Motion Correction Algorithm Research Articles

Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications.

Cardiovascular magnetic resonance (CMR) is an established imaging modality with proven utility in assessing cardiovascular diseases. The ability of CMR to characterize myocardial tissue using T1- and T2-weighted imaging, parametric mapping, and late gadolinium enhancement has allowed for the non-invasive identification of specific pathologies not previously possible with modalities like echocardiography. However, CMR examinations are lengthy and technically complex, requiring multiple pulse sequences and different anatomical planes to comprehensively assess myocardial structure, function, and tissue composition. To increase the overall impact of this modality, there is a need to simplify and shorten CMR exams to improve access and efficiency, while also providing reproducible quantitative measurements. Multiparametric MRI techniques that measure multiple tissue properties offer one potential solution to this problem. This review provides an in-depth look at one such multiparametric approach, cardiac magnetic resonance fingerprinting (MRF). The article is structured as follows. First, a brief review of single-parametric and (non-Fingerprinting) multiparametric CMR mapping techniques is presented. Second, a general overview of cardiac MRF is provided covering pulse sequence implementation, dictionary generation, fast k-space sampling methods, and pattern recognition. Third, recent technical advances in cardiac MRF are covered spanning a variety of topics, including simultaneous multislice and 3D sampling, motion correction algorithms, cine MRF, synthetic multicontrast imaging, extensions to measure additional clinically important tissue properties (proton density fat fraction, T2*, and T1ρ), and deep learning methods for image reconstruction and parameter estimation. The last section will discuss potential clinical applications, concluding with a perspective on how multiparametric techniques like MRF may enable streamlined CMR protocols. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Effect of a second-generation motion correction algorithm on image quality and measurement reproducibility of coronary CT angiography in patients with a myocardial bridge and mural coronary artery

To determine the effect of second-generation motion correction (MC2) on image quality and measurement reproducibility of cardiac CT images in patients with a myocardial bridge and mural coronary artery (MB-MCA) compared to standard (STD) images without motion correction and with first-generation motion correction (MC1). A total of 66 patients with MB-MCA in the left anterior descending branch who underwent 256-detector CT with single-heartbeat acquisition were included. Images were reconstructed at 45% and 75% R-R intervals using STD, MC1, and MC2 algorithms. Image quality for MB-MCA was assessed by two observers on a four-point scale (1=poor and 4=excellent) and compared among STD, MC1, and MC2. Depth and length of MB, lumen area, and minimal diameter of MCA were measured and compared. At 45% R-R interval, image quality scores were 1.59±0.78, 2.21±0.97, and 3.21±0.62 for MCA, and 2.48±0.79, 2.76±0.75, and 3.58±0.58 for MB with STD, MC1 and MC2, respectively. At 75% R-R interval, these values were 2.26±0.60, 3.03±0.89, and 3.59±0.55 for MCA and 3.00±0.93, 3.17±0.83, and 3.80±0.44 for MB. Although MC1 was superior to STD in displaying MCA, there was no statistical difference between the two algorithms for MB (p>0.05). Compared with STD and MC1, MC2 statistically improved image quality and interpretability for both MCA and MB and had narrower limits in interobserver agreement for measurements at both 45% and 75% R-R intervals. MC2 improves CT image quality and measurement reproducibility in patients with MB-MCA compared to STD and MC1.

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.

New Whole-Heart motion correction algorithm enables diagnostic CT of aortic valve and coronary arteries in systolic phase for transcatheter aortic valve implantation candidates

ObjectivesTo investigate the ability of new generation snapshot freeze (NGSSF) algorithm in improving diagnostic image quality of both aortic valve and coronary arteries for transcatheter aortic valve implantation (TAVI) candidates in TAVI planning CT. MethodsSixty-four TAVI candidates underwent TAVI planning CT were enrolled. Scans from coronary CT angiography were reconstructed at 20%, 30%, 40%, and 75% R-R cardiac phases with NGSSF and standard (STD) algorithm. In each phase, following parameters were compared: aortic valve measurements and their reproducibility; image quality of aortic valve and coronary arteries. The diagnostic accuracies of TAVI planning CT for coronary artery stenosis in 30% R-R phase with NGSSF and STD algorithms were calculated in 47out of 64 patients with invasive coronary angiography as reference standard. ResultsFor subjective image quality evaluation, the excellent rate for aortic valve improved from 25.0% to 93.8% and the interpretable rate for coronary arteries increased from 20.3% to 95.3% in the 30% phase images with NGSSF compared with images with STD. For the detection of > 50% coronary artery stenosis, the 30% phase images with NGSSF provided a sensitivity of 90%, specificity of 81.48%, negative predictive value of 91.7%, and positive predictive value of 78.3% on a per-patient basis; While images with STD, had a corresponding results of 95.0%, 33.33%, 90.0%, and 51.4%, respectively. ConclusionsNGSSF significantly improves image quality for both aortic valve and coronary arteries compared with STD for TAVI patients of all heart rates. NGSSF enables the accurate measurement for aortic valve and satisfactory diagnostic performance for coronary arteries stenosis in the same systolic phase for TAVI planning.

