An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.

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Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization

Methods We conducted a multicenter study to examine the accuracy of 64-row, 0.5-mm multidetector CT angiography as compared with conventional coronary angiography in patients with suspected coronary artery disease. Nine centers enrolled patients who underwent calcium scoring and multidetector CT angiography before conventional coronary angiography. In 291 patients with calcium scores of 600 or less, segments 1.5 mm or more in diameter were analyzed by means of CT and conventional angiography at independent core laboratories. Stenoses of 50% or more were considered obstructive. The area under the receiver-operating-characteristic curve (AUC) was used to evaluate diagnostic accuracy relative to that of conventional angiography and subsequent revascularization status, whereas disease severity was assessed with the use of the modified Duke Coronary Artery Disease Index. Results A total of 56% of patients had obstructive coronary artery disease. The patient-based diagnostic accuracy of quantitative CT angiography for detecting or ruling out stenoses of 50% or more according to conventional angiography revealed an AUC of 0.93 (95% confidence interval [CI], 0.90 to 0.96), with a sensitivity of 85% (95% CI, 79 to 90), a specificity of 90% (95% CI, 83 to 94), a positive predictive value of 91% (95% CI, 86 to 95), and a negative predictive value of 83% (95% CI, 75 to 89). CT angiography was similar to conventional angiography in its ability to identify patients who subsequently underwent revascularization: the AUC was 0.84 (95% CI, 0.79 to 0.88) for multidetector CT angiography and 0.82 (95% CI, 0.77 to 0.86) for conventional angiography. A per-vessel analysis of 866 vessels yielded an AUC of 0.91 (95% CI, 0.88 to 0.93). Disease severity ascertained by CT and conventional angiography was well correlated (r = 0.81; 95% CI, 0.76 to 0.84). Two patients had important reactions to contrast medium after CT angiography. Conclusions Multidetector CT angiography accurately identifies the presence and severity of obstructive coronary artery disease and subsequent revascularization in symptomatic patients. The negative and positive predictive values indicate that multidetector CT angiography cannot replace conventional coronary angiography at present. (ClinicalTrials.gov number, NCT00738218.)

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Diagnostic Performance of Coronary Angiography by 64-Row CT

Artifacts can seriously degrade the quality of computed tomographic (CT) images, sometimes to the point of making them diagnostically unusable. To optimize image quality, it is necessary to understand why artifacts occur and how they can be prevented or suppressed. CT artifacts originate from a range of sources. Physics-based artifacts result from the physical processes involved in the acquisition of CT data. Patient-based artifacts are caused by such factors as patient movement or the presence of metallic materials in or on the patient. Scanner-based artifacts result from imperfections in scanner function. Helical and multisection technique artifacts are produced by the image reconstruction process. Design features incorporated into modern CT scanners minimize some types of artifacts, and some can be partially corrected by the scanner software. However, in many instances, careful patient positioning and optimum selection of scanning parameters are the most important factors in avoiding CT artifacts.

Artifacts in CT: Recognition and Avoidance

Introduction Preliminaries Image Reconstruction Image Presentation Key Performance Parameters of a CT Scanner Major Components of CT Scanner Image Artifacts: Appearances, Causes, and Corrections Computer Simulation and Analysis. Helical or Spiral CT Multislice CT X-ray Dose and Reduction Techniques Advanced CT Applications.

Computed Tomography: Principles, Design, Artifacts, and Recent Advances

This scientific statement reviews the scientific data for cardiac computed tomography (CT) related to imaging of coronary artery disease (CAD) and atherosclerosis. Cardiac CT is a CT imaging technique that accounts for cardiac motion, typically through the use of ECG gating. The utility and limitations of generations of cardiac CT systems are reviewed in this statement with emphasis on CT measurement of CAD and coronary artery calcified plaque (CACP) and noncalcified plaque. Successive generations of CT technology have been applied to cardiac imaging beginning in the early 1980s with conventional CT, electron beam CT (EBCT) in 1987, and multidetector CT (MDCT) in 1999. Compared with other imaging modalities, cardiac CT has undergone an accelerated …

