Enhancing gait cadence through rhythm-modulated music: A study on healthy adults

BACKGROUND AND OBJECTIVE

Gait disorders stemming from brain lesions or chemical imbalances, pose significant challenges for patients. Proposed treatments encompass medication, deep brain stimulation, physiotherapy, and visual stimulation. Music, with its harmonious structures, serves as a continuous reference, synchronizing muscle activities through neural connections between hearing and motor functions, can show promise in gait disorder management. This study explores the influence of heightened music rhythm on young healthy participants' gait cadence in three conditions: FeedForward (independent rhythm), FeedBack (cadence-synced rhythm), and Adaptive (cadence-controlled musical experience). The objective is to increase gait cadence through rhythm modulation during walking.

METHOD

The study involved 18 young healthy participants (13 males and 5 females) who did not have any gait or hearing disorders. Each participant completed the gait task in the three aforementioned conditions. Each condition was comprised of three sessions: 1) Baseline, where participants walked while listening to the original music; 2) Intervention, changing the music rhythm to affect the gait cadence; and 3) Realign, replaying the original music and measuring the durability of the effect of the Intervention session. The measurement tool was a pair of footwear equipped with push-button switches that transmited the foot-to-ground contact to the LabVIEW® software, all designed by the research team. Repeated measures of ANOVA was employed to evaluate the impact of the sessions and conditions.

RESULTS

In all three conditions, there was a significant effect of music on increasing gait cadence during Intervention and Realign sessions (p < 0.001). Additionally, the immediate impact of music on gait cadence in the Adaptive condition was superior to the other conditions.

CONCLUSION

The study findings indicate that increasing the rhythm of music during walking has a significant impact on gait cadence among young healthy participants. This effect remained significant even after realigning the music to normal. It could be harnessed to support the rehabilitation of individuals with movement disorders characterized by a decrease in movement speed, such as Parkinson's disease. Moreover, the results indicate that the Adaptive method showed promising outcomes, suggesting its potential for further exploration as an effective means to control gait cadence.

Extended MRI-based PET motion correction for cardiac PET/MRI

PURPOSE

A 2D image navigator (iNAV) based 3D whole-heart sequence has been used to perform MRI and PET non-rigid respiratory motion correction for hybrid PET/MRI. However, only the PET data acquired during the acquisition of the 3D whole-heart MRI is corrected for respiratory motion. This study introduces and evaluates an MRI-based respiratory motion correction method of the complete PET data.

METHODS

Twelve oncology patients scheduled for an additional cardiac 18F-Fluorodeoxyglucose (18F-FDG) PET/MRI and 15 patients with coronary artery disease (CAD) scheduled for cardiac 18F-Choline (18F-FCH) PET/MRI were included. A 2D iNAV recorded the respiratory motion of the myocardium during the 3D whole-heart coronary MR angiography (CMRA) acquisition (~ 10 min). A respiratory belt was used to record the respiratory motion throughout the entire PET/MRI examination (~ 30-90 min). The simultaneously acquired iNAV and respiratory belt signal were used to divide the acquired PET data into 4 bins. The binning was then extended for the complete respiratory belt signal. Data acquired at each bin was reconstructed and combined using iNAV-based motion fields to create a respiratory motion-corrected PET image. Motion-corrected (MC) and non-motion-corrected (NMC) datasets were compared. Gating was also performed to correct cardiac motion. The SUVmax and TBRmax values were calculated for the myocardial wall or a vulnerable coronary plaque for the 18F-FDG and 18F-FCH datasets, respectively.

RESULTS

A pair-wise comparison showed that the SUVmax and TBRmax values of the motion corrected (MC) datasets were significantly higher than those for the non-motion-corrected (NMC) datasets (8.2 ± 1.0 vs 7.5 ± 1.0, p < 0.01 and 1.9 ± 0.2 vs 1.2 ± 0.2, p < 0.01, respectively). In addition, the SUVmax and TBRmax of the motion corrected and gated (MC_G) reconstructions were also higher than that of the non-motion-corrected but gated (NMC_G) datasets, although for the TBRmax this difference was not statistically significant (9.6 ± 1.3 vs 9.1 ± 1.2, p = 0.02 and 2.6 ± 0.3 vs 2.4 ± 0.3, p = 0.16, respectively). The respiratory motion-correction did not lead to a change in the signal to noise ratio.

CONCLUSION

The proposed respiratory motion correction method for hybrid PET/MRI improved the image quality of cardiovascular PET scans by increased SUVmax and TBRmax values while maintaining the signal-to-noise ratio. Trial registration METC162043 registered 01/03/2017.

High efficiency free-breathing 3D thoracic aorta vessel wall imaging using self-gating image reconstruction

PURPOSE

To improve the scan efficiency of thoracic aorta vessel wall imaging using a self-gating (SG)-based motion correction scheme.

MATERIALS AND METHODS

A slab-selective variable-flip-angle 3D turbo spin-echo (SPACE) sequence was modified to acquire SG signals and imaging data. Cartesian sampling with a tiny golden-step spiral profile ordering was used to obtain the imaging data during the systolic period, and then the image data were subsequently corrected based on the SG signals and binned to different respiratory cycles. Finally, respiratory artifacts were estimated from image-based registration of 3D undersampled respiratory bins that were reconstructed with L1 iterative self-consistent parallel imaging reconstruction (SPIRiT). This method was evaluated in 11 healthy volunteers and compared against conventional diaphragmatic navigator-gated acquisition to assess the feasibility of the proposed framework.

RESULTS

Results showed that the proposed method achieved image quality comparable to that of conventional diaphragmatic navigator-gated acquisition with an average scan time of 4 min. The sharpness of the vessel wall and the definition of the liver boundary were in good agreement with the navigator-gated acquisition, which took approximately above 8.5 min depend on the respiratory rate. Further valuation of this technique in patients will be conducted to determine its clinical use.

A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease

BACKGROUND

Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acquisition involve long and unpredictable scan times and methods that accelerate scans via k-space undersampling often rely on long iterative reconstructions. Deep-learning-based reconstruction methods have recently attracted much interest due to their capacity to provide fast reconstructions while often outperforming existing state-of-the-art methods. In this study, we sought to adapt and validate a non-rigid motion-corrected model-based deep learning (MoCo-MoDL) reconstruction framework for 3D whole-heart MRI in a CHD patient cohort.

METHODS

The previously proposed deep-learning reconstruction framework MoCo-MoDL, which incorporates a non-rigid motion-estimation network and a denoising regularization network within an unrolled iterative reconstruction, was trained in an end-to-end manner using 39 CHD patient datasets. Once trained, the framework was evaluated in eight CHD patient datasets acquired with seven-fold prospective undersampling. Reconstruction quality was compared with the state-of-the-art non-rigid motion-corrected patch-based low-rank reconstruction method (NR-PROST) and against reference images (acquired with three-or-four-fold undersampling and reconstructed with NR-PROST).

RESULTS

Seven-fold undersampled scan times were 2.1 ± 0.3 minutes and reconstruction times were ∼30 seconds, approximately 240 times faster than an NR-PROST reconstruction. Image quality comparable to the reference images was achieved using the proposed MoCo-MoDL framework, with no statistically significant differences found in any of the assessed quantitative or qualitative image quality measures. Additionally, expert image quality scores indicated the MoCo-MoDL reconstructions were consistently of a higher quality than the NR-PROST reconstructions of the same data, with the differences in 12 of the 22 scores measured for individual vascular structures found to be statistically significant.

CONCLUSION

The MoCo-MoDL framework was applied to an adult CHD patient cohort, achieving good quality 3D whole-heart images from ∼2-minute scans with reconstruction times of ∼30 seconds.

Intra-bin correction and inter-bin compensation of respiratory motion in free-running five-dimensional whole-heart magnetic resonance imaging

BACKGROUND

Free-running cardiac and respiratory motion-resolved whole-heart five-dimensional (5D) cardiovascular magnetic resonance (CMR) can reduce scan planning and provide a means of evaluating respiratory-driven changes in clinical parameters of interest. However, respiratory-resolved imaging can be limited by user-defined parameters which create trade-offs between residual artifact and motion blur. In this work, we develop and validate strategies for both correction of intra-bin and compensation of inter-bin respiratory motion to improve the quality of 5D CMR.

METHODS

Each component of the reconstruction framework was systematically validated and compared to the previously established 5D approach using simulated free-running data (N = 50) and a cohort of 32 patients with congenital heart disease. The impact of intra-bin respiratory motion correction was evaluated in terms of image sharpness while inter-bin respiratory motion compensation was evaluated in terms of reconstruction error, compression of respiratory motion, and image sharpness. The full reconstruction framework (intra-acquisition correction and inter-acquisition compensation of respiratory motion [IIMC] 5D) was evaluated in terms of image sharpness and scoring of image quality by expert reviewers.

RESULTS

Intra-bin motion correction provides significantly (p < 0.001) sharper images for both simulated and patient data. Inter-bin motion compensation results in significant (p < 0.001) lower reconstruction error, lower motion compression, and higher sharpness in both simulated (10/11) and patient (9/11) data. The combined framework resulted in significantly (p < 0.001) sharper IIMC 5D reconstructions (End-expiration (End-Exp): 0.45 ± 0.09, End-inspiration (End-Ins): 0.46 ± 0.10) relative to the previously established 5D implementation (End-Exp: 0.43 ± 0.08, End-Ins: 0.39 ± 0.09). Similarly, image scoring by three expert reviewers was significantly (p < 0.001) higher using IIMC 5D (End-Exp: 3.39 ± 0.44, End-Ins: 3.32 ± 0.45) relative to 5D images (End-Exp: 3.02 ± 0.54, End-Ins: 2.45 ± 0.52).

CONCLUSION

The proposed IIMC reconstruction significantly improves the quality of 5D whole-heart MRI. This may be exploited for higher resolution or abbreviated scanning. Further investigation of the diagnostic impact of this framework and comparison to gold standards is needed to understand its full clinical utility, including exploration of respiratory-driven changes in physiological measurements of interest.

