2D Cine Research Articles

CSF flow measurement in the mesencephalic aqueduct using 2D cine phase-contrast MRI in dogs with communicating internal hydrocephalus, ventriculomegaly, and physiologic ventricular spaces.

Brachycephalic dogs are overrepresented with ventricular enlargement. Cerebrospinal fluid (CSF) flow dynamics are not completely understood. MRI techniques have been used for the visualization of CSF dynamics including phase-contrast imaging. This study aimed to determine a causality between CSF flow and ventriculomegaly or hydrocephalus and to compare CSF flow dynamics among dogs with ventriculomegaly, internal hydrocephalus, and physiologic ventricles. A total of 51 client-owned dogs were included in the study. Magnetic resonance imaging (MRI)-based FLASH sequences and phase-contrast images of the brain were obtained, and the ROI was placed at the level of the mesencephalic aqueduct. ECG monitoring was performed parallel to MRI acquisition. Evaluation of flow diagrams and processing of phase-contrast images were performed using commercially available software (Argus VA80A, Siemens AG Healthcare Sector, Erlangen, Germany). Dogs were divided into three groups: Group 1 consisted of brachycephalic dogs with ventriculomegaly (group 1A) or internal hydrocephalus (group 1B), group 2 consisted of brachycephalic dogs with normal ventricles, and group 3 consisted of meso- to dolichocephalic dogs with normal ventricles. Group 1 had a higher median Vrost (4.32 cm/s; CI: 2.94-6.33 cm/s) and Vcaud (-6.1 cm/s, CI: 3.99-9.33 cm/s) than group 2 (Vrost: 1.99 cm/s; CI 1.43-2.78 cm/s; Vcaud: 2.91 cm/s, CI: 2.01-4.21 cm/s; p = 0.008; p = 0.03) and group 3 (Vrost:1.85 cm/s, CI: 1.31-2.60 cm/s; Vcaud - 2.46 cm/s, CI 1.68-3.58 cm/s; p = 0.01; p = 0.02). The median Volcaud of group 1 (-0.23 mL/min, CI: 0.13-0.42 mL/min) was higher than that of group 2 (-0.09 mL/min, CI: 0.05 mL/min and 0.15 mL/min) (p = 0.03). Groups 1A and 1B did not differ in Vcaud, Vrost, Volcaud, and Volrost. Group 1A and 1B showed a higher median Vrost (4.01 cm/s, CI: 2.30-7.05 cm/s; 5.94 cm/s, CI: 2.16-7.88 cm/s) than group 2 (1.85 cm/s, CI: 1.24-2.80 cm/s.) (p = 0.03; p = 0.004). Increased CSF flow velocities in rostral and caudal directions are present in dogs with ventriculomegaly and internal hydrocephalus compared to normal controls.

Super-resolution reconstruction of time-resolved four-dimensional computed tomography (TR-4DCT) with multiple breathing cycles based on TR-4DMRI.

