Dynamic Image Series Research Articles

Parametric Imaging With PET and SPECT

In molecular imaging modalities such as positron emission tomography (PET) or single photon emission computed tomography (SPECT), parametric imaging is the process of creating fully quantitative 3-D maps of pharmacokinetic parameters from a dynamic series of radiotracer concentration images. An overview of the pharmacokinetic parameters that have been assessed in PET or SPECT studies, and the kinetic models that have been proposed to compute parametric images is presented. Parametric imaging is challenging due to the high level of noise in the raw images obtained from the scanner, and the additional needs to obtain the input function of the kinetic model and to correct for subject motion during the usually long dynamic scans. Methods that have been proposed to assess each of these challenges are been reviewed.

Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer.

This study aimed to predict the molecular subtypes of breast cancer via intratumoural and peritumoural radiomic analysis with subregion identification based on the decomposition of contrast-enhanced magnetic resonance imaging (DCE-MRI). The study included 211 women with histopathologically confirmed breast cancer. We utilised a completely unsupervised convex analysis of mixtures (CAM) method by unmixing dynamic imaging series from heterogeneous tissues. Each tumour and the surrounding parenchyma were thus decomposed into multiple subregions, representing different vascular characterisations, from which radiomic features were extracted. A random forest model was trained and tested using a leave-one-out cross-validation (LOOCV) method to predict breast cancer subtypes. The predictive models from tumoural and peritumoural subregions were fused for classification. Tumour and peritumour DCE-MR images were decomposed into three compartments, representing plasma input, fast-flow kinetics, and slow-flow kinetics. The tumour subregion related to fast-flow kinetics showed the best performance among the subregions for differentiating between patients with four molecular subtypes (area under the receiver operating characteristic curve (AUC) = 0.832), exhibiting an AUC value significantly (p < 0.0001) higher than that obtained with the entire tumour (AUC = 0.719). When the tumour- and parenchyma-based predictive models were fused, the performance, measured as the AUC, increased to 0.897; this value was significantly higher than that obtained with other tumour partition methods. Radiomic analysis of intratumoural and peritumoural heterogeneity based on the decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features and serve as a valuable clinical marker to enhance the prediction of breast cancer subtypes. • Decomposition of image time-series signals has the potential to more accurately identify tumour kinetic features. • Fusion of intratumoural- and peritumoural-based predictive models improves the prediction of breast cancer subtypes.

Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images.

Automatic vessel segmentation can be used to process the considerable amount of data generated by four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) images. Previous segmentation approaches for dynamic series of images propose either reducing the series to a temporal average (tAIP) or maximum intensity projection (tMIP) prior to vessel segmentation, or a separate segmentation of each image. This paper introduces a method that combines both approaches to overcome the specific drawbacks of each technique. Vessels in the tAIP are enhanced by using the ranking orientation responses of path operators and multiscale vesselness enhancement filters. Then, tAIP segmentation is performed using a seed-based algorithm. In parallel, this algorithm is also used to segment each frame of the series and identify small vessels, which might have been lost in the tAIP segmentation. The results of each individual time frame segmentation are fused using an or boolean operation. Finally, small vessels found only in the fused segmentation are added to the tAIP segmentation. In a quantitative analysis using ten 4D ASL MRA image series from healthy volunteers, the proposed combined approach reached an average Dice coefficient of 0.931, being more accurate than the corresponding tMIP, tAIP, and single time frame segmentation methods with statistical significance. The novel combined vessel segmentation strategy can be used to obtain improved vessel segmentation results from 4D ASL MRA and other dynamic series of images. Improved vessel segmentation of 4D ASL MRA allows a fast and accurate assessment of cerebrovascular structures.