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Assessment of coronary artery disease by cardiac computed tomography : A scientific statement from the american heart association committee on cardiovascular imaging and intervention, council on cardiovascular radiology and intervention, and committee on cardiac imaging, council on clinical cardiology

Purpose: While modern clinical CT scanners under normal circumstances produce high quality images, severe artifacts degrade the image quality and the diagnostic value if metal prostheses or other metal objects are present in the field of measurement. Standard methods for metal artifact reduction (MAR) replace those parts of the projection data that are affected by metal (the so-called metal trace or metal shadow) by interpolation. However, while sinogram interpolation methods efficiently remove metal artifacts, new artifacts are often introduced, as interpolation cannot completely recover the information from the metal trace. The purpose of this work is to introduce a generalized normalization technique for MAR, allowing for efficient reduction of metal artifacts while adding almost no new ones. The method presented is compared to a standard MAR method, as well as MAR using simple length normalization.

Methods: In the first step, metal is segmented in the image domain by thresholding. A 3D forward projection identifies the metal trace in the original projections. Before interpolation, the projections are normalized based on a 3D forward projection of a prior image. This prior image is obtained, for example, by a multithreshold segmentation of the initial image. The original rawdata are divided by the projection data of the prior image and, after interpolation, denormalized again. Simulations and measurements are performed to compare normalized metal artifact reduction (NMAR) to standard MAR with linear interpolation and MAR based on simple length normalization.

Results: Promising results for clinical spiral cone-beam data are presented in this work. Included are patients with hip prostheses, dental fillings, and spine fixation, which were scanned at pitch values ranging from 0.9 to 3.2. Image quality is improved considerably, particularly for metal implants within bone structures or in their proximity. The improvements are evaluated by comparing profiles through images and sinograms for the different methods and by inspecting ROIs. NMAR outperforms both other methods in all cases. It reduces metal artifacts to a minimum, even close to metal regions. Even for patients with dental fillings, which cause most severe artifacts, satisfactory results are obtained with NMAR. In contrast to other methods, NMAR prevents the usual blurring of structures close to metal implants if the metal artifacts are moderate.

Conclusions: NMAR clearly outperforms the other methods for both moderate and severe artifacts. The proposed method reliably reduces metal artifacts from simulated as well as from clinical CT data. Computationally efficient and inexpensive compared to iterative methods, NMAR can be used as an additional step in any conventional sinogram inpainting-based MAR method.

Normalized metal artifact reduction (NMAR) in computed tomography

Background—We investigated the applicability and image quality of contrast-enhanced coronary artery visualization by multislice spiral CT using retrospective ECG gating. Methods and Results—Twenty-five patients in sinus rhythm (significant coronary artery stenoses ruled out by invasive angiography) were studied with a multislice spiral CT (Siemens SOMATOM Volume Zoom). In inspiration (mean breathhold, 37 seconds), a volume data set of the heart was acquired (intravenous contrast agent; 4×1-mm slice thickness; 500-ms rotation; table feed, 1.5 mm/360°). Simultaneous recording of the ECG permitted retrospective reconstruction of contiguous cross sections in intervals of 1 mm at any desired interval of the cardiac cycle. The mean duration of the image reconstruction window was 185 ms. Next to 3-dimensional reconstructions of the heart and coronary arteries, multiplanar reconstructions were rendered to determine the visualized length of the coronary arteries, the contrast-to-noise ratio, and the correlation of...

Noninvasive coronary angiography by retrospectively ECG-gated multislice spiral CT.

In modern computed tomography (CT) there is a strong desire to reduce patient dose and/or to improve image quality by increasing spatial resolution and decreasing image noise. These are conflicting demands since increasing resolution at a constant noise level or decreasing noise at a constant resolution level implies a higher demand on x-ray power and an increase of patient dose. X-ray tube power is limited due to technical reasons. We therefore developed a generalized multi-dimensional adaptive filtering approach that applies nonlinear filters in up to three dimensions in the raw data domain. This new method differs from approaches in the literature since our nonlinear filters are applied not only in the detector row direction but also in the view and in the z-direction. This true three-dimensional filtering improves the quantum statistics of a measured projection value proportional to the third power of the filter size. Resolution tradeoffs are shared among these three dimensions and thus are considerably smaller as compared to one-dimensional smoothing approaches. Patient data of spiral and sequential single- and multi-slice CT scans as well as simulated spiral cone-beam data were processed to evaluate these new approaches. Image quality was assessed by evaluation of difference images, by measuring the image noise and the noise reduction, and by calculating the image resolution using point spread functions. The use of generalized adaptive filters helps to reduce image noise or, alternatively, patient dose. Image noise structures, typically along the direction of the highest attenuation, are effectively reduced. Noise reduction values of typically 30%-60% can be achieved in noncylindrical body regions like the shoulder. The loss in image resolution remains below 5% for all cases. In addition, the new method has a great potential to reduce metal artifacts, e.g., in the hip region.

Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT.

In computed tomography there are different situations where reconstruction has to be performed with limited raw data. In the past few years it has been shown that algorithms which are based on compressed sensing theory are able to handle incomplete datasets quite well. As a cost function these algorithms use the l(1)-norm of the image after it has been transformed by a sparsifying transformation. This yields to an inequality-constrained convex optimization problem. Due to the large size of the optimization problem some heuristic optimization algorithms have been proposed in the past few years. The most popular way is optimizing the raw data and sparsity cost functions separately in an alternating manner. In this paper we will follow this strategy and present a new method to adapt these optimization steps. Compared to existing methods which perform similarly, the proposed method needs no a priori knowledge about the raw data consistency. It is ensured that the algorithm converges to the lowest possible value of the raw data cost function, while holding the sparsity constraint at a low value. This is achieved by transferring the step-size determination of both optimization procedures into the raw data domain, where they are adapted to each other. To evaluate the algorithm, we process measured clinical datasets. To cover a wide field of possible applications, we focus on the problems of angular undersampling, data lost due to metal implants, limited view angle tomography and interior tomography. In all cases the presented method reaches convergence within less than 25 iteration steps, while using a constant set of algorithm control parameters. The image artifacts caused by incomplete raw data are mostly removed without introducing new effects like staircasing. All scenarios are compared to an existing implementation of the ASD-POCS algorithm, which realizes the step-size adaption in a different way. Additional prior information as proposed by the PICCS algorithm can be incorporated easily into the optimization process.

https://iopscience.iop.org/article/10.1088/0031-9155/56/6/003/pdf

Improved total variation-based CT image reconstruction applied to clinical data

Subsecond spiral computed tomography (CT) offers great potential for improving heart imaging. The new multi-row detector technology adds significantly to this potential. We therefore developed and validated dedicated cardiac reconstruction algorithms for imaging the heart with subsecond multi-slice spiral CT utilizing electrocardiogram (ECG) information. The single-slice cardiac z-interpolation algorithms 180 degrees CI and 180 degrees CD [Med. Phys. 25, 2417-2431 (1998)] were generalized to allow imaging of the heart for M-slice scanners. Two classes of algorithms were investigated: 180 degrees MCD (multi-slice cardio delta), a partial scan reconstruction of 180 degrees + delta data with a or = 70 min(-1) the partial scan approach 180 degrees MCD yields unsatisfactory results as compared to 180 degrees MCI. Our theoretical considerations show that a freely selectable scanner rotation time chosen as a function of the patient's heart rate, would further improve the relative temporal resolution and thus further reduce motion artifacts. In our case an additional 0.6 s mode besides the available 0.5 s mode would be very helpful. Moreover, if technically feasible, lower rotation times such as 0.3 s or even less would result in improved image quality. The use of multi-slice techniques for cardiac CT together with the new z-interpolation methods improves the quality of heart imaging significantly. The high temporal resolution of 180 degrees MCI is adequate for spatial and temporal tracking of anatomic structures of the heart (4D reconstruction).

http://www.imp.uni-erlangen.de/pdfs/Cardio_MedPhys082000.pdf

Marc Kachelrieß

Papers

Normalized metal artifact reduction (NMAR) in computed tomography

Noninvasive coronary angiography by retrospectively ECG-gated multislice spiral CT.

Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT.

Improved total variation-based CT image reconstruction applied to clinical data

ECG-correlated image reconstruction from subsecond multi-slice spiral CT scans of the heart.