Automatic 3D registration of dynamic stress and rest (82)Rb and flurpiridaz F 18 myocardial perfusion PET data for patient motion detection and correction.

PURPOSE: The authors aimed to develop an image-based registration scheme to detect and correct patient motion in stress and rest cardiac positron emission tomography (PET)/CT images. The patient motion correction was of primary interest and the effects of patient motion with the use of flurpiridaz F 18 and (82)Rb were demonstrated. METHODS: The authors evaluated stress/rest PET myocardial perfusion imaging datasets in 30 patients (60 datasets in total, 21 male and 9 female) using a new perfusion agent (flurpiridaz F 18) (n = 16) and (82)Rb (n = 14), acquired on a Siemens Biograph-64 scanner in list mode. Stress and rest images were reconstructed into 4 ((82)Rb) or 10 (flurpiridaz F 18) dynamic frames (60 s each) using standard reconstruction (2D attenuation weighted ordered subsets expectation maximization). Patient motion correction was achieved by an image-based registration scheme optimizing a cost function using modified normalized cross-correlation that combined global and local features. For comparison, visual scoring of motion was performed on the scale of 0 to 2 (no motion, moderate motion, and large motion) by two experienced observers. RESULTS: The proposed registration technique had a 93% success rate in removing left ventricular motion, as visually assessed. The maximum detected motion extent for stress and rest were 5.2 mm and 4.9 mm for flurpiridaz F 18 perfusion and 3.0 mm and 4.3 mm for (82)Rb perfusion studies, respectively. Motion extent (maximum frame-to-frame displacement) obtained for stress and rest were (2.2 ± 1.1, 1.4 ± 0.7, 1.9 ± 1.3) mm and (2.0 ± 1.1, 1.2 ±0 .9, 1.9 ± 0.9) mm for flurpiridaz F 18 perfusion studies and (1.9 ± 0.7, 0.7 ± 0.6, 1.3 ± 0.6) mm and (2.0 ± 0.9, 0.6 ± 0.4, 1.2 ± 1.2) mm for (82)Rb perfusion studies, respectively. A visually detectable patient motion threshold was established to be ≥2.2 mm, corresponding to visual user scores of 1 and 2. After motion correction, the average increases in contrast-to-noise ratio (CNR) from all frames for larger than the motion threshold were 16.2% in stress flurpiridaz F 18 and 12.2% in rest flurpiridaz F 18 studies. The average increases in CNR were 4.6% in stress (82)Rb studies and 4.3% in rest (82)Rb studies. CONCLUSIONS: Fully automatic motion correction of dynamic PET frames can be performed accurately, potentially allowing improved image quantification of cardiac PET data.

Motion frozen (18)F-FDG cardiac PET.

BACKGROUND: PET reconstruction incorporating spatially variant 3D Point Spread Function (PSF) improves contrast and image resolution. "Cardiac Motion Frozen" (CMF) processing eliminates the influence of cardiac motion in static summed images. We have evaluated the combined use of CMF- and PSF-based reconstruction for high-resolution cardiac PET. METHODS: Static and 16-bin ECG-gated images of 20 patients referred for (18)F-FDG myocardial viability scans were obtained on a Siemens Biograph-64. CMF was applied to the gated images reconstructed with PSF. Myocardium to blood contrast, maximum left ventricle (LV) counts to defect contrast, contrast-to-noise (CNR) and wall thickness with standard reconstruction (2D-AWOSEM), PSF, ED-gated PSF, and CMF-PSF were compared. RESULTS: The measured wall thickness was 18.9 ± 5.2 mm for 2D-AWOSEM, 16.6 ± 4.5 mm for PSF, and 13.8 ± 3.9 mm for CMF-PSF reconstructed images (all P < .05). The CMF-PSF myocardium to blood and maximum LV counts to defect contrasts (5.7 ± 2.7, 10.0 ± 5.7) were higher than for 2D-AWOSEM (3.5 ± 1.4, 6.5 ± 3.1) and for PSF (3.9 ± 1.7, 7.7 ± 3.7) (CMF vs all other, P < .05). The CNR for CMF-PSF (26.3 ± 17.5) was comparable to PSF (29.1 ± 18.3), but higher than for ED-gated dataset (13.7 ± 8.8, P < .05). CONCLUSION: Combined CMF-PSF reconstruction increased myocardium to blood contrast, maximum LV counts to defect contrast and maintained equivalent noise when compared to static summed 2D-AWOSEM and PSF reconstruction.