Respiratory motion irregularities in lung cancer patients are common and can be severe during multi-fractional (∼20 mins/fraction) radiotherapy. However, the current clinical standard of motion management is to use a single-breath respiratory-correlated four-dimension computed tomography (RC-4DCT or 4DCT) to estimate tumor motion to delineate the internal tumor volume (ITV), covering the trajectory of tumor motion, as a treatment target. To develop a novel multi-breath time-resolved (TR) 4DCT using the super-resolution reconstruction framework with TR 4D magnetic resonance imaging (TR-4DMRI) as guidance for patient-specific breathing irregularity assessment, overcoming the shortcomings of RC-4DCT, including binning artifacts and single-breath limitations. Six lung cancer patients participated in the IRB-approved protocol study to receive multiple T1w MRI scans, besides an RC-4DCT scan on the simulation day, including 80 low-resolution (lowR: 5×5×5mm3) free-breathing (FB) 3D cine MRFB images in 40s (2Hz) and a high-resolution (highR: 2×2×2mm3) 3D breath-hold (BH) MRBH image for each patient. A CT (1×1×3mm3) image was selected from 10-bin RC-4DCT with minimal binning artifacts and a close diaphragm match (<1cm) to the MRBH image. A mutual-information-based Freeform deformable image registration (DIR) was used to register the CT and MRBH via the opposite directions (namely F1: and F2: ) to establish CT-MR voxel correspondences. An intensity-based enhanced Demons DIR was then applied for , in which the original MRBH was used in D1: , while the deformed MRBH was used in D2: . The deformation vector fields (DVFs) obtained from each DIR were composed to apply to the deformed CT (D1) and original CT (D2) to reconstruct TR-4DCT images. A digital 4D-XCAT phantom at the end of inhalation (EOI) and end of exhalation (EOE) with 2.5cm diaphragmatic motion and three spherical targets (ϕ=2, 3, 4cm) were first tested to reconstruct TR-4DCT. For each of the six patients, TR-4DCT images at the EOI, middle (MID), and EOE were reconstructed with both D1 and D2 approaches. TR-4DCT image quality was evaluated with mean distance-to-agreement (MDA) at the diaphragm compared with MRFB, tumor volume ratio (TVR) referenced to MRBH, and tumor shape difference (DICE index) compared with the selected input CT. Additionally, differences in the tumor center of mass (|∆COMD1-D2|), together with TVR and DICE comparison, was assessed in the D1 and D2 reconstructed TR-4DCT images. In the phantom, TR-4DCT quality is assessed by MDA=2.0±0.8mm at the diaphragm, TVR=0.8±0.0 for all tumors, and DICE=0.83±0.01, 0.85±0.02, 0.88±0.01 for ϕ=2, 3, 4cm tumors, respectively. In six patients, the MDA in diaphragm match is -1.6±3.1mm (D1) and 1.0±3.9mm (D2) between the reconstructed TR-4DCT and lowR MRFB among 18 images (3 phases/patient). The tumor similarity is TVR=1.2±0.2 and DICE=0.70±0.07 for D1 and TVR=1.4±0.3 (D2) and DICE=0.73±0.07 for D2. The tumor position difference is |∆COMD1-D2|=1.2±0.8mm between D1 and D2 reconstructions. The feasibility of super-resolution reconstruction of multi-breathing-cycle TR-4DCT is demonstrated and image quality at the diaphragm and tumor is assessed in both the 4D-XCAT phantom and six lung cancer patients. The similarity of D1 and D2 reconstruction suggests consistent and reliable DIR results. Clinically, TR-4DCT has the potential for breathing irregularity assessment and dosimetry evaluation in radiotherapy.

Patient-Specific Deep Learning Tracking Framework for Real-Time 2D Target Localization in Magnetic Resonance Imaging-Guided Radiation Therapy

Purpose: We propose a tumor tracking framework for 2D cine MRI based on a pair of deep learning (DL) models relying on patient-specific (PS) training.Methods and materials: The chosen DL models are: 1) an image registration transformer and 2) an auto-segmentation convolutional neural network (CNN). We collected over 1,400,000 cine MRI frames from 219 patients treated on a 0.35 T MRI-linac plus 7,500 frames from additional 35 patients which were manually labelled and subdivided into fine-tuning, validation, and testing sets. The transformer was first trained on the unlabeled data (without segmentations). We then continued training (with segmentations) either on the fine-tuning set or for PS models based on eight randomly selected frames from the first 5 s of each patient's cine MRI. The PS auto-segmentation CNN was trained from scratch with the same eight frames for each patient, without pre-training. Furthermore, we implemented B-spline image registration as a conventional model, as well as different baselines. Output segmentations of all models were compared on the testing set using the Dice similarity coefficient (DSC), the 50% and 95% Hausdorff distance (HD50%/HD95%), and the root-mean-square-error of the target centroid in superior-inferior direction (RMSESI).Results: The PS transformer and CNN significantly outperformed all other models, achieving a median (inter-quartile range) DSC of 0.92 (0.03)/0.90 (0.04), HD50% of 1.0 (0.1)/1.0 (0.4) mm, HD95% of 3.1 (1.9)/3.8 (2.0) mm and RMSESI of 0.7 (0.4)/0.9 (1.0) mm on the testing set. Their inference time was about 36/8 ms per frame and PS fine-tuning required 3 min for labelling and 8/4 min for training. The transformer was better than the CNN in 9/12 patients, the CNN better in 1/12 patients and the two PS models achieved the same performance on the remaining 2/12 testing patients.Conclusion: For targets in the thorax, abdomen and pelvis, we found two PS DL models to provide accurate real-time target localization during MRI-guided radiotherapy.

