Dong Liang

• Ph.D.
• Professor (Full) at Chinese Academy of Sciences

Dynamic Magnetic Resonance Imaging (MRI) exhibits transformation symmetries, including spatial rotation symmetry within individual frames and temporal symmetry along the time dimension. Explicit incorporation of these symmetry priors in the reconstruction model can significantly improve image quality, especially under aggressive undersampling scena...

Reconstructing visual information from brain activity bridges the gap between neuroscience and computer vision. Even though progress has been made in decoding images from fMRI using generative models, a challenge remains in accurately recovering highly complex visual stimuli. This difficulty stems from their elemental density and diversity, sophist...

Background Self‐supervised methods for magnetic resonance imaging (MRI) reconstruction have garnered significant interest due to their ability to address the challenges of slow data acquisition and scarcity of fully sampled labels. Current regularization‐based self‐supervised techniques merge the theoretical foundations of regularization with the r...

Objective. To develop an accurate myocardial T1 mapping technique at 5T using Look-Locker-based multiple inversion-recovery with the real-time spoiled gradient echo (GRE) acquisition. Approach. The proposed T1 mapping technique (mIR-rt) samples the recovery of inverted magnetization using the real-time GRE and the images captured during diastole ar...

Physiological and external motion cause inter-frame misalignment in chemical exchange saturation transfer magnetic resonance imaging (CEST-MRI), thereby compromising quantitative accuracy. In CEST-MRI, saturation effects induce intensity variations, resulting in motion-intensity coupling that makes registration particularly challenging. To address...

Dynamic MRI plays a vital role in clinical practice by capturing both spatial details and dynamic motion, but its high spatiotemporal resolution is often limited by long scan times. Deep learning (DL)-based methods have shown promising performance in accelerating dynamic MRI. However, most existing algorithms rely on large fully-sampled datasets fo...

Objective. Magnetic resonance imaging (MRI) is critical in medical diagnosis and treatment by capturing detailed features, such as subtle tissue changes, which help clinicians make precise diagnoses. However, the widely used single diffusion model has limitations in accurately capturing more complex details. This study aims to address these limitat...

Objective The purpose of this study is to perform multiple ( ≥ 3 ) material decomposition with deep learning method for spectral cone-beam CT (CBCT) imaging based on ultra-slow kV switching. Approach In this work, a novel deep neural network called SkV-Net is developed to reconstruct multiple material density images from the ultra-sparse spectral...

Keywords: Synthetic MR, MR Fingerprinting/Synthetic MR Motivation: Quantitative MRI techniques like MRF provide multi-parametric maps, but traditional dictionary-based methods face issues with model simplifications and quantificationerrors, resulting in parametric maps that often lack consistency with clinically relevant weighted contrast images. G...

Keywords: fMRI Analysis, fMRI Analysis Motivation: Currently, Sparse and Subspace Constraints have not been used for ultra-short TR Line-scanning fMRI accelerated reconstruction. Goal(s): Shorten Line-scanning fMRI scanning time and improve image quality. Approach: Reconstruct simulated undersampled Line-scanning fMRI data using reconstruction algo...

Keywords: Image Reconstruction, AI/ML Image Reconstruction Motivation: Applying quantum computing to the reconstruction of magnetic resonance imaging. Goal(s): Improving the quality of magnetic resonance reconstruction images by introducing quantum computing. Approach: Building a quantum hybrid classical neural network for undersampling magnetic re...

Keywords: Microstructure, Diffusion Analysis and Visualization Motivation: Understanding how different MRI field strengths affect NODDI metrics is crucial for ensuring diagnostic stability in TSC. This study addresses the gap in knowledgeregarding the impact of 3T and 5T MRI scanners on NODDI's diagnostic accuracy. Goal(s): To assess how varying MR...

Keywords: AI Diffusion Models, AI/ML Image Reconstruction, schobridge Motivation: Magnetic Resonance Imaging (MRI) suffers from slow acquisition speeds, particularly in multi-modal imaging. Existing techniques experience image qualitydegradation and lack robustness under high-factor undersampling. Goal(s): A novel guided reconstruction framework is...

Keywords: Diffusion Acquisition, Diffusion Acquisition, Low-field MRI, L+S decomposition, diffusion reconstruction Motivation: Low-field MRI systems face challenges in diffusion imaging due to limited gradient strength and switching rates. Overcoming these limits is essential to broadenlow-field MRI applications. Goal(s): Develop an advanced, gradi...