Automated algorithm for atlas-based segmentation of the heart and pericardium from non-contrast CT.

Automated segmentation of the 3D heart region from non-contrast CT is a pre-requisite for automated quantification of coronary calcium and pericardial fat. We aimed to develop and validate an automated, efficient atlas-based algorithm for segmentation of the heart and pericardium from non-contrast CT.A co-registered non-contrast CT atlas is first created from multiple manually segmented non-contrast CT data. Non-contrast CT data included in the atlas are co-registered to each other using iterative affine registration, followed by a deformable transformation using the iterative demons algorithm; the final transformation is also applied to the segmented masks. New CT datasets are segmented by first co-registering to an atlas image, and by voxel classification using a weighted decision function applied to all co-registered/pre-segmented atlas images. This automated segmentation method was applied to 12 CT datasets, with a co-registered atlas created from 8 datasets. Algorithm performance was compared to expert manual quantification.Cardiac region volume quantified by the algorithm (609.0 ± 39.8 cc) and the expert (624.4 ± 38.4 cc) were not significantly different (p=0.1, mean percent difference 3.8 ± 3.0%) and showed excellent correlation (r=0.98, p<0.0001). The algorithm achieved a mean voxel overlap of 0.89 (range 0.86-0.91). The total time was <45 sec on a standard windows computer (100 iterations). Fast robust automated atlas-based segmentation of the heart and pericardium from non-contrast CT is feasible.

Pericardial fat burden on ECG-gated noncontrast CT in asymptomatic patients who subsequently experience adverse cardiovascular events.

OBJECTIVES: We aimed to evaluate whether pericardial fat has value in predicting the risk of future adverse cardiovascular outcomes. BACKGROUND: Pericardial fat volume (PFV) and thoracic fat volume (TFV) can be routinely measured from noncontrast computed tomography (NCT) performed for calculating coronary calcium score (CCS) and may predict major adverse cardiac event (MACE) risk. METHODS: From a registry of 2,751 asymptomatic patients without known cardiac artery disease and 4-year follow-up for MACE (cardiac death, myocardial infarction, stroke, late revascularization) after NCT, we compared 58 patients with MACE with 174 same-sex, event-free control subjects matched by a propensity score to account for age, risk factors, and CCS. The TFV was automatically calculated, and PFV was calculated with manual assistance in defining the pericardial contour, within which fat voxels were automatically identified. Independent relationships of PFV and TFV to MACE were evaluated using conditional multivariable logistic regression. RESULTS: Patients experiencing MACE had higher mean PFV (101.8 +/- 49.2 cm(3) vs. 84.9 +/- 37.7 cm(3), p = 0.007) and TFV (204.7 +/- 90.3 cm(3) vs. 177 +/- 80.3 cm(3), p = 0.029) and higher frequencies of PFV >125 cm(3) (33% vs. 14%, p = 0.002) and TFV >250 cm(3) (31% vs. 17%, p = 0.025). After adjustment for Framingham risk score (FRS), CCS, and body mass index, PFV and TFV were significantly associated with MACE (odds ratio [OR]: 1.74, 95% confidence interval [CI]: 1.03 to 2.95 for each doubling of PFV; OR: 1.78, 95% CI: 1.01 to 3.14 for TFV). The area under the curve from receiver-operator characteristic analyses showed a trend of improved MACE prediction when PFV was added to FRS and CCS (0.73 vs. 0.68, p = 0.058). Addition of PFV, but not TFV, to FRS and CCS improved estimated specificity (0.72 vs. 0.66, p = 0.008) and overall accuracy (0.70 vs. 0.65, p = 0.009) in predicting MACE. CONCLUSIONS: Asymptomatic patients who experience MACE exhibit greater PFV on pre-MACE NCT when they are compared with event-free control subjects with similar cardiovascular risk profiles. Our preliminary findings suggest that PFV may help improve prediction of MACE.

Nonlinear registration of serial coronary CT angiography (CCTA) for assessment of changes in atherosclerotic plaque.