Cerebrospinal and interstitial fluid transport via the glymphatic pathway modeled by optimal mass transport

The glymphatic pathway is a system which facilitates continuous cerebrospinal fluid (CSF) and interstitial fluid (ISF) exchange and plays a key role in removing waste products from the rodent brain. Dysfunction of the glymphatic pathway may be implicated in the pathophysiology of Alzheimer's disease. Intriguingly, the glymphatic system is most active during deep wave sleep general anesthesia. By using paramagnetic tracers administered into CSF of rodents, we previously showed the utility of MRI in characterizing a macroscopic whole brain view of glymphatic transport but we have yet to define and visualize the specific flow patterns. Here we have applied an alternative mathematical analysis approach to a dynamic time series of MRI images acquired every 4min over ∼3h in anesthetized rats, following administration of a small molecular weight paramagnetic tracer into the CSF reservoir of the cisterna magna. We use Optimal Mass Transport (OMT) to model the glymphatic flow vector field, and then analyze the flow to find the network of CSF-ISF flow channels. We use 3D visualization computational tools to visualize the OMT defined network of CSF-ISF flow channels in relation to anatomical and vascular key landmarks from the live rodent brain. The resulting OMT model of the glymphatic transport network agrees largely with the current understanding of the glymphatic transport patterns defined by dynamic contrast-enhanced MRI revealing key CSF transport pathways along the ventral surface of the brain with a trajectory towards the pineal gland, cerebellum, hypothalamus and olfactory bulb. In addition, the OMT analysis also revealed some interesting previously unnoticed behaviors regarding CSF transport involving parenchymal streamlines moving from ventral reservoirs towards the surface of the brain, olfactory bulb and large central veins.

Respiratory dynamics in phonation and breathing—A real-time MRI study

The respiratory system is a central part of voice production, but for phonation neither the underlying functional relations between diaphragm (DPH) and rib cage (RC), nor differences to normal breathing are yet understood. This study aims to compare respiratory dynamics in phonation and breathing via dynamic MRI of the lung. Images of the breathing apparatus of 6 professional singers were captured in a 1.5T MRI system in supine position during vital capacity breathing and maximal long sustained phonation at 3 different pitches and loudness conditions. In a dynamic series of cross-sectional images of the lung, distances between characteristic anatomical landmarks were measured. During exhalation in normal breathing the diaphragm and rib cage moved synchronously to reduce lung volume, but during phonation different functional units could be identified, which support phonation by facilitating the control of subglottic pressure.

Model-Based Characterization of the Transpulmonary Circulation by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Heart Failure and Healthy Volunteers

Novel quantitative measures of transpulmonary circulation status may allow the improvement of heart failure (HF) patient management. In this work, we propose a method for the assessment of the transpulmonary circulation using measurements from indicator time intensity curves, derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series. The derived indicator dilution parameters in healthy volunteers (HVs) and HF patients were compared, and repeatability was assessed. Furthermore, we compared the parameters derived using the proposed method with standard measures of cardiovascular function, such as left ventricular (LV) volumes and ejection fraction. In total, 19 HVs and 33 HF patients underwent a DCE-MRI scan on a 1.5 T MRI scanner using a T1-weighted spoiled gradient echo sequence. Image loops with 1 heartbeat temporal resolution were acquired in 4-chamber view during ventricular late diastole, after the injection of a 0.1-mmol gadoteriol bolus. In a subset of subjects (8 HFs, 2 HVs), a second injection of a 0.3-mmol gadoteriol bolus was performed with the same imaging settings. The study was approved by the local institutional review board.Indicator dilution curves were derived, averaging the MR signal within regions of interest in the right and left ventricle; parametric deconvolution was performed between the right and LV indicator dilution curves to identify the impulse response of the transpulmonary dilution system. The local density random walk model was used to parametrize the impulse response; pulmonary transit time (PTT) was defined as the mean transit time of the indicator. λ, related to the Péclet number (ratio between convection and diffusion) for the dilution process, was also estimated. Pulmonary transit time was significantly prolonged in HF patients (8.70 ± 1.87 seconds vs 6.68 ± 1.89 seconds in HV, P < 0.005) and even stronger when normalized to subject heart rate (normalized PTT, 9.90 ± 2.16 vs 7.11 ± 2.17 in HV, dimensionless, P < 0.001). λ was significantly smaller in HF patients (8.59 ± 4.24 in HF vs 12.50 ± 17.09 in HV, dimensionless, P < 0.005), indicating a longer tail for the impulse response. Pulmonary transit time correlated well with established cardiovascular parameters (LV end-diastolic volume index, r = 0.61, P < 0.0001; LV ejection fraction, r = -0.64, P < 0.0001). The measurement of indicator dilution parameters was repeatable (correlation between estimates based on the 2 repetitions for PTT: r = 0.94, P < 0.001, difference between 2 repetitions 0.01 ± 0.60 second, for λ: r = 0.74, P < 0.01, difference 0.69 ± 4.39). Characterization of the transpulmonary circulation by DCE-MRI is feasible in HF patients and HVs. Significant differences are observed between indicator dilution parameters measured in HVs and HF patients; preliminary results suggest good repeatability for the proposed parameters.