Chronic brain functional ultrasound imaging in freely moving rodents performing cognitive tasks

BACKGROUND

Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume. Chronic fUS imaging during cognitive tasks in freely moving animals faces multiple exceptional challenges: performing large durable craniotomies with chronic implants, designing behavioral experiments matching the hemodynamic timescale, stabilizing the ultrasound probe during freely moving behavior, accurately assessing motion artifacts, and validating that the animal can perform cognitive tasks while tethered.

NEW METHOD

We provide validated solutions for those technical challenges. In addition, we present standardized step-by-step reproducible protocols, procedures, and data processing pipelines. Finally, we present proof-of-concept analysis of brain dynamics during a decision making task.

RESULTS

We obtain stable recordings from which we can robustly decode task variables from fUS data over multiple months. Moreover, we find that brain wide imaging through hemodynamic response is nonlinearly related to cognitive variables, such as task difficulty, as compared to sensory responses previously explored.

COMPARISON WITH EXISTING METHODS

Computational pipelines in fUS are nascent and we present an initial development of a full processing pathway to correct and segment fUS data.

CONCLUSIONS

Our methods provide stable imaging and analysis of behavior with fUS that will enable new experimental paradigms in understanding brain-wide dynamics in naturalistic behaviors.

Motion correction of wide-detector 4DCT images for cardiac resynchronization therapy planning

BACKGROUND

Lead placement at the latest mechanically activated left ventricle (LV) segments is strongly correlated with response to cardiac resynchronization therapy (CRT). We demonstrate the feasibility of a cardiac 4DCT motion correction algorithm (ResyncCT) in estimating LV mechanical activation for guiding lead placement in CRT.

METHODS

Subjects with full cardiac cycle 4DCT images acquired using a wide-detector CT scanner for CRT planning/upgrade were included. 4DCT images exhibited motion artifact-induced false-dyssynchrony, hindering LV mechanical activation time estimation. Motion-corrupted images were processed with ResyncCT to yield motion-corrected images. Time to onset of shortening (TOS) was estimated in each of 72 endocardial segments. A false-dyssynchrony index (FDI) was used to quantify the extent of motion artifacts in the uncorrected and the ResyncCT images. After motion correction, the change in classification of LV free-wall segments as optimal target sites for lead placement was investigated.

RESULTS

Twenty subjects (70.7 ​± ​13.9 years, 6 female) were analyzed. Motion artifacts in the ResyncCT-processed images were significantly reduced (FDI: 28.9 ​± ​9.3 ​% vs 47.0 ​± ​6.0 ​%, p ​< ​0.001). In 10 (50 ​%) subjects, ResyncCT motion correction yielded statistically different TOS estimates (p ​< ​0.05). Additionally, 43 ​% of LV free-wall segments were reclassified as optimal target sites for lead placement after motion correction.

CONCLUSIONS

ResyncCT significantly reduced motion artifacts in wide-detector cardiac 4DCT images, yielded statistically different time to onset of shortening estimates, and changed the location of optimal target sites for lead placement. These results highlight the potential utility of ResyncCT motion correction in CRT planning when using wide-detector 4DCT imaging.

Diffusion weighted imaging combining respiratory triggering and navigator echo tracking in the upper abdomen

OBJECTIVES

To evaluate a new motion correction method, named RT + NV Track, for upper abdominal DWI that combines the respiratory triggering (RT) method using a respiration sensor and the Navigator Track (NV Track) method using navigator echoes.

MATERIALS AND METHODS

To evaluate image quality acquired upper abdominal DWI and ADC images with RT, NV, and RT + NV Track in 10 healthy volunteers and 35 patients, signal-to-noise efficiency (SNRefficiency) and the coefficient of variation (CV) of ADC values were measured. Five radiologists independently performed qualitative image-analysis assessments.

RESULTS

RT + NV Track showed significantly higher SNRefficiency than RT and NV (14.01 ± 4.86 vs 12.05 ± 4.65, 10.05 ± 3.18; p < 0.001, p < 0.001). RT + NV Track was superior to RT and equal or better quality than NV in CV and visual evaluation of ADC values (0.033 ± 0.018 vs 0.080 ± 0.042, 0.057 ± 0.034; p < 0.001, p < 0.001). RT + NV Track tends to acquire only expiratory data rather than NV, even in patients with relatively rapid breathing, and can correct for respiratory depth variations, a weakness of RT, thus minimizing image quality degradation.

CONCLUSION

The RT + NV Track method is an efficient imaging method that combines the advantages of both RT and NV methods in upper abdominal DWI, providing stably good images in a short scan time.

Stop moving: MR motion correction as an opportunity for artificial intelligence

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction.

Quality control in clinical raster-scan optoacoustic mesoscopy

Optoacoustic (photoacoustic) mesoscopy bridges the gap between optoacoustic microscopy and macroscopy and enables high-resolution visualization deeper than optical microscopy. Nevertheless, as images may be affected by motion and noise, it is critical to develop methodologies that offer standardization and quality control to ensure that high-quality datasets are reproducibly obtained from patient scans. Such development is particularly important for ensuring reliability in applying machine learning methods or for reliably measuring disease biomarkers. We propose herein a quality control scheme to assess the quality of data collected. A reference scan of a suture phantom is performed to characterize the system noise level before each raster-scan optoacoustic mesoscopy (RSOM) measurement. Using the recorded RSOM data, we develop a method that estimates the amount of motion in the raw data. These motion metrics are employed to classify the quality of raw data collected and derive a quality assessment index (QASIN) for each raw measurement. Using simulations, we propose a selection criterion of images with sufficient QASIN, leading to the compilation of RSOM datasets with consistent quality. Using 160 RSOM measurements from healthy volunteers, we show that RSOM images that were selected using QASIN were of higher quality and fidelity compared to non-selected images. We discuss how this quality control scheme can enable the standardization of RSOM images for clinical and biomedical applications.

Feasibility of a Prototype Image Reconstruction Algorithm for Motion Correction in Interventional Cone-Beam CT Scans

RATIONALE AND OBJECTIVES

Assess the feasibility of a prototype image reconstruction algorithm in correcting motion artifacts in cone-beam computed tomography (CBCT) scans of interventional instruments in the lung.

MATERIALS AND METHODS

First, phantom experiments were performed to assess the algorithm, using the Xsight lung phantom with custom inserts containing straight or curved catheters. During scanning, the inserts moved in a continuous sinusoidal or breath-hold mimicking pattern, with varying amplitudes and frequencies. Subsequently, the algorithm was applied to CBCT data from navigation bronchoscopy procedures. The algorithm's performance was assessed quantitatively via edge-sharpness measurements and qualitatively by three specialists.

RESULTS

In the phantom study, the algorithm improved sharpness in 13 out of 14 continuous sinusoidal motion and five out of seven breath-hold mimicking scans, with more significant effects at larger motion amplitudes. Analysis of 27 clinical scans showed that the motion corrected reconstructions had significantly sharper edges than standard reconstructions (2.81 (2.24-6.46) vs. 2.80 (2.16-4.75), p = 0.003). These results were consistent with the qualitative assessment, which showed higher scores in the sharpness of bronchoscope-tissue interface and catheter-tissue interface in the motion-corrected reconstructions. However, the tumor demarcation ratings were inconsistent between raters, and the overall image quality of the new reconstructions was rated lower.

CONCLUSION

Our findings suggest that applying the new prototype algorithm for motion correction in CBCT images is feasible. The algorithm improved the sharpness of medical instruments in CBCT scans obtained during diagnostic navigation bronchoscopy procedures, which was demonstrated both quantitatively and qualitatively.

Automatic Rejection based on Tissue Signal (ARTS) for motion-corrected quantification of cerebral venous oxygenation in neonates and older adults

OBJECTIVE

Cerebral venous oxygenation (Yv) is a key parameter for the brain's oxygen utilization and has been suggested to be a valuable biomarker in various brain diseases including hypoxic ischemic encephalopathy in neonates and Alzheimer's disease in older adults. T2-Relaxation-Under-Spin-Tagging (TRUST) MRI is a widely used technique to measure global Yv level and has been validated against gold-standard PET. However, subject motion during TRUST MRI scan can introduce considerable errors in Yv quantification, especially for noncompliant subjects. The aim of this study was to develop an Automatic Rejection based on Tissue Signal (ARTS) algorithm for automatic detection and exclusion of motion-contaminated images to improve the precision of Yv quantification.

METHODS

TRUST MRI data were collected from a neonatal cohort (N = 37, 16 females, gestational age = 39.12 ± 1.11 weeks, postnatal age = 1.89 ± 0.74 days) and an older adult cohort (N = 223, 134 females, age = 68.02 ± 9.01 years). Manual identification of motion-corrupted images was conducted for both cohorts to serve as a gold-standard. 9.3% of the images in the neonatal datasets and 0.4% of the images in the older adult datasets were manually identified as motion-contaminated. The ARTS algorithm was trained using the neonatal datasets. TRUST Yv values, as well as the estimation uncertainty (ΔR2) and test-retest coefficient-of-variation (CoV) of Yv, were calculated with and without ARTS motion exclusion. The ARTS algorithm was tested on datasets of older adults: first on the original adult datasets with little motion, and then on simulated adult datasets where the percentage of motion-corrupted images matched that of the neonatal datasets.

RESULTS

In the neonatal datasets, the ARTS algorithm exhibited a sensitivity of 0.95 and a specificity of 0.97 in detecting motion-contaminated images. Compared to no motion exclusion, ARTS significantly reduced the ΔR2 (median = 3.68 Hz vs. 4.89 Hz, P = 0.0002) and CoV (median = 2.57% vs. 6.87%, P = 0.0005) of Yv measurements. In the original older adult datasets, the sensitivity and specificity of ARTS were 0.70 and 1.00, respectively. In the simulated adult datasets, ARTS demonstrated a sensitivity of 0.91 and a specificity of 1.00. Additionally, ARTS significantly reduced the ΔR2 compared to no motion exclusion (median = 2.15 Hz vs. 3.54 Hz, P < 0.0001).

