An investigation into the effects of temporal resolution on hepatic dynamic contrast-enhanced MRI in volunteers and in patients with hepatocellular carcinoma
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This study investigated the effect of temporal resolution on the dual-input pharmacokinetic (PK) modelling of dynamic contrast-enhanced MRI (DCE-MRI) data from normal volunteer livers and from patients with hepatocellular carcinoma. Eleven volunteers and five patients were examined at 3 T. Two sections, one optimized for the vascular input functions (VIF) and one for the tissue, were imaged within a single heart-beat (HB) using a saturation-recovery fast gradient echo sequence. The data was analysed using a dual-input single-compartment PK model. The VIFs and/or uptake curves were then temporally sub-sampled (at interval ▵t = [2-20] s) before being subject to the same PK analysis. Statistical comparisons of tumour and normal tissue PK parameter values using a 5% significance level gave rise to the same study results when temporally sub-sampling the VIFs to HB < ▵t <4 s. However, sub-sampling to ▵t > 4 s did adversely affect the statistical comparisons. Temporal sub-sampling of just the liver/tumour tissue uptake curves at ▵t ≤ 20 s, whilst using high temporal resolution VIFs, did not substantially affect PK parameter statistical comparisons. In conclusion, there is no practical advantage to be gained from acquiring very high temporal resolution hepatic DCE-MRI data. Instead the high temporal resolution could be usefully traded for increased spatial resolution or SNR.Keywords:
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Dynamic Contrast-Enhanced MRI
Abstract : This fellowship research project focuses on improving the temporal and spatial resolution in dynamic contrast-enhanced magnetic resonance imaging (MRI) of the breast. Dynamic contrast-enhanced MRI has been investigated as a possible means for non- invasive determination of the benign or malignant status of a breast tumor due to the differential rate of enhancement following injection of a contrast agent (1-9). In order to capitalize on the time of greatest differentiation between malignant and benign lesions, a sequence of images of the breast must be acquired during the first 1 or 2 minutes following contrast injection (10,11), leading to a requirement for high temporal resolution. In addition, high spatial resolution in 3 dimensions is imperative to allow the visualization of very small tumors with complete coverage of the breast. High signal-to-noise ratio (SNR) is necessary so that noise does not interfere with the differentiation between the malignant and benign enhancement rates. However, with conventional MRI techniques, since each of the dynamic images is collected independently, the requirements for increasing the temporal and spatial resolutions are conflicting. For example, if N encodings are collected for each image where TR is the time to collect one encoding and Ak is the spatial frequency step between encodings, the spatial resolution will be N1Ak' but the temporal resolution will be limited to NTR which may not be acceptable for large N.
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Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for interventions to visualise movements or changes of the target organ. However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution - also known as the spatio-temporal trade-off of dynamic MRI. Several approaches, including deep learning based super-resolution approaches, have been proposed to mitigate this trade-off. Nevertheless, such an approach typically aims to super-resolve each time-point separately, treating them as individual volumes. This research addresses the problem by creating a deep learning model which attempts to learn both spatial and temporal relationships. A modified 3D UNet model, DDoS-UNet, is proposed - which takes the low-resolution volume of the current time-point along with a prior image volume. Initially, the network is supplied with a static high-resolution planning scan as the prior image along with the low-resolution input to super-resolve the first time-point. Then it continues step-wise by using the super-resolved time-points as the prior image while super-resolving the subsequent time-points. The model performance was tested with 3D dynamic data that was undersampled to different in-plane levels. The proposed network achieved an average SSIM value of 0.951$\pm$0.017 while reconstructing the lowest resolution data (i.e. only 4\% of the k-space acquired) - which could result in a theoretical acceleration factor of 25. The proposed approach can be used to reduce the required scan-time while achieving high spatial resolution.