Dynamic positron emission tomography restoration with low-rank representation incorporating edge preservation.

Dynamic positron emission tomography (PET) is a powerful tool that provides useful quantitative information on physiological and biochemical processes. However, the low signal-to-noise ratio (SNR) in short dynamic frames is a challenge. To get high SNR in the dynamic PET and to achieve high-quality PET parametric image are the objective of this study. Low-rank (LR) modeling and edge-preserving prior are incorporated in this study with a unified mathematical framework to improve the SNR of a dynamic PET image series. The proposed algorithm is designed to reduce noise in homogeneous areas while preserving the edges of regions of interest. The performance of the proposed method (LRH) is compared both visually and quantitatively by using the classic Gaussian filter and an LR expression filter on a digital brain phantom and in vivo rat study. Experimental results demonstrate that the proposed filter can achieve superior visual and quantitative performance without sacrificing spatial resolution. The proposed LRH is considerably effective and exhibits great potential in processing dynamic PET data with high noise levels.

Dynamic contrast enhanced CT in nodule characterization: How we review and report.

Incidental indeterminate solitary pulmonary nodules (SPN) that measure less than 3 cm in size are an increasingly common finding on computed tomography (CT) worldwide. Once identified there are a number of imaging strategies that can be performed to help with nodule characterization. These include interval CT, dynamic contrast enhanced computed tomography (DCE-CT), 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG-PET-CT). To date the most cost effective and efficient non-invasive test or combination of tests for optimal nodule characterization has yet to be determined.DCE-CT is a functional test that involves the acquisition of a dynamic series of images of a nodule before and following the administration of intravenous iodinated contrast medium. This article provides an overview of the current indications and limitations of DCE- CT in nodule characterization and a systematic approach to how to perform, analyse and interpret a DCE-CT scan.Electronic supplementary materialThe online version of this article (doi:10.1186/s40644-016-0074-4) contains supplementary material, which is available to authorized users.

Patient motion effects on the quantification of regional myocardial blood flow with dynamic PET imaging.