CONCLUSION

ARTS can improve the reliability of Yv estimation in noncompliant subjects, which may enhance the utility of Yv as a biomarker for brain diseases.

A robust motion correction technique for infrared thermography during awake craniotomy

PURPOSE

Intraoperative infrared thermography is an emerging technique for image-guided neurosurgery, whereby physiological and pathological processes result in temperature changes over space and time. However, motion during data collection leads to downstream artifacts in thermography analyses. We develop a fast, robust technique for motion estimation and correction as a preprocessing step for brain surface thermography recordings.

METHODS

A motion correction technique for thermography was developed which approximates the deformation field associated with motion as a grid of two-dimensional bilinear splines (Bispline registration), and a regularization function was designed to constrain motion to biomechanically feasible solutions. The performance of the proposed Bispline registration technique was compared to phase correlation, a band-stop filter, demons registration, and the Horn-Schunck and Lucas-Kanade optical flow techniques.

RESULTS

All methods were analyzed using thermography data from ten patients undergoing awake craniotomy for brain tumor resection, and performance was compared using image quality metrics. The proposed method had the lowest mean-squared error and the highest peak-signal-to-noise ratio of all methods tested and performed slightly worse than phase correlation and Demons registration on the structural similarity index metric (p < 0.01, Wilcoxon signed-rank test). Band-stop filtering and the Lucas-Kanade method were not strong attenuators of motion, while the Horn-Schunck method was well-performing initially but weakened over time.

CONCLUSION

Bispline registration had the most consistently strong performance out of all the techniques tested. It is relatively fast for a nonrigid motion correction technique, capable of processing ten frames per second, and could be a viable option for real-time use. Constraining the deformation cost function through regularization and interpolation appears sufficient for fast, monomodal motion correction of thermal data during awake craniotomy.

Transverse oscillation tensor velocity imaging using a row-column addressed array: Experimental validation

Tensor velocity imaging (TVI) performance with a row-column probe was assessed for constant flow in a straight vessel phantom and pulsatile flow in a carotid artery phantom. TVI, i.e., estimating the 3-D velocity vector as a function of time and spatial position, was performed using the transverse oscillation cross-correlation estimator, and the flow was acquired with a Vermon 128+128 row-column array probe connected to a Verasonics 256 research scanner. The emission sequence used 16 emissions per image, and a TVI volume rate of 234 Hz was obtained for a pulse repetition frequency (f_prf) of 15 kHz. The TVI was validated by comparing estimates of the flow rate through several cross-sections with the flow rate set by the pump. For the constant 8 mL/s flow in the straight vessel phantom with relative estimator bias (RB) and standards deviation (RSD) was found in the range of -2.18% to 0.55% and 4.58% to 2.48% in measurements performed with an f_prf of 15, 10, 8, and 5 kHz. The pulsatile flow in the carotid artery phantom the was set to an average flow rate of 2.44 mL/s, and the flow was acquired with an f_prf of 15, 10, and 8 kHz. The pulsatile flow was estimated from two measurement sites: one at a straight section of the artery and one at the bifurcation. In the straight section, the estimator predicted the average flow rate with an RB value ranging from -7.99% to 0.10% and an RSD value ranging from 10.76% to 6.97%. At the bifurcation, RB and RSD values were between -7.47% to 2.02% and 14.46% to 8.89%. This demonstrates that an RCA with 128 receive elements can accurately capture the flow rate through any cross-section at a high sampling rate.

Impact of motion correction on [18F]-MK6240 tau PET imaging

OBJECTIVE

PET imaging of tau deposition using [¹⁸F]-MK6240 often involves long acquisitions in older subjects, many of whom exhibit dementia symptoms. The resulting unavoidable head motion can greatly degrade image quality. Motion increases the variability of PET quantitation for longitudinal studies across subjects, resulting in larger sample sizes in clinical trials of Alzheimer's disease (AD) treatment.&#xD;Approach: After using an ultra-short frame-by-frame motion detection method based on the list-mode data, we applied an event-by-event list-mode reconstruction to generate the motion-corrected images from 139 scans acquired in 65 subjects. This approach was initially validated in two phantoms experiments against optical tracking data. We developed a motion metric based on the average voxel displacement in the brain to quantify the level of motion in each scan and consequently evaluate the effect of motion correction on images from studies with substantial motion. We estimated the rate of tau accumulation in longitudinal studies (51 subjects) by calculating the difference in the ratio of standard uptake values in key brain regions for AD. We compared the regions' standard deviations across subjects from motion and non-motion corrected images.&#xD;Main Results: Individually, 14% of the scans exhibited notable motion quantified by the proposed motion metric, affecting 48% of the longitudinal datasets with three time points and 25% of all subjects. Motion correction decreased the blurring in images from scans with notable motion and improved the accuracy in quantitative measures. Motion correction reduced the standard deviation of the rate of tau accumulation by -49%, -24%, -18%, and -16% in the entorhinal, inferior temporal, precuneus, and amygdala regions, respectively. &#xD;Significance: The list-mode-based motion correction method is capable of correcting both fast and slow motion during brain PET scans. It leads to improved brain PET quantitation, which is crucial for imaging AD.

Characterization of motion dependent magnetic field inhomogeneity for DWI in the kidneys

PURPOSE

Diffusion-weighted imaging (DWI) of the abdomen has increased dramatically for both research and clinical purposes. Motion and static field inhomogeneity related challenges limit image quality of abdominopelvic imaging with the most conventional echo-planar imaging (EPI) pulse sequence. While reversed phase encoded imaging is increasingly used to facilitate distortion correction, it typically assumes one motionindependent magnetic field distribution. In this study, we describe a more generalized workflow for the case of kidney DWI in which the field inhomogeneity at multiple respiratory phases is mapped and used to correct all images in a multi-contrast DWI series.

METHODS

In this HIPAA-compliant and IRB-approved prospective study, 8 volunteers (6 M, ages 28-51) had abdominal imaging performed in a 3 T MRI system (MAGNETOM Prisma; Siemens Healthcare, Erlangen, Germany) with ECG gating. Coronal oblique T2-weighted HASTE images were collected for anatomical reference. Sagittal phasecontrast (PC) MRI images through the left renal artery were collected to determine systolic and diastolic phases. Cardiac triggered oblique coronal DWI were collected at 10 b-values between 0 and 800 s/mm2 and 12 directions. DWI series were distortion corrected using field maps generated by forward and reversed phase encoded b = 0 images collected over the full respiratory cycle and matched by respiratory phase. Morphologic accuracy, intraseries spatial variability, and diffusion tensor imaging (DTI) metrics mean diffusivity (MD) and fractional anisotropy (FA) were compared for results generated with no distortion correction, correction with only one respiratory bin, and correction with multiple respiratory bins across the breathing cycle.

RESULTS

Computed field maps showed significant variation in static field with kidney laterality, region, and respiratory phase. Distortion corrected images showed significantly better registration to morphologic images than uncorrected images; for the left kidney, the multiple bin correction outperformed one bin correction. Line profile analysis showed significantly reduced spatial variation with multiple bins than one bin correction. DTI metrics were mostly similar between correction methods, with some differences observed in MD between uncorrected and corrected datasets.

CONCLUSIONS

Our results indicate improved morphology of kidney DWI and derived parametric maps as well as reduced variability over the full image series using the motion-resolved distortion correction. This work highlights some morphologic and quantitative metric improvements can be obtained for kidney DWI when distortion correction is performed in a respiratory-resolved manner.

Robust high spatio-temporal line-scanning fMRI in humans at 7T using multi-echo readouts, denoising and prospective motion correction

BACKGROUND

Functional magnetic resonance imaging (fMRI), typically using blood oxygenation level-dependent (BOLD) contrast weighted imaging, allows the study of brain function with millimeter spatial resolution and temporal resolution of one to a few seconds. At a mesoscopic scale, neurons in the human brain are spatially organized in structures with dimensions of hundreds of micrometers, while they communicate at the millisecond timescale. For this reason, it is important to develop an fMRI method with simultaneous high spatial and temporal resolution. Line-scanning promises to reach this goal at the cost of volume coverage.

NEW METHOD

Here, we release a comprehensive update to human line-scanning fMRI. First, we investigated multi-echo line-scanning with five different protocols varying the number of echoes and readout bandwidth while keeping the TR constant. In these, we compared different echo combination approaches in terms of BOLD activation (sensitivity) and temporal signal-to-noise ratio. Second, we implemented an adaptation of NOise reduction with DIstribution Corrected principal component analysis (NORDIC) thermal noise removal for line-scanning fMRI data. Finally, we tested three image-based navigators for motion correction and investigated different ways of performing fMRI analysis on the timecourses which were influenced by the insertion of the navigators themselves.

RESULTS

The presented improvements are relatively straightforward to implement; multi-echo readout and NORDIC denoising together, significantly improve data quality in terms of tSNR and t-statistical values, while motion correction makes line-scanning fMRI more robust.

COMPARISON WITH EXISTING METHODS

Multi-echo acquisitions and denoising have previously been applied in 3D magnetic resonance imaging. Their combination and application to 1D line-scanning is novel. The current proposed method greatly outperforms the previous line-scanning acquisitions with single-echo acquisition, in terms of tSNR (4.0 for single-echo line-scanning and 36.2 for NORDIC-denoised multi-echo) and t-statistical values (3.8 for single-echo line-scanning and 25.1 for NORDIC-denoised multi-echo line-scanning).

CONCLUSIONS

Line-scanning fMRI was advanced compared to its previous implementation in order to improve sensitivity and reliability. The improved line-scanning acquisition could be used, in the future, for neuroscientific and clinical applications.

