Automatic Motion Correction Research Articles

MR Cranial Bone Imaging: Evaluation of Both Motion-Corrected and Automated Deep Learning Pseudo-CT Estimated MR Images.

CT imaging exposes patients to ionizing radiation. MR imaging is radiation free but previously has not been able to produce diagnostic-quality images of bone on a timeline suitable for clinical use. We developed automated motion correction and use deep learning to generate pseudo-CT images from MR images. We aim to evaluate whether motion-corrected pseudo-CT produces cranial images that have potential to be acceptable for clinical use. Patients younger than age 18 who underwent CT imaging of the head for either trauma or evaluation of cranial suture patency were recruited. Subjects underwent a 5-minute golden-angle stack-of-stars radial volumetric interpolated breath-hold MR image. Motion correction was applied to the MR imaging followed by a deep learning-based method to generate pseudo-CT images. CT and pseudo-CT images were evaluated and, based on indication for imaging, either presence of skull fracture or cranial suture patency was first recorded while viewing the MR imaging-based pseudo-CT and then recorded while viewing the clinical CT. A total of 12 patients underwent CT and MR imaging to evaluate suture patency, and 60 patients underwent CT and MR imaging for evaluation of head trauma. For cranial suture patency, pseudo-CT had 100% specificity and 100% sensitivity for the identification of suture closure. For identification of skull fractures, pseudo-CT had 100% specificity and 90% sensitivity. Our early results show that automated motion-corrected and deep learning-generated pseudo-CT images of the pediatric skull have potential for clinical use and offer a high level of diagnostic accuracy when compared with standard CT scans.

Downward myocardial creep during stress PET imaging is inversely associated with mortality

PurposeThe myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables.MethodsConsecutive patients undergoing regadenoson rest-stress [82Rb]Cl PET-MPI were included. A newly developed 3D motion correction algorithm quantified myocardial creep, the maximum motion at stress during the first pass (60 s), in each direction. All-cause mortality (ACM) served as the primary endpoint.ResultsA total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3–1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91–0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5).ConclusionsDownward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI.

Automated Motion Correction for Myocardial Blood Flow Measurements and Diagnostic Performance of 82Rb PET Myocardial Perfusion Imaging.

Motion correction (MC) affects myocardial blood flow (MBF) measurements in 82Rb PET myocardial perfusion imaging (MPI); however, frame-by-frame manual MC of dynamic frames is time-consuming. This study aims to develop an automated MC algorithm for time-activity curves used in compartmental modeling and compare the predictive value of MBF with and without automated MC for significant coronary artery disease (CAD). Methods: In total, 565 patients who underwent PET-MPI were considered. Patients without angiographic findings were split into training (n = 112) and validation (n = 112) groups. The automated MC algorithm used simplex iterative optimization of a count-based cost function and was developed using the training group. MBF measurements with automated MC were compared with those with manual MC in the validation group. In a separate cohort, 341 patients who underwent PET-MPI and invasive coronary angiography were enrolled in the angiographic group. The predictive performance in patients with significant CAD (≥70% stenosis) was compared between MBF measurements with and without automated MC. Results: In the validation group (n = 112), MBF measurements with automated and manual MC showed strong correlations (r = 0.98 for stress MBF and r = 0.99 for rest MBF). The automatic MC took less time than the manual MC (<12 s vs. 10 min per case). In the angiographic group (n = 341), MBF measurements with automated MC decreased significantly compared with those without (stress MBF, 2.16 vs. 2.26 mL/g/min; rest MBF, 1.12 vs. 1.14 mL/g/min; MFR, 2.02 vs. 2.10; all P < 0.05). The area under the curve (AUC) for the detection of significant CAD by stress MBF with automated MC was higher than that without (AUC, 95% CI, 0.76 [0.71-0.80] vs. 0.73 [0.68-0.78]; P < 0.05). The addition of stress MBF with automated MC to the model with ischemic total perfusion deficit showed higher diagnostic performance for detection of significant CAD (AUC, 95% CI, 0.82 [0.77-0.86] vs. 0.78 [0.74-0.83]; P = 0.022), but the addition of stress MBF without MC to the model with ischemic total perfusion deficit did not reach significance (AUC, 95% CI, 0.81 [0.76-0.85] vs. 0.78 [0.74-0.83]; P = 0.067). Conclusion: Automated MC on 82Rb PET-MPI can be performed rapidly with excellent agreement with experienced operators. Stress MBF with automated MC showed significantly higher diagnostic performance than without MC.