Abdominal MR Multitasking for radiotherapy treatment planning: A motion-resolved and multicontrast 3D imaging approach.

Radiotherapy treatment planning (RTP) using MR has been used increasingly for the abdominal site. Multiple contrast weightings and motion-resolved imaging are desired for accurate delineation of the target and various organs-at-risk and patient-tailored planning. Current MR protocols achieve these through multiple scans with distinct contrast and variable respiratory motion management strategies and acquisition parameters, leading to a complex and inaccurate planning process. This study presents a standalone MR Multitasking (MT)-based technique to produce volumetric, motion-resolved, multicontrast images for abdominal radiotherapy treatment planning. The MT technique resolves motion and provides a wide range of contrast weightings by repeating a magnetization-prepared (saturation recovery and T2 preparations) spoiled gradient-echo readout series and adopting the MT image reconstruction framework. The performance of the technique was assessed through digital phantom simulations and in vivo studies of both healthy volunteers and patients with liver tumors. In the digital phantom study, the MT technique presented structural details and motion in excellent agreement with the digital ground truth. The in vivo studies showed that the motion range was highly correlated (R2 = 0.82) between MT and 2D cine imaging. MT allowed for a flexible contrast-weighting selection for better visualization. Initial clinical testing with interobserver analysis demonstrated acceptable target delineation quality (Dice coefficient = 0.85 ± 0.05, Hausdorff distance = 3.3 ± 0.72 mm). The developed MT-based, abdomen-dedicated technique is capable of providing motion-resolved, multicontrast volumetric images in a single scan, which may facilitate abdominal radiotherapy treatment planning.

O-218 Prospective validation of an artificial intelligence platform for automated follicle counting

Abstract Study question How often are fully automated follicle counts and measurements modified in expert review? Summary answer Automatic follicle annotations provided by an artificial intelligence platform in regular operation of a clinic were edited 3.27% of the time. What is known already Ovarian follicle counting is frequently performed, time-consuming, and subject to noticeable inter-observer variability; as such, it is well-suited to automatizing with artificial intelligence. FOLLISCAN (MIM Fertility) is a software platform that automatically annotates follicles on 2D or 3D ultrasound cine videos, with exact outlines and measurements, without any manual pre-processing. It has undergone several retrospective tests of its precision and recall in detecting ovarian follicles, accuracy of measurements, and time-savings compared to manual counting Study design, size, duration The platform was integrated with a clinic’s existing picture archiving and medical record systems. Ultrasound scans of ovaries were performed as part of regular infertility treatment in two IVF centers. Scans were made by 3 experts in a period from November to December 2023, automatically sent to the platform, analyzed, and then immediately visualized for review by the person performing the ultrasound, in place of manual annotation. Participants/materials, setting, methods The study included 294 cine videos from 147 examinations of 101 patients, with 4347 follicles in total (14.7 per ovary on average, from 2mm). The platform allows users to modify proposed measurements and introduce new follicle annotations. After confirming the review, results were automatically sent back to the clinic’s system to be used in medical decisions just as manual annotations would be. Main results and the role of chance Among all videos the average number of editions (follicle additions, modifications, or deletions) was 0.48 (CI: 0.37-0.61). In total, 142 follicles out of 4347 (3.27%, CI: 2.74-3.80) were edited: 66 follicles were added (1.52%, CI: 1.15-1.88), 26 were modified, (0.60%, CI: 0.37-0.83 ), and 50 were deleted (1.15%, CI: 0.83–1.47). Out of the 151 edited follicles, 13 (9.2%) had 2–5 mm, 29 (20.4%) had 5-10 mm, 51 (35.9%) had 10-15 mm, 42 (29.6%) had 15-20 mm, and 7 (4.9%) had 20 mm or more in diameter. Among follicles of 15 mm or more in size, 49 were edited. Out of 294 videos, 87 (29.6%, 95% Confidence Interval: 24.4-34.6) were edited in review in any way. Limitations, reasons for caution The study was limited in scope and did not track patients until success rates in IVF treatments could be observed. A multi-center randomized control trial could compare pregnancy rates for treatments performed with and without the platform. Wider implications of the findings An integrated platform allows for easy review, while significantly reducing the time spent on follicle counting in a real-world setting. Automated annotations result in consistent, reliable, and quick measurements, with the number of expert modifications smaller than the inter-observer reliability reported in previous studies. Trial registration number Project support was provided by the Polish National Center for Research and Development no. POIR.01.01.01-00-1634/20-00 and ERC Consolidator Grant TUgbOAT no. 772346. This study was conducted following the approval of the research protocol by the review board of the Regional Medical Chamber in Gdańsk (approval no. KB – 51/22).

