Motion-compensated image reconstruction for improved kidney function assessment using dynamic contrast-enhanced MRI.

Accurately measuring renal function is crucial for pediatric patients with kidney conditions. Traditional methods have limitations, but dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a safe and efficient approach for detailed anatomical evaluation and renal function assessment. However, motion artifacts during DCE-MRI can degrade image quality and introduce misalignments, leading to unreliable results. This study introduces a motion-compensated reconstruction technique for DCE-MRI data acquired using golden-angle radial sampling. Our proposed method achieves three key objectives: (1) identifying and removing corrupted data (outliers) using a Gaussian process model fitting with a k -space center navigator, (2) efficiently clustering the data into motion phases and performing interphase registration, and (3) utilizing a novel formulation of motion-compensated radial reconstruction. We applied the proposed motion correction (MoCo) method to DCE-MRI data affected by varying degrees of motion, including both respiratory and bulk motion. We compared the outcomes with those obtained from the conventional radial reconstruction. Our evaluation encompassed assessing the quality of images, concentration curves, and tracer kinetic model fitting, and estimating renal function. The proposed MoCo reconstruction improved the temporal signal-to-noise ratio for all subjects, with a 21.8% increase on average, while total variation values of the aorta, right, and left kidney concentration were improved for each subject, with 32.5%, 41.3%, and 42.9% increases on average, respectively. Furthermore, evaluation of tracer kinetic model fitting indicated that the median standard deviation of the estimated filtration rate ( σ F T ), mean normalized root-mean-squared error (nRMSE), and chi-square goodness-of-fit of tracer kinetic model fit were decreased from 0.10 to 0.04, 0.27 to 0.24, and, 0.43 to 0.27, respectively. The proposed MoCo technique enabled more reliable renal function assessment and improved image quality for detailed anatomical evaluation in the case of bulk and respiratory motion during the acquisition of DCE-MRI.

Ultrasound guidance in navigated liver surgery: toward deep-learning enhanced compensation of deformation and organ motion.

PURPOSE: Accuracy of image-guided liver surgery is challenged by deformation of the liver during the procedure. This study aims at improving navigation accuracy by using intraoperative deep learning segmentation and nonrigid registration of hepatic vasculature from ultrasound (US) images to compensate for changes in liver position and deformation. METHODS: This was a single-center prospective study of patients with liver metastases from any origin. Electromagnetic tracking was used to follow US and liver movement. A preoperative 3D model of the liver, including liver lesions, and hepatic and portal vasculature, was registered with the intraoperative organ position. Hepatic vasculature was segmented using a reduced 3D U-Net and registered to preoperative imaging after initial alignment followed by nonrigid registration. Accuracy was assessed as Euclidean distance between the tumor center imaged in the intraoperative US and the registered preoperative image. RESULTS: Median target registration error (TRE) after initial alignment was 11.6 mm in 25 procedures and improved to 6.9 mm after nonrigid registration (p = 0.0076). The number of TREs above 10 mm halved from 16 to 8 after nonrigid registration. In 9 cases, registration was performed twice after failure of the first attempt. The first registration cycle was completed in median 11 min (8:00-18:45 min) and a second in 5 min (2:30-10:20 min). CONCLUSION: This novel registration workflow using automatic vascular detection and nonrigid registration allows to accurately localize liver lesions. Further automation in the workflow is required in initial alignment and classification accuracy.

Framework for lumen-based nonrigid tomographic coregistration of intravascular images.