Free-breathing cardiovascular cine magnetic resonance imaging using compressed-sensing and retrospective motion correction: accurate assessment of biventricular volume at 3T

PURPOSE

We applied a combination of compressed-sensing (CS) and retrospective motion correction to free-breathing cine magnetic resonance (MR) (FBCS cine MoCo). We validated FBCS cine MoCo by comparing it with breath-hold (BH) conventional cine MR.

MATERIALS AND METHODS

Thirty-five volunteers underwent both FBCS cine MoCo and BH conventional cine MR imaging. Twelve consecutive short-axis cine images were obtained. We compared the examination time, image quality and biventricular volumetric assessments between the two cine MR.

RESULTS

FBCS cine MoCo required a significantly shorter examination time than BH conventional cine (135 s [110-143 s] vs. 198 s [186-349 s], p < 0.001). The image quality scores were not significantly different between the two techniques (End-diastole: FBCS cine MoCo; 4.7 ± 0.5 vs. BH conventional cine; 4.6 ± 0.6; p = 0.77, End-systole: FBCS cine MoCo; 4.5 ± 0.5 vs. BH conventional cine; 4.5 ± 0.6; p = 0.52). No significant differences were observed in all biventricular volumetric assessments between the two techniques. The mean differences with 95% confidence interval (CI), based on Bland-Altman analysis, were - 0.3 mL (- 8.2 - 7.5 mL) for LVEDV, 0.2 mL (- 5.6 - 5.9 mL) for LVESV, - 0.5 mL (- 6.3 - 5.2 mL) for LVSV, - 0.3% (- 3.5 - 3.0%) for LVEF, - 0.1 g (- 8.5 - 8.3 g) for LVED mass, 1.4 mL (- 15.5 - 18.3 mL) for RVEDV, 2.1 mL (- 11.2 - 15.3 mL) for RVESV, - 0.6 mL (- 9.7 - 8.4 mL) for RVSV, - 1.0% (- 6.5 - 4.6%) for RVEF.

CONCLUSION

FBCS cine MoCo can potentially replace multiple BH conventional cine MR and improve the clinical utility of cine MR.

An objective evaluation method for head motion estimation in PET-Motion corrected centroid-of-distribution

Head motion presents a continuing problem in brain PET studies. A wealth of motion correction (MC) algorithms had been proposed in the past, including both hardware-based methods and data-driven methods. However, in most real brain PET studies, in the absence of ground truth or gold standard of motion information, it is challenging to objectively evaluate MC quality. For MC evaluation, image-domain metrics, e.g., standardized uptake value (SUV) change before and after MC are commonly used, but this measure lacks objectivity because 1) other factors, e.g., attenuation correction, scatter correction and parameters used in the reconstruction, will confound MC effectiveness; 2) SUV only reflects final image quality, and it cannot precisely inform when an MC method performed well or poorly during the scan time period; 3) SUV is tracer-dependent and head motion may cause increases or decreases in SUV for different tracers, so evaluating MC effectiveness is complicated. Here, we present a new algorithm, i.e., motion corrected centroid-of-distribution (MCCOD) to perform objective quality control for measured or estimated rigid motion information. MCCOD is a three-dimensional surrogate trace of the center of tracer distribution after performing rigid MC using the existing motion information. MCCOD is used to inform whether the motion information is accurate, using the PET raw data only, i.e., without PET image reconstruction, where inaccurate motion information typically leads to abrupt changes in the MCCOD trace. MCCOD was validated using simulation studies and was tested on real studies acquired from both time-of-flight (TOF) and non-TOF scanners. A deep learning-based brain mask segmentation was implemented, which is shown to be necessary for non-TOF MCCOD generation. MCCOD is shown to be effective in detecting abrupt translation motion errors in slowly varying tracer distribution caused by the motion tracking hardware and can be used to compare different motion estimation methods as well as to improve existing motion information.

Motion-corrected 4D-Flow MRI for neurovascular applications

Neurovascular 4D-Flow MRI has emerged as a powerful tool for comprehensive cerebrovascular hemodynamic characterization. Clinical studies in at risk populations such as aging adults indicate hemodynamic markers can be confounded by motion-induced bias. This study develops and characterizes a high fidelity 3D self-navigation approach for retrospective rigid motion correction of neurovascular 4D-Flow data. A 3D radial trajectory with pseudorandom ordering was combined with a multi-resolution low rank regularization approach to enable high spatiotemporal resolution self-navigators from extremely undersampled data. Phantom and volunteer experiments were performed at 3.0T to evaluate the ability to correct for different amounts of induced motions. In addition, the approach was applied to clinical-research exams from ongoing aging studies to characterize performance in the clinical setting. Simulations, phantom and volunteer experiments with motion correction produced images with increased vessel conspicuity, reduced image blurring, and decreased variability in quantitative measures. Clinical exams revealed significant changes in hemodynamic parameters including blood flow rates, flow pulsatility index, and lumen areas after motion correction in probed cerebral arteries (Flow: P<0.001 Lt ICA, P=0.002 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; Area: P<0.001 Lt ICA, P<0.001 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; flow pulsatility index: P=0.042 Rt ICA, P=0.002 Lt MCA). Motion induced bias can lead to significant overestimation of hemodynamic markers in cerebral arteries. The proposed method reduces measurement bias from rigid motion in neurovascular 4D-Flow MRI in challenging populations such as aging adults.

Motion detection and correction for carotid MRI using a markerless optical system

PURPOSE

Motion related artifact is a challenge for MRI, especially when imaging regions like the carotid artery where complex motion (abrupt and bulk motion) may occur. This study aims to develop a non-contact motion detection and correction system for carotid MRI using a markerless optical tracking system.

METHODS

The proposed markerless optical tracking system consisted of a cross-line laser, an MRI-compatible camera and plastic holders mounted inside the scanner bore. The neck motion of the subject can be captured by monitoring the change of the projected laser position in real-time. The system was used to correct both abrupt motion and bulk motion for carotid MRI. The abrupt motion (e.g. coughing) was compensated by discarding the corrupted k-space lines and re-estimating the missing lines using SPIRiT algorithm. The bulk motion was corrected by phase adjustment of k-space lines according to the measured 1D-translational bulk motion (along anterior-posterior direction) and optimized in-plane translation parameters. Ten volunteers underwent carotid MRI with real-time neck motion detection and retrospective motion correction. Artery sharpness, vessel wall thickness and overall image quality score were compared between the motion-corrupted image and motion-corrected images of different correction strategies.

RESULTS

Both the abrupt motion and the bulk motion during carotid scanning were successfully detected and corrected. The results of ten volunteers demonstrated significant improvement in carotid artery sharpness, vessel wall thickness measurement, and overall image quality score using the proposed markerless optical tracking system and motion correction strategies.

CONCLUSION

The proposed markerless structured light based motion detection and correction system can sensitively detect both abrupt and bulk motion during carotid MR scans. By correcting for both abrupt and bulk motion, vessel wall delineation was improved in carotid MR images, which could potentially facilitate carotid plaque identification and atherosclerosis diagnosis in the future.

Respiratory motion correction in F-18-FDG PET/CT impacts lymph node assessment in lung cancer patients

BACKGROUNDS

Elastic motion correction in PET has been shown to increase image quality and quantitative measurements of PET datasets affected by respiratory motion. However, little is known on the impact of respiratory motion correction on clinical image evaluation in oncologic PET. This study evaluated the impact of motion correction on expert readers' lymph node assessment of lung cancer patients.

METHODS

Forty-three patients undergoing F-18-FDG PET/CT for the staging of suspected lung cancer were included. Three different PET reconstructions were investigated: non-motion-corrected ("static"), belt gating-based motion-corrected ("BG-MC") and data-driven gating-based motion-corrected ("DDG-MC"). Assessment was conducted independently by two nuclear medicine specialists blinded to the reconstruction method on a six-point scale [Formula: see text] ranging from "certainly negative" (1) to "certainly positive" (6). Differences in [Formula: see text] between reconstruction methods, accounting for variation caused by readers, were assessed by nonparametric regression analysis of longitudinal data. From [Formula: see text], a dichotomous score for N1, N2, and N3 ("negative," "positive") and a subjective certainty score were derived. SUV and metabolic tumor volumes (MTV) were compared between reconstruction methods.

RESULTS

BG-MC resulted in higher scores for N1 compared to static (p = 0.001), whereas DDG-MC resulted in higher scores for N2 compared to static (p = 0.016). Motion correction resulted in the migration of N1 from tumor free to metastatic on the dichotomized score, consensually for both readers, in 3/43 cases and in 2 cases for N2. SUV was significantly higher for motion-corrected PET, while MTV was significantly lower (all p < 0.003). No significant differences in the certainty scores were noted.

CONCLUSIONS

PET motion correction resulted in significantly higher lymph node assessment scores of expert readers. Significant effects on quantitative PET parameters were seen; however, subjective reader certainty was not improved.

Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network

Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration methods are generally computationally intense and time-consuming. Deep learning approaches are promising in achieving high accuracy with fast speed, but have yet been investigated with consideration for tracer distribution changes or in the whole-body scope. In this work, we developed an unsupervised automatic deep learning-based framework to correct inter-frame body motion. The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information. Our dataset contains 27 subjects each under a 90-min FDG whole-body dynamic PET scan. Evaluating performance in motion simulation studies and a 9-fold cross-validation on the human subject dataset, compared with both traditional and deep learning baselines, we demonstrated that the proposed network achieved the lowest motion prediction error, obtained superior performance in enhanced qualitative and quantitative spatial alignment between parametric Ki and Vb images, and significantly reduced parametric fitting error. We also showed the potential of the proposed motion correction method for impacting downstream analysis of the estimated parametric images, improving the ability to distinguish malignant from benign hypermetabolic regions of interest. Once trained, the motion estimation inference time of our proposed network was around 460 times faster than the conventional registration baseline, showing its potential to be easily applied in clinical settings.

