Self-supervised learning for denoising of multidimensional MRI data.

PURPOSE: To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image. THEORY AND METHODS: Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability. RESULTS: The proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images. CONCLUSION: The proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps.

A noise robust image reconstruction using slice aware cycle interpolator network for parallel imaging in MRI.

BACKGROUND: Reducing Magnetic resonance imaging (MRI) scan time has been an important issue for clinical applications. In order to reduce MRI scan time, imaging acceleration was made possible by undersampling k-space data. This is achieved by leveraging additional spatial information from multiple, independent receiver coils, thereby reducing the number of sampled k-space lines. PURPOSE: The aim of this study is to develop a deep-learning method for parallel imaging with a reduced number of auto-calibration signals (ACS) lines in noisy environments. METHODS: A cycle interpolator network is developed for robust reconstruction of parallel MRI with a small number of ACS lines in noisy environments. The network estimates missing (unsampled) lines of each coil data, and these estimated missing lines are then utilized to re-estimate the sampled k-space lines. In addition, a slice aware reconstruction technique is developed for noise-robust reconstruction while reducing the number of ACS lines. We conducted an evaluation study using retrospectively subsampled data obtained from three healthy volunteers at 3T MRI, involving three different slice thicknesses (1.5, 3.0, and 4.5 mm) and three different image contrasts (T1w, T2w, and FLAIR). RESULTS: Despite the challenges posed by substantial noise in cases with a limited number of ACS lines and thinner slices, the slice aware cycle interpolator network reconstructs the enhanced parallel images. It outperforms RAKI, effectively eliminating aliasing artifacts. Moreover, the proposed network outperforms GRAPPA and demonstrates the ability to successfully reconstruct brain images even under severe noisy conditions. CONCLUSIONS: The slice aware cycle interpolator network has the potential to improve reconstruction accuracy for a reduced number of ACS lines in noisy environments.

RenseNet: A Deep Learning Network Incorporating Residual and Dense Blocks with Edge Conservative Module to Improve Small-Lesion Classification and Model Interpretation.

Deep learning has become an essential tool in medical image analysis owing to its remarkable performance. Target classification and model interpretability are key applications of deep learning in medical image analysis, and hence many deep learning-based algorithms have emerged. Many existing deep learning-based algorithms include pooling operations, which are a type of subsampling used to enlarge the receptive field. However, pooling operations degrade the image details in terms of signal processing theory, which is significantly sensitive to small objects in an image. Therefore, in this study, we designed a Rense block and edge conservative module to effectively manipulate previous feature information in the feed-forward learning process. Specifically, a Rense block, an optimal design that incorporates skip connections of residual and dense blocks, was demonstrated through mathematical analysis. Furthermore, we avoid blurring of the features in the pooling operation through a compensation path in the edge conservative module. Two independent CT datasets of kidney stones and lung tumors, in which small lesions are often included in the images, were used to verify the proposed RenseNet. The results of the classification and explanation heatmaps show that the proposed RenseNet provides the best inference and interpretation compared to current state-of-the-art methods. The proposed RenseNet can significantly contribute to efficient diagnosis and treatment because it is effective for small lesions that might be misclassified or misinterpreted.

Unsupervised motion artifact correction of turbo spin-echo MRI using deep image prior.

PURPOSE: In MRI, motion artifacts can significantly degrade image quality. Motion artifact correction methods using deep neural networks usually required extensive training on large datasets, making them time-consuming and resource-intensive. In this paper, an unsupervised deep learning-based motion artifact correction method for turbo-spin echo MRI is proposed using the deep image prior framework. THEORY AND METHODS: The proposed approach takes advantage of the high impedance to motion artifacts offered by the neural network parameterization to remove motion artifacts in MR images. The framework consists of parameterization of MR image, automatic spatial transformation, and motion simulation model. The proposed method synthesizes motion-corrupted images from the motion-corrected images generated by the convolutional neural network, where an optimization process minimizes the objective function between the synthesized images and the acquired images. RESULTS: In the simulation study of 280 slices from 14 subjects, the proposed method showed a significant increase in the averaged structural similarity index measure by 0.2737 in individual coil images and by 0.4550 in the root-sum-of-square images. In addition, the ablation study demonstrated the effectiveness of each proposed component in correcting motion artifacts compared to the corrected images produced by the baseline method. The experiments on real motion dataset has shown its clinical potential. CONCLUSION: The proposed method exhibited significant quantitative and qualitative improvements in correcting rigid and in-plane motion artifacts in MR images acquired using turbo spin-echo sequence.