PURPOSE: Coronary CT angiography (CCTA) is a high-resolution three-dimensional imaging technique for the evaluation of coronary arteries in suspected or confirmed coronary artery disease (CAD). Coregistration of serial CCTA scans would allow precise superimposition of images obtained at two different points in time, which could aid in recognition of subtle changes and precise monitoring of coronary plaque progression or regression. To this end, the authors aimed at developing a fully automatic nonlinear volume coregistration for longitudinal CCTA scan pairs. METHODS: The algorithm combines global displacement and local deformation using nonlinear volume coregistration with a volume-preserving constraint. Histogram matching of intensities between two serial scans is performed prior to nonlinear coregistration with dense nonparametric local deformation in which sum of squared differences is used as a similarity measure. The approximate segmentation of coronary arteries obtained from commercially available software provides initial anatomical landmarks for the coregistration algorithm that help localize and emphasize the structure of interest. To avoid possible bias caused by incorrect segmentation, the authors convolve the Gaussian kernel with the segmented binary coronary tree mask and define an extended weighted region of interest. A multiresolution approach is employed to represent coarse-to-fine details of both volumes and the energy function is optimized using a gradient descent method. The authors applied the algorithm in ten paired CCTA datasets (20 scans in total) obtained within 10.7 +/- 5.7 months from each other on a dual source CT scanner to monitor progression of CAD. RESULTS: Serial CCTA coregistration was successful in 9/10 cases as visually confirmed. The global displacement and local deformation of target registration error obtained from four anatomical landmarks were 2.22 +/- 1.15 and 1.56 +/- 0.74 mm, respectively, and the inverse consistency error of local deformation was 0.14 +/- 0.06 mm. The observer variability between two expert observers was 1.31 +/- 0.91 mm. CONCLUSIONS: The proposed coregistration algorithm demonstrates potential to accurately register serial CCTA scans, which may allow direct comparison of calcified and noncalcified atherosclerotic plaque changes between the two scans.

Enhanced definition PET for cardiac imaging.

BACKGROUND: We aimed to determine in phantom and cardiac clinical studies the impact of a new high-resolution PET image reconstruction. METHODS: A phantom with cardiac insert filled with (18)F, 14 (18)F-FDG viability studies and 15 (82)Rb perfusion studies were acquired on a Siemens Biograph-64 (4-ring). The data were reconstructed with 2D- and 3D-attenuation weighted ordered subsets expectation maximization (AWOSEM), and high-definition reconstruction (HD.PET). We calculated wall/cavity contrast, contrast-to-noise ratio (CNR), wall thickness, motion/thickening and ejection fraction. RESULTS: In the phantom study, we found an increase in defect size (up to 26%), contrast (up to 48%) and CNR (1.9) with HD.PET as compared to standard techniques. The contrast increased on HD.PET images compared to 2D- and 3D-AWOSEM for viability (14.0% +/- 4.8%) and perfusion studies (7.3% +/- 4.3%) (P < .05). Average CNR increased with HD.PET by 79.4% +/- 17.1% and 68.8% +/- 3.0% in viability and perfusion studies respectively (all P < .05). Average wall thickness with HD.PET decreased in the phantom study by 1.3 +/- 0.3 mm and the viability studies by 1.9 +/- 0.7 mm but not in the perfusion studies. The functional measurements were not significantly different for any techniques. CONCLUSIONS: We demonstrated both in phantom and patient cardiac studies that HD.PET improves image contrast, defect definition, and CNR.

Quantification of 3D regional myocardial wall thickening from gated magnetic resonance images.

PURPOSE: To develop 3D quantitative measures of regional myocardial wall motion and thickening using cardiac magnetic resonance imaging (MRI) and to validate them by comparison to standard visual scoring assessment. MATERIALS AND METHODS: In all, 53 consecutive subjects with short-axis slices and mid-ventricular 2-chamber/4-chamber views were analyzed. After correction for breath-hold-related misregistration, 3D myocardial boundaries were fitted to images and edited by an imaging cardiologist. Myocardial thickness was quantified at end-diastole and end-systole by computing the 3D distances using Laplace's equation. 3D thickening was represented using the standard 17-segment polar coordinates. 3D thickening was compared with 3D wall motion and with expert visual scores (6-point visual scoring of wall motion and wall thickening; 0 = normal; 5 = greatest abnormality) assigned by imaging cardiologists. RESULTS: Correlation between ejection fraction and thickening measurements was (r = 0.84; P < 0.001) compared to correlation between ejection fraction and motion measurements (r = 0.86; P < 0.001). Good negative correlation between summed visual scores and global wall thickening and motion measurements were also obtained (r(thick) = -0.79; r(motion) = -0.74). Additionally, overall good correlation between individual segmental visual scores with thickening/wall motion (r(thick) = -0.69; r(motion) = -0.65) was observed (P < 0.0001). CONCLUSION: 3D quantitative regional thickening and wall motion measures obtained from MRI correlate strongly with expert clinical scoring.

Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and Metabolic Syndrome.

