Misregistration Problem Research Articles

An end-to-end anti-shaking multi-focus image fusion approach

In this paper, an end-to-end multi-focus fusion network is proposed to solve the mis-registration problem of the captured images. This paper takes the shaking and camera breathing effects that usually exist when acquiring multiple partially focused images into account. The new network consists of a homography pre-correction module, a deformable convolutional fine-correction module, an attention based fusion module and a reconstruction module. The homography pre-correction module is proposed to achieve a coarse correction for multi-focus images. The deformable convolution fine-correction module enables the fusion algorithm to achieve an effective fine alignment for small shaking in multi-focus images. The attention based fusion module and the image reconstruction module are proposed to achieve high definition fusion results. This paper produces a dedicated shaking dataset for training and testing. The algorithm’s superior performance in depth-of-field extension and generalisation to different scenes can be seen through sufficient experiments on test datasets, real scenes and image sequences. The ablation experiments also demonstrate the necessity of each module in the algorithm.

Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection

Due to the periodicity of circuit boards, the registration algorithm based on keypoints is less robust in circuit board detection and is prone to misregistration problems. In this paper, the binary neighborhood coordinate descriptor (BNCD) is proposed and applied to circuit board image registration. The BNCD consists of three parts: neighborhood description, coordinate description, and brightness description. The neighborhood description contains the grayscale information of the neighborhood, which is the main part of BNCD. The coordinate description introduces the actual position of the keypoints in the image, which solves the problem of inter-period matching of keypoints. The brightness description introduces the concept of bright and dark points, which improves the distinguishability of BNCD and reduces the calculation amount of matching. Experimental results show that in circuit board image registration, the matching precision rate and recall rate of BNCD is better than that of classic algorithms such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF), and the calculation of descriptors takes less time.

Tensor-based keypoint detection and switching regression model for relative radiometric normalization of bitemporal multispectral images

ABSTRACT In some remote sensing applications, such as unsupervised change detection, bitemporal multispectral images must be first aligned/harmonized radiometrically. For doing so, Many Relative Radiometric Normalization (RRN) algorithms exist; however, most suffer from misregistration problems and can only operate on geo/co-registered image pairs, while unregistered multispectral pairs are required. To tackle this situation, keypoint-based RRN methods were introduced, which can radiometrically calibrate unregistered/registered image pairs using keypoint matching algorithms. However, they ignore the spatial and spectral characteristics of spectral bands of input images, resulting in potential RRN errors. They also employ a linear mapping function for RRN modelling, which can not handle non-linear radiometric distortions. To address these limitations, this paper proposes a robust algorithm for RRN of bitemporal multispectral images, using a new extension of SURF detector for multispectral images, namely the Weighted Spectral Structure Tensor SURF (WSST-SURF), and a flexible Switching Regression (SR) model. Taking advantage of the tensor theory, WSST-SURF efficiently preserves both spatial and spectral information distributed over all bands of multispectral images for keypoint detection, resulting in extracting reliable inliers (or keypoints) for RRN. An adaptive SR model is introduced based on the normalized mutual information, accurately approximating a linear/non-linear relationship between inliers in multispectral images. Six unregistered multispectral image pairs captured by inter/intra remote sensing sensors were employed to validate the efficacy of the proposed method. The results indicate that adopting spectral tensor-based SURF methods in the RRN process exhibits better local and global performance than using the original SURF. Furthermore, the proposed method outperforms the existing conventional RRN methods in terms of accuracy and visual quality, indicating its competence for RRN of bitemporal multispectral images with high illumination, viewpoint, and scale differences.

Thoracic hybrid PET/CT registration using improved hybrid feature intensity multimodal demon