Influence of a new motion correction algorithm (CardioCapture) on the correlation between heart rate and optimal reconstruction phase

PurposeTo investigate the effect of a new motion correction algorithm (CardioCapture) on the correlation between heart rate and optimal reconstruction phase by evaluating the impact of wide detector CT combined with CardioCapture on CCTA image quality. Materials and methodsAll cases were examined from April 2021 to September 2021. Two experienced radiologists scored these images on a four-point Likert scale. First, all images were divided into eight groups according to HR (at an interval of 5 bpm). The subjective score of images, the frequency of used CardioCapture, and the proportion of the diastolic reconstruction phase were compared in each group. Then, all cases were divided into two groups, one group was reconstructed using the automatic temporal reconstruction algorithm (Ephase) only, and the other group was reconstructed using the Ephase with the CardioCapture. The relationship between HR and the diastolic reconstruction phase was analyzed by the receiver operator characteristic curve (ROC). ResultThe data of 515 patients were studied. With the increase in HR, the subjective image score decreased, the frequency of CardioCapture increased, and the phase ratio of diastolic reconstruction decreased. When the HR was less than 70 bpm, the percentage of excellence image in each group surpassed 94.90%. The highest utilization rate of CardioCapture was 65.22%, and the lowest proportion of diastolic reconstruction was 72.46%. When 70 bpm < HR ≤ 75 bpm, the image excellence rate was 90.43%, the CardioCapture utilization rate was 82.05%, and the diastolic reconstruction rate was 56.41%.When 75 bpm < HR ≤ 80 bpm, the image excellence rate was 87.91%, the CardioCapture utilization rate was 80.65%, and the diastolic reconstruction was 6.45%.When the HR > 80 bpm, the image excellence rate was 80.00%, the CardioCapture utilization rate was 75.00%, and the diastolic reconstruction rate was 22.50%. The best cut-off point between HR and the diastolic reconstruction ROC curve in the groups without CardioCapture was 65 bpm, while that in groups with CardioCapture was 68 bpm. ConclusionThe CardioCapture can effectively improve the image quality of CCTA with high HR. By maintaining the HR below 68 bpm and utilizing the prospective ECG-gated narrow phase axial scan, it is possible to ensure optimal image quality and concurrently reduce radiation dose.

Evaluation of Image Quality for High Heart Rates for Coronary Computed Tomographic Angiography with Advancement in CT Technology: The CONVERGE Registry.