P-694 Fully automated follicle counting matches human accuracy levels in predicting stimulation outcomes

Abstract Study question Can automatic measurement of follicles be used to predict the number of oocytes retrieved as accurately as manual physicians’ reports? Summary answer Automated follicular annotations generated by an artificial intelligence platform have similar predictive ability of the number of retrieved and mature oocytes as physician reports. What is known already Ovarian follicle counting is a time-consuming, frequently performed assessment, which has significant inter-observer variability. It’s a perfect candidate for automation using artificial intelligence. The main purpose of this monitoring is to determine the optimal day for trigger administration which maximises the number of retrieved oocytes and increases the chance of pregnancy. This decision typically depends on follicle sizes and hormone levels, while also considering the unique conditions of each patient. Research in this area is ongoing, but it primarily focuses on follicle sizes as measured by physicians, rather than by automated systems. Study design, size, duration This study included 589 in vitro cycles of 545 patients performed in 5 centers in Poland between 2019 and 2021. The 719 examinations that were done at most 3 days before the administration of the trigger were analyzed. Manual reports from follicle measurements have been collected, together with 2D cine loop scans (videos) of the ovaries, and final counts of retrieved oocytes and mature (stage MII) oocytes. The Folliscan artificial intelligence platform provided automatic measurements. Participants/materials, setting, methods Follicles in each ovary were grouped by size, starting from 8mm, then every 2mm up to 24mm, and one group for follicles larger than 24mm. The number of follicles in each group, with the number of days until the trigger shot, was used as features in a linear regression model to predict the number of retrieved oocytes, or MIIs. Two datasets were created: one based on physician reports, and the other on automatic measurements. Main results and the role of chance Ten-fold cross-validation was performed. The model based on manual measurements achieved an R² of 0.62 (Confidence Interval: 0.56–0.68) for predicting the number of retrieved oocytes and 0.51 (CI: 0.45–0.57) for MIIs. In comparison, the model based on automatic annotations achieved an R² of 0.63 (CI: 0.57–0.69) for oocytes and 0.50 (0.43–0.56) for MIIs. The mean average errors (MAE) in the predicted number of oocytes and MIIs were 3.12 (CI: 2.88–3.36) and 2.54 (CI: 2.36–2.72), respectively, for manual measurements. They amounted to 3.09 (CI: 2.85–3.33) and 2.56 (CI: 2.38–2.73) for automatic annotations. Two one-sided tests for equivalence were conducted on the mean average error, with a margin set at 0.2. The results yielded a p-value of 0.026 for oocytes errors and 0.0023 for MIIs errors. Limitations, reasons for caution The study had a limited scope, focusing on research from 5 in vitro clinics. A linear regression model was used, comparing only the potential of automatic measurements with manual measurements. Further development of the model is needed to achieve better accuracy. Wider implications of the findings Manual follicular measurements and automated measurements provide similar predictive values in predicting the number of retrieved and mature oocytes. Automated measurements can be used during stimulation monitoring, offering a reliable and efficient alternative to manual methods. However, clinical studies focusing on interpretable metrics are needed to further validate these findings. Trial registration number Polish National Center for Research and Development no. POIR.01.01.01-00-1634/20-00 and ERC Consolidator Grant TUgbOAT no. 772346.

