SHILO, a novel dual imaging approach for simultaneous HI-/LOw temporal (Low-/Hi-spatial) resolution imaging for vascular dynamic contrast enhanced cardiovascular magnetic resonance: numerical simulations and feasibility in the carotid arteries
© Calcagno et al.; licensee BioMed Central Ltd. 2013
Received: 27 February 2012
Accepted: 23 April 2013
Published: 24 May 2013
Dynamic contrast enhanced (DCE) cardiovascular magnetic resonance (CMR) is increasingly used to quantify microvessels and permeability in atherosclerosis. Accurate quantification depends on reliable sampling of both vessel wall (VW) uptake and contrast agent dynamic in the blood plasma (the so called arterial input function, AIF). This poses specific challenges in terms of spatial/temporal resolution and matched dynamic MR signal range, which are suboptimal in current vascular DCE-CMR protocols. In this study we describe a novel dual-imaging approach, which allows acquiring simultaneously AIF and VW images using different spatial/temporal resolution and optimizes imaging parameters for the two compartments. We refer to this new acquisition as SHILO, Simultaneous HI-/LOw-temporal (low-/hi-spatial) resolution DCE-imaging.
In SHILO, the acquisition of low spatial resolution single-shot AIF images is interleaved with segments of higher spatial resolution images of the VW. This allows sampling the AIF and VW with different spatial/temporal resolution and acquisition parameters, at independent spatial locations. We show the adequacy of this temporal sampling scheme by using numerical simulations. Following, we validate the MR signal of SHILO against a standard 2D spoiled gradient recalled echo (SPGR) acquisition with in vitro and in vivo experiments. Finally, we show feasibility of using SHILO imaging in subjects with carotid atherosclerosis.
Our simulations confirmed the superiority of the SHILO temporal sampling scheme over conventional strategies that sample AIF and tissue curves at the same time resolution. Both the median relative errors and standard deviation of absolute parameter values were lower for the SHILO than for conventional sampling schemes. We showed equivalency of the SHILO signal and conventional 2D SPGR imaging, using both in vitro phantom experiments (R2 =0.99) and in vivo acquisitions (R2 =0.95). Finally, we showed feasibility of using the newly developed SHILO sequence to acquire DCE-CMR data in subjects with carotid atherosclerosis to calculate plaque perfusion indices.
We successfully demonstrate the feasibility of using the newly developed SHILO dual-imaging technique for simultaneous AIF and VW imaging in DCE-CMR of atherosclerosis. Our initial results are promising and warrant further investigation of this technique in wider studies measuring kinetic parameters of plaque neovascularization with validation against gold standard techniques.
Inflammation accompanied by the proliferation of adventitial microvessels and intra-plaque neovessels is an important hallmark of vulnerable atherosclerotic plaques, at high risk of causing severe clinical events . Dynamic Contrast Enhanced (DCE) CMR is a well-established non-invasive method used to quantify the extent and properties of tissue microvasculature [2–4]. In recent years DCE-CMR has been applied to quantify neovascularization in atherosclerosis [5, 6] and to track its changes after the application of anti-atherosclerotic therapies [7–9]. Nowadays DCE-CMR is used as a novel endpoint in pre-clinical and clinical trials testing the efficacy of anti-atherosclerotic drugs [10, 11].
DCE-CMR of atherosclerosis currently suffers from inherent limitations. Reliable estimation of plaque perfusion indices with DCE-CMR requires accurate sampling of both the blood plasma (arterial input function, AIF) and vessel wall (VW) enhancement curves [12, 13]. Accurate AIF sampling requires acquiring data with high temporal resolution, which is very challenging when simultaneously imaging tissues that require high spatial resolution, such as atherosclerotic plaques [5, 6, 14]. To maintain acceptable spatial and temporal resolution, few axial slices are usually imaged, therefore sacrificing coverage along a vascular bed [5, 6]. Moreover, AIF and VW enhancements reach very different peak contrast agent concentrations which makes it difficult to optimize imaging parameters  for both compartments in the same acquisition. These limitations may result in inadequate characterization of plaque neovessels and may impact the significance of DCE-CMR findings.
Recent studies have shown by numerical simulations that if the AIF is acquired with a substantially higher temporal resolution (≈1 s) than is necessary for the tissue response, then the accuracy of perfusion parameters is higher than if both curves were sampled at the same slower temporal resolution [16, 17]. Many k-space sharing techniques, such as TRICKS [18–20], CURE , keyhole [22–24], and other segmented radial [25, 26]- or spiral -trajectory strategies, which are densely sampled at the center of k-space, allow for increased temporal resolution in a low-spatial resolution AIF image while still acquiring time-course tissue data at higher spatial resolutions. However, these approaches still do not address the fact that AIF and tissue response need to be acquired with different dynamic signal range.