This study aims to evaluate image quality in patients with heart rates above or equal to 70 beats per minute (bpm), performed on a 16 cm scanner (256-slice General Electric Revolution) in comparison to a CT scanner with only 4 cm of coverage (64 slice Volume CT). Recent advancements in image acquisition, such as whole-heart coverage in a single rotation and post-processing methods in coronary computed tomographic angiography (CCTA), include motion-correction algorithms, such as SnapShot Freeze (SSF), which improve temporal resolution and allow for the assessment of coronary artery disease (CAD) with lower motion scores and better image qualities. Studies from the comprehensive evaluation of high temporal- and spatial-resolution cardiac CT using a wide coverage system (CONVERGE) registry (a multicenter registry at four centers) have shown the 16 cm CT scanner having a better image quality in comparison to the 4 cm scanner. However, these studies failed to include patients with undesirable or high heart rates due to well-documented poor image acquisition on prior generations of CCTA scanners. A prospective, observational, multicenter cohort study comparing image quality, quantitively and qualitatively, on scans performed on a 16 cm CCTA in comparison to a cohort of images captured on a 4 cm CCTA at four centers. Participants were recruited based on broad inclusion criteria, and each patient in the 16 cm CCTA arm of the study received a CCTA scan using a 256-slice, whole-heart, single-beat scanner. These patients were then matched by age, gender, and heart rate to patients who underwent CCTA scans on a 4 cm CT scanner. Image quality was graded based on the signal-to-noise ratio, contrast-to-noise ratio, and on a Likert scale of 0-4: 0, very poor-4, excellent. 104 patients were evaluated for this study. The mean heart rate was 75 ± 7 in the 4 cm scanner and 75 ± 7 in the 16 cm one (p = 0.426). The signal-to-noise and contrast-to-noise ratios were higher in the 16 cm scanner (p = 0.0001). In addition, more scans were evaluated as having an excellent quality on the 16 cm scanner than on the 4 cm scanner (p < 0.0001) based on a 4-point Likert scale. The 16 cm scanner has a superior image quality for fast heart rates compared to the 4 cm scanner. This study shows that there is a significantly higher frequency of excellent and good studies showing better contrast-to-noise and signal-to-noise ratios with the 16 cm scanner compared to the 4 cm scanner.

Breast MRI – The champion in the millimeter league: MIO breast MRI – The method of choice in women with dense breasts

We perform MRI of the breast as a first pass technique. We successfully established 10-minute-protocols (including T2 images) with a fixed dosage of 5 ml 1 M CM. A high spatial resolution of 526 × 526, better 672 × 672 or maximum (1.024 × 1.024, MIO MRI) is vital to achieve best results. We use fixation tools to avoid motion artifacts. Motion correction algorithms can, however, often eliminate such artifacts when they are present. In initial breast MRI exams, morphologic features are the most important criteria for lesion evaluation. If previous exams are available for comparison, the main criteria indicating a suspicious lesion are an increase in lesion size or the depiction of new lesions.High quality HR MRI of the breast is the method of choice in women with dense or extremely dense breasts in all cases (screening, assessment, follow up). In density type A or B, MRI can be helpful in defined constellations, e.g. when MX and US are limited or contraindicated.According to our experience, 95% or more of all carcinomas of the breast are detectable on MRI. The remaining 5% of MRI-occult lesions are intraductal tumors or very small invasive carcinomas depicted with mammography due to associated microcalcifications. MRI is, however, superior to all other imaging modalities in the detection of the clinically relevant DCIS (high risk DCIS, intermediate type).Consecutive MRI examinations in intervals of 12 to 24 months allow a reliable detection of invasive breast cancer with an average size of 7–8 mm. This corresponds to a rate of metastasis-free locoregional lymph nodes in >95% of cases. The rate of interval cancers is <2%. In conclusion, this strategy may increase the overall-lifetime survival of breast cancer patients to more than 95%. Inversely, mortality may be reduced to <5%.Taking these improvements in early breast cancer detection and survival that can be achieved through the implementation of QA HR MRI of the breast into account, it should be discussed to modify oncologic guidelines for the treatment of breast cancer.MRI is the best diagnostic tool we have and according to our experience, a first pass, quality-assured high-resolution breast MRI protocol provides best diagnostic results at minimal procedural effort.

Impact of a novel whole-heart motion-correction algorithm on non-contrast cardiac CT images and coronary artery calcium scores

Impact of a novel whole-heart motion-correction algorithm on non-contrast cardiac CT images and coronary artery calcium scores

Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.

To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological and functional evaluation. The acquired image data of 53 CCTA cases, where the patient heart rate (HR) was ≥75bpm, were reconstructed at 0,±2,±4,±6, and±8% deviations from each optimal systolic phase, with and without the MCA, yielding a total of 954 images (53 cases × 9 phases × 2 reconstructions). The overall image quality and diagnostic confidence were graded by two radiologists using a 5-point scale, with scores ≥3 being deemed clinically interpretable. Signal-to-noise ratio, contrast-to-noise ratio, vessel sharpness, and circularity were measured. The CCTA-derived fractional flow reserve (CT-FFR) was calculated in 38 vessels on 24 patients to identify functionally significant stenosis, using the invasive fractional flow reserve (FFR) as reference. All metrics were compared between two reconstructions at various phases. Inferior image quality was observed as the phase deviation was enlarged. However, MCA significantly improved the image quality at nonoptimal phases and the optimal phase. Coronary artery evaluation was feasible within 4% phase deviation using MCA, with interpretable overall image quality and high diagnostic confidence. With MCA, the performance of identifying functionally significant stenosis via CT-FFR was increased for images at various phase deviations. However, obvious decrease in accuracy, as compared to the image at the optimal phase, was found on those with deviations>4%. The deep learning-based MCA allows up to 4% phase deviation in acquiring CCTA for reliable morphological and functional evaluation on patients with high HRs.