PET/CT images are commonly used for cancer detection and management. In order to obtain high accuracy of tumour interpretation as well as the following treatment planning, accurate registration of these two is highly important. The use of hybrid PET/CT scanner to physically co-register these images does not fully solve the nonlinear misregistration problem due to physiological motions such as breathing and cardiac motions. This paper proposes an improved hybrid feature intensity multimodal demon registration algorithm to register PET/CT images acquired from hybrid PET/CT scanner. In basis, the method comprises of 3 major steps: PET sinogram filtering based on 3D hybrid mean-median filter; segmentation of structures from both PET/CT images (the lungs, the heart and the tumour) and registration of PET/CT images using feature intensity multimodal demon method. The segmented structures acted as “features” to be integrated in the registration process in which an improved intensity based multimodal demon registration was used. The improved multimodal demon used a combination of sum of conditional variations (SCV) and multimodality independent neighbourhood descriptive (MIND) similarity measures. Overall, the proposed hybrid feature intensity multimodal registration method was tested on the simulated NCAT PET/CT images and 21 clinical hybrid PET/CT datasets. Experimental results showed that the combination of SCV and MIND similarity measures produced the best registration result for PET/CT misregistration problem. The success of the registration of the patient datasets was validated through improved lung volume overlap between the PET and the CT lung post registration according to Dice coefficient as well as Hausdorff distance score. The registration method increased the Dice and the Hausdorff distance measures by 4.46% and 22.38% in average respectively after registration in which the registration improvement is more than double as achieved by FFD and previous method. It is concluded that our proposed PET/CT registration algorithm is better than the standard FFD registration technique; thus has the potential to be utilised in clinical application.

Polarimetric Channel Misregistration Evaluation for the GaoFen-3 QPSI Mode

This letter presents two main contributions to the data quality assessment of China’s new GaoFen-3 radar satellite. First, we observed a half-pixel misregistration between the horizontal (H) and vertical (V) transmitting channels in the azimuth direction. This was determined by investigating the corner reflector (CR) response of some stripmap products of GaoFen-3 quad-pol stripmap (QPSI) mode. Second, to check whether the azimuth misregistration exists in different beams of QPSI mode, we improved the RADARSAT-2 channel checking method as a tool and evaluated more than 300 stripmap scenes. This letter confirms that the half-pixel misregistration problem, which can cause about 10% decoherence in the co-pol and cross-pol channel correlation coefficients, is common in the GaoFen-3 stripmap products of QPSI mode. Furthermore, the improved method can be considered as an effective way to fix the misregistration problem.

Building Change Detection Improvement Using Topographic Correction Models

In the change detection application of remote sensing, commonly the variation in the brightness values of the pixels/objects in bi-temporal image is used as an indicator for detecting changes. However, there exist effects, other than a change in the objects that can cause variations in the brightness values. One of the effects is the illumination difference on steep surfaces mainly steeproofs of houses in very high resolution images, specifically in off-nadir images. This can introduce the problem of false change detection results. This problem becomes more serious in images with different view-angles. In this study, we propose a methodology to improve the building change detection accuracy using imagery taken under different illumination conditions and different view-angles. This is done by using the Patch-Wise Co-Registration (PWCR) method to overcome the misregistration problem caused by view-angle difference and applying Topographic Correction (TC) methods on pixel intensities to attenuate the effect of illumination angle variation on the building roofs. To select a proper TC method, four of the most widely used correction methods, namely C-correction, Minnaert, Enhanced Minnaert (for slope), and Cosine Correction are evaluated in this study. The results proved that the proposed methodology is capable to improve the change detection accuracy. Specifically, the correction using the C-correction and Enhanced Minnaert improved the change detection accuracy by around 35% in an area with a large number of steep-roof houses imaged under various solar angles.

RPC-Based Coregistration of VHR Imagery for Urban Change Detection

In urban change detection, coregistration between bi-temporal Very High Resolution (vhr) images taken from different viewing angles, especially from high off-nadir angles, is very challenging. The relief displacements of elevated objects in such images usually lead to significant misregistration that negatively affects the accuracy of change detection. This paper presents a novel solution, called Patch-Wise CoRegistration (pwcr), that can overcome the misregistration problem caused by viewing angle difference and accordingly improve the accuracy of urban change detection. The pwcr method utilizes a Digital Surface Model (dsm) and the Rational Polynomial Coefficients (rpcs) of the images to find corresponding points in a bi-temporal image set. The corresponding points are then used to generate corresponding patches in the image set. To prove that the pwcr method can overcome the misregistration problem and help achieving accurate change detection, two change detection criteria are tested and incorporated into a change detection framework. Experiments on four bi-temporal image sets acquired by Ikonos, GeoEye-1, and Worldview-2 satellites from different viewing angles show that the pwcr method can achieve highly accurate image patch coregistration (up to 80 percent higher than traditional coregistration for elevated objects), so that the change detection framework can produce accurate urban change detection results (over 90 percent).