Dual bolus [28–35] and dual imaging techniques [15, 36–40] can address this additional need for different dynamic signal range for AIF and VW. Dual bolus techniques allow acquiring AIF and VW images with different spatial and temporal resolutions, spatial locations and volumetric coverage. They provide different dynamic signal range for AIF and tissue by using two injections of contrast media: the first low dose injection is used for AIF acquisition, while the second full dose injection is used to acquire the tissue enhancement curves. However, to achieve good correspondence, either the two injections have to be matched in terms of volumes and injection rates , or appropriate steps have to be followed while processing the data . Both approaches are either not practical in a clinical setting, or render data analysis more complicated, with a higher degree of uncertainty.
Differently from dual-bolus, dual-imaging techniques can acquire AIF and VW data with different imaging parameters simultaneously, in the same scan, after only one injection of the full dose required for imaging uptake in the target tissue, such as the vessel wall [5, 15, 36–38, 40]. This eliminates the need for dilution and matching of volume and rate of separate injections and allows measuring the AIF during uptake in the tissue of interest. In addition, since it requires only a single injection, this approach is ideal for ease of workflow in a clinical setting. However, in our knowledge the currently available dual imaging approaches do not allow acquiring images with different temporal and spatial resolution, spatial location and volumetric coverage. This would be particularly useful for tissues requiring imaging with high spatial resolution, such as atherosclerotic plaques.
Here we describe a novel dual-imaging method to address the described limitations of DCE-CMR of atherosclerosis. We name the new sequence SHILO (Simultaneous HI-/LOw-Temporal (Low-/Hi-Spatial) Resolution DCE-imaging). Being a dual imaging approach, both AIF and VW images can be acquired with their optimal dynamic signal range. Differently from previous dual imaging approaches this new sequence allows the acquisition of high temporal/low spatial resolution AIF images and low temporal/high spatial resolution tissue images with different temporal and spatial resolution, imaging parameters and slice location. The high temporal/low spatial resolution AIF images allow sampling the time course of the contrast agent concentration in the blood plasma at a sufficient rate. The low temporal/high spatial resolution tissue images allow sampling the slower uptake in the vessel wall with sufficient spatial resolution to capture plaques heterogeneity. Since these features are implemented in a dual-imaging approach, optimal AIF and VW curves can be obtained using only one injection of contrast agent, which is most practical in a clinical setting.
In the first part of this manuscript we present a description of the new SHILO sequence. Secondly, we perform numerical simulations to demonstrate that our chosen temporal sampling strategy (high temporal/low spatial resolution AIF interleaved with low temporal/high spatial resolution VW images) allows more accurate estimates of plaque perfusion, with respect to sampling both tissues at the same, slower rate. Thirdly, we present phantom and in vivo validation experiments, to demonstrate equivalency of the SHILO MR signal to the original 2D spoiled gradient recalled echo (SPGR) acquisition and its adequacy in sampling the AIF and tissue signals. Finally, we show feasibility of acquiring and analyzing SHILO DCE-MR images in human subjects with carotid atherosclerosis.