Brain PET motion correction using 3D face-shape model: the first clinical study

OBJECTIVE

Head motions during brain PET scan cause degradation of brain images, but head fixation or external-maker attachment become burdensome on patients. Therefore, we have developed a motion correction method that uses a 3D face-shape model generated by a range-sensing camera (Kinect) and by CT images. We have successfully corrected the PET images of a moving mannequin-head phantom containing radioactivity. Here, we conducted a volunteer study to verify the effectiveness of our method for clinical data.

METHODS

Eight healthy men volunteers aged 22-45 years underwent a 10-min head-fixed PET scan as a standard of truth in this study, which was started 45 min after ¹⁸F-fluorodeoxyglucose (285 ± 23 MBq) injection, and followed by a 15-min head-moving PET scan with the developed Kinect based motion-tracking system. First, selecting a motion-less period of the head-moving PET scan provided a reference PET image. Second, CT images separately obtained on the same day were registered to the reference PET image, and create a 3D face-shape model, then, to which Kinect-based 3D face-shape model matched. This matching parameter was used for spatial calibration between the Kinect and the PET system. This calibration parameter and the motion-tracking of the 3D face shape by Kinect comprised our motion correction method. The head-moving PET with motion correction was compared with the head-fixed PET images visually and by standard uptake value ratios (SUVRs) in the seven volume-of-interest regions. To confirm the spatial calibration accuracy, a test-retest experiment was performed by repeating the head-moving PET with motion correction twice where the volunteer's pose and the sensor's position were different.

RESULTS

No difference was identified visually and statistically in SUVRs between the head-moving PET images with motion correction and the head-fixed PET images. One of the small nuclei, the inferior colliculus, was identified in the head-fixed PET images and in the head-moving PET images with motion correction, but not in those without motion correction. In the test-retest experiment, the SUVRs were well correlated (determinant coefficient, r² = 0.995).

CONCLUSION

Our motion correction method provided good accuracy for the volunteer data which suggested it is useable in clinical settings.

Performance evaluation of dedicated brain PET scanner with motion correction system

OBJECTIVE

Various motion correction (MC) algorithms for positron emission tomography (PET) have been proposed to accelerate the diagnostic performance and research in brain activity and neurology. We have incorporated MC system-based optical motion tracking into the brain-dedicated time-of-flight PET scanner. In this study, we evaluate the performance characteristics of the developed PET scanner when performing MC in accordance with the standards and guidelines for the brain PET scanner.

METHODS

We evaluate the spatial resolution, scatter fraction, count rate characteristics, sensitivity, and image quality of PET images. The MC evaluation is measured in terms of the spatial resolution and image quality that affect movement.

RESULTS

In the basic performance evaluation, the average spatial resolution by iterative reconstruction was 2.2 mm at 10 mm offset position. The measured peak noise equivalent count rate was 38.0 kcps at 16.7 kBq/mL. The scatter fraction and system sensitivity were 43.9% and 22.4 cps/(Bq/mL), respectively. The image contrast recovery was between 43.2% (10 mm sphere) and 72.0% (37 mm sphere). In the MC performance evaluation, the average spatial resolution was 2.7 mm at 10 mm offset position, when the phantom stage with the point source translates to ± 15 mm along the y-axis. The image contrast recovery was between 34.2 % (10 mm sphere) and 66.8 % (37 mm sphere).

CONCLUSIONS

The reconstructed images using MC were restored to their nearly identical state as those at rest. Therefore, it is concluded that this scanner can observe more natural brain activity.

Adaptive data-driven motion detection and optimized correction for brain PET

Head motion during PET scans causes image quality degradation, decreased concentration in regions with high uptake and incorrect outcome measures from kinetic analysis of dynamic datasets. Previously, we proposed a data-driven method, center of tracer distribution (COD), to detect head motion without an external motion tracking device. There, motion was detected using one dimension of the COD trace with a semiautomatic detection algorithm, requiring multiple user defined parameters and manual intervention. In this study, we developed a new data-driven motion detection algorithm, which is automatic, self-adaptive to noise level, does not require user-defined parameters and uses all three dimensions of the COD trace (3DCOD). 3DCOD was first validated and tested using 30 simulation studies (¹⁸F-FDG, N = 15; ¹¹C-raclopride (RAC), N = 15) with large motion. The proposed motion correction method was tested on 22 real human datasets, with 20 acquired from a high resolution research tomograph (HRRT) scanner (¹⁸F-FDG, N = 10; ¹¹C-RAC, N = 10) and 2 acquired from the Siemens Biograph mCT scanner. Real-time hardware-based motion tracking information (Vicra) was available for all real studies and was used as the gold standard. 3DCOD was compared to Vicra, no motion correction (NMC), one-direction COD (our previous method called 1DCOD) and two conventional frame-based image registration (FIR) algorithms, i.e., FIR1 (based on predefined frames reconstructed with attenuation correction) and FIR2 (without attenuation correction) for both simulation and real studies. For the simulation studies, 3DCOD yielded -2.3 ± 1.4% (mean ± standard deviation across all subjects and 11 brain regions) error in region of interest (ROI) uptake for ¹⁸F-FDG (-3.4 ± 1.7% for ¹¹C-RAC across all subjects and 2 regions) as compared to Vicra (perfect correction) while NMC, FIR1, FIR2 and 1DCOD yielded -25.4 ± 11.1% (-34.5 ± 16.1% for ¹¹C- RAC), -13.4 ± 3.5% (-16.1 ± 4.6%), -5.7 ± 3.6% (-8.0 ± 4.5%) and -2.6 ± 1.5% (-5.1 ± 2.7%), respectively. For real HRRT studies, 3DCOD yielded -0.3 ± 2.8% difference for ¹⁸F-FDG (-0.4 ± 3.2% for ¹¹C-RAC) as compared to Vicra while NMC, FIR1, FIR2 and 1DCOD yielded -14.9 ± 9.0% (-24.5 ± 14.6%), -3.6 ± 4.9% (-13.4 ± 14.3%), -0.6 ± 3.4% (-6.7 ± 5.3%) and -1.5 ± 4.2% (-2.2 ± 4.1%), respectively. In summary, the proposed motion correction method yielded comparable performance to the hardware-based motion tracking method for multiple tracers, including very challenging cases with large frequent head motion, in studies performed on a non-TOF scanner.

Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data

Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement and maternal breathing and consequently to suppress erroneous signal correlations. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint with least motion artefacts as reference volume, and perform interpolation for the reconstruction of the motion corrected time series. The results can suffer, if no low-motion frame is available, and if reconstruction does not exploit any assumptions about the continuity of the fMRI signal. Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI. We performed an extensive parameter study to investigate the effectiveness of motion estimation and present in this work benchmark metrics to quantify the effect of motion correction and regularised volumetric reconstruction approaches on functional connectivity computations. We demonstrate the proposed framework's ability to improve functional connectivity estimates, reproducibility and signal interpretability, which is clinically highly desirable for the establishment of prognostic noninvasive imaging biomarkers. The motion correction and volumetric reconstruction framework is made available as an open-source package of NiftyMIC.

Rigid real-time prospective motion-corrected three-dimensional multiparametric mapping of the human brain

PURPOSE

To develop a rigid real-time prospective motion-corrected multiparametric mapping technique and to test the performance of quantitative estimates.

METHODS

Motion tracking and correction were performed by integrating single-shot spiral navigators into a multiparametric imaging technique, three-dimensional quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). The spiral navigator was optimized, and quantitative measurements were validated using a standard system phantom. The effect of motion correction on whole-brain T1 and T2 mapping under different types of head motion during the scan was evaluated in 10 healthy volunteers. Finally, six patients with Parkinson's disease, which is known to be associated with a high prevalence of motion artifacts, were scanned to evaluate the effectiveness of our method in the real world.

RESULTS

The phantom study demonstrated that the proposed motion correction method did not introduce quantitative bias. Improved parametric map quality and repeatability were shown in volunteer experiments with both in-plane and through-plane motions, comparable to the no-motion ground truth. In real-life validation in patients, the approach showed improved parametric map quality compared to images obtained without motion correction.

CONCLUSIONS

Real-time prospective motion-corrected multiparametric relaxometry based on 3D-QALAS provided robust and repeatable whole-brain multiparametric mapping.

Respiration-averaged CT versus standard CT attenuation map for correction of 18F-sodium fluoride uptake in coronary atherosclerotic lesions on hybrid PET/CT

BACKGROUND

To evaluate the impact of respiratory-averaged computed tomography attenuation correction (RACTAC) compared to standard single-phase computed tomography attenuation correction (CTAC) map, on the quantitative measures of coronary atherosclerotic lesions of 18F-sodium fluoride (18F-NaF) uptake in hybrid positron emission tomography and computed tomography (PET/CT).

METHODS

This study comprised 23 patients who underwent 18F-NaF coronary PET in a hybrid PET/CT system. All patients had a standard single-phase CTAC obtained during free-breathing and a 4D cine-CT scan. From the cine-CT acquisition, RACTAC maps were obtained by averaging all images acquired over 5 seconds. PET reconstructions using either CTAC or RACTAC were compared. The quantitative impact of employing RACTAC was assessed using maximum target-to-background (TBRMAX) and coronary microcalcification activity (CMA). Statistical differences were analyzed using reproducibility coefficients and Bland-Altman plots.

RESULTS

In 23 patients, we evaluated 34 coronary lesions using CTAC and RACTAC reconstructions. There was good agreement between CTAC and RACTAC for TBRMAX (median [Interquartile range]): CTAC = 1.65 [1.23 to 2.38], RACTAC = 1.63 [1.23 to 2.33], p = 0.55), with coefficient of reproducibility of 0.18, and CMA: CTAC = 0.10 [0 to 1.0], RACTAC = 0.15 [0 to 1.03], p = 0.55 with coefficient of reproducibility of 0.17 CONCLUSION: Respiratory-averaged and standard single-phase attenuation correction maps provide similar and reproducible methods of quantifying coronary 18F-NaF uptake on PET/CT.