Motion-Compensated MR CINE Reconstruction With Reconstruction-Driven Motion Estimation.

In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames. In this work, we propose a novel perspective for addressing the MCMR problem and a more integrated and efficient solution to the MCMR field. Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i.e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization. Our approach is unique in that the motion estimation is directly driven by the ultimate goal, reconstruction, but not by the canonical motion-warping loss (similarity measurement between motion-warped images and target images). We align the objectives of motion estimation and reconstruction, eliminating the drawbacks of artifacts-affected motion estimation and therefore error-propagated reconstruction. Further, we can deliver high-quality reconstruction and realistic motion without applying any regularization/smoothness loss terms, circumventing the non-trivial weighting factor tuning. We evaluate our method on two datasets: 1) an in-house acquired 2D CINE dataset for the retrospective study and 2) the public OCMR cardiac dataset for the prospective study. The conducted experiments indicate that the proposed MCMR framework can deliver artifact-free motion estimation and high-quality MR images even for imaging accelerations up to 20x, outperforming SOTA non-MCMR and MCMR methods in both qualitative and quantitative evaluation across all experiments. The code is available at https://github.com/JZPeterPan/MCMR-Recon-Driven-Motion.

Inter-scanner super-resolution of 3D cine MRI using a transfer-learning network for MRgRT

Objective. Deep-learning networks for super-resolution (SR) reconstruction enhance the spatial-resolution of 3D magnetic resonance imaging (MRI) for MR-guided radiotherapy (MRgRT). However, variations between MRI scanners and patients impact the quality of SR for real-time 3D low-resolution (LR) cine MRI. In this study, we present a personalized super-resolution (psSR) network that incorporates transfer-learning to overcome the challenges in inter-scanner SR of 3D cine MRI. Approach: Development of the proposed psSR network comprises two-stages: (1) a cohort-specific SR (csSR) network using clinical patient datasets, and (2) a psSR network using transfer-learning to target datasets. The csSR network was developed by training on breath-hold and respiratory-gated high-resolution (HR) 3D MRIs and their k-space down-sampled LR MRIs from 53 thoracoabdominal patients scanned at 1.5 T. The psSR network was developed through transfer-learning to retrain the csSR network using a single breath-hold HR MRI and a corresponding 3D cine MRI from 5 healthy volunteers scanned at 0.55 T. Image quality was evaluated using the peak-signal-noise-ratio (PSNR) and the structure-similarity-index-measure (SSIM). The clinical feasibility was assessed by liver contouring on the psSR MRI using an auto-segmentation network and quantified using the dice-similarity-coefficient (DSC). Results. Mean PSNR and SSIM values of psSR MRIs were increased by 57.2% (13.8–21.7) and 94.7% (0.38–0.74) compared to cine MRIs, with the reference 0.55 T breath-hold HR MRI. In the contour evaluation, DSC was increased by 15% (0.79–0.91). Average time consumed for transfer-learning was 90 s, psSR was 4.51 ms per volume, and auto-segmentation was 210 ms, respectively. Significance. The proposed psSR reconstruction substantially increased image and segmentation quality of cine MRI in an average of 215 ms across the scanners and patients with less than 2 min of prerequisite transfer-learning. This approach would be effective in overcoming cohort- and scanner-dependency of deep-learning for MRgRT.

Motion-robust free-running volumetric cardiovascular MRI.

To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion. The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets. Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications.

Convolutional LSTM model for cine image prediction of abdominal motion.