Automated dynamic motion correction improves repeatability and reproducibility of myocardial blood flow quantification with rubidium-82 PET imaging

BackgroundPatient motion reduces the accuracy of PET myocardial blood flow (MBF) measurements. This study evaluated the effect of automatic motion correction on test-retest repeatability and inter-observer variability in a clinically relevant population. MethodsPatients with known or suspected CAD underwent repeat rest 82Rb PET scans within minutes as part of their scheduled rest-stress perfusion study. Two trained observers evaluated the presence of heart motion in each scan. Global LV and per-vessel MBF were computed from the dynamic rest images before and after automatic motion correction. Test-retest and inter-observer variability were assessed using intra-class correlation and Bland-Altman analysis. Results140 pairs of test-retest scans were included, with visual motion noted in 18%. Motion correction decreased the global MBF values by 3.5% (0.80 ± 0.24 vs 0.82 ± 0.25 mL⋅min−1⋅g−1; P < 0.001) suggesting that the blood input function was underestimated in cases with patient motion. Test-retest repeatability of global MBF improved by 9.7% (0.25 vs 0.28 mL⋅min−1⋅g−1; P < 0.001) and inter-observer repeatability was improved by 7.1% (0.073 vs 0.079 mL⋅min−1⋅g−1; P = 0.012). There was a marked impact on both test-retest repeatability as well as inter-observer repeatability in the LCX territory, with improvements of 16.5% (0.30 vs 0.36 mL⋅min−1⋅g−1; P < 0.0000) and 18.4% (0.13 vs 0.16 mL⋅min−1⋅g−1; P < 0.001), respectively. ConclusionAutomatic motion correction improved test-retest repeatability and reduced differences between observers.

Automated motion artifact correction for dynamic contrast-enhanced fluorescence imaging during open orthopedic surgery.

Indocyanine green (ICG)-based dynamic contrast-enhanced fluorescence imaging (DCE-FI) can objectively assess bone perfusion intraoperatively. However, it is susceptible to motion artifacts due to patient's involuntary respiration during the 4.5-minute DCE-FI data acquisition. An automated motion correction approach based on mutual information (MI) frameby-frame was developed to overcome this problem. In this approach, MIs were calculated between the reference and the adjacent frame translated and the maximal MI corresponded to the optimal translation. The images obtained from eighteen amputation cases were utilized to validate the approach and the results show that this correction can significantly reduce the motion artifacts and can improve the accuracy of bone perfusion assessment.

Detection of spinal long fiber tract degeneration in HSP: Improved diffusion tensor imaging