SHILO imaging sequence
SHILO imaging parameters
Field of view (FOV)
160 mm × 160 mm
In-plane spatial resolution
0.5 mm × 2 mm
0.5 mm × 0.5 mm
N of slices
320 × 80
320 × 320
N of acquisition segments
Turbo field echo (TFE) factor
Repetition time (TR)
Echo time (TE)
Dual imaging with SHILO: temporal sampling simulations
Here we show that accuracy and precision of kinetic parameters related to atherosclerotic plaque microvascularization can be improved by sampling the AIF at faster time resolution than the plaque enhancement. This feature is implemented in the novel SHILO sequence by interleaving the low spatial/high temporal resolution AIF images with high spatial/low temporal resolution tissue acquisitions. A model AIF  with time resolution 0.1 s over a 5 minutes interval was used to compute tissue uptake curves using a modified Tofts-Kermode  model and a range of kinetic parameters, representative of vessel wall perfusion indices reported in the literature [45, 46]. Kinetic parameters used in the simulation were v p , the fraction of intra-vascular volume; K trans , expressing the inflow of contrast agent from the plasma to the tissue compartment; v e , the fraction of extra-vascular extra-cellular space. The parameter K ep expressing the backflow of contrast agent from the tissue to the plasma compartment was calculated as K trans / ve . 150 different combination of kinetic parameters were used, with v p values 0.001, 0.005, 0.02, 0.05, 0.1, K trans values 0.02, 0.06, 0.1, 0.15, 0.2 min-1, and v e values 0.1, 0.2, 0.3, 0.4, 0.6, 0.8. Two different sampling schemes were then implemented while fitting for kinetic parameters : 1) sampling of both AIF and tissue curves at the same increasingly lower rate (referred to as “same time resolution”, STR) to emulate deriving the AIF curve from the same tissue frames 2) sampling of the tissue curves only (referred to as “different time resolution”, DTR) at an increasingly lower rate to emulate the SHILO dual imaging or other dual bolus techniques. These different levels of under-sampling simulate situations in which an increasing number of tissue slices is acquired using both sampling schemes. In the STR scheme both AIF and tissue curves were under-sampled at rates 1.6, 3.2, 6.4, 12.8, 25.6 and 51.2 s (corresponding to multiples from 1 to 32 of the AIF time resolution in SHILO). In the DTR scheme, tissue curves were under-sampled at the same rate used for the STR case. As for AIF, two DTR schemes were investigated with the AIF sampled at 1.6 s and 0.8 s. The first case (1.6 s AIF sampling) corresponds to the temporal resolution of the AIF in the proposed SHILO acquisition. The second case correspond to a hypothetical test bolus acquisition, where the time resolution of the AIF is equal to the acquisition time of one single shot image as mentioned in the above description of the SHILO sequence, without interleaving the acquisition of segments of tissue slices. Comparing both these DTR schemes allows assessing the impact on kinetic parameters estimation of interleaving AIF and tissue acquisitions, such as is proposed in the SHILO sequence, as opposed to the corresponding test-bolus acquisition. For each parameter set and sampling scheme, the simulation was repeated 6 times, each time shifting the sampling grid by one sixth of the sampling interval to account for a variable temporal registration of the image sampling and bolus passage. Finally, kinetic parameters were estimated with the same modified Tofts-Kermode model used to simulate tissue curves , using non linear least squares fitting routines implemented in Matlab (MathWorks, Natick, MA). The average and standard deviation of estimated kinetic parameters across the 6 variable temporal registration cases were recorded. Relative errors, normalized by the true parameter value were recorded and the magnitude taken. The median of relative magnitude errors and standard deviation over the span of kinetic parameters simulated was plotted as a function of tissue slice temporal sampling resolution.
Validation of SHILO imaging sequence: phantom experiments
The equivalency of the MR signal of the newly developed SHILO sequence with its low and high spatial resolution standard SPGR counterparts was investigated using phantom experiments. Ten vials containing different dilutions of Gd-DTPA in saline with concentrations between 0 to 5 mM were imaged using the SHILO acquisition and using standard saturation-prepared 2D-SPGR acquisitions equivalent to the single-shot low-resolution and 4-fold segmented high-resolution SHILO images. For the high resolution SPGR image a saturation delay of 0.8 s was used as in the SHILO acquisition. SHILO imaging parameters were the same as described above (Table 1). An additional proton-density-weighted image (PD-w) was acquired with the same imaging parameters described in Table 1 except TR/TE 300/2.3 ms and flip angle = 4° to normalize the signal. Regions-of-interest were drawn in each vial using custom made Matlab software (MathWorks, Natick, MA). Normalized signal from SHILO and SPGR images were plotted against each other and compared using Pearson’s correlation coefficient.
Validation of SHILO imaging sequence: in-vivo imaging
Correspondence between AIF curves derived from SHILO and standard SPGR acquisitions was evaluated by visual inspection. AIF curves from all vessels of each subject were averaged after appropriate translation in time to overlay the initial bolus arrival. The Pearson’s correlation coefficient for this set of data points was found.