Deep Learning-Based Motion Correction in Projection Domain for Coronary Computed Tomography Angiography: A Clinical Evaluation.

This study aimed to evaluate the clinical performance of a deep learning-based motion correction algorithm (MCA) in projection domain for coronary computed tomography angiography (CCTA). A total of 192 patients who underwent CCTA examinations were included and divided into 2 groups based on the average heart rate (HR): group 1, 82 patients with HR of <75 beats per minute; group 2, 110 patients with HR of ≥75 beats per minute. The CCTA images were reconstructed with and without MCA. The subjective image quality was graded in terms of vessel visualization, sharpness, diagnostic confidence, and overall image quality using a 5-point scale, where cases with all scores of ≥3 were deemed interpretable. Objective image quality was measured through signal-to-noise ratio and contrast-to-noise ratio in regions relative to the vessels. The image quality scores for 2 reconstructions and effective dose between 2 groups were compared. The mean effective dose was similar between 2 groups. Neither group showed significant difference on objective image quality for 2 reconstructions. Images reconstructed with and without MCA were both found interpretable for group 1, whereas the subjective image quality was significantly improved by the MCA for all 4 metrics in group 2, with the interpretability increased from 80.91% to 99.09%. Compared with group 1, group 2 showed similar interpretability and diagnostic confidence, despite inferior overall image quality. In CCTA examinations, the deep learning-based MCA is capable of improving the image quality and diagnostic confidence for patients with increased HR to a similar level as for those with low HR.

Impact of Motion Correction tools on reliability and agreement of three software packages for dynamic- perfusion PET/CT processing

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): University of Groningen/University Medical Centre Groningen. Background PET/CT plays an important role in the evaluation of patients with CAD. It allows for non-invasive assessment of coronary function, by means of software based estimations of regional and global myocardial blood flow (MBF) and coronary flow reserve (CFR). However, measurements can differ between software vendors. Also, motion artifacts can impact their reliability. Literature stands that motion can lead to differences up to 500%. In-software motion correction (ISMC) tools are available in some processing software packages, however they rely on different algorithms and their specific role is yet unknown. Recently, an innovative data driven motion correction (DDMC) algorithm was developed, that specifically tracks and corrects for motion prior the reconstruction process. This opens up the possibility of an standardized MC technique. Purpose This study aims to determine the impact of ISMC and DDMC on the reproducibility and agreement of MBF/CFR values between three software packages for dynamic PET processing. It was hypothesized that the use of MC techniques improves agreement/reproducibility in these parameters between vendors. Methods Twenty-three patients categorized as normal after a 13N-ammonia PET/CT examination acquired between July-December 2020 were retrospectively included. Inclusion criteria: no history of CAD, no perfusion defects, normal LV-function and no MACEs and/or relapse at follow up. These criteria were used to prevent possible interference to MBF/CFR values by concomitant CAD. Reconstructions were made without motion correction (NMC), with ISMC and with DDMC. These reconstructions were analyzed in three different software: Corridor 4DM [4DM], Cedars-Sinai QPET [QPET], and SyngoMBF [Syngo]. Clinical data, as well as regional and global values of MBF in rest and stress and CFR were acquired for the final analysis. Paired T-tests, Bland Altman plots and intraclass correlation coefficients were used for statistical analysis. Results Study population consisted of 15(65%) woman, all patients &amp;gt;60 yo, and 21(91%) of adenosine stress (9% regadenoson). The impact of the MC techniques varied between technique, software and region. Differences ranged from −0.12 (QPET DDMC in RCA stress-MBF) to +0.81 (Syngo ISMC in RCA CFR) ml/g/min. QPET proved to be more sensitive to MC, as significant changes in all regional and global stress-MBF and CFR were detected after applying any MC technique (p&amp;lt;0.001). Bland-Altman plots showed heterogeneous results after the use of MC, with some territories improving the Bias and LoA but the opposite in others. Finally, absolute agrement by ICC was predominantly better when using DDMC compared to NMC and ISMC. Only DDMC achieved an ICC &amp;gt;0.9 (excellent) in the RCA CFR. Conclusions The use of DDMC improves the reliability of regional and global stress MBF and CFR in all packages when compared to the use of ISMC or NMC, however, the impact of DDMC is heterogeneous between the packages.

