtained using electrocardiogram-gated CT. Motion estimation (ME) and motion-compensated image reconstruction (MCR) were performed alternately until convergence was achieved. The ME method estimated the cardiac MVF using 4D nonrigid image registration between a cardiac reference phase and all the other phases. The nonrigid deformation of the heart was modeled using cubic B-splines. The cost function consisted of a sum of squared weighted differences and spatial and temporal regularization terms. A nested conjugate gradient optimization algorithm was applied to minimize the cost function and estimate the MVFs. Cardiac images were reconstructed using a motion-tracking algorithm that utilized the MVFs estimated by the ME method. The reconstructed images supplied the input to the ME of the next iteration. The performance of the proposed method was evaluated using four patient data sets acquired with a 64-slice CT scanner. The heart rates of the patients ranged from 52 to 71 beats/min.

RESULTS

Motion artifacts were significantly reduced, and the image quality increased with the number of iterations. Without MCR, the right coronary artery (RCA) was deformed into an arc in axial images of rapid phases. With the proposed method the RCA appeared sharper and was reconstructed similar in shape to the reconstruction at the quiescent phase at mid-diastole. The boundary between the interventricular septum and the right ventricle was also clearer and sharper using the proposed algorithm. The steepness of the transition range at a rapid phase (35% R-R) was increased from 6.8 HU∕pixel to 11.5 HU∕pixel. The ME-MCR algorithm converged in just four iterations.

CONCLUSION

We developed a fully 4D image reconstruction method that alternates ME and MCR algorithms in an iterative fashion. Performance tests using clinical patient data resulted in reduced motion artifacts.

Automatic extraction of 3D dynamic left ventricle model from 2D rotational angiocardiogram

In this paper, we propose an automatic method to directly extract 3D dynamic left ventricle (LV) model from sparse 2D rotational angiocardiogram (each cardiac phase contains only five projections). The extracted dynamic model provides quantitative cardiac function for analysis. The overlay of the model onto 2D real-time fluoroscopic images provides valuable visual guidance during cardiac intervention. Though containing severe cardiac motion artifacts, an ungated CT reconstruction is used in our approach to extract a rough static LV model. The initialized LV model is projected onto each 2D projection image. The silhouette of the projected mesh is deformed to match the boundary of LV blood pool. The deformation vectors of the silhouette are back-projected to 3D space and used as anchor points for thin plate spline (TPS) interpolation of other mesh points. The proposed method is validated on 12 synthesized datasets. The extracted 3D LV meshes match the ground truth quite well with a mean point-to-mesh error of 0.51 +/- 0.11 mm. The preliminary experiments on two real datasets (included a patient and a pig) show promising results too.

ECG gated tomographic reconstruction for 3-D rotational coronary angiography

A method is proposed for 3-D reconstruction of coronary from a limited number of projections in rotational angiography. A Bayesian maximum a posteriori (MAP) estimation is applied with a Poisson distributed projection to reconstruct the 3D coronary tree at a given instant of the cardiac cycle. Several regularizers are investigated L0-norm, L1 and L2 -norm in order to take into account the sparsity of the data. Evaluations are reported on simulated data obtained from a 3D dynamic sequence acquired on a 64-slice GE LightSpeed CT scan. A performance study is conducted to evaluate the quality of the reconstruction of the structures.

Potential role of three-dimensional rotational angiography and C-arm CT for valvular repair and implantation

Imaging modalities utilized in the interventional cardiology suite have seen an impressive evolution and expansion recently, particularly with regard to the recent interest in three-dimensional (3D) imaging. Despite this, the backbone of visualization in the catheterization laboratory remains two-dimensional (2D) X-ray fluoroscopy and cine-angiography. New imaging techniques under development, referred to as three-dimensional rotational angiography (RA) and C-arm CT, hold great promise for improving current device implantation and understanding of cardiovascular anatomy. This paper reviews the evolution of rotational angiography and advanced 3D X-ray imaging applications to interventional cardiology.

Reduction of motion artefacts in on-board cone beam CT by warping of projection images

OBJECTIVE

We describe the development and testing of a motion correction method for flat panel imager-based cone beam CT (CBCT) based on warping of projection images.

METHODS

Markers within or on the surface of the patient were tracked and their mean three-dimensional (3D) position calculated. The two-dimensional (2D) cone beam projection images were then warped before reconstruction to place each marker at the projection from its mean 3D position. The motion correction method was tested using simulated cone beam projection images of a deforming virtual phantom, real CBCT images of a moving breast phantom and clinical CBCT images of a patient with breast cancer and another with pancreatic cancer undergoing radiotherapy.

RESULTS

In phantom studies, the method was shown to greatly reduce motion artefacts in the locality of the radiotherapy target and allowed the true surface shape to be accurately recovered. The breast phantom motion-compensated surface was within 1 mm of the true surface shape for 90% of surface points and greater than 2 mm from the true surface at only 2% of points. Clinical CBCT images showed improved image quality in the locality of the radiotherapy target after motion correction.

CONCLUSION

The proposed method is effective in reducing motion artefacts in CBCT images.