Purpose: Modern medical imaging enables clinicians to effectively diagnose, monitor, and treat diseases. However, clinical decision-making often relies on combined evaluation of either longitudinal or disparate image sets, necessitating coregistration of multiple acquisitions. Promising coregistration techniques have been proposed; however, available methods predominantly rely on time-consuming manual alignments or nontrivial feature extraction with limited clinical applicability. Addressing these issues, we present a fully automated, robust, nonrigid registration method, allowing for coregistering of multimodal tomographic vascular image datasets using luminal annotation as the sole alignment feature. Approach: Registration is carried out by the use of the registration metrics defined exclusively for lumens shapes. The framework is primarily broken down into two sequential parts: longitudinal and rotational registration. Both techniques are inherently nonrigid in nature to compensate for motion and acquisition artifacts in tomographic images. Results: Performance was evaluated across multimodal intravascular datasets, as well as in longitudinal cases assessing pre-/postinterventional coronary images. Low registration error in both datasets highlights method utility, with longitudinal registration errors-evaluated throughout the paired tomographic sequences-of 0.29 ± 0.14 mm ( < 2 longitudinal image frames) and 0.18 ± 0.16 mm ( < 1 frame) for multimodal and interventional datasets, respectively. Angular registration for the interventional dataset rendered errors of 7.7 ° ± 6.7 ° , and 29.1 ° ± 23.2 ° for the multimodal set. Conclusions: Satisfactory results across datasets, along with additional attributes such as the ability to avoid longitudinal over-fitting and correct nonlinear catheter rotation during nonrigid rotational registration, highlight the potential wide-ranging applicability of our presented coregistration method.

Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries.

X-ray angiography is commonly used in the diagnosis and treatment of coronary artery disease with the advantage of visualization of the inside of blood vessels in real-time. However, it has several disadvantages that occur in the acquisition process, which causes inconvenience and difficulty. Here, we propose a novel segmentation and nonrigid registration method to provide useful real-time assistive images and information. A convolutional neural network is used for the segmentation of coronary arteries in 2D X-ray angiography acquired from various angles in real-time. To compensate for errors that occur during the 2D X-ray angiography acquisition process, 3D CT angiography is used to analyze the topological structure. A novel energy function-based 3D deformation and optimization is utilized to implement real-time registration. We evaluated the proposed method for 50 series from 38 patients by comparing the ground truth. The proposed segmentation method showed that Precision, Recall, and F1 score were 0.7563, 0.6922, and 0.7176 for all vessels, 0.8542, 0.6003, and 0.7035 for markers, and 0.8897, 0.6389, and 0.7386 for bifurcation points, respectively. In the nonrigid registration method, the average distance of 0.8705, 1.06, and 1. 5706 mm for all vessels, markers, and bifurcation points was achieved. The overall process execution time was 0.179 s.

Delayed PET imaging using image synthesis network and nonrigid registration without additional CT scan.

PURPOSE: Attenuation correction is critical for positron emission tomography (PET) image reconstruction. The standard protocol for obtaining attenuation information in a clinical PET scanner is via coregistered computed tomography (CT) images. Therefore, for delayed PET imaging, the CT scan is repeated twice, which increases the radiation dose for the patient. In this paper, we propose a zero-extradose delayed PET imaging method that requires no additional CT scans. METHODS: A deep learning-based synthesis network is designed to convert PET data into pseudo-CT images for delayed scans. Then, nonrigid registration is performed between this pseudo CT image and the CT image of the first scan, warping the CT image of the first scan to an estimated CT image for the delayed scan. Finally, the PET image attenuation correction in the delayed scan is obtained from this estimated CT image. Experiments with clinical datasets are implemented to assess the effectiveness of the proposed method with the well-recognized Generative Adversarial Networks (GAN) method. The average peak signal-to-noise ratio (PSNR) and the mean absolute percent error (MAPE) are used for comparison. We also use scoring from three experienced radiologists as subjective measurement means based on the diagnostic consistency of the PET images reconstructed from GAN and the proposed method with respect to the ground truth images. RESULTS: The experiments show that the average PSNR is 47.04 dB (the proposed method) vs. 44.41 dB (the traditional GAN method) for the reconstructed delayed PET images in our evaluation dataset. The average MAPEs are 1.59% for the proposed method and 3.32% for the traditional GAN method across five organ regions of interest (ROIs). The scores for the GAN and the proposed method rated by three experienced radiologists are 8.08±0.60 and 9.02±0.52, indicating that the proposed method yields more consistent PET images with the ground truth. CONCLUSIONS: This work proposes a novel method for CT-less delayed PET imaging based on image synthesis network and nonrigid image registration. The PET image reconstructed using the proposed method yields delayed PET images with high image quality without artifacts and is quantitatively more accurate than the traditional GAN method.