Patient motion is a common problem during dynamic positron emission tomography (PET) scans for quantification of myocardial blood flow (MBF). The purpose of this study was to quantify the prevalence of body motion in a clinical setting and evaluate with realistic phantoms the effects of motion on blood flow quantification, including CT attenuation correction (CTAC) artifacts that result from PET-CT misalignment. A cohort of 236 sequential patients was analyzed for patient motion under resting and peak stress conditions by two independent observers. The presence of motion, affected time-frames, and direction of motion was recorded; discrepancy between observers was resolved by consensus review. Based on these results, patient body motion effects on MBF quantification were characterized using the digital NURBS-based cardiac-torso phantom, with characteristic time activity curves (TACs) assigned to the heart wall (myocardium) and blood regions. Simulated projection data were corrected for attenuation and reconstructed using filtered back-projection. All simulations were performed without noise added, and a single CT image was used for attenuation correction and aligned to the early- or late-frame PET images. In the patient cohort, mild motion of 0.5 ± 0.1 cm occurred in 24% and moderate motion of 1.0 ± 0.3 cm occurred in 38% of patients. Motion in the superior/inferior direction accounted for 45% of all detected motion, with 30% in the superior direction. Anterior/posterior motion was predominant (29%) in the posterior direction. Left/right motion occurred in 24% of cases, with similar proportions in the left and right directions. Computer simulation studies indicated that errors in MBF can approach 500% for scans with severe patient motion (up to 2 cm). The largest errors occurred when the heart wall was shifted left toward the adjacent lung region, resulting in a severe undercorrection for attenuation of the heart wall. Simulations also indicated that the magnitude of MBF errors resulting from motion in the superior/inferior and anterior/posterior directions was similar (up to 250%). Body motion effects were more detrimental for higher resolution PET imaging (2 vs 10 mm full-width at half-maximum), and for motion occurring during the mid-to-late time-frames. Motion correction of the reconstructed dynamic image series resulted in significant reduction in MBF errors, but did not account for the residual PET-CTAC misalignment artifacts. MBF bias was reduced further using global partial-volume correction, and using dynamic alignment of the PET projection data to the CT scan for accurate attenuation correction during image reconstruction. Patient body motion can produce MBF estimation errors up to 500%. To reduce these errors, new motion correction algorithms must be effective in identifying motion in the left/right direction, and in the mid-to-late time-frames, since these conditions produce the largest errors in MBF, particularly for high resolution PET imaging. Ideally, motion correction should be done before or during image reconstruction to eliminate PET-CTAC misalignment artifacts.

Evaluation of oesophageal transit velocity using the improved Demons technique.

This paper presents a novel method to compute oesophageal transit velocity in a direct and automatized manner by the registration of scintigraphy images. A total of 36 images from nine healthy volunteers were processed. Four dynamic image series per volunteer were acquired after a minimum 8 h fast. Each acquisition was made following the ingestion of 5 ml saline labelled with about 26 MBq (700 µCi) technetium-99m phytate in a single swallow. Between the acquisitions, another two swallows of 5 ml saline were performed to clear the oesophagus. The composite acquired files were made of 240 frames of anterior and posterior views. Each frame is the accumulate count for 250 ms.At the end of acquisitions, the images were corrected for radioactive decay, the geometric mean was computed between the anterior and posterior views and the registration of a set of subsequent images was performed. Utilizing the improved Demons technique, we obtained from the deformation field the regional resultant velocity, which is directly related to the oesophagus transit velocity. The mean regional resulting velocities decreases progressively from the proximal to the distal oesophageal portions and, at the proximal portion, is virtually identical to the primary peristaltic pump typical velocity. Comparison between this parameter and 'time-activity' curves reveals consistency in velocities obtained using both methods, for the proximal portion. Application of the improved Demons technique, as an easy and automated method to evaluate velocities of oesophageal bolus transit, is feasible and seems to yield consistent data, particularly for the proximal oesophagus.

Image manifold revealing for breast lesion segmentation in DCE-MRI.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for breast lesion differentiation. Manual segmentation in DCE-MRI is difficult and open to viewer interpretation. In this paper, an automatic segmentation method based on image manifold revealing was introduced to overcome the problems of the currently used method. First, high dimensional datasets were constructed from a dynamic image series. Next, an embedded image manifold was revealed in the feature image by nonlinear dimensionality reduction technique. In the last stage, k-means clustering was performed to obtain final segmentation results. The proposed method was applied in actual clinical cases and compared with the gold standard. Statistical analysis showed that the proposed method achieved an acceptable accuracy, sensitivity, and specificity rates.