Spinal diffusion tensor imaging (sDTI) is still a challenging technique for selectively evaluating anatomical areas like the pyramidal tracts (PT), dorsal columns (DC), and anterior horns (AH) in clinical routine and for reliably quantifying white matter anisotropy and diffusivity. In neurodegenerative diseases, the value of sDTI is promising but not yet well understood.The objective of this prospective, single-center study was to evaluate the long fiber tract degeneration within the spinal cord in normal aging (n = 125) and to prove its applicability in pathologic conditions as in patients with molecular genetically confirmed hereditary spastic paraplegias (HSP; n = 40), a prototypical disease of the first motor neuron and in some genetic variants with affection of the dorsal columns. An optimized monopolar Stejskal-Tanner sequence for high-resolution, axial sDTI of the cervical spinal cord at 3.0 T with advanced standardized evaluation methods was developed for a robust DTI value estimation of PT, DC, and AH in both groups.After sDTI measurement at C2, an automatic motion correction and an advanced semi-automatic ROI-based, standardized evaluation of white matter anisotropy and diffusivity was performed to obtain regional diffusivity measures for PT, DC, and AH. Reliable and stable sDTI values were acquired in a healthy population without significant decline between age 20 and 65. Reference values for PT, DC, and AH for fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were established.In HSP patients, the decline of the long spinal fiber tracts could be demonstrated by diffusivity abnormalities in the pyramidal tracts with significantly reduced PTFA (p < 0.001), elevated PTRD (p = 0.002) and reduced PTMD (p = 0.003) compared to healthy controls. Furthermore, FA was significantly reduced in DCFA (p < 0.001) with no differences in AH. In a genetically homogeneous subgroup of SPG4 patients (n = 12) with affection of the dorsal columns, DCRD significantly correlated with the overall disease severity as measured by the Spastic Paraplegia Rating Scale (SPRS) (r = − 0.713, p = 0.009).With the most extensive sDTI study in vivo to date, we showed that axial sDTI combined with motion correction and advanced data post-processing strategies enables robust measurements and is ready to use, allowing recognition and quantification of disease- and age-related changes of the PT, DC, and AH. These results may also encourage the usage of sDTI in other neurodegenerative diseases with spinal cord involvement to explore its capability as selective biomarkers.

Inter- and Intraobserver Repeatability of Myocardial Flow Reserve Values Determined with SPECT Study Using a Discovery NM530c Camera and Corridor 4DM Software.

Background: Myocardial blood flow (MBF) and flow reserve (MFR) examination, especially useful in the diagnosis of multivessel coronary artery disease (CAD), can be assessed with a cadmium-zinc-telluride (CZT) SPECT gamma camera, as an alternative to the expensive and less available PET. However, study processing is not free from subjective factors. Therefore, this paper aims to evaluate intra- and interobserver repeatability of MBF and MFR values obtained by the same operator and two independent operators. Methods: This study included 57 adult patients. MBF and MFR were assessed using a Discovery NM530c camera in a two-day, rest/dipyridamople protocol, using 99mTc-MIBI. Data were processed using Corridor4DM software, twice by one operator and once by another operator. Results: The repeatability of the assessed values was quite good in the whole myocardium, LAD and LCX vascular territories, but was poor in the RCA territory. Conclusions: The poor repeatability of MBF and MFR in RCA vascular territory can be explained by poor automatic orientation of the heart axis during post-processing and a so-called “cardiac creep” phenomenon. Better automatic heart orientation and introduction of automatic motion correction is likely to drastically improve this repeatability. In the present state of the software, PET is better for patients requiring assessment of MFR in the RCA territory.

Reducing motion-correction-induced variability in 82rubidium myocardial blood-flow quantification

BackgroundClinical use of myocardial blood flow (MBF) and flow reserve (MFR) is increasing. Motion correction is necessary to obtain accurate results but can introduce variability when performed manually. We sought to reduce that variability with an automated motion-correction algorithm. MethodsA blinded randomized controlled trial of two technologists was performed on the motion correction of 100 dynamic 82Rb patient studies comparing manual motion correction with manual review and adjustment of automated motion correction. Inter-rater variability between technologists for MBF and MFR was the primary outcome with comparison made by analysis of the limits of agreement. Processing time was the secondary outcome. ResultsLimits of agreements between the two technologists decreased significantly for both MBF and MFR, going from [− 0.22, 0.22] mL/min/g and [− 0.31, 0.36] to [− 0.12, 0.15] mL/min/g and [− 0.15, 0.18], respectively (both P < .002). In addition, the average time spent on motion correcting decreased by 1 min per study from 5:21 to 4:21 min (P = .001). ConclusionsIn this randomized controlled trial, the use of automated motion correction significantly decreased inter-user variability and reduced processing time.

Motion Quantification and Automated Correction in Clinical RSOM.