Objective.In this study, we tackle the challenge of latency in magnetic resonance linear accelerator (MR-Linac) systems, which compromises target coverage accuracy in gated real-time radiotherapy. Our focus is on enhancing motion prediction precision in abdominal organs to address this issue. We developed a convolutional long short-term memory (convLSTM) model, utilizing 2D cine magnetic resonance (cine-MR) imaging for this purpose.Approach.Our model, featuring a sequence-to-one architecture with six input frames and one output frame, employs structural similarity index measure (SSIM) as loss function. Data was gathered from 17 cine-MRI datasets using the Philips Ingenia MR-sim system and an Elekta Unity MR-Linac equivalent sequence, focusing on regions of interest (ROIs) like the stomach, liver, pancreas, and kidney. The datasets varied in duration from 1 to 10 min.Main results.The study comprised three main phases: hyperparameter optimization, individual training, and transfer learning with or without fine-tuning. Hyperparameters were initially optimized to construct the most effective model. Then, the model was individually applied to each dataset to predict images four frames ahead (1.24-3.28 s). We evaluated the model's performance using metrics such as SSIM, normalized mean square error, normalized correlation coefficient, and peak signal-to-noise ratio, specifically for ROIs with target motion. The average SSIM values achieved were 0.54, 0.64, 0.77, and 0.66 for the stomach, liver, kidney, and pancreas, respectively. In the transfer learning phase with fine-tuning, the model showed improved SSIM values of 0.69 for the liver and 0.78 for the kidney, compared to 0.64 and 0.37 without fine-tuning.Significance. The study's significant contribution is demonstrating the convLSTM model's ability to accurately predict motion for multiple abdominal organs using a Unity-equivalent MR sequence. This advancement is key in mitigating latency issues in MR-Linac radiotherapy, potentially improving the precision and effectiveness of real-time treatment for abdominal cancers.

Non-contrast free-breathing 2D CINE compressed SENSE T1-TFE cardiovascular MRI at 3T in sedated young children for assessment of congenital heart disease.

Cardiac MRI is a crucial tool for assessing congenital heart disease (CHD). However, its application remains challenging in young children when performed at 3T. The aim of this retrospective single center study was to compare a non-contrast free-breathing 2D CINE T1-weighted TFE-sequence with compressed sensing (FB 2D CINE CS T1-TFE) with 3D imaging for diagnostic accuracy of CHD, image quality, and vessel diameter measurements in sedated young children. FB 2D CINE CS T1-TFE was compared with a 3D non-contrast whole-heart sequence (3D WH) and 3D contrast-enhanced MR angiography (3D CE-MRA) at 3T in 37 CHD patients (20♂, 1.5±1.4 years). Two radiologists independently assessed image quality, type of CHD, and diagnostic confidence. Diameters and measures of contrast and sharpness of the aorta and pulmonary vessels were determined. A non-parametric multi-factorial approach was used to estimate diagnostic accuracy for the diagnosis of CHD. Linear mixed models were calculated to compare contrast and vessel sharpness. Krippendorff's alpha was determined to quantify vessel diameter agreement. FB 2D CINE CS T1-TFE was rated superior regarding image quality, diagnostic confidence, and diagnostic sensitivity for both intra- and extracardiac pathologies compared to 3D WH and 3D CE-MRA (all p<0.05). FB 2D CINE CS T1-TFE showed superior contrast and vessel sharpness (p<0.001) resulting in the highest proportion of measurable vessels (740/740; 100%), compared to 3D WH (530/620; 85.5%) and 3D CE-MRA (540/560; 96.4%). Regarding vessel diameter measurements, FB 2D CINE CS T1-TFE revealed the closest inter-reader agreement (Krippendorff's alpha: 0.94-0.96; 3D WH: 0.78-0.94; 3D CE-MRA: 0.76-0.93). FB 2D CINE CS T1-TFE demonstrates robustness at 3T and delivers high-quality diagnostic results to assess CHD in sedated young children. Its ability to function without contrast injection and respiratory compensation enhances ease of use and could encourage widespread adoption in clinical practice.

Manifold-based denoising for Ferumoxytol-enhanced 3D cardiac cine MRI.