Automatic indicator dilution curve extraction in dynamic-contrast enhanced imaging using spectral clustering

Indicator dilution theory provides a framework for the measurement of several cardiovascular parameters. Recently, dynamic imaging and contrast agents have been proposed to apply the method in a minimally invasive way. However, the use of contrast-enhanced sequences requires the definition of regions of interest (ROIs) in the dynamic image series; a time-consuming and operator dependent task, commonly performed manually. In this work, we propose a method for the automatic extraction of indicator dilution curves, exploiting the time domain correlation between pixels belonging to the same region. Individual time intensity curves were projected into a low dimensional subspace using principal component analysis; subsequently, clustering was performed to identify the different ROIs. The method was assessed on clinically available DCE-MRI and DCE-US recordings, comparing the derived IDCs with those obtained manually. The robustness to noise of the proposed approach was shown on simulated data. The tracer kinetic parameters derived on real images were in agreement with those obtained from manual annotation. The presented method is a clinically useful preprocessing step prior to further ROI-based cardiac quantifications.

SU-F-303-06: Effect of Multiband RF Pulse Duration On Cine MRI Tracking Accuracy for MRI-GRT

Purpose: Exception gating and dynamic MLC tracking on MRI-gRT systems require segmentation of high temporal resolution images. Multi-band excitation is an emerging MRI acceleration technique that simultaneously excites a short comb of 2D slices, thereby increasing effective temporal resolution. However, the design of multiband RF pulses imposes tradeoffs between slice profiles and the temporal duration of the pulse. The goal of this study was to determine whether slice profiles of shorter duration multiband RF pulses affect organ tracking accuracy for MRI-gRT. Methods: Three-band Shinnar-Le Roux (SLR) RF pulses were designed with slice bandwidths of 1.5kHz and durations ranging from 750 to 3000 µs. The pulses were implemented into a golden angle radial pulse sequence of our own design. Time series of dynamic 2D sagittal cine images of an MRI-compatible dynamic phantom (Model 008M, CIRS) with 2.5 cm superior/inferior target motion were acquired at 3T using each of the RF pulse durations. Images were reconstructed offline in Matlab. The target was contoured on the last image of the time series and tracked through the time series using normalized cross-correlation with a Powell optimizer and B-spline interpolator. Peak-to-peak (P2P) centroid displacements along the superior/inferior dimension were estimated and compared against the phantom motion trajectory. Results: Transition band sizes of the multiband RF slice profiles ranged from 2.1 to 4.6 mm for intended 5 mm slices. P2P centroid displacements ranged from 2.3 to 2.6 cm, demonstrating good agreement with the 2.5 cm phantom displacement, and indicating that slice profile has minimal effect on tracking ability. Conclusion: Slice profile does not affect tracking ability, permitting the use of shorter duration RF pulses to increase temporal resolution during MRI-gRT. Future work will investigate the advantages of multiband excitation for 2D/3D image registration and will combine multiband excitation with in-plane acceleration to further improve temporal resolution. Elekta Instruments, AB