An Endodontic Forecasting Model Based on the Analysis of Preoperative Dental Radiographs: A Pilot Study on an Endodontic Predictive Deep Neural Network.

INTRODUCTION: This study aimed to evaluate the use of deep convolutional neural network (DCNN) algorithms to detect clinical features and predict the three-year outcome of endodontic treatment on preoperative periapical radiographs. METHODS: A database of single-root premolars that received endodontic treatment or retreatment by endodontists with presence of three-year outcome was prepared (n = 598). We constructed a 17-layered DCNN with a self-attention layer (Periapical Radiograph Explanatory System with Self-Attention Network [PRESSAN-17]), and the model was trained, validated, and tested to 1) detect 7 clinical features, that is, full coverage restoration, presence of proximal teeth, coronal defect, root rest, canal visibility, previous root filling, and periapical radiolucency and 2) predict the three-year endodontic prognosis by analyzing preoperative periapical radiographs as an input. During the prognostication test, a conventional DCNN without a self-attention layer (residual neural network [RESNET]-18) was tested for comparison. Accuracy and area under the receiver-operating-characteristic curve were mainly evaluated for performance comparison. Gradient-weighted class activation mapping was used to visualize weighted heatmaps. RESULTS: PRESSAN-17 detected full coverage restoration (area under the receiver-operating-characteristic curve = 0.975), presence of proximal teeth (0.866), coronal defect (0.672), root rest (0.989), previous root filling (0.879), and periapical radiolucency (0.690) significantly, compared to the no-information rate (P < .05). Comparing the mean accuracy of 5-fold validation of 2 models, PRESSAN-17 (67.0%) showed a significant difference to RESNET-18 (63.4%, P < .05). Also, the area under average receiver-operating-characteristic of PRESSAN-17 was 0.638, which was significantly different compared to the no-information rate. Gradient-weighted class activation mapping demonstrated that PRESSAN-17 correctly identified clinical features. CONCLUSIONS: Deep convolutional neural networks can detect several clinical features in periapical radiographs accurately. Based on our findings, well-developed artificial intelligence can support clinical decisions related to endodontic treatments in dentists.

A Bounding-Box Regression Model for Colorectal Tumor Detection in CT Images Via Two Contrary Networks.

The field of medical image analysis has been attracted to deep learning. Various deep learning-based techniques have been introduced to aid diagnosis in the CT image of the patient. The auxiliary model for diagnosis that we proposed is to detect colorectal tumors in the CT image. The model is combined with two contrary networks of 'Detection Transformer" and 'Hourglass". Furthermore., to improve the performance of the model., we propose an efficient connection method for two contrary models by using intermediate prediction information. A total of 3.,509 patients (193.,567 CT images) were applied to the experiment and our model outperforms the conventional models in colorectal tumor detection. Clinical Relevance - The proposed model in this paper automatically detects colorectal tumors and provides the bounding box in the CT images. Colorectal tumor is one of the common diseases. In addition, the mortality rate is so high that in-time treatment is required. The model we present here has a sensitivity (or recall) of 84.73 % for tumor detection and a precision of 88.25 % in the patient CT data. The in-slice performance of the tumor detection shows an IoU of 0.56, a sensitivity of 0.67, and a precision of 0.68.

The Role of N-Glycosylation in the Intracellular Trafficking and Functionality of Neuronal Growth Regulator 1.