Feasibility of SHILO imaging for carotid atherosclerosis
In the same cohort of patients, and in the same imaging session, after two low dose injections for in vivo validation of SHILO, an additional higher dose injection was used to assess feasibility of using SHILO for perfusion studies in carotid atherosclerosis (Figure 2). For this purpose, 0.05 mmol/kg of Gd-DTPA (Magnevist) was injected at a rate of 4 ml/s, followed by 20 ml saline flush. In this case also, contrast agent injection started after the acquisition of 3 high spatial resolution tissue images of the SHILO- DCE sequence (approximately 19.2 sec). Following acquisition, images were transferred to a workstation for image analysis. After image registration and de-noising  image signals were normalized by the PD-w images. One ROI was drawn in the vessel lumen of each common carotid for the low resolution AIF image, while for the high resolution tissue image ROIs encompassed the whole visible vessel wall of both common carotids and areas in the sternocleidomastoid muscle. The average ROI signal intensity versus time for both AIF and tissues was converted to T1 by using the SPGR signal equation, and converted to concentration using [Eq. 1]. Contrast agent relaxivity was assumed to be 4.2 s-1mM-1. The initial value of T1,0 was taken from the average of pre-contrast images before the influx of contrast agent. AIF concentration curves were corrected for hematocrit before kinetic modeling. A modified Tofts model  was used to analyze concentration curves, by using the AIF and tissue data from SHILO images. Kinetic parameters vp (fraction of vascular space), Ktrans (wash-in constant from plasma to tissue compartment), ve (fraction of extravascular extracellular space), and Kep (Ktrans/ve wash-out constant from tissue to plasma compartment) were calculated using standard non-linear least squares fitting procedures  written in Matlab.
Kinetic modeling simulations
Validation of SHILO imaging sequence: phantom experiments
Validation of SHILO imaging sequence: in vivo experiments
In-vivo imaging of carotid atherosclerosis
Abundant and permeable adventitial microvessels and intra-plaque neovascularization are considered histological hallmarks of plaque vulnerability [45, 54] and therefore an attractive target for non-invasive imaging methods aimed at identifying individuals at high-risk for acute cardiovascular events [55, 56]. DCE-MRI is a non-invasive technique used to quantify tissues’ perfusion in several organs , which has been applied in recent years to quantify perfusion in atherosclerotic plaques [5–8, 50–52, 57].
Accurate quantification of plaque perfusion is particularly challenging, because of the high in-plane spatial resolution and slice coverage required to capture the heterogeneity of highly complex plaques. Despite the initial encouraging results [5–9, 48, 50–52, 57], current vascular DCE-CMR protocols used to assess plaque perfusion have failed to meet these requirements.
In this manuscript we propose a novel dual imaging technique for improved quantification of perfusion parameters for DCE-CMR of atherosclerosis. We name the new sequence SHILO (Simultaneous HI-/LOw-temporal (low-/hi-spatial) resolution DCE-imaging). The new SHILO sequence proposes to overcome challenges presented by the need to acquire both accurate AIF and tissue DCE curves simultaneously when high-spatial resolution and slice coverage are required for tissue acquisition, such as is the case of atherosclerotic plaques.
In this respect, the SHILO technique has several advantages over other dual imaging DCE techniques. The segmented nature of the tissue acquisition allows acquiring the tissue scan with higher spatial resolution than the AIF scan, while still maintaining a high temporal resolution on the order of 1 s for the AIF curve. In particular, segmentation allows simultaneous measurement of the AIF and the tissue response, allowing accurate sampling of the initial phases of both the plasma and tissue uptake curve (Figure 1).
Importantly, the AIF and tissue scans can be acquired with different dynamic signal ranges, accounting for the expected differences in contrast agent concentration in the lumen and tissue. Additionally, the separation of imaging segments makes the acquisition flexible, since imaging parameters can be manipulated to change signal contrast properties of either scan.
Finally, the AIF image can be acquired in a separate location from the main tissue scan as demonstrated here. This concept can furthermore be extended to multi-slice imaging of the tissue, while retaining a single slice for the AIF. This will allow adequate spatial resolution and slice coverage to capture the complexity of atherosclerotic plaques, while improving estimation of kinetic parameters because of more accurate AIF sampling and will be addressed in future work.
In this study we demonstrate the need for the dual imaging approach that is the basis of our proposed new acquisition sequence. Through numerical simulations, we quantify the errors that are likely to arise from DCE measurements if both AIF and VW wall enhancements are sampled at the same, slow, time resolution (STR sampling scheme). On the contrary, we demonstrate the efficacy of our proposed sampling scheme, in which the AIF is sampled with faster time resolution than the tissue (DTR sampling scheme). Relative errors and standard deviations for the DTR scheme were vastly lower than for the STR scheme, especially at lower temporal resolution (Figures 4 and 5). Also evident was the close similarity of the two DTR schemes with AIF temporal resolutions of 0.8 sec and 1.6 sec. This indicates that there is minimal penalty due to reducing the temporal resolution of the AIF to accommodate the tissue image segment in the interleaved SHILO DTR scheme. The median accuracy and precision of kinetic parameter estimation for the DTR schemes was found to be less than 1% while for STR sampling the accuracy and precision was between 1% and 100%. This may have significant impact on the estimation of plaque perfusion, since the indices v p and K trans have been previously shown to correlate with plaque neovascularization and several systemic risk factors in patients with carotid atherosclerosis [6, 51].