The two-dimensional (2D) cine cardiovascular magnetic resonance (CMR) technique is the reference standard for assessing cardiac function. However, one challenge with 2D cine is that the acquisition time for the whole cine stack is long and requires multiple breath holds, which may not be feasible for pediatric or ill patients. Though single breath-hold multi-slice cine may address the issue, it can only acquire low-resolution images, and hence, affect the accuracy of cardiac function assessment. To address these challenges, a Ferumoxytol-enhanced, free breathing, isotropic high-resolution 3D cine technique was developed. The method produces high-contrast cine images with short acquisition times by using compressed sensing together with a manifold-based method for image denoising. This study included fifteen patients (9.1 $ \pm $ 5.6 yrs.) who were referred for clinical cardiovascular magnetic resonance imaging (MRI) with Ferumoxytol contrast and were prescribed the 3D cine sequence. The data was acquired on a 1.5T scanner. Statistical analysis shows that the manifold-based denoised 3D cine can accurately measure ventricular function with no significant differences when compared to the conventional 2D breath-hold (BH) cine. The multiplanar reconstructed images of the proposed 3D cine method are visually comparable to the golden standard 2D BH cine method in terms of clarity, contrast, and anatomical precision. The proposed method eliminated the need for breath holds, reduced scan times, enabled multiplanar reconstruction within an isotropic data set, and has the potential to be used as an effective tool to access cardiovascular conditions.

Assessing aortic motion with automated 3D cine balanced steady state free precession cardiovascular magnetic resonance segmentation

PurposeTo apply a free-running three-dimensional (3D) cine balanced steady state free precession (bSSFP) cardiovascular magnetic resonance (CMR) framework in combination with artificial intelligence (AI) segmentations to quantify time-resolved aortic displacement, diameter and diameter change. MethodsIn this prospective study, we implemented a free-running 3D cine bSSFP sequence with scan time of approximately 4 min facilitated by pseudo-spiral Cartesian undersampling and compressed-sensing reconstruction. Automated segmentation of the aorta in all cardiac timeframes was applied through the use of nnU-Net. Dynamic 3D motion maps were created for three repeated scans per volunteer, leading to the detailed quantification of aortic motion, as well as the measurement and change in diameter of the ascending aorta. ResultsA total of 14 adult healthy volunteers (median age, 28 years (interquartile range [IQR]: 26.0–31.3), 6 females) were included. Automated segmentation compared to manual segmentation of the aorta test set showed a Dice score of 0.93 ± 0.02. The median (IQR) over all volunteers for the largest maximum and mean ascending aorta (AAo) displacement in the first scan was 13.0 (4.4) mm and 5.6 (2.4) mm, respectively. Peak mean diameter in the AAo was 25.9 (2.2) mm and peak mean diameter change was 1.4 (0.5) mm. The maximum individual variability over the three repeated scans of maximum and mean AAo displacement was 3.9 (1.6) mm and 2.2 (0.8) mm, respectively. The maximum individual variability of mean diameter and diameter change were 1.2 (0.5) mm and 0.9 (0.4) mm. ConclusionA free-running 3D cine bSSFP CMR scan with a scan time of four minutes combined with an automated nnU-net segmentation consistently captured the aorta’s cardiac motion-related 4D displacement, diameter, and diameter change.

Clinical utility of a rapid two-dimensional balanced steady-state free precession sequence with deep learning reconstruction