Coronary artery assessment on pre transcatheter aortic valve implantation computed tomography may avoid the need for additional coronary angiography

PurposeThe purpose of this study was to evaluate the percentage of coronary angiography that can be securely avoided by the interpretation of coronary arteries on pre transcatheter aortic valve implantation CT (TAVI-CT), using CT images obtained with deep-learning reconstruction and motion correction algorithms. Material and methodAll consecutive patients who underwent TAVI-CT and coronary angiography, from December 2021 to July 2022 were screened for inclusion in the study. Patients who had previous coronary artery revascularization or who did not undergo TAVI were excluded. All TAVI-CT examinations were obtained using deep-learning reconstruction and motion correction algorithms. On TAVI-CT examinations, quality and stenosis of coronary artery were analyzed retrospectively. When insufficient image quality and/or when diagnosis or doubt of one significant coronary artery stenosis, patients were considered as having possible coronary artery stenosis. The results of coronary angiography were used as the standard of reference for significant CAS. ResultsA total of 206 patients (92 men; mean age, 80.6 years) were included; of these 27/206 (13%) had significant coronary artery stenosis on coronary angiography and were referred for potential revascularization. Sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of TAVI-CT to identify patients requiring coronary artery revascularization was 100% (95% confidence interval [CI]: 87.2–100%), 100% (95% CI: 96.3–100%), 54% (95% CI: 46.6–61.6), 25% (95% CI: 17.0–34.0%) and 60% (95% CI: 53.1–66.9%) respectively. Intra- and inter observer variability was substantial agreement for quality and decision to recommend coronary angiography. Mean reading time was 2 ± 1.2 (standard deviation) min (range: 1–5 min). Overall, TAVI-CT could potentially rule out indication for revascularization for 97 patients (47%). ConclusionAnalysis of coronary artery on TAVI-CT using deep-learning reconstruction and motion correction algorithms can potentially safely avoid coronary angiography in 47% of patients.

Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies.

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTATATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.

Effect of fixational eye movements in corneal topography measurements with optical coherence tomography.

There is an increasing interest in applying optical coherence tomography (OCT) to quantify the topography of ocular structures. However, in its most usual configuration, OCT data is acquired sequentially while a beam is scanned through the region of interest, and the presence of fixational eye movements can affect the accuracy of the technique. Several scan patterns and motion correction algorithms have been proposed to minimize this effect, but there is no consensus on the ideal parameters to obtain a correct topography. We have acquired corneal OCT images with raster and radial patterns, and modeled the data acquisition in the presence of eye movements. The simulations replicate the experimental variability in shape (radius of curvature and Zernike polynomials), corneal power, astigmatism, and calculated wavefront aberrations. The variability of the Zernike modes is highly dependent on the scan pattern, with higher variability in the direction of the slow scan axis. The model can be a useful tool to design motion correction algorithms and to determine the variability with different scan patterns.

Motion Correction for Separate Mandibular and Cranial Movements in Cone Beam CT Reconstructions.

Patient motions are a repeatedly reported phenomenon in oral and maxillofacial cone beam CT scans, leading to reconstructions of limited usability. In certain cases, independent movements of the mandible induce unpredictable motion patterns. Previous motion correction methods are not able to handle such complex cases of patient movements. Our goal was to design a combined motion estimation and motion correction approach for separate cranial and mandibular motions, solely based on the 2D projection images from a single scan. Our iterative three-step motion correction algorithm models the two articulated motions as independent rigid motions. First of all, we segment cranium and mandible in the projection images using a deep neural network. Next, we compute a 3D reconstruction with the poses of the object's trajectories fixed. Third, we improve all poses by minimizing the projection error while keeping the reconstruction fixed. Step two and three are repeated alternately. We find that our marker-free approach delivers reconstructions of up to 85% higher quality, with respect to the projection error, and can improve on already existing techniques, which model only a single rigid motion. We show results of both synthetic and real data created in different scenarios. The reconstruction of motion parameters in a real environment was evaluated on acquisitions of a skull mounted on a hexapod, creating a realistic, easily reproducible motion profile. The proposed algorithm consistently enhances the visual quality of motion impaired CBCT scans, thus eliminating the need for a re-scan in certain cases, considerably lowering radiation dosage for the patient. It can flexibly be used with differently sized regions of interest and is even applicable to localtomography. This article is protected by copyright. All rights reserved.