A markerless beam′s eye view tumor tracking algorithm based on VoxelMorph-a learning-based unsupervised registration framework for images with missing data / 中华放射医学与防护杂志

Objective:To propose a machine learning-based markerless beam′s eye view (BEV) tumor tracking algorithm that can be applied to low-quality megavolt (MV) images with multileaf collimator (MLC)-induced occlusion and non-rigid deformation.Methods:This study processed the registration of MV images using the window template matching method and end-to-end unsupervised network Voxelmorph and verified the accuracy of the tumor tracking algorithm using dynamic chest models. Phantom QA plans were executed after the treatment offset was manually set on the accelerator, and 682 electronic portal imaging device (EPID) images obtained during the treatment were collected as fixed images. Moreover, the digitally reconstructed radiography (DRR) images corresponding to the portal angles in the planning system were collected as floating images for the study of target volume tracking. In addition, 533 pairs of EPID and DRR images of 21 lung tumor patients treated with radiotherapy were collected to conduct the study of tumor tracking and provide quantitative result of changes in tumor locations during the treatment. Image similarity was used for third-party validation of the algorithm.Results:The algorithm could process images with different degrees (10%-80%) of data missing and performed well in non-rigid registration of images with data missing. As shown by the phantom verification, 86.8% and 80% of the tracking errors were less than 3 mm and less than 2 mm, respectively, and the normalized mutual information (NMI) varied from 1.18 ± 0.02 to 1.20 ± 0.02 after registration ( t = -6.78, P = 0.001). The tumor motion of the clinical cases was dominated by translation, with an average displacement of 3.78 mm and a maximum displacement of 7.46 mm. The registration result of the cases showed the presence of non-rigid deformations, and the corresponding NMI varied from 1.21 ± 0.03 before registration to 1.22 ± 0.03 after registration ( t = -2.91, P = 0.001). Conclusions:The tumor tracking algorithm proposed in this study has reliable tracking accuracy and high robustness and can be used for non-invasive and real-time tumor tracking requiring no additional equipment and radiation dose.

Comparison of Accuracies between Real-Time Nonrigid and Rigid Registration in the MRI-US Fusion Biopsy of the Prostate.

Magnetic resonance imaging (MRI) is increasingly important in the detection and localization of prostate cancer. Regarding suspicious lesions on MRI, a targeted biopsy using MRI fused with ultrasound (US) is widely used. To achieve a successful targeted biopsy, a precise registration between MRI and US is essential. The purpose of our study was to show any decrease in errors using a real-time nonrigid registration technique for prostate biopsy. Nineteen patients with suspected prostate cancer were prospectively enrolled in this study. Registration accuracy was calculated by the measuring distance of corresponding points by rigid and nonrigid registration between MRI and US, and compared for rigid and nonrigid registration methods. Overall cancer detection rates were also evaluated by patient and by core. Prostate volume was measured automatically from MRI and manually from US, and compared to each other. Mean distances between the corresponding points in MRI and US were 5.32 ± 2.61 mm for rigid registration and 2.11 ± 1.37 mm for nonrigid registration (p < 0.05). Cancer was diagnosed in 11 of 19 patients (57.9%), and in 67 of 266 biopsy cores (25.2%). There was no significant difference in prostate-volume measurement between the automatic and manual methods (p = 0.89). In conclusion, nonrigid registration reduces targeting errors.

Optimizing Nonrigid Registration for Scanning Transmission Electron Microscopy Image Series.