RPC-Based Coregistration of VHR Imagery for Urban Change Detection

In urban change detection, coregistration between bi-temporal Very High Resolution (VHR) images taken from different viewing angles, especially from high off-nadir angles, is very challenging. The relief displacements of elevated objects in such images usually lead to significant misregistration that negatively affects the accuracy of change detection. This paper presents a novel solution, called Patch-Wise CoRegistration (PWCR), that can overcome the misregistration problem caused by viewing angle difference and accordingly improve the accuracy of urban change detection. The PWCR method utilizes a Digital Surface Model (DSM) and the Rational Polynomial Coefficients (RPCs) of the images to find corresponding points in a bi-temporal image set. The corresponding points are then used to generate corresponding patches in the image set. To prove that the PWCR method can overcome the misregistration problem and help achieving accurate change detection, two change detection criteria are tested and incorporated into a change detection framework. Experiments on four bi-temporal image sets acquired by Ikonos, GeoEye-1, and Worldview-2 satellites from different viewing angles show that the PWCR method can achieve highly accurate image patch coregistration (up to 80 percent higher than traditional coregistration for elevated objects), so that the change detection framework can produce accurate urban change detection results (over 90 percent).

Cine viability magnetic resonance imaging of the heart without increased scan time

Cardiac magnetic resonance imaging (MRI) provides information about myocardial morphology, function, and viability from cine, tagged, and late gadolinium enhancement (LGE) images, respectively. While the cine and tagged images are acquired in a time-resolved fashion, the LGE images are acquired at a single timeframe. The purpose of this work is to develop a method for generating cine LGE images without additional scan time. The motion field is extracted from the tagged images, and is then used to guide the deformation of the infarcted region from the acquired LGE image at the acquired timeframe to any other timeframe. Major techniques for motion estimation, including harmonic phase (HARP) and optical flow analysis, are tested in this work for motion estimation. The proposed method is tested on numerical phantom and images from four human subjects. The generated cine LGE images showed both viability and wall motion information in the same set of images without additional scan time or image misregistration problems. The band-pass optical flow analysis resulted in the most accurate motion estimation compared to other methods, especially HARP, which fails to track points at the myocardial boundary. Infarct transmurality from the generated images showed good agreement with myocardial strain, and wall thickening showed good agreement with that measured from conventional cine images. In conclusion, the developed technique allows for generating cine LGE images that enable simultaneous display of wall motion and viability information. The generated images could be useful for estimating myocardial contractility reserve and for treatment prognosis.

Real-Time Mobile 3D Temperature Mapping

The ability to measure surface temperature and represent it on a metrically accurate 3D model has proven applications in many areas, such as medical imaging, building energy auditing, and search and rescue. A system is proposed that enables this task to be performed with a handheld sensor, and for the first time with results able to be visualized and analyzed in real time. A device comprising a thermal-infrared camera and range sensor is calibrated geometrically and used for data capture. The device is localized using a combination of iterative closest point and video-based pose estimation from the thermal-infrared video footage, which is shown to reduce the occurrence of failure modes. Furthermore, the problem of misregistration, which can introduce severe distortions in assigned surface temperatures is avoided through the use of a risk-averse neighborhood weighting mechanism. Results demonstrate that the system is more stable and accurate than previous approaches, and can be used to accurately model complex objects and environments for practical tasks.

Cine delayed hyper-enhancement CMR imaging without additional scan time

Background A typical CMR exam includes cine, tagging, first-pass, and delayed-hyper-enhancement (DHE) imaging. The DHE image is usually acquired at a single timeframe. Nevertheless, obtaining cine DHE images would provide both viability and functional information in single set of images without misregistration problems for better image interpretation. However, repeated breath-hold scans are required to obtain cine DHE images, which increases scan-time with varying level of tissue enhancement with time. In this work, we propose a new technique for generating cine DHE images based on the acquired DHE image and motion field derived from the corresponding tagged images. Methods A mid-diastolic short-axis DHE image and the corresponding set of tagged images were acquired on 3T-MRI scanner. The corresponding cine images were acquired for reference. Image analysis has two steps: 1) apply the Lucas-Kanade optical-flow algorithm to the tagged images to extract the motion field of the myocardium through the cardiac cycle; 2) apply the measured motion field to the known (acquired) DHE image to generate the unknown DHE images. The infarcted regions were identified in the DHE images using the full-width at halfmaximum method. Radial thickening was measured from both cine and DHE images to evaluate the generated images. Infarction transmurality, circumferential strain, and radial thickening were measured from the DHE, tagged, and cine images, respectively, and the relationships between the generated parameters were studied using regression and correlation analyses. Results Figure 1 shows tagged and DHE images. Figure 2 shows transmurality and strain at different segments. Peak infarction transmurality (circumferential strain) were 91% (0%) and 0% (-14%) in the infarcted and lateral regions, respectively. Myocardial thickening, calculated from the DHE images, showed good correlation with the results from the gold-standard cine images (r = 0.72). Further, there was an excellent agreement between ED and ES infarction transmurality calculated from the DHE images (r = 0.998). Infarction transmurality showed good correlation with strain (r = 0.81; y = 0.19x+11.27). Conclusions The developed technique generates composite images that show both viability and functional information in one set of images, which is important for prognostic evaluation of the patient’s condition, e.g. determining candidates for revascularization. The generated images could be also useful for differentiating between gadolinium recess in the muscle and myocardial scar, usually conducted by comparing cine and DHE images side-by-side. Future work includes further improvement of the optical-flow algorithm to minimize error propagation with time, and implementing the algorithm on large number of patients with different degrees of infarction.