Raster-scan optoacoustic mesoscopy (RSOM) offers high-resolution non-invasive insights into skin pathophysiology, which holds promise for disease diagnosis and monitoring in dermatology and other fields. However, RSOM is quite vulnerable to vertical motion of the skin, which can depend on the part of the body being imaged. Motion correction algorithms have already been proposed, but they are not fully automated, they depend on anatomical segmentation pre-processing steps that might not be performed successfully, and they are not site- specific. Here, we determined for the first time the magnitude of the micrometric vertical skin displacements at different sites on the body that affect RSOM. The quantifi- cation of motion allowed us to develop a site-specific correction algorithm. The algorithm is fully automated and does not need prior anatomical information. We found that the magnitude of the vertical motion depends strongly on the site of imaging and is caused by breathing, heart beating, and arterial pulsation. The developed algorithm resulted in more than 2-fold improvement in the signal-to-noise ratio of the reconstructed images at every site tested. Proposing an effective automated motion correction algorithm paves the way for realizing the full clinical potential of RSOM.

Real-time registration of 3D to 2D ultrasound images for image-guided prostate biopsy.

During image-guided prostate biopsy, needles are targeted at tissues that are suspicious of cancer to obtain specimen for histological examination. Unfortunately, patient motion causes targeting errors when using an MR-transrectal ultrasound (TRUS) fusion approach to augment the conventional biopsy procedure. This study aims to develop an automatic motion correction algorithm approaching the frame rate of an ultrasound system to be used in fusion-based prostate biopsy systems. Two modes of operation have been investigated for the clinical implementation of the algorithm: motion compensation using a single user initiated correction performed prior to biopsy, and real-time continuous motion compensation performed automatically as a background process. Retrospective 2D and 3D TRUS patient images acquired prior to biopsy gun firing were registered using an intensity-based algorithm utilizing normalized cross-correlation and Powell's method for optimization. 2D and 3D images were downsampled and cropped to estimate the optimal amount of image information that would perform registrations quickly and accurately. The optimal search order during optimization was also analyzed to avoid local optima in the search space. Error in the algorithm was computed using target registration errors (TREs) from manually identified homologous fiducials in a clinical patient dataset. The algorithm was evaluated for real-time performance using the two different modes of clinical implementations by way of user initiated and continuous motion compensation methods on a tissue mimicking prostate phantom. After implementation in a TRUS-guided system with an image downsampling factor of 4, the proposed approach resulted in a mean ± std TRE and computation time of 1.6 ± 0.6 mm and 57 ± 20 ms respectively. The user initiated mode performed registrations with in-plane, out-of-plane, and roll motions computation times of 108 ± 38 ms, 60 ± 23 ms, and 89 ± 27 ms, respectively, and corresponding registration errors of 0.4 ± 0.3 mm, 0.2 ± 0.4 mm, and 0.8 ± 0.5°. The continuous method performed registration significantly faster (P < 0.05) than the user initiated method, with observed computation times of 35 ± 8 ms, 43 ± 16 ms, and 27 ± 5 ms for in-plane, out-of-plane, and roll motions, respectively, and corresponding registration errors of 0.2 ± 0.3 mm, 0.7 ± 0.4 mm, and 0.8 ± 1.0°. The presented method encourages real-time implementation of motion compensation algorithms in prostate biopsy with clinically acceptable registration errors. Continuous motion compensation demonstrated registration accuracy with submillimeter and subdegree error, while performing < 50 ms computation times. Image registration technique approaching the frame rate of an ultrasound system offers a key advantage to be smoothly integrated to the clinical workflow. In addition, this technique could be used further for a variety of image-guided interventional procedures to treat and diagnose patients by improving targeting accuracy.

Automatic motion correction for in vivo human skin optical coherence tomography angiography through combined rigid and nonrigid registration.