Achieving sub-picometer precision measurements of atomic column positions in high-resolution scanning transmission electron microscope images using nonrigid registration (NRR) and averaging of image series requires careful optimization of experimental conditions and the parameters of the registration algorithm. On experimental data from SrTiO3 [100], sub-pm precision requires alignment of the sample to the zone axis to within 1 mrad tilt and sample drift of less than 1 nm/min. At fixed total electron dose for the series, precision in the fast scan direction improves with shorter pixel dwell time to the limit of our microscope hardware, but the best precision along the slow scan direction occurs at 6 µs/px dwell time. Within the NRR algorithm, the "smoothness factor" that penalizes large estimated shifts is the most important parameter for sub-pm precision, but in general, the precision of NRR images is robust over a wide range of parameters.

Assessment and application of the coherent point drift algorithm to augmented reality surgical navigation for laparoscopic partial nephrectomy.

PURPOSE: The surface-based registration approach to laparoscopic augmented reality (AR) has clear advantages. Nonrigid point-set registration paves the way for surface-based registration. Among current non-rigid point set registration methods, the coherent point drift (CPD) algorithm is rarely used because of two challenges: (1) volumetric deformation is difficult to predict, and (2) registration from intraoperative visible tissue surface to whole anatomical preoperative model is a "part-to-whole" registration that CPD cannot be applied directly to. We preliminarily applied CPD on surgical navigation for laparoscopic partial nephrectomy (LPN). However, it introduces normalization errors and lacks navigation robustness. This paper presents important advances for more effectively applying CPD to LPN surgical navigation while attempting to quantitatively evaluate the accuracy of CPD-based surgical navigation. METHODS: First, an optimized volumetric deformation (Op-VD) algorithm is proposed to achieve accurate prediction of volume deformation. Then, a projection-based partial selection method is presented to conveniently and robustly apply the CPD to LPN surgical navigation. Finally, kidneys with different deformations in vitro, phantom and in vivo experiments are performed to evaluate the accuracy and effectiveness of our approach. RESULTS: The average root-mean-square error of volume deformation was refined to 0.84 mm. The mean target registration error (TRE) of the surface and inside markers in the in vitro experiments decreased to 1.51 mm and 1.29 mm, respectively. The robustness and precision of CPD-based navigation were validated in phantom and in vivo experiments, and the mean navigation TRE of the phantom experiments was found to be [Formula: see text] mm. CONCLUSION: Accurate volumetric deformation and robust navigation results can be achieved in AR navigation of LPN by using surface-based registration with CPD. Evaluation results demonstrate the effectiveness of our proposed methods while showing the clinical application potential of CPD. This work has important guiding significance for the application of the CPD in laparoscopic AR.

Nonrigid registration for tracking incompressible soft tissues with sliding motion.

PURPOSE: Respiration causes the deformation and sliding motion of the soft tissues, and affects the accuracy of the assessment of minimally invasive abdominal surgery. Nonrigid registration is used to eliminate the effects of respiration for the assessment. Because the soft tissues with high water content are volume preserving during deformation, incompressibility has to be considered when tracking soft tissues for nonrigid registration. The purpose of the study was to develop an incompressible nonrigid registration for tracking deformable soft tissues with sliding motion. METHODS: The nonrigid registration framework proposed in the present study includes two main steps: (a) The solution in the subspace of diffeomorphisms is searched and encoded to stationary velocity field in the log domain. (b) The divergence-free component and harmonic remainder are extracted by Fourier-based Helmholtz-Hodge decomposition (FHHD) and further integrated by an adaptive weight to simultaneously retain the incompressibility of deformation and compensate the sliding motion. The method was evaluated on 11 groups of synthetic datasets and five groups of clinical images. Registration accuracy is evaluated by using four quantitative measures, including mean surface distance (MSD), Hausdorff distance (HD), mean corresponding distance (MCD), and Dice similarity coefficient (DSC). Incompressibility is evaluated by using two quantitative measures, including relative volume change (RVC) and Jacobian determinant (J). RESULTS: Compared with three state-of-the-art nonrigid registration methods, the proposed method shows an advantage in handling the incompressible deformation of images with large sliding motion. The lowest (MSD, 0.631 mm), (HD, 6.000 mm), and (MCD, 3.555 mm) and the highest (DSC, 0.970) are obtained proving the high registration accuracy with sliding motion compensation of the proposed method. The (RVC, 0.006) and Jacobian determinant (J, 1.008 ± 0.070) are nearly close to 0 and 1, respectively, showing the strong incompressibility of the proposed method. The proposed method improves registration accuracy in nearly all cases, which maintains the incompressibility of tissue transformation while simultaneously compensating the sliding motion on clinical datasets. CONCLUSIONS: The proposed method improves the registration accuracy of incompressible tissues when dealing with large sliding motion, and thus has the potential to improve current minimally invasive abdominal surgery.