Following, we show equivalency of the SHILO MR signal to the signal of corresponding low and high resolution standard SPGR sequences (Figures 6 and 7). We demonstrate with both in vitro and in vivo experiments that the modifications applied to the native SPGR sequence to implement the SHILO sampling scheme, do not affect the properties of the SPGR MR signal. This is important when dealing with quantitative MR imaging techniques, such as DCE-CMR, where the signal has to be appropriately converted to concentration to extract meaningful quantitative information.
Finally, we show feasibility of using SHILO for DCE-CMR of subjects with carotid atherosclerosis (Figure 8). We demonstrate ability to extract meaningful kinetic parameters from SHILO acquisitions, in the same range of published literature values for both vessel wall and skeletal muscle, used as a reference tissue. In these early experiments we are encouraged by the comparison of kinetic parameters to those already in the literature. Rather than measuring substantially different kinetic parameters, we anticipate the benefit of SHILO to be in improved precision of kinetic parameter estimates that will improve the sensitivity of DCE measurements to neovascularization in the tissue. This will be investigated in further studies including correlation between DCE measurements and histological evaluation.
This feasibility study presents several limitations. As shown by numerical simulations, some advantages of the DTR sampling scheme implemented in the SHILO sequence can be mostly appreciated when the disparity between the AIF and tissue time resolution is significant. This is the case when imaging more than one tissue slice, simultaneously with one AIF slice. In this feasibility study we omitted extensive exploration of comparison with acquisitions of multiple tissue slices, instead demonstrating the technique with a single tissue slice. Our aim was to demonstrate the feasibility of applying the DTR sampling scheme, while providing physical separation between AIF and tissue slices and independence of imaging parameters, attributes not available in other k-space sharing techniques. Comparison of tissue curves between SHILO and SPGR in the low dose scans was not possible, because of the too low uptake of contrast agent in the vessel wall. Addition of a second high-dose to make such a comparison would have increased total patient dose beyond the recommended guidelines. Finally, the small number of patients included and the lack of histological samples limits the extent of the assessment of the SHILO technique. However, we feel that the data presented in this feasibility study will warrant the future investigation of this novel approach in a more extensive study, where the absolute accuracy of plaque perfusion parameters will be compared against histological features as gold standard.
In conclusion, the successful demonstration, in vivo, of the new SHILO dual-imaging technique for simultaneous AIF and tissue-curve imaging in DCE-CMR of atherosclerosis is promising, and warrants further investigation in wider studies measuring kinetic parameters of atherosclerotic plaque neovascularization.
We wish to thank Dr. Anita Gianella, PhD at Icahn School of Medicine at Mount Sinai for her support in the phantom experiments performed as part of this study. Grant support: NIH NHLBI R01 HL071021, NIH/NCATS CTSA UL1TR000067 (Imaging Core), NIH/NIBIB EB009638 and NIH/NHLBI Program of Excellence in Nanotechnology (PEN) Award, Contract #HHSN268201000045C.