BackgroundCardiovascular magnetic resonance (CMR) cine imaging is still limited by long acquisition times. This study evaluated the clinical utility of an accelerated two-dimensional (2D) cine sequence with deep learning reconstruction (Sonic DL) to decrease acquisition time without compromising quantitative volumetry or image quality. MethodsA sub-study using 16 participants was performed using Sonic DL at two different acceleration factors (8x and 12x). Quantitative left-ventricular volumetry, function and mass measurements were compared between the two acceleration factors against a standard cine method. Following this sub-study, 108 participants were prospectively recruited and imaged using a standard cine method and the Sonic DL method with the acceleration factor that more closely matched the reference method. Two experienced clinical readers rated images based on their diagnostic utility and performed all image contouring. Quantitative contrast difference and endocardial border sharpness were also assessed. Left- and right-ventricular volumetry, left-ventricular mass and myocardial strain measurements were compared between cine methods using Bland-Altman plots, Pearson’s correlation, and paired t-tests. Comparative analysis of image quality was measured using Wilcoxon-signed-rank tests and visualized using bar graphs. ResultsSonic DL at an acceleration factor of 8 more closely matched the reference cine method. There were no significant differences found across left ventricular volumetry, function, or mass measurements. In contrast, an acceleration factor of 12 resulted in a 6% reduction of measured ejection fraction when compared to the standard cine method and a 4% reduction of measured ejection fraction when compared to Sonic DL at an acceleration factor of 8. Thus, Sonic DL at an acceleration factor of 8 was chosen for downstream analysis. In the larger cohort, this accelerated cine sequence was successfully performed in all participants and significantly reduced the acquisition time of cine images compared to the standard 2D method (reduction of 40%, p < 0.0001). Diagnostic image quality ratings and quantitative image quality evaluations were statistically not different between the two methods (p > 0.05). Left- and right-ventricular volumetry and circumferential and radial strain were also similar between methods (p > 0.05) but left-ventricular mass and longitudinal strain were over-estimated using the proposed accelerated cine method (mass over-estimated by 3.36g/m2, p < 0.0001; longitudinal strain over-estimated by 1.97%, p = 0.001). ConclusionsThis study found that an accelerated 2D cine method with DL reconstruction at an acceleration factor of 8 can reduce CMR cine acquisition time by 40% without significantly affecting volumetry or image quality. Given the increase of scan time efficiency, this undersampled acquisition method using deep learning reconstruction should be considered for routine clinical CMR.

Kiosk 8R-FB-08 - Automated Cloud-deployable Pipeline for 3D Cardiac Shape Phenomics FBom 2D Cine Cardiac Magnetic Resonance Imaging

Kiosk 8R-FB-08 - Automated Cloud-deployable Pipeline for 3D Cardiac Shape Phenomics FBom 2D Cine Cardiac Magnetic Resonance Imaging

Kiosk 8R-FB-10 - Exploration of Geometric Deep Learning FBom 3D Cardiac Shape Models for the Classification of Cardiac Amyloid Sub-type FBom 2D Cine Cardiovascular Magnetic Resonance Imaging

Kiosk 8R-FB-10 - Exploration of Geometric Deep Learning FBom 3D Cardiac Shape Models for the Classification of Cardiac Amyloid Sub-type FBom 2D Cine Cardiovascular Magnetic Resonance Imaging

CMR 3-94 - Fully Automated Motion-corrected 2D CINE MRI of the Fetal Heart

CMR 3-94 - Fully Automated Motion-corrected 2D CINE MRI of the Fetal Heart

Kiosk 3R-TC-06 - Feasibility of Ventricular Volumetry in 101 Third Trimester Fetuses Using Motion-corrected Whole Heart 4D Cine MRI with Image-based Gating

Kiosk 3R-TC-06 - Feasibility of Ventricular Volumetry in 101 Third Trimester Fetuses Using Motion-corrected Whole Heart 4D Cine MRI with Image-based Gating

Kiosk 8R-TC-11 - Time to Obviate Gating for Cardiac MR of Mice: FBee Breathing, Motion Resolved 3D CINE t2*-mapping with Whole Heart and Whole Cardiac Cycle Coverage

Kiosk 8R-TC-11 - Time to Obviate Gating for Cardiac MR of Mice: FBee Breathing, Motion Resolved 3D CINE t2*-mapping with Whole Heart and Whole Cardiac Cycle Coverage

Kiosk 11R-TB-10 - Motion-robust 3D Cine Imaging Using Compressive Recovery with Outlier Rejection (CORe)

Kiosk 11R-TB-10 - Motion-robust 3D Cine Imaging Using Compressive Recovery with Outlier Rejection (CORe)