Nonrigid registration of medical image based on adaptive local structure tensor and normalized mutual information.

Nonrigid registration of medical images is especially critical in clinical treatment. Mutual information is a popular similarity measure for medical image registration; however, only the intensity statistical characteristics of the global consistency of image are considered in MI, and the spatial information is ignored. In this paper, a novel intensity-based similarity measure combining normalized mutual information with spatial information for nonrigid medical image registration is proposed. The different parameters of Gaussian filtering are defined according to the regional variance, the adaptive Gaussian filtering is introduced into the local structure tensor. Then, the obtained adaptive local structure tensor is used to extract the spatial information and define the weighting function. Finally, normalized mutual information is distributed to each pixel, and the discrete normalized mutual information is multiplied with a weighting term to obtain a new measure. The novel measure fully considers the spatial information of the image neighborhood, gives the location of the strong spatial information a larger weight, and the registration of the strong gradient regions has a priority over the small gradient regions. The simulated brain image with single-modality and multimodality are used for registration validation experiments. The results show that the new similarity measure improves the registration accuracy and robustness compared with the classical registration algorithm, reduces the risk of falling into local extremes during the registration process.

Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation.

Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive and either infrastructure-heavy or subject to motion artefacts. In this paper we employ a single consumer-grade RGBD camera with an ICP-based registration approach to jointly align all points from a sequence of depth images non-rigidly. Subtle body deformation due to postural sway and respiration is successfully mitigated leading to a higher geometric accuracy through regularised locally affine transformations. We present results from 6 clinical cases where our method compares well with the gold standard and outperforms a previous approach. We show that our method produces better reconstructions qualitatively by visual assessment and quantitatively by consistently obtaining lower landmark error scores and yielding more accurate breast volume estimates.

Nonrigid active shape model-based registration framework for motion correction of cardiac T1 mapping.

PURPOSE: Accurate reconstruction of myocardial T1 maps from a series of T1 -weighted images consists of cardiac motions induced from breathing and diaphragmatic drifts. We propose and evaluate a new framework based on active shape models to correct for motion in myocardial T1 maps. METHODS: Multiple appearance models were built at different inversion time intervals to model the blood-myocardium contrast and brightness changes during the longitudinal relaxation. Myocardial inner and outer borders were automatically segmented using the built models, and the extracted contours were used to register the T1 -weighted images. Data acquired from 210 patients using a free-breathing acquisition protocol were used to train and evaluate the proposed framework. Two independent readers evaluated the quality of the T1 maps before and after correction using a four-point score. The mean absolute distance and Dice index were used to validate the registration process. RESULTS: The testing data set from 180 patients at 5 short axial slices showed a significant decrease of mean absolute distance (from 3.3 ± 1.6 to 2.3 ± 0.8 mm, P < 0.001) and increase of Dice (from 0.89 ± 0.08 to 0.94 ± 0.4%, P < 0.001) before and after correction, respectively. The T1 map quality improved in 70 ± 0.3% of the motion-affected maps after correction. Motion-corrupted segments of the myocardium reduced from 21.8 to 8.5% (P < 0.001) after correction. CONCLUSION: The proposed method for nonrigid registration of T1 -weighted images allows T1 measurements in more myocardial segments by reducing motion-induced T1 estimation errors in myocardial segments. Magn Reson Med 80:780-791, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Accurate nonrigid 3D human body surface reconstruction using commodity depth sensors.