- Moreno PR, Purushothaman KR, Fuster V, Echeverri D, Truszczynska H, Sharma SK, Badimon JJ, O’Connor WN: Plaque neovascularization is increased in ruptured atherosclerotic lesions of human aorta: implications for plaque vulnerability. Circulation. 2004, 110: 2032-8. 10.1161/01.CIR.0000143233.87854.23.View ArticlePubMedGoogle Scholar
- Choyke PL, Dwyer AJ, Knopp MV: Functional tumor imaging with dynamic contrast-enhanced magnetic resonance imaging. J Magn Reson Imaging. 2003, 17: 509-20. 10.1002/jmri.10304.View ArticlePubMedGoogle Scholar
- Notohamiprodjo M, Reiser MF, Sourbron SP: Diffusion and perfusion of the kidney. Eur J Radiol. 2010, 76: 337-47. 10.1016/j.ejrad.2010.05.033.View ArticlePubMedGoogle Scholar
- Padhani AR: Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions. J Magn Reson Imaging. 2002, 16: 407-22. 10.1002/jmri.10176.View ArticlePubMedGoogle Scholar
- Calcagno C, Cornily JC, Hyafil F, Rudd JH, Briley-Saebo KC, Mani V, Goldschlager G, Machac J, Fuster V, Fayad ZA: Detection of neovessels in atherosclerotic plaques of rabbits using dynamic contrast enhanced MRI and 18F-FDG PET. Arterioscler Thromb Vasc Biol. 2008, 28: 1311-7. 10.1161/ATVBAHA.108.166173.PubMed CentralView ArticlePubMedGoogle Scholar
- Kerwin W, Hooker A, Spilker M, Vicini P, Ferguson M, Hatsukami T, Yuan C: Quantitative magnetic resonance imaging analysis of neovasculature volume in carotid atherosclerotic plaque. Circulation. 2003, 107: 851-6. 10.1161/01.CIR.0000048145.52309.31.View ArticlePubMedGoogle Scholar
- Lobatto ME, Fayad ZA, Silvera S, Vucic E, Calcagno C, Mani V, Dickson SD, Nicolay K, Banciu M, Schiffelers RM, Metselaar JM, van Bloois L, Wu HS, Fallon JT, Rudd JH, Fuster V, Fisher EA, Storm G, Mulder WJ:Multimodal clinical imaging to longitudinally assess a nanomedical anti-inflammatory treatment in experimental atherosclerosis. Mol Pharm. 2010, 7: 2020-2029. 10.1021/mp100309y.PubMed CentralView ArticlePubMedGoogle Scholar
- Vucic E, Dickson SD, Calcagno C, Rudd JH, Moshier E, Hayashi K, Mounessa JS, Roytman M, Moon MJ, Lin J, Tsimikas S, Fisher EA, Nicolay K, Fuster V, Fayad ZA:Pioglitazone modulates vascular inflammation in atherosclerotic rabbits noninvasive assessment with FDG-PET-CT and dynamic contrast-enhanced MR imaging. JACC Cardiovasc Imaging. 2011, 4: 1100-1109. 10.1016/j.jcmg.2011.04.020.PubMed CentralView ArticlePubMedGoogle Scholar
- Vucic E, Calcagno C, Dickson SD, Rudd JH, Hayashi K, Bucerius J, Moshier E, Mounessa JS, Roytman M, Moon MJ, Lin J, Ramachandran S, Tanimoto T, Brown K, Kotsuma M, Tsimikas S, Fisher EA, Nicolay K, Fuster V, Fayad ZA:Regression of inflammation in atherosclerosis by the LXR agonist R211945: a noninvasive assessment and comparison with atorvastatin. JACC Cardiovasc Imaging. 2012, 5: 819-828. 10.1016/j.jcmg.2011.11.025.PubMed CentralView ArticlePubMedGoogle Scholar
- Fayad ZA, Mani V, Woodward M, Kallend D, Bansilal S, Pozza J, Burgess T, Fuster V, Rudd JH, Tawakol A, Farkouh ME: Rationale and design of dal-PLAQUE: a study assessing efficacy and safety of dalcetrapib on progression or regression of atherosclerosis using magnetic resonance imaging and 18F-fluorodeoxyglucose positron emission tomography/computed tomography. Am Heart J. 2011, 162: 214-21. 10.1016/j.ahj.2011.05.006. e212PubMed CentralView ArticlePubMedGoogle Scholar
- Fayad ZA, Mani V, Woodward M, Kallend D, Abt M, Burgess T, Fuster V, Ballantyne CM, Stein EA, Tardif JC, Rudd JH, Farkouh ME, Tawakol A, dal-PLAQUE Investigators:Safety and efficacy of dalcetrapib on atherosclerotic disease using novel non-invasive multimodality imaging (dal-PLAQUE): a randomised clinical trial. Lancet. 2011, 378: 1547-1559. 10.1016/S0140-6736(11)61383-4.PubMed CentralView ArticlePubMedGoogle Scholar