When using optical coherence tomography angiography (OCTA), the development of artifacts due to involuntary movements can severely compromise the visualization and subsequent quantitation of tissue microvasculatures. To correct such an occurrence, we propose a motion compensation method to eliminate artifacts from human skin OCTA by means of step-by-step rigid affine registration, rigid subpixel registration, and nonrigid B-spline registration. To accommodate this remedial process, OCTA is conducted using two matching all-depth volume scans. Affine transformation is first performed on the large vessels of the deep reticular dermis, and then the resulting affine parameters are applied to all-depth vasculatures with a further subpixel registration to refine the alignment between superficial smaller vessels. Finally, the coregistration of both volumes is carried out to result in the final artifact-free composite image via an algorithm based upon cubic B-spline free-form deformation. We demonstrate that the proposed method can provide a considerable improvement to the final en face OCTA images with substantial artifact removal. In addition, the correlation coefficients and peak signal-to-noise ratios of the corrected images are evaluated and compared with those of the original images, further validating the effectiveness of the proposed method. We expect that the proposed method can be useful in improving qualitative and quantitative assessment of the OCTA images of scanned tissue beds.

Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes.

Patient motion artifacts are often visible in densely sampled or large wide field-of-view (FOV) retinal optical coherence tomography (OCT) volumes. A popular strategy for reducing motion artifacts is to capture two orthogonally oriented volumetric scans. However, due to larger volume sizes, longer acquisition times, and corresponding larger motion artifacts, the registration of wide FOV scans remains a challenging problem. In particular, gaps in data acquisition due to eye motion, such as saccades, can be significant and their modeling becomes critical for successful registration. In this article, we develop a complete computational pipeline for the automatic motion correction and accurate registration of wide FOV orthogonally scanned OCT images of the human retina. The proposed framework utilizes the retinal boundary segmentation as a guide for registration and requires only a minimal transformation of the acquired data to produce a successful registration. It includes saccade detection and correction, a custom version of the optical flow algorithm for dense lateral registration and a linear optimization approach for axial registration. Utilizing a wide FOV swept source OCT system, we acquired retinal volumes of 12 subjects and we provide qualitative and quantitative experimental results to validate the state-of-the-art effectiveness of the proposed technique. The source code corresponding to the proposed algorithm is available online.

Investigation into diagnostic accuracy of common strategies for automated perfusion motion correction.

Respiratory motion is a significant obstacle to the use of quantitative perfusion in clinical practice. Increasingly complex motion correction algorithms are being developed to correct for respiratory motion. However, the impact of these improvements on the final diagnosis of ischemic heart disease has not been evaluated. The aim of this study was to compare the performance of four automated correction methods in terms of their impact on diagnostic accuracy. Three strategies for motion correction were used: (1)independent translation correction for all slices, (2)translation correction for the basal slice with transform propagation to the remaining two slices assuming identical motion in the remaining slices, and (3)rigid correction (translation and rotation) for the basal slice. There were no significant differences in diagnostic accuracy between the manual and automatic motion-corrected datasets ([Formula: see text]). The area under the curve values for manual motion correction and automatic motion correction were 0.93 and 0.92, respectively. All of the automated motion correction methods achieved a comparable diagnostic accuracy to manual correction. This suggests that the simplest automated motion correction method (method 2 with translation transform for basal location and transform propagation to the remaining slices) is a sufficiently complex motion correction method for use in quantitative myocardial perfusion.

Frequency based gating: An alternative, conformal, approach to 4D PET data utilization.