In the last decade, 3D modeling techniques enjoyed a booming development in both hardware and software. High-end hardware generates high fidelity results, but the cost is prohibitive, whereas consumer-level devices generate plausible results for entertainment purposes but are not appropriate for medical uses. We present a cost-effective and easy-to-use 3D body reconstruction system using consumer-grade depth sensors, which provides reconstructed body shapes with a high degree of accuracy and reliability appropriate for medical applications. Our surface registration framework integrates the articulated motion assumption, global loop closure constraint, and a general as-rigid-as-possible deformation model. To enhance the reconstruction quality, we propose a novel approach to accurately infer skeletal joints from anatomical data using multimodality registration. We further propose a supervised predictive model to infer the skeletal joints for arbitrary subjects independent from anatomical data reference. A rigorous validation test has been conducted on real subjects to evaluate the reconstruction accuracy and repeatability. Our system has the potential to make accurate body surface scanning systems readily available for medical professionals and the general public. The system can be used to obtain additional health data derived from 3D body shapes, such as the percentage of body fat.

Fully automatic deformable registration of pretreatment MRI/CT for image-guided prostate radiotherapy planning.

PURPOSE: In prostate radiotherapy, dose distribution may be calculated on CT images, while the MRI can be used to enhance soft tissue visualization. Therefore, a registration between MR and CT images could improve the overall treatment planning process, by improving visualization with a demonstrated interobserver delineation variability when segmenting the prostate, which in turn can lead to a more precise planning. This registration must compensate for prostate deformations caused by changes in size and form between the acquisitions of both modalities. METHODS: We present a fully automatic MRI/CT nonrigid registration method for prostate radiotherapy treatment planning. The proposed registration methodology is a two-step registration process involving both a rigid and a nonrigid registration step. The registration is constrained to volumes of interest in order to improve robustness and computational efficiency. The method is based on the maximization of the mutual information in combination with a deformation field parameterized by cubic B-Splines. RESULTS: The proposed method was validated on eight clinical patient datasets. Quantitative evaluation, using Hausdorff distance between prostate volumes in both images, indicated that the overall registration errors is 1.6 ± 0.2 mm, with a maximum error of less than 2.3 mm, for all patient datasets considered in this study. CONCLUSIONS: The proposed approach provides a promising solution for an effective and accurate prostate radiotherapy treatment planning since it satisfies the desired clinical accuracy.

Local incompressible registration for liver ablation surgery assessment.

PURPOSE: In liver microwave ablation (MWA) surgery, the ablation area covers the tumor to generate tissue necrosis and treat the cancer. As the liver deforms during the operation, deviation between the target area determined during preoperative planning and the resultant ablation area is inevitable. Therefore, an accurate assessment of tumor coverage is crucial for treatment. Through registration between the pre- and postoperative livers, the ablation area is warped on the preoperative liver for the computation of tumor coverage. However, large deformations between the pre- and postoperative livers are caused by multiple factors, and these diverse deformations make registration a challenging task. The purpose of this paper was to develop an automatic method that can accurately register post- to preoperative livers. METHODS: In the proposed method, nonrigid deformations caused by respiratory movement and edema are separately considered and estimated by the local incompressible model in the registration of livers. The pre- and postoperative livers are first aligned by a rigid registration based on a convex hull. In the nonrigid registrations, local incompressible constraints are then set on the liver and the ablation area to estimate the deformations caused by respiratory movement and edema, respectively. The concatenation of the rigid and nonrigid deformations is used to warp the ablation area on the preoperative liver. RESULTS: The proposed method was evaluated using clinical CT datasets from 20 patients. The Dice similarity coefficient (DSC) between the preoperative and warped postoperative livers is 94.35%, the mean surface distance (MSD) between the livers is 1.65 mm, the mean Hausdorff distance (HDD) between the livers is 3.36 mm, and the mean corresponding distance (MCD) between the corresponding landmarks is 1.70 mm. Compared with five other state-of-the-art methods, the proposed method achieves automatic ablation assessment with highly accurate registration. CONCLUSIONS: The proposed method achieves a high accuracy for registering the livers. The sizes and positions of the ablation area and tumor are accurately compared for the assessment of ablation surgery.