- Evelhoch JL: Key factors in the acquisition of contrast kinetic data for oncology. J Magn Reson Imaging. 1999, 10: 254-9. 10.1002/(SICI)1522-2586(199909)10:3<254::AID-JMRI5>3.0.CO;2-9.View ArticlePubMedGoogle Scholar
- Parker GJ, Tanner SF, Leach MO: International society for magnetic resonance in medicine (ISMRM). Pitfalls in the measurement of tissue permeability iver short time-scales using multi-compartment models with a low temporal resolution bloo input function. 1996, New York, NY, USA: Proc 4th Annual Meeting ISMRM, 1582-Google Scholar
- Chen H, Ricks J, Rosenfeld M, Kerwin WS: Progression of experimental lesions of atherosclerosis: assessment by kinetic modeling of black-blood dynamic contrast-enhanced MRI. Magn Reson Med. 2012, Epub aheadGoogle Scholar
- Kim D, Axel L: Multislice, dual-imaging sequence for increasing the dynamic range of the contrast-enhanced blood signal and CNR of myocardial enhancement at 3T. J Magn Reson Imaging. 2006, 23: 81-6. 10.1002/jmri.20471.View ArticlePubMedGoogle Scholar
- Li KL, Buonaccorsi G, Thompson G, Cain JR, Watkins A, Russell D, Qureshi S, Evans DG, Lloyd SK, Zhu X, Jackson A: An improved coverage and spatial resolution-using dual injection dynamic contrast-enhanced (ICE-DICE) MRI: a novel dynamic contrast-enhanced technique for cerebral tumors. Magn Reson Med. 2012, 68: 452-62. 10.1002/mrm.23252.View ArticlePubMedGoogle Scholar
- Henderson E, Rutt BK, Lee TY: Temporal sampling requirements for the tracer kinetics modeling of breast disease. Magn Reson Imaging. 1998, 16: 1057-73. 10.1016/S0730-725X(98)00130-1.View ArticlePubMedGoogle Scholar
- Mistretta CA, Grist TM, Korosec FR, Frayne R, Peters DC, Mazaheri Y, Carrol TJ: 3D time-resolved contrast-enhanced MR DSA: advantages and tradeoffs. Magn Reson Med. 1998, 40: 571-81. 10.1002/mrm.1910400410.View ArticlePubMedGoogle Scholar
- Kershaw LE, Cheng HL: A general dual-bolus approach for quantitative DCE-MRI. Magn Reson Imaging. 2011, 29: 160-6. 10.1016/j.mri.2010.08.009.View ArticlePubMedGoogle Scholar
- Korosec FR, Frayne R, Grist TM, Mistretta CA: Time-resolved contrast-enhanced 3D MR angiography. Magn Reson Med. 1996, 36: 345-51. 10.1002/mrm.1910360304.View ArticlePubMedGoogle Scholar
- Parrish T, Hu X: Continuous update with random encoding (CURE): a new strategy for dynamic imaging. Magn Reson Med. 1995, 33: 326-36. 10.1002/mrm.1910330307.View ArticlePubMedGoogle Scholar
- Jones RA, Haraldseth O, Muller TB, Rinck PA, Oksendal AN: K-space substitution: a novel dynamic imaging technique. Magn Reson Med. 1993, 29: 830-4. 10.1002/mrm.1910290618.View ArticlePubMedGoogle Scholar
- Coenegrachts K: Magnetic resonance imaging of the liver: New imaging strategies for evaluating focal liver lesions. World J Radiol. 2009, 1: 72-85. 10.4329/wjr.v1.i1.72.PubMed CentralView ArticlePubMedGoogle Scholar
- van Vaals JJ, Brummer ME, Dixon WT, Tuithof HH, Engels H, Nelson RC, Gerety BM, Chezmar JL, den Boer JA: “Keyhole” method for accelerating imaging of contrast agent uptake. J Magn Reson Imaging. 1993, 3: 671-5. 10.1002/jmri.1880030419.View ArticlePubMedGoogle Scholar
- Mistretta CA: Undersampled radial MR acquisition and highly constrained back projection (HYPR) reconstruction: potential medical imaging applications in the post-Nyquist era. J Magn Reson Imaging. 2009, 29: 501-16. 10.1002/jmri.21683.View ArticlePubMedGoogle Scholar
- Song HK, Dougherty L, Schnall MD: Simultaneous acquisition of multiple resolution images for dynamic contrast enhanced imaging of the breast. Magn Reson Med. 2001, 46: 503-9. 10.1002/mrm.1220.View ArticlePubMedGoogle Scholar
- Sigfridsson A, Petersson S, Carlhall CJ, Ebbers T:Four-dimensional flow MRI using spiral acquisition. Magn Reson Med. 2012, 68 (4): 1065-1073. 10.1002/mrm.23297.View ArticlePubMedGoogle Scholar