Keywords: AI Diffusion Models, PET/MR, Radiomics Motivation: PET imaging offers valuable diagnostic insights but is limited by high costs and radiation exposure. Synthesizing PET-like images from MRI offers a viablealternative, though current methods largely overlook quantitative metrics such as radiomics, which could enhance diagnostic accuracy. G...

Keywords: AI/ML Image Reconstruction, Cardiomyopathy Motivation: Current cardiac T1 mapping technique suffers from long acquisition time and its sensitivity to noise or motion artifacts. Goal(s): To reduce the acquisition time and to improve the robustness against motion or noise artifacts in cardiac T1 mapping. Approach: A deep learning framework...

Fast PET imaging is clinically important for reducing motion artifacts and improving patient comfort. While recent diffusion-based deep learning methods have shown promise, they often fail to capture the true PET degradation process, suffer from accumulated inference errors, introduce artifacts, and require extensive reconstruction iterations. To a...

Reconstructing images from undersampled k-space data is crucial for accelerating MRI acquisition. While deep learning methods have shown advantages, they typically rely on fully sampled reference data, which are difficult to obtain and time-consuming. To address this issue, we propose a self-supervised method based on two parallel reconstruction ne...

Pathological image and genomic data have been widely explored in cancer survival analysis. However, due to the instability of gene sequencing and the randomness of tumor tissue sampling, it remains challenging to extract robust feature representation from each modality. Besides, the high semantic heterogeneity also adds an extra burden to the integ...

Integrating multimodal data can uncover causal features hidden in single-modality analyses, offering a comprehensive understanding of disease complexity. This study introduces a multimodal fusion subtyping (MOFS) framework that integrates radiological, pathological, genomic, transcriptomic, and proteomic data from 122 patients with IDH-wildtype adu...

Background Magnetic resonance fingerprinting (MRF) is a rapid imaging technique for simultaneous mapping of multiple tissue properties such as T1 and T2 relaxation times. However, conventional pattern matching reconstruction and iterative low rank reconstruction methods may not take full advantage of the spatiotemporal content of MRF data and can r...

Background To minimize radiation exposure while obtaining high‐quality Positron Emission Tomography (PET) images, various methods have been developed to derive standard‐count PET (SPET) images from low‐count PET (LPET) images. Although deep learning methods have enhanced LPET images, they rarely utilize the rich complementary information from MR im...

Purpose: To develop 5T-SRIS, an improved 5T myocardial T1 mapping method based on MOLLI, which addresses limitations in inversion efficiency, readout perturbations, and imperfect magnetization recovery. Methods: The proposed 5T-SRIS method is based on a modified 5-(3)-3 MOLLI sequence with ECG gating and gradient echo readout. To improve inversion...

Objective. Dynamic positron emission tomography (dPET) is an important molecular imaging technology that is used for the clinical diagnosis, staging, and treatment of various human cancers. Higher temporal imaging resolutions are desired for the early stages of radioactive tracer metabolism. However, images reconstructed from raw data with shorter...

Purpose Whole-brain segmentation via positron emission tomography (PET) imaging is crucial for advancing neuroscience research and clinical medicine, providing essential insights into biological metabolism and activity within different brain regions. However, the low resolution of PET images may have limited the segmentation accuracy of multiple br...

Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate 3D-MC-CMR acquisition by a novel method based on score-based diffusion models with self-supervised learning. Specifically, we first establish a mapping between the undersa...

In clinical practice, particularly in neurology assessments, imaging multiparametric MR images with a single‐sequence scan is often limited by either insufficient imaging contrast or the constraints of accelerated imaging techniques. A novel single scan 3D imaging method, incorporating Wave‐CAIPI and MULTIPLEX technologies and named WAMP, has been...

Purpose: To develop an accurate myocardial T1 mapping technique at 5T using Look-Locker-based multiple inversion-recovery with the real-time spoiled gradient echo (GRE) acquisition. Methods: The proposed T1 mapping technique (mIR-rt) samples the recovery of inverted magnetization using the real-time GRE and the images captured during diastole are s...

Low-count Positron Emission Tomography reconstruction is critical for maintaining high imaging quality while minimizing tracer doses and radiation exposure. Although integrating structural information from CT and MR data has been shown to enhance PET reconstruction, this typically requires simultaneous PET and CT/MRI scans, complicating workflows a...

Pathological image and genomic data have been widely explored in cancer survival analysis. However, due to the instability of gene sequencing and the randomness of tumor tissue sampling, it remains challenging to extract robust feature representation from each modality. Besides, the high semantic het-erogeneity also adds an extra burden to the inte...