Creating hemodynamic atlases of cardiac 4D flow MRI.

PURPOSE: Hemodynamic atlases can add to the pathophysiological understanding of cardiac diseases. This study proposes a method to create hemodynamic atlases using 4D Flow magnetic resonance imaging (MRI). The method is demonstrated for kinetic energy (KE) and helicity density (Hd ). MATERIALS AND METHODS: Thirteen healthy subjects underwent 4D Flow MRI at 3T. Phase-contrast magnetic resonance cardioangiographies (PC-MRCAs) and an average heart were created and segmented. The PC-MRCAs, KE, and Hd were nonrigidly registered to the average heart to create atlases. The method was compared with 1) rigid, 2) affine registration of the PC-MRCAs, and 3) affine registration of segmentations. The peak and mean KE and Hd before and after registration were calculated to evaluate interpolation error due to nonrigid registration. RESULTS: The segmentations deformed using nonrigid registration overlapped (median: 92.3%) more than rigid (23.1%, P < 0.001), and affine registration of PC-MRCAs (38.5%, P < 0.001) and affine registration of segmentations (61.5%, P < 0.001). The peak KE was 4.9 mJ using the proposed method and affine registration of segmentations (P = 0.91), 3.5 mJ using rigid registration (P < 0.001), and 4.2 mJ using affine registration of the PC-MRCAs (P < 0.001). The mean KE was 1.1 mJ using the proposed method, 0.8 mJ using rigid registration (P < 0.001), 0.9 mJ using affine registration of the PC-MRCAs (P < 0.001), and 1.0 mJ using affine registration of segmentations (P = 0.028). The interpolation error was 5.2 ± 2.6% at mid-systole, 2.8 ± 3.8% at early diastole for peak KE; 9.6 ± 9.3% at mid-systole, 4.0 ± 4.6% at early diastole, and 4.9 ± 4.6% at late diastole for peak Hd . The mean KE and Hd were not affected by interpolation. CONCLUSION: Hemodynamic atlases can be obtained with minimal user interaction using nonrigid registration of 4D Flow MRI. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1389-1399.

Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images.

BACKGROUND: Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images. METHODS: In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects. RESULTS: We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 ([Formula: see text]) for left ventricle myocardium and from 0.6307 to 0.6519 ([Formula: see text]) for right atrium. CONCLUSIONS: The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.

Value of Nonrigid Registration of Pre-Procedure MR with Post-Procedure CT After Radiofrequency Ablation for Hepatocellular Carcinoma.