Neuronal growth regulator 1 (NEGR1) is a brain-enriched membrane protein that is involved in neural cell communication and synapse formation. Accumulating evidence indicates that NEGR1 is a generic risk factor for various psychiatric diseases including autism and depression. Endoglycosidase digestion of single NEGR1 mutants revealed that the wild type NEGR1 has six putative N-glycosylation sites partly organized in a Golgi-dependent manner. To understand the role of each putative N-glycan residue, we generated a series of multi-site mutants (2MT-6MT) with additive mutations. Cell surface staining and biotinylation revealed that NEGR1 mutants 1MT to 4MT were localized on the cell surface at different levels, whereas 5MT and 6MT were retained in the endoplasmic reticulum to form highly stable multimer complexes. This indicated 5MT and 6MT are less likely to fold correctly. Furthermore, the removal of two N-terminal sites N75 and N155 was sufficient to completely abrogate membrane targeting. An in vivo binding assay using the soluble NEGR1 protein demonstrated that glycans N286, N294 and N307 on the C-terminal immunoglobulin-like domain play important roles in homophilic interactions. Taken together, these results suggest that the N-glycan moieties of NEGR1 are closely involved in the folding, trafficking, and homodimer formation of NEGR1 protein in a site-specific manner.

Deep Neural Network With Consistency Regularization of Multi-Output Channels for Improved Tumor Detection and Delineation.

Deep learning is becoming an indispensable tool for imaging applications, such as image segmentation, classification, and detection. In this work, we reformulate a standard deep learning problem into a new neural network architecture with multi-output channels, which reflects different facets of the objective, and apply the deep neural network to improve the performance of image segmentation. By adding one or more interrelated auxiliary-output channels, we impose an effective consistency regularization for the main task of pixelated classification (i.e., image segmentation). Specifically, multi-output-channel consistency regularization is realized by residual learning via additive paths that connect main-output channel and auxiliary-output channels in the network. The method is evaluated on the detection and delineation of lung and liver tumors with public data. The results clearly show that multi-output-channel consistency implemented by residual learning improves the standard deep neural network. The proposed framework is quite broad and should find widespread applications in various deep learning problems.

The effect of boiled feed on trace elements of longissimus dorsi muscle in Hanwoo steers.

Boiled feed is obtained by mixing and boiling agricultural by-products such as rice straw, rice bran, and bean curd with grains. The study explored the change in fatty acid, free amino acid, nucleotide, mineral, cholesterol, myoglobin and collagen of longissimus dorsi muscle in Hanwoo steers fed with boiled feed. Forty steers, 20 heads per group, were divided into two groups: a control group and a boiled feed group. The steers were raised for 10 months. The boiled feed group was enriched with palmitoleic acid, oleic acid, arachidonic acid and unsaturated fatty acids compared with the control group. There were no significant differences in amino acid and nucleic acid composition between the two groups. The boiled feed group contained higher levels of iron and manganese in the boiled feed group compared with the control group. The total cholesterol level was significantly increased, whereas calorie levels, myoglobin and collagen composition showed no differences. As the supply of boiled feed increases the content of fatty acids, unsaturated fatty acids and minerals related to flavor, it should be a feed that leads to the production of high-quality beef.

Na/K-ATPase beta1-subunit associates with neuronal growth regulator 1 (NEGR1) to participate in intercellular interactions.

Neuronal growth regulator 1 (NEGR1) is a GPI-anchored membrane protein that is involved in neural cell adhesion and communication. Multiple genome wide association studies have found that NEGR1 is a generic risk factor for multiple human diseases, including obesity, autism, and depression. Recently, we reported that Negr1-/- mice showed a highly increased fat mass and affective behavior. In the present study, we identified Na/K-ATPase, beta1-subunit (ATP1B1) as an NEGR1 binding partner by yeast two-hybrid screening. NEGR1 and ATP1B1 were found to form a relatively stable complex in cells, at least partially co-localizing in membrane lipid rafts. We found that NEGR1 binds with ATP1B1 at its C-terminus, away from the binding site for the alpha subunit, and may contribute to intercellular interactions. Collectively, we report ATP1B1 as a novel NEGR1-interacting protein, which may help deciphering molecular networks underlying NEGR1-associated human diseases. [BMB Reports 2021; 54(3): 164-169].

Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions.

Deep learning is becoming an indispensable tool for various tasks in science and engineering. A critical step in constructing a reliable deep learning model is the selection of a loss function, which measures the discrepancy between the network prediction and the ground truth. While a variety of loss functions have been proposed in the literature, a truly optimal loss function that maximally utilizes the capacity of neural networks for deep learning-based decision-making has yet to be established. Here, we devise a generalized loss function with functional parameters determined adaptively during model training to provide a versatile framework for optimal neural network-based decision-making in small target segmentation. The method is showcased by more accurate detection and segmentation of lung and liver cancer tumors as compared with the current state-of-the-art. The proposed formalism opens new opportunities for numerous practical applications such as disease diagnosis, treatment planning, and prognosis.

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications.

In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. We review classical machine learning algorithms such as Markov random fields, k-means clustering, random forest, etc. Although such classical learning models are often less accurate compared to the deep-learning techniques, they are often more sample efficient and have a less complex structure. We also review different deep-learning architectures, such as the artificial neural networks (ANNs), the convolutional neural networks (CNNs), and the recurrent neural networks (RNNs), and present the segmentation results attained by those learning models that were published in the past 3 yr. We highlight the successes and limitations of each machine learning paradigm. In addition, we discuss several challenges related to the training of different machine learning models, and we present some heuristics to address those challenges.

A locally segmented reconstruction method for parallel imaging.

PURPOSE: A locally segmented parallel imaging reconstruction method is proposed that efficiently utilizes sensitivity distribution of multichannel receiver coil. THEORY AND METHODS: A method of locally segmenting a MR signal is introduced to maximize the differences in sensitivity between receiver channels. A 1D Fourier transformation of the undersampled k-space data is performed along the readout direction, which generates a hybrid 2D space. The hybrid space is partitioned into localized segments along the readout direction. In every localized segment, kernels representing relation between adjacent signals are estimated from autocalibration signals, and data at unsampled points are estimated using the kernels. Then, the images are reconstructed from full k-space data that consists of the sampled data and the estimated data at unsampled points. RESULTS: In a computer simulation and in vivo experiments, the locally segmented reconstruction method produced fewer residual artifacts compared to the conventional parallel imaging reconstruction methods with the same kernel geometry. The performance gain of the proposed method comes from maximizing encoding capability of receiver channels, thus resulting in the accurately estimated kernel weights that reflect the relation between adjacent signals. CONCLUSION: The proposed spatial segmentation method maximally utilizes differences in the sensitivity of receiver channels to reconstruct images with reduced artifacts.

Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images.

Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for image analysis tasks and has shown promising results. Conventional U-Net architectures, however, suffer from three major drawbacks. First, skip connections allow for the duplicated transfer of low resolution information in feature maps to improve efficiency in learning, but this often leads to blurring of extracted image features. Secondly, high level features extracted by the network often do not contain enough high resolution edge information of the input, leading to greater uncertainty where high resolution edge dominantly affects the network's decisions such as liver and liver-tumor segmentation. Thirdly, it is generally difficult to optimize the number of pooling operations in order to extract high level global features, since the number of pooling operations used depends on the object size. To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features. In the case of small object inputs, features in the skip connection are not incorporated with features in the residual path. Furthermore, the proposed architecture has additional convolution layers in the skip connection in order to extract high level global features of small object inputs as well as high level features of high resolution edge information of large object inputs. Efficacy of the modified U-Net (mU-Net) was demonstrated using the public dataset of Liver tumor segmentation (LiTS) challenge 2017. For liver-tumor segmentation, Dice similarity coefficient (DSC) of 89.72 %, volume of error (VOE) of 21.93 %, and relative volume difference (RVD) of - 0.49 % were obtained. For liver segmentation, DSC of 98.51 %, VOE of 3.07 %, and RVD of 0.26 % were calculated. For the public 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb), DSCs were 96.01 % for the liver and 68.14 % for liver-tumor segmentations, respectively. The proposed mU-Net outperformed existing state-of-art networks.