Marker-less and calibration-less motion correction method for brain PET

Marker-less head motion correction methods have been well-studied; however, no reports discussing potential issues in positional calibration between a PET system and an external sensor remain limited. In this study, we develop a method for positional calibration between the PET system and an external range sensor to achieve practical head motion correction. The basic concept of the developed method involves using the subject's face model as a marker not only for head motion detection but also for the system positional calibration. The face model of the subject, which can be obtained easily using the range sensor, can also be calculated from a computed tomography (CT) image of the same subject. The CT image, which is acquired separately for attenuation correction in PET, has the same coordinates as the PET image because of the appropriate matching algorithm between CT and PET images. The proposed method was implemented in the helmet-type PET and the motion correction accuracy was assessed quantitatively using a mannequin head. The phantom experiments demonstrated the performance of the developed motion correction method; high-resolution images with no trace of the applied motion were obtained as if no motion was provided. Statistical analysis supported the visual assessment results in terms of the spatial resolution, contrast recovery; uniformity, and the results implied that motion with correction slightly improved image quality compared with the motionless case. The tolerance of the developed method against potential tracking errors had a minimum 10% difference in the amplitude of the rotation angle.

Evaluating different methods of MR-based motion correction in simultaneous PET/MR using a head phantom moved by a robotic system

Background

Due to comparatively long measurement times in simultaneous positron emission tomography and magnetic resonance (PET/MR) imaging, patient movement during the measurement can be challenging. This leads to artifacts which have a negative impact on the visual assessment and quantitative validity of the image data and, in the worst case, can lead to misinterpretations. Simultaneous PET/MR systems allow the MR-based registration of movements and enable correction of the PET data. To assess the effectiveness of motion correction methods, it is necessary to carry out measurements on phantoms that are moved in a reproducible way. This study explores the possibility of using such a phantom-based setup to evaluate motion correction strategies in PET/MR of the human head.

Method

An MR-compatible robotic system was used to generate rigid movements of a head-like phantom. Different tools, either from the manufacturer or open-source software, were used to estimate and correct for motion based on the PET data itself (SIRF with SPM and NiftyReg) and MR data acquired simultaneously (e.g. MCLFIRT, BrainCompass). Different motion estimates were compared using data acquired during robot-induced motion. The effectiveness of motion correction of PET data was evaluated by determining the segmented volume of an activity-filled flask inside the phantom. In addition, the segmented volume was used to determine the centre-of-mass and the change in maximum activity concentration.

Results

The results showed a volume increase between 2.7 and 36.3% could be induced by the experimental setup depending on the motion pattern. Both, BrainCompass and MCFLIRT, produced corrected PET images, by reducing the volume increase to 0.7–4.7% (BrainCompass) and to -2.8–0.4% (MCFLIRT). The same was observed for example for the centre-of-mass, where the results show that MCFLIRT (0.2–0.6 mm after motion correction) had a smaller deviation from the reference position than BrainCompass (0.5–1.8 mm) for all displacements.

Conclusions

The experimental setup is suitable for the reproducible generation of movement patterns. Using open-source software for motion correction is a viable alternative to the vendor-provided motion-correction software.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00442-6.

Observer repeatability and interscan reproducibility of 18F-sodium fluoride coronary microcalcification activity

BACKGROUND

We aimed to establish the observer repeatability and interscan reproducibility of coronary 18F-sodium-fluoride positron emission tomography (PET) uptake using a novel semi-automated approach, coronary microcalcification activity (CMA).

METHODS

Patients with multivessel coronary artery disease underwent repeated hybrid PET and computed tomography angiography (CTA) imaging (PET/CTA). CMA was defined as the integrated standardized uptake values (SUV) in the entire coronary tree exceeding 2 standard deviations above the background SUV. Coefficients of repeatability between the same observer (intraobserver repeatability), between 2 observers (interobserver repeatability) and coefficient of reproducibility between 2 scans (interscan reproducibility), were determined at vessel and patient level.

RESULTS

In 19 patients, CMA was assessed twice in 43 coronary vessels on two PET/CT scans performed 12 ± 5 days apart. There was excellent intraclass correlation for intraobserver and interobserver repeatability as well as interscan reproducibility (all ≥ 0.991). There was 100% intraobserver, interobserver and interscan agreement for the presence (CMA > 0) or absence (CMA = 0) of coronary18F-NaF uptake. Mean CMA was 3.12 ± 0.62 with coefficients of repeatability of ≤ 10% for all measures: intraobserver 0.24 and 0.22, interobserver 0.30 and 0.29 and interscan 0.33 and 0.32 at a per-vessel and per-patient level, respectively.

CONCLUSIONS

CMA is a repeatable and reproducible global measure of coronary atherosclerotic activity.

Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction

BACKGROUND

Current 18F-NaF assessments of aortic valve microcalcification using 18F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements.

MATERIALS AND METHODS

Fourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUVmax values measured in the aortic valve and maximum target-to-background ratios (TBRmax) values after correcting for blood pool activity.

RESULTS

Compared to standard and ECG-MC reconstructions, increases in both SUVmax and TBRmax were observed following 3 × MC (SUVmax: Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBRmax: Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R2 value) to the AVCS when compared to the standard methods (SUVmax: Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBRmax: Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46).

CONCLUSION

3 × MC improves the correlation between the AVCS and SUVmax and TBRmax and should be considered in PET studies of aortic valves using 18F-NaF.

Improved myocardial blood flow estimation with residual activity correction and motion correction in 18F-flurpiridaz PET myocardial perfusion imaging

PURPOSE

We sought to evaluate the diagnostic performance for coronary artery disease (CAD) of myocardial blood flow (MBF) quantification with ¹⁸F-flurpiridaz PET using motion correction (MC) and residual activity correction (RAC).

METHODS

In total, 231 patients undergoing same-day pharmacologic rest and stress ¹⁸F-flurpiridaz PET from Phase III Flurpiridaz trial (NCT01347710) were studied. Frame-by-frame MC was performed and RAC was accomplished by subtracting the rest residual counts from the dynamic stress polar maps. MBF and myocardial flow reserve (MFR) were derived with a two-compartment early kinetic model for the entire left ventricle (global), each coronary territory, and 17-segment. Global and minimal values of three territorial (minimal vessel) and segmental estimation (minimal segment) of stress MBF and MFR were evaluated in the prediction of CAD. MBF and MFR were evaluated with and without MC and RAC (1: no MC/no RAC, 2: no MC/RAC, 3: MC/RAC).

RESULTS

The area-under the receiver operating characteristics curve (AUC [95% confidence interval]) of stress MBF with MC/RAC was higher for minimal segment (0.89 [0.85-0.94]) than for minimal vessel (0.86 [0.81-0.92], p = 0.03) or global estimation (0.81 [0.75-0.87], p < 0.0001). The AUC of MFR with MC/RAC was higher for minimal segment (0.87 [0.81-0.93]) than for minimal vessel (0.83 [0.76-0.90], p = 0.014) or global estimation (0.77 [0.69-0.84], p < 0.0001). The AUCs of minimal segment stress MBF and MFR with MC/RAC were higher compared to those with no MC/RAC (p < 0.001 for both) or no MC/no RAC (p < 0.0001 for both).

CONCLUSIONS

Minimal segment MBF or MFR estimation with MC and RAC improves the diagnostic performance for obstructive CAD compared to global assessment.

Endogenous assessment of myocardial injury with single-shot model-based non-rigid motion-corrected T1 rho mapping

BACKGROUND

Cardiovascular magnetic resonance T1ρ mapping may detect myocardial injuries without exogenous contrast agent. However, multiple co-registered acquisitions are required, and the lack of robust motion correction limits its clinical translation. We introduce a single breath-hold myocardial T1ρ mapping method that includes model-based non-rigid motion correction.

METHODS

A single-shot electrocardiogram (ECG)-triggered balanced steady state free precession (bSSFP) 2D adiabatic T1ρ mapping sequence that collects five T1ρ-weighted (T1ρw) images with different spin lock times within a single breath-hold is proposed. To address the problem of residual respiratory motion, a unified optimization framework consisting of a joint T1ρ fitting and model-based non-rigid motion correction algorithm, insensitive to contrast change, was implemented inline for fast (~ 30 s) and direct visualization of T1ρ maps. The proposed reconstruction was optimized on an ex vivo human heart placed on a motion-controlled platform. The technique was then tested in 8 healthy subjects and validated in 30 patients with suspected myocardial injury on a 1.5T CMR scanner. The Dice similarity coefficient (DSC) and maximum perpendicular distance (MPD) were used to quantify motion and evaluate motion correction. The quality of T1ρ maps was scored. In patients, T1ρ mapping was compared to cine imaging, T2 mapping and conventional post-contrast 2D late gadolinium enhancement (LGE). T1ρ values were assessed in remote and injured areas, using LGE as reference.

RESULTS

Despite breath holds, respiratory motion throughout T1ρw images was much larger in patients than in healthy subjects (5.1 ± 2.7 mm vs. 0.5 ± 0.4 mm, P < 0.01). In patients, the model-based non-rigid motion correction improved the alignment of T1ρw images, with higher DSC (87.7 ± 5.3% vs. 82.2 ± 7.5%, P < 0.01), and lower MPD (3.5 ± 1.9 mm vs. 5.1 ± 2.7 mm, P < 0.01). This resulted in significantly improved quality of the T1ρ maps (3.6 ± 0.6 vs. 2.1 ± 0.9, P < 0.01). Using this approach, T1ρ mapping could be used to identify LGE in patients with 93% sensitivity and 89% specificity. T1ρ values in injured (LGE positive) areas were significantly higher than in the remote myocardium (68.4 ± 7.9 ms vs. 48.8 ± 6.5 ms, P < 0.01).

CONCLUSIONS

The proposed motion-corrected T1ρ mapping framework enables a quantitative characterization of myocardial injuries with relatively low sensitivity to respiratory motion. This technique may be a robust and contrast-free adjunct to LGE for gaining new insight into myocardial structural disorders.

High-resolution Structural Magnetic Resonance Imaging and Quantitative Susceptibility Mapping

High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.