Respiratory gating is a strategy for overcoming image degradation caused by patient motion in Positron Emission Tomography (PET) imaging. Traditional methods for sorting data, namely, phase-based gating or amplitude-based gating, come with an inherent trade-off between resolution improvements and added noise present in the subjugated data. If the goal of motion correction in PET is realigned from creating 4D images that attempt to mimic nongated images, towards ideal utilization of the information available, then new paths for data management emerge. In this work, the authors examine the application of a method in a new class of frequency based data subjugation algorithms, termed gating +. This strategy utilizes data driven information to locally adapt signal to its optimal segregation, thereby creating a new approach to 4D data utilization PET. 189 (18)F-fluorodeoxyglucose (FDG) PET scans were acquired at a single bed position centered on the thorax region. 4D gated image sets were reconstructed using data driven gating. The gating+ signal optimization algorithm, previously presented in small animal PET images and simulations, was used to segregate data in frequency space to generate optimized 4D images in the population-the first application and analysis of gating+ in human PET scans. The nongated, phase gated, and gating+ representations of the data were compared using FDG uptake analysis in the identified lesions and noise measurements from background regions. Optimized processing required less than 1 min per scan on a standard PC (plus standard reconstruction time), and yielded entire 4D optimized volumes plus motion maps. Optimized scans had noise characteristics similar to nongated images, yet also contained much of the resolution and motion information found in the gated images. The average SUVmax increase in the lesion sample between gated/nongated and gating+/nongated (±SD in population) was 35.8% ± 34.6% and 28.6% ± 27.9%, respectively. The average percent standard deviation (%SD ± SD in population) in liver volumes of interest (VOIs) across the sample for the nongated, gated, and gating+ scans was 6.7% ± 2.4%, 13.6% ± 3.3%, and 7.1% ± 2.5%, respectively. In all cases, the noise in the gating+ liver VOIs was closer to the nongated measurements than to the gated. The gating+ algorithm introduces the notion of conforming 4D data segregation to the local information and statistics that support it. By segregating data in frequency space, the authors are able to generate low noise motion information rich image sets, derived solely from selective use of raw data. Their work shows that the gating+ algorithm can be robustly applied in populations, and across varying qualities of motion and scans statistics, and be integrated as part of a fully automated motion correction workflow. Furthermore, the idea of smart signal utilization underpins a new concept of low risk or even risk-free motion correction application in PET.

TH-AB-303-11: Automatic Prostate Intrafraction Motion Management

Purpose: Intrafraction motion management is important for prostate radiotherapy when tight margin is used especially for hypo-fractional treatment. We evaluate the benefits of imaging strategies in the situation of manual and automatic motion correction. Methods: Prostate position information was acquired continuously using electromagnetic transponder at two institutions from 17 and 30 patients (536 and 883 trajectories), respectively. We simulated 7 -field IMRT delivery with periodic imaging at various frequencies and with continuous adaptive imaging. With manual correction, we assumed correction is applied when the displacement is greater than a predefined threshold and there is a 30 sec delay after imaging to determine and apply the couch shift. With automatic correction, we assumed 1 sec delay. For adaptive imaging, real-time “dose-free” cine-MV imaging during treatment is used in conjunction with online-updated motion pattern to estimate 3D displacement. Automatic MV-kV imaging is only used to confirm over-threshold motion and calculate couch shift, therefore very low additional patient dose from kV imaging. Results: There was no significant bias from either cohort since the results from the two institutions are similar. Compared to the initial setup position, without intervention, on average, the prostates moved out of a 3-mm margin for 9.2% of the beam-on time. With automatic correction, 3-min imaging and correction frequency reduces the mean percentage to 4.7%. With manual correction, however, the percentage is only reduced to 8.4%, minor improvement compared to no imaging. The maximal patient-specific over-3mm time is 24.3%. The adaptive imaging strategy reduces the mean percentage to 0.9% and the maximum to 1.5%. On average, only 2.5 kV images are needed per fraction, similar to 3-min periodic imaging. The targeting performance is achieved by only 1.0 couch shift per fraction. Conclusion: Adaptive continuous monitoring with automatic motion compensation is more beneficial than periodic imaging surveillance at similar imaging dose.

Improved Image Quality of Coronary CT Angiography Using Automatic Motion Correction

Background: Motion artifacts that degrade image quality of coronary CT angiography (CCTA) in patients with high heart rates may be reduced with specific automatic motion correction algorithms (AMC). Objectives: We compared coronary-artery delineation between AMC and conventional CCTA reconstruction (CR). Patients and Methods: CCTA images (clinical single-source-64-slice-CT system) of 17 consecutive patients with heart rates exceeding 55 bpm were reconstructed with both CR and AMC during the individually best-suited phase of the cardiac cycle. Two independent readers who were blinded to the reconstruction algorithm scored image quality of each coronary artery segment (AHA 15-segment-model; 1: non-diagnostic 4: excellent). In case of disagreement a third blinded reader assigned a final score. Two-tailed statistical tests (Wilcoxonmatched-pairs, Pearson-correlation) were significant at P 70 bpm; P = 0.021). Inter-reader agreement was good. (inter-class-correlation, 0.762). Conclusions: CCTA image quality improves significantly with AMC in patients with high heart rates and reduces the proportion of nondiagnostic examinations.