Simultaneous Multi-Slice(SMS) is a magnetic resonance imaging (MRI) technique which excites several slices concurrently using multiband radiofrequency pulses to reduce scanning time. However, due to its variable data structure and difficulty in acquisition, it is challenging to integrate SMS data as training data into deep learning frameworks.This...

Background Cervical cancer remains a critical global health issue, responsible for over 600,000 new cases and 300,000 deaths annually. Pathological imaging of cervical cancer is a crucial diagnostic tool. However, distinguishing specific areas of cellular differentiation remains challenging because of the lack of clear boundaries between cells at v...

Multi-organ segmentation in total-body PET images is crucial for accurately locating abnormalities and assisting in the observation of corresponding metabolic regions in the human body. Despite the emergence of numerous advanced methods in the field of multi-organ segmentation in recent years, available PET image segmentation techniques remain rela...

The field of accelerated magnetic resonance imaging (AMRI) has garnered significant attention, focusing on reconstructing target image from compressively sampled k-space measurements to address an ill-posed linear inverse problem. In this study, we exploit the multiparameterization of MRI to propose a new plug-and-play prior (P3) for enhancing reco...

Dynamic positron emission tomography (PET) parametric imaging typically requires a 60-minute acquisition period, causing patient discomfort and reducing clinical efficiency. This study explores the feasibility of generating parametric Ki images from 10-minute dynamic PET images acquired in the early or late scanning phases employing a multi-channel...

Background The complementary absorption contrast CT (ACT) and differential phase contrast CT (DPCT) can be generated simultaneously from an x‐ray computed tomography (CT) imaging system incorporated with grating interferometer. However, it has been reported that ACT images exhibit better spatial resolution than DPCT images. By far, the primary caus...

Purpose This study aimed to implement high-end positron emission tomography (PET) equipment to assist conventional PET equipment in improving image quality via a distribution learning-based diffusion model. Methods A diffusion model was first trained on a dataset of high-quality (HQ) images acquired by a high-end PET device (uEXPLORER scanner), an...

Background Recently, the popularity of dual‐layer flat‐panel detector (DL‐FPD) based dual‐energy cone‐beam CT (CBCT) imaging has been increasing. However, the image quality of dual‐energy CBCT remains constrained by the Compton scattered x‐ray photons. Purpose The objective of this study is to develop a novel scatter correction method, named e‐Gri...

Purposes Positron emission tomography (PET) imaging is widely used to detect focal lesions or diseases and to study metabolic abnormalities between organs. However, analyzing organ correlations alone does not fully capture the characteristics of the metabolic network. Our work proposes a graph‐based analysis method for quantifying the topological p...

The precise segmentation of different brain regions and tissues is usually a prerequisite for the detection and diagnosis of various neurological disorders in neuroscience. Considering the abundance of functional and structural dual-modality information for positron emission tomography/magnetic resonance (PET/MR) images, we propose a novel 3D whole...

To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm cone-beam computed tomography (CBCT) equipped in the interventional suite. However, limited by the slow gantry rotation speed, the temporal resolution and temporal sampling d...

Magnetic Resonance Imaging (MRI) is a multi-contrast imaging technique in which different contrast images share similar structural information. However, conventional diffusion models struggle to effectively leverage this structural similarity. Recently, the Schr\"odinger Bridge (SB), a nonlinear extension of the diffusion model, has been proposed t...

Background Multi‐material decomposition is an interesting topic in dual‐energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms. Purpose In this work, a novel multi‐material decomposition network (MMD‐Net) is proposed to improve the multi‐material decomposition performance of DECT imaging....

Diffusion model-based approaches recently achieved re-markable success in MRI reconstruction, but integration into clinical routine remains challenging due to its time-consuming convergence. This phenomenon is partic-ularly notable when directly apply conventional diffusion process to k-space data without considering the inherent properties of k-sp...

Diffusion models have recently demonstrated considerable advancement in the generation and reconstruction of magnetic resonance imaging (MRI) data. These models exhibit great potential in handling unsampled data and reducing noise, highlighting their promise as generative models. However, their application in dynamic MRI remains relatively underexp...

Purpose Total-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations (\documentclass[12pt]{minimal} \usepackage{amsmath} \...

Objective. The aim of this study was to investigate the impact of the bowtie filter on the image quality of the photon-counting detector (PCD) based CT imaging. Approach. Numerical simulations were conducted to investigate the impact of bowtie filters on image uniformity using two water phantoms, with tube potentials ranging from 60 to 140 kVp with...

Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient methods for motion correction in cardiac T1 mapping. In this paper, we propose a deep learning-based and topology-preserving image registration framework for motion correction in cardiac T1 mapping....

Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms to recover these features. In recent years, the development of quantum computing has discovered that quantum convolution can improve network accuracy, possibly due to pote...

Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative imaging technique within the field of Magnetic Resonance Imaging (MRI), offers comprehensive insights into tissue properties by simultaneously acquiring multiple tissue parameter maps in a single acquisition. Sequence optimization is crucial for improving the accuracy a...

Recently, diffusion models have shown considerable promise for MRI reconstruction. However, extensive experimentation has revealed that these models are prone to generating artifacts due to the inherent randomness involved in generating images from pure noise. To achieve more controlled image reconstruction, we reexamine the concept of interpolatab...

Dynamic MR images possess various transformation symmetries,including the rotation symmetry of local features within the image and along the temporal dimension. Utilizing these symmetries as prior knowledge can facilitate dynamic MR imaging with high spatiotemporal resolution. Equivariant CNN is an effective tool to leverage the symmetry priors. Ho...

Objective. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastases (BMs). The delivery of drugs to the central nervous system is challenging because of the blood–brain barrier, leading to a relatively poor prognosis for patients with BMs. Therefore, early detection and treatment of BMs are highly importa...

Dynamic cerebral perfusion CT (PCT) is an effective imaging technique for the clinical diagnosis and therapy guidance of many kinds of cerebrovascular diseases (CVDs), but the large radiation dose imposed on a patient during repeated CT scans greatly limits its clinical applications. Achieving low-dose PCT imaging with the help of advanced and sati...

Dual-energy computed tomography (DECT) has been widely used to obtain quantitative elemental composition of imaged subjects for personalized and precise medical diagnosis. Compared with DECT leveraging advanced X-ray source and/or detector technologies, the use of the sequential-scanning data acquisition scheme to implement DECT may make a broader...

Background: Recently, the popularity of dual-layer flat-panel detector (DL-FPD) based dual-energy cone-beam CT (DE-CBCT) imaging has been increasing. However, the image quality of DE-CBCT remains constrained by the Compton scattered X-ray photons. Purpose: The objective of this study is to develop an energy-modulated scatter correction method for D...

Tuberous sclerosis complex (TSC) manifests as a multisystem disorder with significant neurological implications. This study addresses the critical need for robust classification models tailored to TSC in pediatric patients, introducing QResNet,a novel deep learning model seamlessly integrating conventional convolutional neural networks with quantum...

PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming by PET-MRI systems. We aim to accelerate MRI and improve PET image quality. This paper proposed a novel joint reconstruction model by diffusion stochastic differential equations based on learning the joint probability distribution...

Diffusion model has been successfully applied to MRI reconstruction, including single and multi-coil acquisition of MRI data. Simultaneous multi-slice imaging (SMS), as a method for accelerating MR acquisition, can significantly reduce scanning time, but further optimization of reconstruction results is still possible. In order to optimize the reco...

Objective. This study aims at developing a simple and rapid Compton scatter correction approach for cone-beam CT (CBCT) imaging. Approach. In this work, a new Compton scatter estimation model is established based on two distinct CBCT scans: one measures the full primary and scatter signals without anti-scatter grid (ASG), and the other measures a p...

The rapid development of e-Health provides elderly consumers with more convenient medical services. Alzheimer’s disease is one of the major diseases that threaten the health of the elderly. Its early detection is vital for its effective treatment and management. In this study, an end-to-end model, individual-to-group graph convolutional network (IG...

Objective. This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4–10 mm in size) in computed tomography angiography images. Approach. This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two compon...

Objective. Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. But PET suffers from a low signal-to-noise ratio, while MRI are time-consuming. To address time-consuming, an effective strategy involves reducing k-space data collection, albeit at the cost of lowering image quality....

Quantitative T1rho parameter mapping has shown promise in clinical and research studies. However, it suffers from long scan times. Deep learning-based techniques have been successfully applied in accelerated quantitative MR parameter mapping. However, most methods require fully-sampled training dataset, which is impractical in the clinic. In this s...

A dedicated brain PET scanner can achieve higher spatial resolution, sensitivity, and cost-effectiveness than whole-body PET scanners. In this study, we present the software system for a dedicated brain PET scanner, encompassing data acquisition, detector calibration, sinogram generation, imaging reconstruction, and data correction. The dedicated b...

The low-density imaging performance of a zone plate-based nano-resolution hard x-ray computed tomography (CT) system can be significantly improved by incorporating a grating-based Lau interferometer. Due to the diffraction, however, the acquired nano-resolution phase signal may suffer splitting problem, which impedes the direct reconstruction of ph...