Abstract A10: Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics

Abstract Intratumor functional heterogeneity is the presence of multiple subpopulations or localized regions with different physiological properties that affect cancer biology (e.g., motility, invasion) and/or response to systemic therapy. Functional heterogeneity may reflect unique aspects of the tumor microenvironment or cellular genetic diversity including, but not limited to, the consequences of different localized patterns of vascular perfusion, stromal infiltration, somatic mutation, epigenetic modifications. Given the continuing improvements in tumor imaging technologies and the biological importance of tumor vascularization as a major driver of functional heterogeneity, the goal of the present work was to develop novel mathematical models to study vascular heterogeneity and its changes in tumors using data from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). DCE-MRI provides a noninvasive in vivo method to evaluate tumor vasculature architectures based on contrast accumulation and washout. Understanding the role of vascular heterogeneity in tumors has significant implications for advancing individualized cancer diagnosis and treatment. To improve vascular characterization, we developed multi-tissue compartment modeling (MTCM). Notably, MTCM is a fully unsupervised method for deconvoluting dynamic imaging series data from heterogeneous tumors that contain unknown numbers of distinct vascular compartments. The pixel-wise tracer concentration in a particular vascular compartment is modeled as being proportional to the local volume transfer constant of the vascular compartment. However, the imaging data often contain significant numbers of partial-volume pixels. To address this limitation, MTCM estimates pharmacokinetic parameters (flux rate constants) using the time-courses of pure-volume pixels, i.e., the signals from those pixels highly enriched in a particular vascular compartment. A convex analysis of mixtures scheme is applied to identify those pure-volume pixels present at the vertices of the clustered pixel time series scatter simplex, without any knowledge of compartment distribution. Thus, MTCM offers an unsupervised approach to characterize intratumor heterogeneity. Applying MTCM to dynamic contrast-enhanced MRI of breast cancers revealed characteristic intratumor vascular heterogeneity and therapeutic responses that were otherwise undetectable. We identified differential and heterogeneous changes in tissue-specific vascular pharmacokinetics in tumors during treatment that were undetected using standard analysis, including tumor islands of persistent enhancement that have escaped the effects of therapy. While it is not yet possible to assign causality, these in vivo results allowed us to propose new hypotheses regarding the complex relationships between intratumor heterogeneity, clonal repopulation, cancer stem-cell, and therapeutic efficacy. Citation Format: Li Chen, peter Choyke, Robert Clarke, Zaver Bhujwalla, Yue Wang. Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr A10. doi:10.1158/1538-7445.CHTME14-A10

Reconstruction of dynamic image series from undersampled MRI data using data-driven model consistency condition (MOCCO).

To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models preestimated from training data. We introduce the model consistency condition (MOCCO) technique, which utilizes temporal models to regularize reconstruction without constraining the solution to be low-rank, as is performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Our method was compared with a standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE-MRA) and cardiac CINE imaging. We studied the sensitivity of all methods to rank reduction and temporal subspace modeling errors. MOCCO demonstrated reduced sensitivity to modeling errors compared with the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE-MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data.

Dynamic imaging method using the low n-rank tensor for electrical capacitance tomography

Imaging objects in electrical capacitance tomography (ECT) measurement are often in a dynamic evolution process, and exploiting the spatial–temporal properties of the dynamic reconstruction objects is crucial for the improvement of the reconstruction quality. Based on the multiple measurement vectors, in this paper a robust dynamic reconstruction model that incorporates the ECT measurement information and the dynamic evolution information of a dynamic object, in which a series of dynamic images is cast as a third-order tensor that the first two dimensions are space and the third is time, is proposed. Under the considerations of the two-dimensional spatial structure property of a difference image and the spatial–temporal property of a third-order image tensor, a new objective functional that fuses the ECT measurement information, the dynamic evolution information, the temporal constraint, the spatial constraint, the low rank constraint of a difference image and the low n-rank constraint of a third-order tensor is proposed, where the images are reconstructed by a batching pattern. The split Bregman iteration (SBI) algorithm is developed for solving the proposed objective functional. Numerical simulations are implemented to demonstrate the advantages of the proposed algorithm on improving the reconstruction quality and the robustness.

Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics.

With the existence of biologically distinctive malignant cells originated within the same tumor, intratumor functional heterogeneity is present in many cancers and is often manifested by the intermingled vascular compartments with distinct pharmacokinetics. However, intratumor vascular heterogeneity cannot be resolved directly by most in vivo dynamic imaging. We developed multi-tissue compartment modeling (MTCM), a completely unsupervised method of deconvoluting dynamic imaging series from heterogeneous tumors that can improve vascular characterization in many biological contexts. Applying MTCM to dynamic contrast-enhanced magnetic resonance imaging of breast cancers revealed characteristic intratumor vascular heterogeneity and therapeutic responses that were otherwise undetectable. MTCM is readily applicable to other dynamic imaging modalities for studying intratumor functional and phenotypic heterogeneity, together with a variety of foreseeable applications in the clinic.

Paradoxical effect of the signal-to-noise ratio of GRAPPA calibration lines: A quantitative study.