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Achieving both high spatial and temporal resolution is desired in dynamic Magnetic Resonance Imaging (MRI), however, it is difficult to satisfy because of the slow scanning speed of MRI caused by physical and physiological limits. In order to guarantee the temporal resolution, the amount of acquired k-space data is usually reduced. In this paper, a novel method-patch learning-based dynamic MRI super-resolution reconstruction, is proposed, where high resolution dynamic images are reconstructed based on the sampled low frequency k-space data together with a small amount of fully sampled frames as training data. The proposed method is also demonstrated by in-vivo dynamic MRI data.
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To test the feasibility of a novel "split dynamic" method in which high temporal and high spatial resolution dynamic MR images are acquired during a single bolus injection.High temporal resolution images were acquired using a three-dimensional (3D) dual-echo EPI sequence. The high spatial resolution images were acquired using a 3D T1 -weighted turbo field echo sequence. Simulations were performed to test the split dynamic method in terms of accuracy relative to a continuous acquisition and for temporal sampling requirements for accurate estimation of kinetic parameters. The method was tested in four patients where pharmacokinetic parameters were extracted from the high temporal resolution data.The split dynamic method enabled quantitative evaluation of both T1- and T2*-weighted characteristics. Simulations showed that splitting the dynamic acquisition does not significantly influence the reliability of parameter estimations. Simulation showed a required temporal resolution of 13, 16, and 8 s for accurate estimates of Ktrans, ve, and vp, respectively, and an optimal sampling interval between 2 and 6 s for peak R2*.The split dynamic sequence enabled detailed assessment of dynamic T1- and T2*-weighted contrast kinetics without compromising guidelines concerning spatial resolution.
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Abstract Purpose: To investigate the relationship between temporal resolution of dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) and classification of breast lesions as benign versus malignant. Materials and Methods: Patients underwent T 1 ‐weighted DCE MRI with 15 s/acquisition temporal resolution using 1.5 Tesla (n = 48) and 3.0T (n = 33) MRI scanners. Seventy‐nine patients had pathologically proven diagnosis and 2 had 2 years follow‐up showing no change in lesion size. The temporal resolution of DCE MRI was systematically reduced as a postprocessing step from 15 to 30, 45, and 60 s/acquisition by eliminating intermediate time points. Average wash‐in and wash‐out slopes, wash‐out percentage changes, and kinetic curve shape (persistently enhancing, plateau, or wash‐out) were compared for each temporal resolution. Logistic regression and receiver operating characteristic (ROC) curve analysis were used to compare kinetic parameters and diagnostic accuracy. Results: Sixty patients (74%) had malignant lesions and 21 patients (26%) had benign lesions. All temporal‐resolution parameters significantly predicted benign versus malignant diagnosis ( P < 0.05). However, 45 s/acquisition and higher temporal‐resolution datasets showed higher accuracy than the 60 s/acquisition dataset by ROC curve analysis (0.72 versus 0.69 for average wash‐in slope; 0.85 versus 0.82, for average wash‐out slope; and 0.88 versus 0.80 for kinetic curve shape assessment, for 45 s/acquisition versus 60 s/acquisition temporal‐resolution datasets, respectively ( P = 0.027). Conclusion: DCE MRI data with at least 45‐s temporal resolution maximized the agreement between the kinetic parameters and correct classification of benign versus malignant diagnosis. J. Magn. Reson. Imaging 2009;30:999–1004. © 2009 Wiley‐Liss, Inc.
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T1-shortening contrast agents have been widely used in time-resolved magnetic resonance angiography. To match imaging data acquisition with the short time period of the first pass of contrast agent, temporal resolution and/or spatial resolution have to be compromised in many cases. In this study, a novel non-contrast-enhanced technique was developed for time-resolved magnetic resonance angiography. Alternating magnetization preparation was applied in two consecutive acquisitions of each measurement to eliminate the need for contrast media. Without the constraint of contrast media kinetics, temporal resolution is drastically improved from the order of a second as in conventional contrast-enhanced approach to tens of milliseconds (50.9 msec) in this study, without compromising spatial resolution. Initial results from volunteer studies demonstrate the feasibility of this method to depict anatomic structure and dynamic filling of main vessels in the head. Magn Reson Med 63:835–841, 2010. © 2010 Wiley-Liss, Inc.