LEARNING TO CORRECT AXIAL MOTION IN OCT FOR 3D RETINAL IMAGING

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging of biological tissues at high resolution that has revolutionized retinal imaging. A major challenge in OCT imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose a convolutional neural network that learns to correct axial motion in OCT based on a single volumetric scan. The proposed method is able to correct large motion, while preserving the overall curvature of the retina. The experimental results show significant improvements in visual quality as well as overall error compared to the conventional methods in both normal and disease cases.

RAVE-T2/T1 - Feasibility of a new hybrid MR-sequence for free-breathing abdominal MRI in children and adolescents

BACKGROUND

The new radial volumetric encoding RAVE-T₂/T₁ hybrid sequence is a modern three-dimensional sequence with multiparametric approach, which includes T₂- and T₁-weighted contrasts obtained in identical slice position during one measurement. However, the RAVE-T₂/T₁ hybrid sequence is not yet being used in clinical routine.

PURPOSE

The aim of this study was to evaluate the RAVE-T₂/T₁ hybrid sequence in a pediatric population with a clinical indication for an abdominal MRI examination to demonstrate that the hybrid imaging may be less challenging to perform on children.

MATERIALS AND METHODS

Our retrospective observational study included pediatric patients of all age groups and required for an abdominal MRI examination. Non-contrast standard axial T₁ DIXON and non-contrast RAVE-T₂/T₁ hybrid sequence were obtained at 3 T. MRI studies were analyzed independently by two pediatric radiologists using a 5-point Likert-type scale in five different categories. T₁- and T₂-weighted sequences were each compared with the RAVE-T₂/T₁-sequence using a Wilcoxon signed-rank test.

RESULTS

The analysis included 15 children (mean age, 11 years and 4 months, 7 girls and 8 boys). The Cohens Kappa of interrater agreement measured 0.62. The T₂ weighted part of the RAVE-T₂/T₁ sequence was significantly better than the standard T₂ HASTE sequence in four of five image quality categories: overall image quality (2.2 ± 0.7 vs 1.8 ± 0,7, p = 0.03), respiratory motion artefacts (3.8 ± 0.4 vs 2.0 ± 0.7, p <= 0.01), portal vein clarity (3.3 ± 0.8 vs 2.2 ± 0.7, p <= 0.01), hepatic margin sharpness (2.4 ± 1,0 vs 1.8 ± 0.7, p <= 0.01). The T₁ weighted part of the RAVE-T₂/T₁ sequence was significantly better than the standard T₁ DIXON weighted sequence in three of five image quality categories: respiratory motion artefacts (4.0 ± 0.2 vs 3.6 ± 0.8, p = 0.01), portal vein clarity (2.7 ± 0.9 vs 2.1 ± 0.7, p <= 0.01), hepatic margin sharpness (3.2 ± 0.7 vs 2.6 ± 0.9, p <= 0.01).

CONCLUSIONS

The RAVE-T₂/T₁ hybrid sequence is feasible and equal compared to standard T₁- and T₂-weighted sequences in the assessment of abdominal organs in a pediatric population. Due to non-inferiority to the current standard sequences for abdominal imaging, the RAVE-T₂/T₁ hybrid sequence is a good alternative for children who cannot be examined in breath-hold technique.

Motion correction of free-breathing magnetic resonance renography using model-driven registration

INTRODUCTION

Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR).

MATERIALS AND METHODS

MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data).

RESULTS

DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HD_unregistered = 9.98 ± 9.76, HD_registered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses.

DISCUSSION

MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods.

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Motion compensated whole-heart coronary cardiovascular magnetic resonance angiography using focused navigation (fNAV)

BACKGROUND

Radial self-navigated (RSN) whole-heart coronary cardiovascular magnetic resonance angiography (CCMRA) is a free-breathing technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CCMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV).

METHODS

We applied fNAV to 500 data sets from a numerical simulation, 22 healthy subjects, and 549 cardiac patients. In each of these cohorts we compared fNAV to RSN and respiratory resolved extradimensional golden-angle radial sparse parallel (XD-GRASP) reconstructions of the same data. Reconstruction times for each method were recorded. Motion estimate accuracy was measured as the correlation between fNAV and ground truth for simulations, and fNAV and image registration for in vivo data. Percent vessel sharpness was measured in all simulated data sets and healthy subjects, and a subset of patients. Finally, subjective image quality analysis was performed by a blinded expert reviewer who chose the best image for each in vivo data set and scored on a Likert scale 0-4 in a subset of patients by two reviewers in consensus.

RESULTS

The reconstruction time for fNAV images was significantly higher than RSN (6.1 ± 2.1 min vs 1.4 ± 0.3, min, p < 0.025) but significantly lower than XD-GRASP (25.6 ± 7.1, min, p < 0.025). Overall, there is high correlation between the fNAV and reference displacement estimates across all data sets (0.73 ± 0.29). For simulated data, healthy subjects, and patients, fNAV lead to significantly sharper coronary arteries than all other reconstruction methods (p < 0.01). Finally, in a blinded evaluation by an expert reviewer fNAV was chosen as the best image in 444 out of 571 data sets (78%; p < 0.001) and consensus grades of fNAV images (2.6 ± 0.6) were significantly higher (p < 0.05) than uncorrected (1.7 ± 0.7), RSN (1.9 ± 0.6), and XD-GRASP (1.8 ± 0.8).

CONCLUSION

fNAV is a promising technique for improving the quality of RSN free-breathing 3D whole-heart CCMRA. This novel approach to respiratory self-navigation can derive 3D nonrigid motion estimations from an acquired 1D signal yielding statistically significant improvement in image sharpness relative to 1D translational correction as well as XD-GRASP reconstructions. Further study of the diagnostic impact of this technique is therefore warranted to evaluate its full clinical utility.

Effects of motion correction, sampling rate and parametric modelling in dynamic contrast enhanced MRI of the temporomandibular joint in children affected with juvenile idiopathic arthritis

The temporomandibular joint (TMJ) is typically involved in 45-87% of children with Juvenile Idiopathic Arthritis (JIA). Accurate diagnosis of JIA is difficult as various clinical tests, including MRI, disagree. The purpose of this study is to optimize the methodological aspects of Dynamic Contrast Enhanced (DCE) MRI of the TMJ in children. In this cross-sectional study, including data from 73 JIA affected children, aged 6-15 years, effects of motion correction, sampling rate and parametric modelling on DCE-MRI data is investigated. Consensus among three radiologists determined the regions of interest. Quantitative perfusion parameters were estimated using four perfusion models; the Adiabatic Approximation to Tissue Homogeneity (AATH), Distributed Capillary Adiabatic Tissue Homogeneity (DCATH), Gamma Capillary Transit Time (GCTT) and Two Compartment Exchange (2CXM) models. Effects of motion correction were evaluated by a sum of least squares between corrected raw data and the GCTT model. The effect of systematically down sampling the raw data was tested. The sum of least squares was computed across all pharmacokinetic models. Relative difference perfusion parameters between the left and right TMJ were used for an unsupervised k-means based stratification of the data based on a principal component analysis, as well as for a supervised random forest classification. Diagnostic sensitivity and specificity were computed relative to structural image scorings. Paired sample t-tests, as well as ANOVA tests, were used (significant threshold: p < 0.05) with Tukeys post hoc test. High-level elastic motion correction provides the best least square fit to the GCTT model (percental improvement: 72-84%). A 4 s sampling rate captures more of the potentially disease relevant signal variations. The various parametric models all leave comparable residues (relative standard deviation: 3.4%). In further evaluation of DCE-MRI as a potential diagnostic tool for JIA a high-level elastic motion correction scheme should be adopted, with a sampling rate of at least 4 s. Results suggest that DCE-MRI data can be a valuable part in JIA diagnostics in the TMJ.

Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution

Motion degradation is a central problem in Magnetic Resonance Imaging (MRI). This work addresses the problem of how to obtain higher quality, super-resolved motion-free reconstructions from highly undersampled MRI data. In this work, we present for the first time a variational multi-task framework that allows joining three relevant tasks in MRI: reconstruction, registration and super-resolution. Our framework takes a set of multiple undersampled MR acquisitions corrupted by motion into a novel multi-task optimisation model, which is composed of an L² fidelity term that allows sharing representation between tasks, super-resolution foundations and hyperelastic deformations to model biological tissue behaviors. We demonstrate that this combination yields significant improvements over sequential models and other bi-task methods. Our results exhibit fine details and compensate for motion producing sharp and highly textured images compared to state of the art methods while keeping low CPU time. Our improvements are appraised on both clinical assessment and statistical analysis.

Evaluation of a Motion Correction Algorithm for C-Arm Computed Tomography Acquired During Transarterial Chemoembolization

Purpose

The aim of this retrospective study was to evaluate the feasibility of a motion correction 3D reconstruction prototype technique for C-arm computed tomography (CACT).

Material and Methods

We included 65 consecutive CACTs acquired during transarterial chemoembolization of 54 patients (47 m,7f; 67 ± 11.3 years). All original raw datasets (CACT_Org) underwent reconstruction with and without volume punching of high-contrast objects using a 3D image reconstruction software to compensate for motion (CACT_{MC_bone};CACT_{MC_no bone}). Subsequently, the effect on image quality (IQ) was evaluated using objective (image sharpness metric) and subjective criteria. Subjective criteria were defined by vessel geometry, overall IQ, delineation of tumor feeders, the presence of foreign material-induced artifacts and need for additional imaging, assessed by two independent readers on a 3-(vessel geometry and overall IQ) or 2-point scale, respectively. Friedman rank-sum test and post hoc analysis in form of pairwise Wilcoxon signed-rank test were computed and inter-observer agreement analyzed using kappa test.