Intuitively, GRAPPA auto-calibration signal (ACS) lines with higher signal-to-noise ratio (SNR) may be expected to boost the accuracy of kernel estimation and increase the SNR of GRAPPA reconstructed images. Paradoxically, Sodickson and his colleagues pointed out that using ACS lines with high SNR may actually lead to lower SNR in the GRAPPA reconstructed images. A quantitative study of how the noise in the ACS lines affects the SNR of the GRAPPA reconstructed images is presented. In a phantom, the singular values of the GRAPPA encoding matrix and the root-mean-square error of GRAPPA reconstruction were evaluated using multiple sets of ACS lines with variant SNR. In volunteers, ACS lines with high and low SNR were estimated, and the SNR of corresponding TGRAPPA reconstructed images was evaluated. We show that the condition number of the GRAPPA kernel estimation equations is proportional to the SNR of the ACS lines. In dynamic image series reconstructed with TGRAPPA, high SNR ACS lines result in reduced SNR if appropriate regularization is not applied. Noise has a similar effect to Tikhonov regularization. Without appropriate regularization, a GRAPPA kernel estimated from ACS lines with higher SNR amplifies random noise in the GRAPPA reconstruction. Magn Reson Med 74:231-239, 2015. © 2014 Wiley Periodicals, Inc.

Noncontrast enhanced four‐dimensional dynamic MRA with golden angle radial acquisition and k‐space weighted image contrast (KWIC) reconstruction

To explore the feasibility of 2D and 3D golden-angle radial acquisition strategies in conjunction with k-space weighted image contrast (KWIC) temporal filtering to achieve noncontrast enhanced dynamic MRA (dMRA) with high spatial resolution, low streaking artifacts and high temporal fidelity. Simulations and in vivo examinations in eight normal volunteers and an arteriovenous malformation patient were carried out. Both 2D and 3D golden angle radial sequences, preceded by spin tagging, were used for dMRA of the brain. The radial dMRA data were temporally filtered using the KWIC strategy and compared with matched standard Cartesian techniques. The 2D and 3D dynamic MRA image series acquired with the proposed radial techniques demonstrated excellent image quality without discernible temporal blurring compared with standard Cartesian based approaches. The image quality of radial dMRA was equivalent to or higher than that of Cartesian dMRA by visual inspection. A reduction factor of up to 10 and 3 in scan time was achieved for 2D and 3D radial dMRA compared with the Cartesian-based counterparts. The proposed 2D and 3D radial dMRA techniques demonstrated image quality comparable or even superior to those obtained with standard Cartesian methods, but within a fraction of the scan time.

Perfusion and vascular permeability: Basic concepts and measurement in DCE-CT and DCE-MRI

The microvascular network formed by the capillaries supplies the tissues and permits their function. It provides a considerable surface area for exchanges between blood and tissues. All pathological conditions cause changes in the microcirculation. These changes can be used as imaging biomarkers for the diagnosis of lesions and optimisation of treatment. Among the many imaging techniques developed to study the microcirculation, the analysis of the tissue kinetics of intravenously injected contrast agents is the most widely used, either as positive enhancement for CT, T1-weighted MRI and ultrasound-dynamic contrast-enhanced-imaging (DCE-imaging)-or negative enhancement in T2*-weighted brain MRI-dynamic susceptibility contrast-MRI (DSC-MRI)-. Acquisition involves an injection of contrast agent during the acquisition of a dynamic series of images on a zone of interest. These kinetics may be analyzed visually, to define qualitative criteria, or with software using mathematical modelling, to extract quantitative physiological parameters. The results depend on the acquisition conditions (type of imaging device, imaging mode, frequency and total duration of acquisition), the type of contrast agent, the data pre-processing (motion correction, conversion of the signal into concentration) and the data analysis method. Because of these multiple choices it is necessary to understand the physiological processes involved and understand the advantages and limits of each strategy.