Accelerated and navigator-gated look-locker imaging for cardiac T1 Estimation (ANGIE) with reduced motion artifact

Background T1 mapping of the left ventricle is routinely performed using Modified Look-Locker Inversion Recovery (MOLLI) [1] in a variety of disease settings [2]. MOLLI requires relatively long breathholds, and consequently sequences of MOLLI images may be misregistered due to respiratory drift. We sought to develop a T1 mapping sequence that would not require breathholding and would not have misregistration problems. We used kspace segmentation and navigator gating to eliminate respiratory artifact, and compressed sensing [3] (CS) to accelerate image acquisition. CS utilizes undersampling in k-t space along with nonlinear iterative image reconstruction. CS is well-suited to Look-Locker imaging because these images, which vary smoothly from one inversion time to the next, exhibit time-domain sparsity.

Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure

Finite depth-of-field poses a problem in light optical imaging systems since the objects present outside the range of depth-of-field appear blurry in the recorded image. Effective depth-of-field of a sensor can be enhanced considerably without compromising the quality of the image by combining multi-focus images of a scene. This paper presents a block-based algorithm for multi-focus image fusion. In general, finding a suitable block-size is a problem in block-based methods. A large block is more likely to contain portions from both focused and defocused regions. This may lead to selection of considerable amount of defocused regions. On the other hand, small blocks do not vary much in relative contrast and hence difficult to choose from. Moreover, small blocks are more affected by mis-registration problems. In this work, we present a block-based algorithm which do not use a fixed block-size and rather makes use of a quad-tree structure to obtain an optimal subdivision of blocks. Though the algorithm starts with blocks, it ultimately identifies sharply focused regions in input images. The algorithm is simple, computationally efficient and gives good results. A new focus-measure called energy of morphologic gradients is introduced and is used in the algorithm. It is comparable with other focus measures viz.energy of gradients, variance, Tenengrad, energy of Laplacian and sum modified Laplacian. The algorithm is robust since it works with any of the above focus measures. It is also robust against pixel mis-registration. Performance of the algorithm has been evaluated by using two different quantitative measures.

Snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS)

We present a snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS) for eye imaging applications. The resulting system is capable of simultaneously acquiring 48 spectral channel images in the range 470 nm–650 nm with frame rate at 5.2 fps. The spatial sampling of each measured spectral scene is 350 × 350 pixels. The advantages of this snapshot device are elimination of the eye motion artifacts and pixel misregistration problems in traditional scanning-based hyperspectral retinal cameras, and real-time imaging of oxygen saturation dynamics with sub-second temporal resolution. The spectral imaging performance is demonstrated in a human retinal imaging experiment in vivo. The absorption spectral signatures of oxy-hemoglobin and macular pigments were successfully acquired by using this device.