Evaluation of the second-generation whole-heart motion correction algorithm (SSF2) used to demonstrate the aortic annulus on cardiac CT

The main purpose of pre-transcatheter aortic valve implantation (TAVI) cardiac computed tomography (CT) for patients with severe aortic stenosis is aortic annulus measurements. However, motion artifacts present a technical challenge because they can reduce the measurement accuracy of the aortic annulus. Therefore, we applied the recently developed second-generation whole-heart motion correction algorithm (SnapShot Freeze 2.0, SSF2) to pre-TAVI cardiac CT and investigated its clinical utility by stratified analysis of the patient's heart rate during scanning. We found that SSF2 reconstruction significantly reduced aortic annulus motion artifacts and improved the image quality and measurement accuracy compared to standard reconstruction, especially in patients with high heart rate or a 40% R-R interval (systolic phase). SSF2 may contribute to improving the measurement accuracy of the aortic annulus.

An Iterative Based Motion Correction for SPECT with Simulated Data

Purpose: Single Photon Emission Computed Tomography (SPECT) is one of the important imaging modality in nuclear medicine used for diagnostic imaging of different organs of human body. Patient movement during the acquisition time is a well-known cause of artifacts in reconstructed SPECT data. These motion artifacts may significantly affect the diagnostic accuracy. Therefore, the motion correction of patients in tomography images is very much essential for more accurate diagnosis and hence achieves the quality of the images. Objective: The aim of this project is to develop and evaluate a data driven approach to motion correction without necessity for motion tracking system. Methods: It has been proposed a method to iteratively estimate and compensate this motion during the reconstruction. The rigid motion was estimated view-by-view in every iteration and then used to update the system matrix. The initial reconstructed image is motion contaminated; it can be used to generate a first rough motion estimate. This motion is taken into account in a reconstruction process to generate a motion-corrected image at the first iteration. Then the motion-corrected image and the motion estimate are alternately updated to increase the likelihood, the iterations are stopped when the updated motion seems to have converged. The image update is done by applying multiple iterations of the OSEM algorithm. A final iterative reconstruction was performed with the last motion estimate. Results: The method was evaluated on simulations phantom study. From the simulations phantom data, we produced the several simulated motion induced data. Motion correction algorithms were applied with the simulated motion induced data. The quality of the reconstructed images was improved substantially after the compensation. Mean structural similarity was increased. In the simulation studies, most of the motion blurring in the reconstructed images disappeared after the compensation. Conclusions: The proposed method effectively eliminated motion-induced artifacts in SPECT scans. Since only measured raw data are needed for the motion estimation and compensation, the proposed method can be applied retrospectively to SPECT scans affected by motion. Bangladesh J. Nuclear Med. 26(1): 19-24, 2023

Post-hoc motion correction for coronary computed tomography angiography without additional radiation dose - Improved image quality and interpretability for “free”

AbstractObjectiveTo evaluate the impact of a motion-correction (MC) algorithm, applicable post-hoc and not dependent on extended padding, on the image quality and interpretability of coronary computed tomography angiography (CCTA).MethodsNinety consecutive patients undergoing CCTA on a latest-generation 256-slice CT device were prospectively included. CCTA was performed with prospective electrocardiogram-triggering and the shortest possible acquisition window (without padding) at 75% of the R-R-interval. All datasets were reconstructed without and with MC of the coronaries. The latter exploits the minimal padding inherent in cardiac CT scans with this device due to data acquisition also during the short time interval needed for the tube to reach target currents and voltage (“free” multiphase). Two blinded readers independently assessed image quality on a 4-point Likert scale for all segments.ResultsA total of 1,030 coronary segments were evaluated. Application of MC both with automatic and manual coronary centerline tracking resulted in a significant improvement in image quality as compared to the standard reconstruction without MC (mean Likert score 3.67 [3.50;3.81] vs 3.58 [3.40;3.73], P = 0.005, and 3.7 [3.55;3.82] vs 3.58 [3.40;3.73], P &lt; 0.001, respectively). Furthermore, MC significantly reduced the proportion of non-evaluable segments and patients with at least one non-evaluable coronary segment from 2% to as low as 0.3%, and from 14% to as low as 3%. Reduction of motion artifacts was predominantly observed in the right coronary artery.ConclusionsA post-hoc device-specific MC algorithm improves image quality and interpretability of prospectively electrocardiogram-triggered CCTA and reduces the proportion of non-evaluable scans without any additional radiation dose exposure.