INTRODUCTION: Pericardial fat is emerging as an important parameter for cardiovascular risk stratification. We extended previously developed quantitation of thoracic fat volume (TFV) from non-contrast coronary calcium (CC) CT scans to also quantify pericardial fat volume (PFV) and investigated the associations of PFV and TFV with CC and the Metabolic Syndrome (METS). METHODS: TFV is quantified automatically from user-defined range of CT slices covering the heart. Pericardial fat contours are generated by spline interpolation between 5-7 control points, placed manually on the pericardium within this cardiac range. Contiguous fat voxels within the pericardium are identified as pericardial fat. PFV and TFV were measured from non-contrast CT for 201 patients. In 105 patients, abdominal visceral fat area (VFA) was measured from an additional single-slice CT. In 26 patients, images were quantified by two readers to establish inter-observer variability. TFV and PFV were examined in relation to Body Mass Index (BMI), waist circumference and VFA, standard coronary risk factors (RF), CC (Agatston score >0) and METS. RESULTS: PFV and TFV showed excellent correlation with VFA (R=0.79, R=0.89, p<0.0001), and moderate correlation with BMI (R=0.49, R=0.48, p<0.0001). In 26 scans, the inter-observer variability was greater for PFV (8.0+/-5.3%) than for TFV (4.4+/-3.9%, p=0.001). PFV and TFV, but not RF, were associated with CC [PFV: p=0.04, Odds Ratio 3.1; TFV: p<0.001, OR 7.9]. PFV and TFV were also associated with METS [PFV: p<0.001, OR 6.1; TFV p<0.001, OR 5.7], unlike CC [OR=1.0 p=NS] or RF. PFV correlated with low-HDL and high-glucose; TFV correlated with low-HDL, low-adiponectin, and high glucose and triglyceride levels. CONCLUSIONS: PFV and TFV can be obtained easily and reproducibly from routine CC scoring scans, and may be important for risk stratification and monitoring.

Quantitative analysis of myocardial perfusion SPECT anatomically guided by coregistered 64-slice coronary CT angiography.

UNLABELLED: Sequential testing by coronary CT angiography (CTA) and myocardial perfusion SPECT (MPS) obtained on stand-alone scanners may be needed to diagnose coronary artery disease in equivocal cases. We have developed an automated technique for MPS-CTA registration and demonstrate its utility for improved MPS quantification by guiding the coregistered physiologic (MPS) with anatomic CTA information. METHODS: Automated registration of MPS left ventricular (LV) surfaces with CTA coronary trees was accomplished by iterative minimization of voxel differences between presegmented CTA volumes and motion-frozen MPS data. Studies of 35 sequential patients (26 men; mean age, 67 +/- 12 y) with 64-slice coronary CTA, MPS, and available results of the invasive coronary angiography performed within 3 mo were retrospectively analyzed. Three-dimensional coronary vessels and CTA slices were extracted and fused with quantitative MPS results mapped on LV surfaces and MPS coronary regions. Automatically coregistered CTA images and extracted trees were used to correct the MPS contours and to adjust the standard vascular region definitions for MPS quantification. RESULTS: Automated coregistration of MPS and coronary CTA had the success rate of 96% as assessed visually; the average errors were 4.3 +/- 3.3 mm in translation and 1.5 +/- 2.6 degrees in rotation on stress and 4.2 +/- 3.1 mm in translation and 1.7 +/- 3.2 degrees in rotation on rest. MPS vascular region definition was adjusted in 17 studies, and LV contours were adjusted in 11 studies using coregistered CTA images as a guide. CTA-guided myocardial perfusion analysis, compared with standard MPS analysis, resulted in improved area under the receiver-operating-characteristic (ROC) curves for the detection of right coronary artery (RCA) and left circumflex artery (LCX) lesions (0.84 +/- 0.08 vs. 0.70 +/- 0.11 for LCX, P = 0.03, and 0.92 +/- 0.05 vs. 0.75 +/- 0.09 for RCA, P = 0.02). CONCLUSION: Software image coregistration of stand-alone coronary CTA and MPS obtained on separate scanners can be performed rapidly and automatically, allowing CTA-guided contour and vascular territory adjustment on MPS for improved quantitative MPS analysis.

Automated quality control for segmentation of myocardial perfusion SPECT.