Results

Objective IQ as defined by an image sharpness metric, increased from 273.5 ± 28 (CACT_Org) to 328.5 ± 55.1 (CACT_{MC_bone}) and 331 ± 57.8 (CACT_{MC_no bone}; all p < 0.0001). These results could largely be confirmed by the subjective analysis, which demonstrated predominantly good and moderate inter-observer agreement, with best agreement for CACT_{MC_no bone} in all categories (e.g., vessel geometry: CACT_Org: κ = 0.51, CACT_{MC_bone}: κ = 0.42, CACT_{MC_no bone}: κ = 0.69).

Conclusion

The application of a motion correction algorithm was feasible for all data sets and led to an increase in both objective and subjective IQ parameters.

Level of Evidence

3

Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI

•

Subject motion in dMRI leads to a set of scattered slices with unique contrast.

•

We introduce a slice-to-volume reconstruction framework for multi-shell HARDI data

•

Based on a data-driven representation as spherical harmonics and radial decomposition (SHARD).

•

The method is evaluated in test-retest scans and in the neonatal dHCP cohort.

•

Results show robust reconstruction in severely motion-corrupted scans.

Diffusion MRI offers a unique probe into neural microstructure and connectivity in the developing brain. However, analysis of neonatal brain imaging data is complicated by inevitable subject motion, leading to a series of scattered slices that need to be aligned within and across diffusion-weighted contrasts. Here, we develop a reconstruction method for scattered slice multi-shell high angular resolution diffusion imaging (HARDI) data, jointly estimating an uncorrupted data representation and motion parameters at the slice or multiband excitation level. The reconstruction relies on data-driven representation of multi-shell HARDI data using a bespoke spherical harmonics and radial decomposition (SHARD), which avoids imposing model assumptions, thus facilitating to compare various microstructure imaging methods in the reconstructed output. Furthermore, the proposed framework integrates slice-level outlier rejection, distortion correction, and slice profile correction. We evaluate the method in the neonatal cohort of the developing Human Connectome Project (650 scans). Validation experiments demonstrate accurate slice-level motion correction across the age range and across the range of motion in the population. Results in the neonatal data show successful reconstruction even in severely motion-corrupted subjects. In addition, we illustrate how local tissue modelling can extract advanced microstructure features such as orientation distribution functions from the motion-corrected reconstructions.

Anatomy-Guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI

Fetal Magnetic Resonance Imaging (MRI) is challenged by the fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion commonly occurs in between slices acquisitions. Motion correction for each slice is thus very important for reconstruction of 3D fetal brain MRI, but is highly operator-dependent and time-consuming. Approaches based on convolutional neural networks (CNNs) have achieved encouraging performance on prediction of 3D motion parameters of arbitrarily oriented 2D slices, which, however, does not capitalize on important brain structural information. To address this problem, we propose a new multi-task learning framework to jointly learn the transformation parameters and tissue segmentation map of each slice, for providing brain anatomical information to guide the mapping from 2D slices to 3D volumetric space in a coarse to fine manner. In the coarse stage, the first network learns the features shared for both regression and segmentation tasks. In the refinement stage, to fully utilize the anatomical information, distance maps constructed based on the coarse segmentation are introduced to the second network. Finally, incorporation of the signed distance maps to guide the regression and segmentation together improves the performance in both tasks. Experimental results indicate that the proposed method achieves superior performance in reducing the motion prediction error and obtaining satisfactory tissue segmentation results simultaneously, compared with state-of-the-art methods.

Sharpness in motion corrected quantitative imaging at 7T

Sub-millimeter imaging at 7T has opened new possibilities for qualitatively and quantitatively studying brain structure as it evolves throughout the life span. However, subject motion introduces image blurring on the order of magnitude of the spatial resolution and is thus detrimental to image quality. Such motion can be corrected for, but widespread application has not yet been achieved and quantitative evaluation is lacking. This raises a need to quantitatively measure image sharpness throughout the brain. We propose a method to quantify sharpness of brain structures at sub-voxel resolution, and use it to assess to what extent limited motion is related to image sharpness. The method was evaluated in a cohort of 24 healthy volunteers with a wide and uniform age range, aiming to arrive at results that largely generalize to larger populations. Using 3D fat-excited motion navigators, quantitative R₁, R₂^* and Quantitative Susceptibility Maps and T₁-weighted images were retrospectively corrected for motion. Sharpness was quantified in all modalities for selected regions of interest (ROI) by fitting the sigmoidally shaped error function to data within locally homogeneous clusters. A strong, almost linear correlation between motion and sharpness improvement was observed, and motion correction significantly improved sharpness. Overall, the Full Width at Half Maximum reduced from 0.88 mm to 0.70 mm after motion correction, equivalent to a 2.0 times smaller voxel volume. Motion and sharpness were not found to correlate with the age of study participants. We conclude that in our data, motion correction using fat navigators is overall able to restore the measured sharpness to the imaging resolution, irrespective of the amount of motion observed during scanning.

Correction of out-of-FOV motion artifacts using convolutional neural network

PURPOSE

Subject motion during MRI scan can result in severe degradation of image quality. Existing motion correction algorithms rely on the assumption that no information is missing during motions. However, this assumption does not hold when out-of-FOV motion happens. Currently available algorithms are not able to correct for image artifacts introduced by out-of-FOV motion. The purpose of this study is to demonstrate the feasibility of incorporating convolutional neural network (CNN) derived prior image into solving the out-of-FOV motion problem.

METHODS AND MATERIALS

A modified U-net network was proposed to correct out-of-FOV motion artifacts by incorporating motion parameters into the loss function. A motion model based data fidelity term was applied in combination with the CNN prediction to further improve the motion correction performance. We trained the CNN on 1113 MPRAGE images with simulated oscillating and sudden motion trajectories, and compared our algorithm to a gradient-based autofocusing (AF) algorithm in both 2D and 3D images. Additional experiment was performed to demonstrate the feasibility of transferring the networks to different dataset. We also evaluated the robustness of this algorithm by adding Gaussian noise to the motion parameters. The motion correction performance was evaluated using mean square error (NMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).

RESULTS

The proposed algorithm outperformed AF-based algorithm for both 2D (NMSE: 0.0066 ± 0.0009 vs 0.0141 ± 0.008, P < .01; PSNR: 29.60 ± 0.74 vs 21.71 ± 0.27, P < .01; SSIM: 0.89 ± 0.014 vs 0.73 ± 0.004, P < .01) and 3D imaging (NMSE: 0.0067 ± 0.0008 vs 0.070 ± 0.021, P < .01; PSNR: 32.40 ± 1.63 vs 22.32 ± 2.378, P < .01; SSIM: 0.89 ± 0.01 vs 0.62 ± 0.03, P < .01). Robust reconstruction was achieved with 20% data missed due to the out-of-FOV motion.

CONCLUSION

In conclusion, the proposed CNN-based motion correction algorithm can significantly reduce out-of-FOV motion artifacts and achieve better image quality compared to AF-based algorithm.

Comparison of two elastic motion correction approaches for whole-body PET/CT: motion deblurring vs gate-to-gate motion correction

Background

Respiratory motion in PET/CT leads to well-known image degrading effects commonly compensated using elastic motion correction approaches. Gate-to-gate motion correction techniques are promising tools for improving clinical PET data but suffer from relatively long reconstruction times. In this study, the performance of a fast elastic motion compensation approach based on motion deblurring (DEB-MC) was evaluated on patient and phantom data and compared to an EM-based fully 3D gate-to-gate motion correction method (G2G-MC) which was considered the gold standard.

Methods

Twenty-eight patients were included in this study with suspected or confirmed malignancies in the thorax or abdomen. All patients underwent whole-body [¹⁸F]FDG PET/CT examinations applying hardware-based respiratory gating. In addition, a dynamic anthropomorphic thorax phantom was studied with PET/CT simulating tumour motion under controlled but realistic conditions. PET signal recovery values were calculated from phantom scans by comparing lesion activities after motion correction to static ground truth data. Differences in standardized uptake values (SUV) and metabolic volume (MV) between both reconstruction methods as well as between motion-corrected (MC) and non motion-corrected (NOMC) results were statistically analyzed using a Wilcoxon signed-rank test.

Results

Phantom data analysis showed high lesion recovery values of 91% (2 cm motion) and 98% (1 cm) for G2G-MC and 83% (2 cm) and 90% (1 cm) for DEB-MC. The statistical analysis of patient data found significant differences between NOMC and MC reconstructions for SUV _max, SUV _mean, MV, and contrast-to-noise ratio (CNR) for both reconstruction algorithms. Furthermore, both methods showed similar increases of 11–12% in SUV _max and SUV _mean after MC. The statistical analysis of the MC/NOMC ratio found no significant differences between the methods.

Conclusion

Both motion correction techniques deliver comparable improvements of SUV _max, SUV _mean, and CNR after MC on clinical and phantom data. The fast elastic motion compensation technique DEB-MC may thereby be a valuable alternative to state-of-the art motion correction techniques.

Test-retest of automated segmentation with different motion correction strategies: A comparison of prospective versus retrospective methods

Test-retest of automated image segmentation algorithms (FSL FAST, FSL FIRST, and FREESURFER) are computed on magnetic resonance images from 12 unsedated children aged 9.4±2.6 years ([min,max] ​= ​[6.5 years, 13.8 years]) using different approaches to motion correction (prospective versus retrospective). The prospective technique, PROMO MPRAGE, dynamically estimates motion using specially acquired navigator images and adjusts the remaining acquisition accordingly, whereas the retrospective technique, MPnRAGE, uses a self-navigation property to retrospectively estimate and account for motion during image reconstruction. To increase the likelihood and range of motions, participants heads were not stabilized with padding during repeated scans. When motion was negligible both techniques had similar performance. When motion was not negligible, the automated image segmentation and anatomical labeling software tools showed the most consistent performance with the retrospectively corrected MPnRAGE technique (≥80% volume overlaps for 15 of 16 regions for FIRST and FREESURFER, with greater than 90% volume overlaps for 12 regions with FIRST and 11 regions with FREESURFER). Prospectively corrected MPRAGE with linear view-ordering also demonstrated lower performance than MPnRAGE without retrospective motion correction.