A multicontrast imaging method using steady-state free precession with alternating RF flip angles.

PURPOSE: To obtain multicontrast images including fat-suppressed contrast image, a novel multicontrast imaging method using an SSFP sequence with alternating RF flip angles is proposed. METHODS: The proposed method uses the balanced SSFP sequence with 2 flip angles. In general, the conventional balanced SSFP sequence has its own unique contrast, which combines both FID signal and echo signal under a steady-state condition. By using alternating RF flip angles and RF phase cycling, various image contrasts weighted by proton density, T1 , and T2 can be obtained. The proposed method offers multicontrast images with fat suppression by using the combination of 2 images obtained just after alternating RF pulses, respectively. RESULTS: As demonstrated by simulations, phantom and in vivo experiments, the proposed method provides multicontrast knee images including fat-suppressed contrast images. CONCLUSION: The proposed method can be a useful tool for clinical diagnosis, such as the cartilage segmentation and the fast screening of lesions.

Marinirhabdus citrea sp. nov., a marine bacterium isolated from a seaweed.

A gram-stain-negative, aerobic, rod-shaped (1.3-1.9×0.3-0.5 µm) and non-motile marine bacterium, designated MEBiC09412T, was isolated from seaweed collected at Yeonggwang County, South Korea. 16S rRNA gene sequence analysis demonstrated that strain MEBiC09412T shared high sequence similarity with Marinirhabdus gelatinilytica NH83T (95.4 %). Growth was observed at 17-38 °C (optimum 30 °C), at pH 4.0-8.5 (optimum pH 7.0) and with 0.5-6.0 % (w/v; optimum 2.5 %) NaCl. The predominant cellular fatty acids were iso-C15 : 0 (27.4 %), iso-C15 : 1 G (9.6 %), anteiso-C15 : 0 (14.6 %), iso-C16 : 0 (6.2 %), iso-C17 : 0 3OH (13.2 %) and summed feature 3 (comprising C16 : 1ω6c and/or C16 : 1ω7c; 7.4 %). The DNA G+C content was determined to be 43.1 mol%, while the major respiratory quinone was menaquinone-6. Several phenotypic characteristics such as indole production, the oxidizing patterns of several carbohydrtaes (of glucose, fructose, sucrose, maltose, mannose etc.) and organic acids, and the enzyme activities of α-chymotrypsin and α-glucosidase differentiated strain MEBiC09412T from M. gelatinilytica NH83T. On the basis of this polyphasic taxonomic data, strain MEBiC09412T should be classified as a novel species of the genus Marinirhabduswith the suggested name Marinirhabdus citrea sp. nov. The type strain is MEBiC09412T (=KCCM 43216T=JCM 31588T).

Retrospective motion gating in cardiac MRI using a simultaneously acquired navigator.

A simultaneous acquisition technique of image and navigator signals (simultaneously acquired navigator, SIMNAV) is proposed for cardiac magnetic resonance imaging (CMRI) in Cartesian coordinates. To simultaneously acquire both image and navigator signals, a conventional balanced steady-state free precession (bSSFP) pulse sequence is modified by adding a radiofrequency (RF) pulse, which excites a supplementary slice for the navigator signal. Alternating phases of the RF pulses make it easy to separate the simultaneously acquired magnetic resonance data into image and navigator signals. The navigator signals of the proposed SIMNAV were compared with those of current gating devices and self-gating techniques for seven healthy subjects. In vivo experiments demonstrated that SIMNAV could provide cardiac cine images with sufficient image quality, similar to those from electrocardiogram (ECG) gating with breath-hold. SIMNAV can be used to acquire a cardiac cine image without requiring an ECG device and breath-hold, whilst maintaining feasible imaging time efficiency.

Non-contrast-enhanced peripheral MR angiography using velocity-selective excitation.