UNLABELLED: Left ventricular (LV) segmentation, including accurate assignment of LV contours, is essential for the quantitative assessment of myocardial perfusion SPECT (MPS). Two major types of segmentation failures are observed in clinical practices: incorrect LV shape determination and incorrect valve-plane (VP) positioning. We have developed a technique to automatically detect these failures for both nongated and gated studies. METHODS: A standard Cedars-Sinai perfusion SPECT (quantitative perfusion SPECT [QPS]) algorithm was applied to derive LV contours in 318 consecutive (99m)Tc-sestamibi rest/stress MPS studies consisting of stress/rest scans with or without attenuation correction and gated stress/rest images (1,903 scans total). Two numeric parameters, shape quality control (SQC) and valve-plane quality control, were derived to categorize the respective contour segmentation failures. The results were compared with the visual classification of automatic contour adequacy by 3 experienced observers. RESULTS: The overall success of automatic LV segmentation in the 1,903 scans ranged from 66% on nongated images (incorrect shape, 8%; incorrect VP, 26%) to 87% on gated images (incorrect shape, 3%; incorrect VP, 10%). The overall interobserver agreement for visual classification of automatic LV segmentation was 61% for nongated scans and 80% for gated images; the agreement between gray-scale and color-scale display for these scans was 86% and 91%, respectively. To improve the reliability of visual evaluation as a reference, the cases with intra- and interobserver discrepancies were excluded, and the remaining 1,277 datasets were considered (101 with incorrect LV shape and 102 with incorrect VP position). For the SQC, the receiver-operating-characteristic area under the curve (ROC-AUC) was 1.0 +/- 0.00 for the overall dataset, with an optimal sensitivity of 100% and a specificity of 98%. The ROC-AUC was 1.0 in all specific datasets. The algorithm was also able to detect the VP position errors: VP overshooting with ROC-AUC, 0.91 +/- 0.01; sensitivity, 100%; and specificity, 70%; and VP undershooting with ROC-AUC, 0.96 +/- 0.01; sensitivity, 100%; and specificity, 70%. CONCLUSION: A new automated method for quality control of LV MPS contours has been developed and shows high accuracy for the detection of failures in LV segmentation with a variety of acquisition protocols. This technique may lead to an improvement in the objective, automated quantitative analysis of MPS.

AUTOMATED MULTI-MODALITY REGISTRATION OF 64-SLICE CORONARY CT ANGIOGRAPHY WITH MYOCARDIAL PERFUSION SPECT.

A multi-modality image registration algorithm for the alignment of myocardial perfusion SPECT (MPS) and coronary computed tomography angiography (CTA) scans is presented in this work. Coronary CTA and MPS provides clinically complementary information in the diagnosis of coronary artery disease. An automated registration algorithm is proposed utilizing segmentation results of MPS volumes, where regions of myocardium and blood pools are extracted and used as an anatomical mask. Using a variational framework, we adopt an energy functional with a piecewise constant image model and optimize it numerically with a gradient descent algorithm. The computational efficiency and robustness of the proposed automatic registration of CTA with MPS have been demonstrated by the experiments that yielded an average error smaller than an MPS voxel size.

Geometric feature-based multimodal image registration of contrast-enhanced cardiac CT with gated myocardial perfusion SPECT.

PURPOSE: Cardiac computed tomography (CT) and single photon emission computed tomography (SPECT) provide clinically complementary information in the diagnosis of coronary artery disease (CAD). Fused anatomical and physiological data acquired sequentially on separate scanners can be coregistered to accurately diagnose CAD in specific coronary vessels. METHODS: A fully automated registration method is presented utilizing geometric features from a reliable segmentation of gated myocardial perfusion SPECT (MPS) volumes, where regions of myocardium and blood pools are extracted and used as an anatomical mask to de-emphasize the inhomogeneities of intensity distribution caused by perfusion defects and physiological variations. A multiresolution approach is employed to represent coarse-to-fine details of both volumes. The extracted voxels from each level are aligned using a similarity measure with a piecewise constant image model and minimized using a gradient descent method. The authors then perform limited nonlinear registration of gated MPS to adjust for phase differences by automatic cardiac phase matching between CT and MPS. For phase matching, they incorporate nonlinear registration using thin-plate-spline-based warping. Rigid registration has been compared with manual alignment (n=45) on 20 stress/rest MPS and coronary CTA data sets acquired from two different sites and five stress CT perfusion data sets. Phase matching was also compared to expert visual assessment. RESULTS: As compared with manual alignment obtained from two expert observers, the mean and standard deviation of absolute registration errors of the proposed method for MPS were 4.3 +/- 3.5, 3.6 +/- 2.6, and 3.6 +/- 2.1 mm for translation and 2.1 +/- 3.2 degrees, 0.3 +/- 0.8 degree, and 0.7 +/- 1.2 degrees for rotation at site A and 3.8 +/- 2.7, 4.0 +/- 2.9, and 2.2 +/- 1.8mm for translation and 1.1 +/- 2.0 degrees, 1.6 +/- 3.1 degrees, and 1.9 +/- 3.8 degrees for rotation at site B. The results for CT perfusion were 3.0 +/- 2.9, 3.5 +/- 2.4, and 2.8 +/- 1.0 mm for translation and 3.0 +/- 2.4 degrees, 0.6 +/- 0.9 degree, and 1.2 +/- 1.3 degrees for rotation. The registration error shows that the proposed method achieves registration accuracy of less than 1 voxel (6.4 x 6.4 x 6.4 mm) misalignment. The proposed method was robust for different initializations in the range from -80 to 70, -80 to 70, and -50 to 50 mm in the x-, y-, and z-axes, respectively. Validation results of finding best matching phase showed that best matching phases were not different by more than two phases, as visually determined. CONCLUSIONS: The authors have developed a fast and fully automated method for registration of contrast cardiac CT with gated MPS which includes nonlinear cardiac phase matching and is capable of registering these modalities with accuracy <10 mm in 87% of the cases.

Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography.

INTRODUCTION: We aimed to develop an automated algorithm (APQ) for accurate volumetric quantification of non-calcified (NCP) and calcified plaque (CP) from coronary CT angiography (CCTA). METHODS: APQ determines scan-specific attenuation thresholds for lumen, NCP, CP and epicardial fat, and applies knowledge-based segmentation and modeling of coronary arteries, to define NCP and CP components in 3D. We tested APQ in 29 plaques for 24 consecutive scans, acquired with dual-source CT scanner. APQ results were compared to volumes obtained by manual slice-by-slice NCP/CP definition and by interactive adjustment of plaque thresholds (ITA) by 2 independent experts. RESULTS: APQ analysis time was <2 sec per lesion. There was strong correlation between the 2 readers for manual quantification (r = 0.99, p < 0.0001 for NCP; r = 0.85, p < 0.0001 for CP). The mean HU determined by APQ was 419 +/- 78 for luminal contrast at mid-lesion, 227 +/- 40 for NCP upper threshold, and 511 +/- 80 for the CP lower threshold. APQ showed a significantly lower absolute difference (26.7 mm(3) vs. 42.1 mm(3), p = 0.01), lower bias than ITA (32.6 mm(3) vs 64.4 mm(3), p = 0.01) for NCP. There was strong correlation between APQ and readers (R = 0.94, p < 0.0001 for NCP volumes; R = 0.88, p < 0.0001, for CP volumes; R = 0.90, p < 0.0001 for NCP and CP composition). CONCLUSIONS: We developed a fast automated algorithm for quantification of NCP and CP from CCTA, which is in close agreement with expert manual quantification.

Patient motion correction for multiplanar, multi-breath-hold cardiac cine MR imaging.

PURPOSE: To correct for spatial misregistration of multi-breath-hold short-axis (SA), two-chamber (2CH), and four-chamber (4CH) cine cardiac MR (CMR) images caused by respiratory and patient motion. MATERIALS AND METHODS: Twenty CMR studies from consecutive patients with separate breath-hold 2CH, 4CH, and SA 20-phase cine images were considered. We automatically registered the 2CH, 4CH, and SA images in three dimensions by minimizing the cost function derived from plane intersections for all cine phases. The automatic alignment was compared with manual alignment by two observers. RESULTS: The processing time for the proposed method was <20 seconds, compared to 14-24 minutes for the manual correction. The initial plane displacement identified by the observers was 2.8 +/- 1.8 mm (maximum = 14 mm). A displacement of >/=5 mm was identified in 15 of 20 studies. The registration accuracy (defined as the difference between the automatic parameters and those obtained by visual registration) was 1.0 +/- 0.9 mm, 1.1 +/- 1.0 mm, 1.1 +/- 1.2 mm, and 2.0 +/- 1.8 mm for 2CH-4CH alignment and SA alignment in the mid, basal, and apical regions, respectively. The algorithm variability was higher in the apex (2.0 +/- 1.9 mm) than in the mid (1.4 +/- 1.4 mm) or basal (1.2 +/- 1.2 mm) regions (ANOVA, P < 0.05). CONCLUSION: An automated preprocessing algorithm can reduce spatial misregistration between multiple CMR images acquired at different breath-holds and plane orientations.