Motion correction facilitates the automation of cardiac ASL perfusion imaging

Background Cardiac arterial spin labeling (ASL) perfusion imaging requires subtraction of signals in tagged and control CMR images. In recent clinical studies, perfusion reserve mapping of a single short-axis slice has required laborious manual segmentation of the LV muscle [1]. Here we demonstrate that using free open source software for automatic motion correction reduces the required manual segmentation to just 4 images (2 rest, 2 stress). Methods Framework Images for rest and stress acquisition are processed similarly using the procedure in Figure 1. The control and tagged image pair that has the highest correlation to other image pairs is chosen as a “reference” (Cref, Tref), and is manually segmented to generate masks (MCref, MTref). Remaining control and tagged images are then registered to their respective reference images. The resulting displa-

A comparison of methods for automated motion correction of DCE-MRI perfusion datasets evaluated in terms of diagnostic accuracy: a CE-MARC sub-study

Background Automated mage registration in cardiac myocardial perfusion is a necessity before quantitative perfusion can be widely accepted in clinical practice. Increasingly complex motion correction algorithms are being developed to deal with cardiac motion. However, the impact of these improvements has not been evaluated in terms of the final clinical diagnosis. Advanced motion correction methods are associated with increased computational overhead and the potential of introducing subtle registration errors, which can be hard to detect and quantify. The aim of this study was to compare the performance of the various automated correction methods in terms of their impact on diagnostic accuracy.

1074Myocardial T1 and T2 Mappings with Automatic Motion Correction at 3 Tesla MR: Comparison of T1 and T2 Measurements by Breathhold, Free-Breathing and Cardiac Cycle

1074Myocardial T1 and T2 Mappings with Automatic Motion Correction at 3 Tesla MR: Comparison of T1 and T2 Measurements by Breathhold, Free-Breathing and Cardiac Cycle

Myocardial T1 and T2 mappings with automatic motion correction at 3 Tesla MR: comparison of T1 and T2 measurements by breathhold, free-breathing and cardiac cycle

T1 and T2 mappings are novel quantitative methods to assess microscopic changes in the myocardium. The aim of this study was to assess differences in myocardial T1 and T2 measurements with automatic motion correction according to breathhold, free-breathing and cardiac cycle. Methods Myocardial T1 and T2 mapping at 3 Tesla MR was obtained from 24 healthy volunteers (16 males and 8 females, mean age, 31±4 years). Shortened modified looklocker inversion recovery was used for T1 mapping and T2-prepared single-shot steady-state free precession was utilized for T2 mapping. Automatic motion correction was done by an elastic registration algorithm based on estimating motion-free synthetic images. For each patient, T1 and T2 mapping were performed consecutively at three shortaxis levels in mid-diastole during breathhold (BH), in middiastole during free-breathing (FB), and in end-systole during breathhold (SYS-BH). T1 and T2 values were measured in six segments per each level. Mean and standard deviation of slice-averaged myocardial T1 and T2 values were compared between BH, FB and SYS-BH. Results Mean slice-averaged myocardial T1 and T2 values displayed good agreement between BH, FB and SYS-BH (T1 values, 1127 vs. 1146 vs. 1123; T2 values, 41 vs. 42 vs. 41). Standard deviation of T1 measurements was significantly different between BH and SYS-BH (p=0.006) and between FB and SYS-BH (p<0.000) but no significant difference was found between BH and FB (p=0.753). Standard deviation of T2 measurements was not statistically different between BH, FB and SYS-BH (p=0.068). Standard deviation of T1 and T2 measurements was lowest in SYS-BH followed by BH and FB. Conclusions