Analysis of ASTER Emissivity Product Over an Arid Area in Southern New Mexico, USA

The accuracy of thermal infrared emissivities derived from Advanced Spaceborne Thermal Emission and Reflectance radiometer (ASTER) was assessed in an arid area in southern New Mexico, which includes the White Sands National Monument (WSNM) during 2006-2008. ASTER emissivities retrieved by the temperature and emissivity separation (TES) algorithm were directly compared with laboratory measurements of samples from WSNM. Good agreement was found for the high spectral contrast of gypsum and for the low spectral contrast of water bodies. Furthermore, the day/night consistency of ASTER emissivities was checked, and day/night emissivity differences lower than ±0.013 were observed. However, unexpected emissivity values larger than unity were retrieved by ASTER/TES at 8-9 μm , mainly concentrated over lava flow surfaces. The thermal infrared radiance image data with 90-m spatial resolution was resized to 180 m for the analysis in this paper to avoid misregistration problems due to terrain topography. Emissivity temporal variations were analyzed and attributed, in some cases, to the soil moisture variations. This was particularly noted after periods of high precipitation which occurred in August 2006. The results presented here show the high emissivity accuracy achievable with ASTER data in ideal atmospheric conditions and discuss some problems which should be considered in the future, as the retrieval of overestimated emissivity values.

Validation of the Atmospheric Chemistry Experiment by noncoincident MkIV balloon profiles

[1] We have compared volume mixing ratio profiles of atmospheric trace gases measured by the Atmospheric Chemistry Experiment (ACE) version 2.2 and the MkIV solar occultation Fourier transform infrared spectrometers. These gases are H2O, O3, N2O, CO, CH4, HNO3, HF, HCl, OCS, ClONO2, HCN, CH3Cl, CF4, CCl2F2, CCl3F, COF2, CHF2Cl, and SF6. Due to the complete lack of close spatiotemporal coincidences between the ACE occultations and the MkIV balloon flights, we used potential temperatures and equivalent latitudes from analyzed meteorological fields to find comparable ACE and MkIV profiles. The results show excellent agreement for CH4, N2O, and other long-lived gases but slightly poorer agreement for shorter-lived species like CO, O3, and HCN. For example, in the upper troposphere (∼400–650 K), maximum differences between MkIV and ACE are 2.4% for CH4, 1.7% for N2O, −12.4% for CO, −15.9% for O3, and −5.6% for HCN. In the lower stratosphere (∼650–900 K), maximum MkIV-ACE differences are 7.6% for CH4, 14.1% for N2O, 7.3% for CO, −9.2% for O3, and 31.5% for HCN. Apart from a small vertical misregistration problem, the overall agreement between MkIV and ACE is very good.

구름을 포함한 푸쉬브룸 스캐너 영상의 밴드간 상호등록

푸쉬브룸 스캐너 PAN영상과 MS영상 사이에는 오프셋이 존재하며 서로 다른 시간과 각도로 촬영하고 있다. 이로 인하여 구름과 같이 빠르게 움직이는 물체는 오정합 점들을 생성하며 이는 PAN영상과 MS영상간의 상호영상등록의 오차를 발생시킨다. 특히 구름(안개 및 스모그 포함)이 있는 기상조건 하에서 얻어진 위성영상은 구름에 의해 가려진 지형정보를 추출하는 데 있어 많은 문제를 야기하기 때문에 정확한 영상등록을 위해서는 효과적인 구름 탐지 및 제거 알고리즘이 필요하다. 구름 제거를 위한 관련 연구들은 크게 다음과 같은 세 가지로 나누어지는데 (1) 구름 검출 알고리즘을 통해 구름으로 여겨지는 영역을 분리하여 구름영역을 제거하는 방법 (2) 다중분광영상의 밴드정보를 이용하는 방법 (3) 다시기 영상정보를 이용하는 방법들로 나눌 수 있다. 본 연구에서는 구름 지역을 제거하는 방법과 다시기영상을 이용하는 방법을 사용하여 구름이 포함된 푸쉬브룸 스캐너 밴드간 영상등록의 정확도를 비교, 분석하였다. Since PAN(panchromatic) and MS(multispectral) imagery of pushbroom scanner have the offset between PAN and MS CCD(charge coupled device) in the focal plane, PAN and MS images are acquired at different time and angle. Since clouds are fast moving objects, they should lead mis-registration problem with wrong matching points on clouds. The registration of cloudy imagery to recognize and remove the contamination of clouds can be categorized into three classes: (1) cloud is considered as nose and removed (2) employing multi-spectral imagery (3) using multi-temporal imagery. In this paper, method (1) and (3) are implemented and analysed with cloudy pushbroom scanner images.