PURPOSE: To develop a new non-contrast-enhanced peripheral MR angiography that provides a high contrast angiogram without using electrocardiography triggering and saturation radiofrequency pulses. METHODS: A velocity-selective excitation technique is used in conjunction with the golden-angle radial sampling scheme. The signal amplitude varies according to the velocity of the flow by the velocity-selective excitation technique. Because the arterial blood velocity varies depending on the cardiac phase, the acquired data can be classified into systolic and diastolic phase based on the signal amplitude of the artery. Two images are then reconstructed from the systolic and diastolic phase data, respectively, and an image reflecting the differences between the two images is obtained to eliminate background and vein signals. The performance of the proposed method was compared with the quiescent-interval single shot (QISS) in eight healthy subjects and an elderly subject. RESULTS: The proposed method generated fewer residual venous and background signals than the QISS. Furthermore, the maximum intensity projection images, the relative contrast, and the apparent contrast-to-noise ratio results showed that the proposed method produced a better contrast than the QISS. CONCLUSIONS: The proposed non-contrast-enhanced peripheral MR angiography technique can provide a high contrast angiogram without the use of electrocardiography triggering and saturation radiofrequency pulses. Magn Reson Med 79:779-788, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Oleiagrimonas citrea sp. nov., a marine bacterium isolated from tidal flat sediment and emended description of the genus Oleiagrimonas Fang et al. 2015 and Oleiagrimonas soli.

A Gram-stain-negative, aerobic, rod-shaped (1.4-3.6×0.4-0.6 µm) and motile marine bacterium, designated as MEBiC09124T, was isolated from tidal flat sediment of Suncheon Bay, South Korea. 16S rRNA gene sequence analysis revealed that strain MEBiC09124T showed high similarity to Oleiagrimonas soli 3.5XT (96.7 %). Growth was observed at 18-38 °C (optimum 30 °C), at pH 4.0-8.5 (optimum pH 7.5) and with 0-6 % (w/v) (optimum 2.5 %) NaCl. The predominant cellular fatty acids were iso-C15 : 0, iso-C16 : 0, iso-C17 : 0 and summed feature 9 (comprising 10-methyl C16 : 0 and/or iso-C17 : 1ω9c). The DNA G+C content was 66.1 mol%. The major respiratory quinone was Q-8. Biochemical characteristics such as production of acetoin and enzyme activities of trypsin, α-chymotrypsin and N-acetyl-ß-glucosaminidase differentiated strain MEBiC09124T from O. soli 3.5XT. On the basis of data from this polyphasic taxonomic study, strain MEBiC09124T (=KCCM 43131T=JCM 30904T) is classified as the type strain of a novel species in the genus Oleiagrimonas, for which the name Oleiagrimonas citrea sp. nov. is proposed. Emended descriptions of the genus Oleiagrimonas and Oleiagrimonas soli are also given.

Self-gated cardiac cine imaging using phase information.

PURPOSE: To obtain multiphase cardiac cine images with high resolution, a novel self-gating method for both cardiac and respiratory motions is proposed. METHODS: The proposed method uses the phase of projection data obtained from a separate axial slice to measure cardiac and respiratory motion, after the acquisition of every k-space line in the image plane. Cardiac motion is estimated from the phase of the projection data passing through the aorta, which is amplified by superior-inferior directional bipolar gradients, whereas respiratory motion is estimated from the phase of the left-right directional projection data of the abdomen. To verify the proposed self-gating method, a simulation and in vivo steady state free precession cardiac imaging were performed. RESULTS: The proposed method provides high resolution multiphase cardiac cine images. Using the proposed self-gating method, the phase variation of the projection data offers information about cardiac and respiratory motions that is equivalent to external gating devices. CONCLUSION: The proposed method can capture time-resolved cardiac and respiratory motion from the phase information of the projection data. Because the projection data is obtained from a separate gating slice, the self-gating signals are not affected by imaging planes. Magn Reson Med 77:1216-1222, 2017. © 2016 International Society for Magnetic Resonance in Medicine.