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K-Space Acquisition Method for Dynamic Contrast-Enhanced MRI: Application to Breast Tumors by Sumati Krishnan Chairs: Jeffrey A. Fessler and Thomas L. Chenevert Dynamic contrast-enhanced (DCE)-MRI is increasingly being used for detection and diagnosis of tumors. The primary objective is to elicit diagnostically significant architectural and pharmacokinetic features of lesions. Hence, DCE-MRI of tumors is ideally performed at high spatial resolution while sampling a time-varying event at high temporal resolution. A variety of variable rate sampling strategies and associated reconstructive schemes have been developed to resolve the conflicting demands of simultaneous high resolution sampling of temporal and spatial detail. In all these methods, a minimum desired spatial resolution is specified, and attempts are made to improve the temporal resolution in sampling the dynamic event. In this work a novel method, termed spatio-temporal bandwidth-based (STBB) acquisition, is developed to address the trade-off inherent in DCE-MRI. This technique is constrained only by the overall scan duration, within which the temporal event is expected to reach steady-state, and the imaging sequence repetition time, TR. Neither the spatial nor temporal resolution
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Purpose : The purpose of this work is to implement the CS‐based MRI in DCE‐MRI to develop a new method that can exhibit both high temporal and high spatial resolution results which are significant for DCE‐MRI and related diagnosis. Method and Materials : By exploring the DCE specific characteristic that administration of contrast agent (CA) induces change only in image signal intensity in certain areas such as vessels and lesions no significant change in anatomical structure we have developed a novel approach Reference imAge based Compressed sEnsing (RACE) to capitalize the sparsity and compressibility of DCE‐MRI. Phantom experiments have been performed on an MAGNETOM ESSANZA 1.5T MRI scanner (Siemens Erlangen Germany)focusing on the study of temporal and spatial resolution respectively. Spatial finite difference (SPD) is chosen to be the sparse transformation. 3D radial sampling is implemented as under sampling scheme. The reconstruction is based on solving a constrained total variation (TV)‐norm minimization problem. Results : According to the phantom experiments during a same time course much more (up to 10 times) time frames can be obtained with RACE which means higher temporal resolution. The comparisons demonstrate that the details of the dynamic curves can be detected using RACE especially when the intensity varying rate is high (Fig.3 c). Meanwhile the spatial resolution is not degraded. Conclusion : This work proved that RACE which properly explored the features of DCE‐MRI can significantly improve the temporal resolution of DCE‐MRI without degrading its spatial resolution. Conflict of Interest (only if applicable) : Phantom experiments are supported by Siemens Mindit Magnetic Resonance Ltd. Co.
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Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tuning to maximise spatial information while preserving high temporal resolution of dynamic MRI. An U-Net based network with perceptual loss is trained on a benchmark dataset and fine-tuned using one subject-specific static high resolution MRI as prior knowledge to obtain high resolution dynamic images during the inference stage. 3D dynamic data for three subjects were acquired with different parameters to test the generalisation capabilities of the network. The method was tested for different levels of in-plane undersampling for dynamic MRI. The reconstructed dynamic SR results after fine-tuning showed higher similarity with the high resolution ground-truth, while quantitatively achieving statistically significant improvement. The average SSIM of the lowest resolution experimented during this research (6.25~\% of the k-space) before and after fine-tuning were 0.939 $\pm$ 0.008 and 0.957 $\pm$ 0.006 respectively. This could theoretically result in an acceleration factor of 16, which can potentially be acquired in less than half a second. The proposed approach shows that the super-resolution MRI reconstruction with prior-information can alleviate the spatio-temporal trade-off in dynamic MRI, even for high acceleration factors.
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