Combined functional and viability cardiac MR imaging in a single breathhold

The combination of cardiac viability and functional information enhances the identification of different heart tissues in the setting of ischemic heart disease. A method has recently been proposed for obtaining black-blood delayed-enhancement (DE) viability images using the stimulated-echo acquisition mode (STEAM) MRI pulse sequence in a single short breathhold. The method was validated against conventional inversion-recovery (IR) DE images for identifying regions of myocardial infarction (MI). The method was based on the acquisition of three consecutive images of the same anatomical slice. One image has T(1)-weighted contrast in which infarction appears bright. The two other images are used to construct an anatomical image of the heart, which is combined with the first image to produce a black-blood viability image. However, using appropriate modulation and demodulation frequencies, the latter two images bear useful information about myocardial deformation that results in a cardiac strain-encoding (SENC) functional image. In this work, a method is proposed for obtaining three consecutive SENC images in a single acquisition that can be combined to produce a composite image of the heart, which shows both functional and viability information. The proposed technique reduces scan time by one-half, compared with separate acquisitions of functional and viability images, and alleviates misregistration problems caused by separate breathholds.

Simulation study of respiratory-induced errors in cardiac positron emission tomography/computed tomography

Heart disease is a leading killer in Canada and positron emission tomography (PET) provides clinicians with in vivo metabolic information for diagnosing heart disease. Transmission data are usually acquired with 68Ge, although the advent of PET/CT scanners has made computed tomography (CT) an alternative option. The fast data acquisition of CT compared to PET may cause potential misregistration problems, leading to inaccurate attenuation correction (AC). Using Monte Carlo simulations and an anthropomorphic dynamic computer phantom, this study determines the magnitude and location of respiratory-induced errors in radioactivity uptake measured in cardiac PET/CT. A homogeneous tracer distribution in the heart was considered. The AC was based on (1) a time-averaged attenuation map, (2) CT maps from a single phase of the respiratory cycle, and (3) CT maps phase matched to the emission data. Circumferential profiles of the heart uptake were compared and differences of up to 24% were found between the single-phase CT-AC method and the true phantom values. Simulation results were supported by a PET/CT canine study which showed differences of up to 10% in the heart uptake in the lung-heart boundary region when comparing 68Ge- to CT-based AC with the CT map acquired at end inhalation.

A fast 3D approach for coronary MRA.

Two-dimensional (2D)-breath-hold coronary magnetic resonance angiography (MRA) has been shown to be a fast and reliable method to depict the proximal coronary arteries. Recent developments, however, allow for free-breathing navigator gated and navigator corrected three-dimensional (3D) coronary MRA. These 3D approaches have potential for improved signal-to-noise ratio (SNR) and allow for the acquisition of adjacent thin slices without the misregistration problems known from 2D approaches. Still, a major impediment of a 3D acquisition is the increased scan time. The purpose of this study was the implementation of a free-breathing navigator gated and corrected ultra-fast 3D coronary MRA technique, which allows for scan times of less than 5 minutes. Twelve healthy adult subjects were examined in the supine position using a navigator gated and corrected ECG triggered ultra-fast 3D interleaved gradient echo planar imaging sequence (TFE-EPI). A 3D slab, consisting of 20 slices with a reconstructed slice thickness of 1.5 mm, was acquired with free-breathing. The diastolic TFE-EPI acquisition block was preceded by a T2prep pre-pulse, a diaphragmatic navigator pulse, and a fat suppression pre-pulse. With a TR of 19 ms and an effective TE of 5.4 ms, the duration of the data acquisition window duration was 38 ms. The in-plane spatial resolution was 1.0-1.3 mm*1.5-1.9 mm. In all cases, the entire left main (LM) and extensive portions of the left anterior descending (LAD) and right coronary artery (RCA) could be visualized with an average scan time for the entire 3D-volume data set of 2:57 +/- 0:51 minutes. Average contiguous vessel length visualized was 53 +/- 11 mm (range: 42 to 75 mm) for the LAD and 84 +/- 14 mm (range: 62 to 112 mm) for the RCA. Contrast-to-noise between coronary blood and myocardium was 5.0 +/- 2.3 for the LM/LAD and 8.0 +/- 2.9 for the RCA, resulting in an excellent suppression of myocardium. We present a new approach for free-breathing 3D coronary MRA, which allows for scan times superior to corresponding 2D coronary MRA approaches, and which takes advantage of the enhanced SNR of 3D acquisitions and the post-processing benefits of thin adjacent slices. The robust image quality and the short average scanning time suggest that this approach may be useful for screening the major coronary arteries or identification of anomalous coronary arteries. J. Magn. Reson. Imaging 1999;10:821-825.