PURPOSE: To evaluate the value of pre-radiofrequency ablation (RFA) MR and post-RFA CT registration for the assessment of the therapeutic response of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: A total of 178 patients with single HCC who received RFA as an initial treatment and had available pre-RFA MR and post-RFA CT images were included in this retrospective study. Two independent readers (one experienced radiologist, one inexperienced radiologist) scored the ablative margin (AM) of treated tumors on a four-point scale (1, residual tumor; 2, incomplete AM; 3, borderline AM; 4, sufficient AM), in two separate sessions: (1) visual comparison between pre-and post-RFA images; (2) with addition of nonrigid registration for pre- and post-RFA images. Local tumor progression (LTP) rates between low-risk (response score, 3-4) and high-risk groups (1-2) were analyzed using the Kaplan-Meier method at each interpretation session. RESULTS: The patients' reassignments after using the registered images were statistically significant for inexperienced reader (p < 0.001). In the inexperienced reader, LTP rates of low- and high-risk groups were significantly different with addition of registered images (session 2) (p < 0.001), but not significantly different in session 1 (p = 0.101). However, in the experienced reader, LTP rates of low- and high-risk groups were significantly different in both interpretation sessions (p < 0.001). Using the registered images, the cumulative incidence of LTP at 2 years was 3.0-6.6%, for the low-risk group, and 18.6-27.8% for the high-risk group. CONCLUSION: Registration between pre-RFA MR and post-RFA CT images may allow better assessment of the therapeutic response of HCC after RFA, especially for inexperienced radiologists, helping in the risk stratification for LTP.

Attenuation correction in 4D-PET using a single-phase attenuation map and rigidity-adaptive deformable registration.

PURPOSE: Four-dimensional positron emission tomography (4D-PET) imaging is a potential solution to the respiratory motion effect in the thoracic region. Computed tomography (CT)-based attenuation correction (AC) is an essential step toward quantitative imaging for PET. However, due to the temporal difference between 4D-PET and a single attenuation map from CT, typically available in routine clinical scanning, motion artifacts are observed in the attenuation-corrected PET images, leading to errors in tumor shape and uptake. We introduced a practical method to align single-phase CT with all other 4D-PET phases for AC. METHODS: A penalized non-rigid Demons registration between individual 4D-PET frames without AC provides the motion vectors to be used for warping single-phase attenuation map. The non-rigid Demons registration was used to derive deformation vector fields (DVFs) between PET matched with the CT phase and other 4D-PET images. While attenuated PET images provide useful data for organ borders such as those of the lung and the liver, tumors cannot be distinguished from the background due to loss of contrast. To preserve the tumor shape in different phases, an ROI-covering tumor was excluded from nonrigid transformation. Instead the mean DVF of the central region of the tumor was assigned to all voxels in the ROI. This process mimics a rigid transformation of the tumor along with a nonrigid transformation of other organs. A 4D-XCAT phantom with spherical lung tumors, with diameters ranging from 10 to 40 mm, was used to evaluate the algorithm. The performance of the proposed hybrid method for attenuation map estimation was compared to (a) the Demons nonrigid registration only and (b) a single attenuation map based on quantitative parameters in individual PET frames. RESULTS: Motion-related artifacts were significantly reduced in the attenuation-corrected 4D-PET images. When a single attenuation map was used for all individual PET frames, the normalized root-mean-square error (NRMSE) values in tumor region were 49.3% (STD: 8.3%), 50.5% (STD: 9.3%), 51.8% (STD: 10.8%) and 51.5% (STD: 12.1%) for 10-mm, 20-mm, 30-mm, and 40-mm tumors, respectively. These errors were reduced to 11.9% (STD: 2.9%), 13.6% (STD: 3.9%), 13.8% (STD: 4.8%), and 16.7% (STD: 9.3%) by our proposed method for deforming the attenuation map. The relative errors in total lesion glycolysis (TLG) values were -0.25% (STD: 2.87%) and 3.19% (STD: 2.35%) for 30-mm and 40-mm tumors, respectively, in proposed method. The corresponding values for Demons method were 25.22% (STD: 14.79%) and 18.42% (STD: 7.06%). Our proposed hybrid method outperforms the Demons method especially for larger tumors. For tumors smaller than 20 mm, nonrigid transformation could also provide quantitative results. CONCLUSION: Although non-AC 4D-PET frames include insignificant anatomical information, they are still useful to estimate the DVFs to align the attenuation map for accurate AC. The proposed hybrid method can recover the AC-related artifacts and provide quantitative AC-PET images.