Background Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) stand as pivotal diagnostic tools for brain disorders, offering the potential for mutually enriching disease diagnostic perspectives. However, the costs associated with PET scans and the inherent radioactivity have limited the widespread application of PET. Furthermo...

Positron emission tomography/magnetic resonance imaging (PET/MRI) systems can provide precise anatomical and functional information with exceptional sensitivity and accuracy for neurological disorder detection. Nevertheless, the radiation exposure risks and economic costs of radiopharmaceuticals may pose significant burdens on patients. To mitigate...

Nasopharyngeal carcinoma (NPC) is a malignant tumor primarily treated by radiotherapy. Accurate delineation of the target tumor is essential for improving the effectiveness of radiotherapy. However, the segmentation performance of current models is unsatisfactory due to poor boundaries, large-scale tumor volume variation, and the labor-intensive na...

This paper proposes an ultrafast and robust myocardial T1 mapping technique for myocardial T1 mapping utilizing a minimal set of T1-weighted images obtained from a single inversion recovery (IR) pulse. By integrating a Convolutional Neural Network (CNN) with a Fully Connected (FC) network, UltraMAP proficiently handles three undersampled T1-weighte...

Tuberous sclerosis complex (TSC) presents as a multisystem disorder with profound neurological impacts. Our study introduces Quantum-Residual Neural Network (QR-Net), a novel quantum neural network model that innovatively combines conventional residual convolutional neural networks (CNNs) with quantum layers for pediatric TSC classification. QR-Net...

Semi-supervised segmentation by using large amounts of unlabeled data and small amounts of labeled data have achieved great success. This paper proposed a semi-supervised segmentation method based on consistent learning and contrast learning. It mainly uses a mean-teacher framework to add consistency losses and contrast losses based on multi-scale...

Motivation: Diffusion model has been applied to MRI reconstruction, including single and multi-coil acquisition of MRI data. Simultaneous multi-slice imaging (SMS), as a methodfor accelerating MR acquisition, significantly reduces scanning time, but further optimization of reconstruction results is still possible. Goal(s): In order to optimize the...

Motivation: The high-field-like image reconstruction, mainstream efforts are primarily focused on high or ultra-high fields, lacking in the reconstruction of high-field-like imagesfrom low-field ones. Goal(s): This paper presented a model for reconstructing high-field-like MR images from low-field images with unpaired data. Approach: We execute a p...

Motivation: Supervised deep learning (SDL) has limitations due to data dependency, and self-supervised frameworks like DIP struggle with noise and artifacts. Goal(s): Introducing PEARL, a novel self-supervised accelerated parallel MRI approach. Approach: PEARL leverages joint deep decoders coupling with cross-fusion schemes based on multi-parameter...

Motivation: When using complex prior parameters for regularization in the MPI reconstruction method based on the system matrix, the process is very time-consuming and thepreprocessing process is complex. Goal(s): The purpose of this study is to simplify the reconstruction process and achieve efficient real-time reconstruction of magnetic particle i...

Motivation: This study seeks to address the challenge of human layer-specific fMRI at 5.0T whole-body MRI scanner. Goal(s): Develop Layer-specific VASO fMRI based on the slice-selective slab-inversion vascular space occupancy (SS-SI VASO) pulse sequence at 5.0T scanner. Approach: The SS-SI VASO pulse sequence with GOIA RF pulse at 5.0T scanner has...

Motivation: PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming by PET-MRI systems. Goal(s): We aim to accelerate MRI and improve PET image quality. Approach: This paper proposed a novel joint reconstruction model by diffusion stochastic differential equations based on learning the...

Motivation: The beating of the heart is predictable, and existing methods mainly focus on sparsity and low rank, ignore the predictability. Goal(s): Our goal is to improve the quality of dMRI reconstruction through predictability. Approach: Introduced a method to extract predictability latent vectors and reconstruct images based on it. Results: We...

In the planning phase of radiation therapy, PET images are frequently integrated with CT and MRI to accurately delineate the target region for treatment. However, obtaining additional CT or MR images solely for localization purposes proves to be financially burdensome, time-intensive, and may increase patient radiation exposure. To alleviate these...

Objective. In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) da...

Purpose CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging....

Purpose To develop a novel deep learning‐based method inheriting the advantages of data distribution prior and end‐to‐end training for accelerating MRI. Methods Langevin dynamics is used to formulate image reconstruction with data distribution before facilitate image reconstruction. The data distribution prior is learned implicitly through the end...

Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework...

Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term...

Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (C...