Motion-Compensated PET Image Reconstruction via Separable Parabolic Surrogates

The effective resolution of positron emission tomography (PET) can be significantly degraded by patient motion during data acquisition. This is especially true in the thorax due to respiratory motion. This study concentrates on the improvement of motion correction algorithms both in terms of image quality and computational cost. In this paper, we present a novel motion-compensated image reconstruction (MCIR) algorithm based on a parabolic surrogate likelihood function instead of the loglikelihood function of the expectation maximization (EM) algorithm. The theoretical advantage of the parabolic surrogate algorithm lies within the fact that its loglikelihood is upper bounded by the EM loglikelihood, thus it will converge faster than EM. This is of particular importance in PET motion correction, where reconstructions are very computationally demanding. Relaxation parameters were also introduced to converge closer to the maximum likelihood (ML) solution and achieve lower noise levels. Image reconstructions with embedded relaxation parameters actually converged to better solutions than the corresponding ones without relaxation. Motion-compensated parabolic surrogates were indeed shown to accelerate convergence compared to EM, without reaching a limit cycle. Nonetheless, with the incorporation of ordered subsets in the reconstruction setting, the improvement was less evident.

Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction.

Intra-scan rigid-body motion is a costly and ubiquitous problem in clinical magnetic resonance imaging (MRI) of thehead. State-of-the-art methods for retrospective motion correction in MRI are often computationally expensive or in the case of image-to-image deep learning (DL) based methods can be prone to undesired alterations of the image (hallucinations'). In this work we introduce a novel rigid-body motion correction method which combines the advantages of classical model-driven and data-consistency (DC) preserving approaches with a novel DL algorithm, to provide fast and robust retrospective motion correction. The proposed Motion Parameter Estimating Densenet (MoPED) retrospectively estimates subject head motion during MRI acquisitions using a DL network with DenseBlocks and multitask learning. It quantifies the 2D rigid in-plane motion parameters slice-wise for each echo train (ET) of a Cartesian T2-weighted 2D Turbo-Spin-Echo sequence. The network receives a center patch of the motion corrupted k-space as well as an additional motion-free low-resolution reference scan to provide the ground truth orientation. The supervised training utilizes motion simulations based on 28 acquisitions with subject-wise training, validation, and test data splits of 70%, 23%, and 7%. During inference, MoPED is embedded in an iterative DC-driven motion correction algorithm which alternatingly updates estimates of the motion parameters and motion-corrected low-resolution k-space data. The estimated motion parameters are then used to reconstruct the final motion correctedimage. The mean absolute/squared error and the Pearson correlation coefficient were used to analyze the motion parameter estimation quality on in-silico data in a quantitative evaluation. Structural similarity (SSIM), DC error and root mean squared error (RMSE) were used as metrics of image quality improvement. Furthermore, the generalization capability of the network was analyzed on two in-vivo motion volumes with 28 slices each and on one simulated T1-weightedvolume. The motion estimation achieves a Pearson correlation of 0.968 to the simulated ground-truth of the 2433 test data slices used. In-silico results indicate that MoPED decreases the time for the optimization by a factor of around 27 compared to a conventional method and is able to reduce the RMSE of the reconstructions and average DC error by more than a factor of two compared to uncorrected images. In-vivo experiments show a decrease in computation time by a factor of around 20, a RMSE decrease from 0.055 to 0.033 and an SSIM increase from 0.795 to 0.862. Furthermore, contrast independence is demonstrated as MoPED is also able to correct T1-weighted images in simulations without retraining. Due to the model-based correction, no hallucinations wereobserved. Incorporating DL in a model-based motion correction algorithm shows great benefit on the optimization and computation time. The k-space-based estimation also allows a data consistent correction and therefore avoids the risk of hallucinations of image-to-imageapproaches.