- Groothuis JG, Kremers FP, Beek AM, Brinckman SL, Tuinenburg AC, Jerosch-Herold M, van Rossum AC, Hofman MB: Comparison of dual to single contrast bolus magnetic resonance myocardial perfusion imaging for detection of significant coronary artery disease. J Magn Reson Imaging. 2010, 32: 88-93. 10.1002/jmri.22231.View ArticlePubMedGoogle Scholar
- Ishida M, Schuster A, Morton G, Chiribiri A, Hussain S, Paul M, Merkle N, Steen H, Lossnitzer D, Schnackenburg B, Alfakih K, Plein S, Nagel E:Development of a universal dual-bolus injection scheme for the quantitative assessment of myocardial perfusion cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2011, 13: 28-10.1186/1532-429X-13-28.PubMed CentralView ArticlePubMedGoogle Scholar
- Kim TH, Pack NA, Chen L, DiBella EV: Quantification of myocardial perfusion using CMR with a radial data acquisition: comparison with a dual-bolus method. J Cardiovasc Magn Reson. 2010, 12 (1): 45-10.1186/1532-429X-12-45.PubMed CentralView ArticlePubMedGoogle Scholar
- Utz W, Greiser A, Niendorf T, Dietz R, Schulz-Menger J: Single- or dual-bolus approach for the assessment of myocardial perfusion reserve in quantitative MR perfusion imaging. Magn Reson Med. 2008, 59: 1373-7. 10.1002/mrm.21611.View ArticlePubMedGoogle Scholar
- Christian TF, Aletras AH, Arai AE: Estimation of absolute myocardial blood flow during first-pass MR perfusion imaging using a dual-bolus injection technique: comparison to single-bolus injection method. J Magn Reson Imaging. 2008, 27: 1271-7. 10.1002/jmri.21383.View ArticlePubMedGoogle Scholar
- Risse F, Semmler W, Kauczor HU, Fink C: Dual-bolus approach to quantitative measurement of pulmonary perfusion by contrast-enhanced MRI. J Magn Reson Imaging. 2006, 24: 1284-90. 10.1002/jmri.20747.View ArticlePubMedGoogle Scholar
- Hsu LY, Rhoads KL, Holly JE, Kellman P, Aletras AH, Arai AE: Quantitative myocardial perfusion analysis with a dual-bolus contrast-enhanced first-pass MRI technique in humans. J Magn Reson Imaging. 2006, 23: 315-22. 10.1002/jmri.20502.View ArticlePubMedGoogle Scholar
- Christian TF, Rettmann DW, Aletras AH, Liao SL, Taylor JL, Balaban RS, Arai AE: Absolute myocardial perfusion in canines measured by using dual-bolus first-pass MR imaging. Radiology. 2004, 232: 677-84. 10.1148/radiol.2323030573.View ArticlePubMedGoogle Scholar
- Jelescu IO, Leppert IR, Narayanan S, Araujo D, Arnold DL, Pike GB: Dual-temporal resolution dynamic contrast-enhanced MRI protocol for blood–brain barrier permeability measurement in enhancing multiple sclerosis lesions. J Magn Reson Imaging. 2011, 33: 1291-300. 10.1002/jmri.22565.View ArticlePubMedGoogle Scholar
- Gatehouse PD, Elkington AG, Ablitt NA, Yang GZ, Pennell DJ, Firmin DN: Accurate assessment of the arterial input function during high-dose myocardial perfusion cardiovascular magnetic resonance. J Magn Reson Imaging. 2004, 20: 39-45. 10.1002/jmri.20054.View ArticlePubMedGoogle Scholar
- Breton E, Kim D, Chung S, Axel L: Quantitative contrast-enhanced first-pass cardiac perfusion MRI at 3 tesla with accurate arterial input function and myocardial wall enhancement. J Magn Reson Imaging. 2011, 34 (3): 676-84. 10.1002/jmri.22647.PubMed CentralView ArticlePubMedGoogle Scholar
- Calcagno C, Ramachandran S, Mani V, Kotys M, Fischer S, Fayad ZA: ISMRM 19th annual meeting and exhibition. SHILO: Simultaneous High/Low spatial/temporal resolution dual-imaging acquisition for improved parameters quantification in dynamic contrast enhanced (DCE) MRI of atherosclerosis. 2011, Montreal-Quebec, Canada: ISMRM 19th Annual Meeting and ExhibitionGoogle Scholar
- Wang J, Chen H, Wilson GJ, Balu N, Kerwin WS, Yuan C, Boernert P: ISMRM 19th annual meeting and exhibition. Interleaved LOcal excited black blood (LOBBI) and bright blood MRI for improved vessel wall DCE. 2011, Montreal - Quebec, Canada: ISMRM 19th Annual Meeting and ExhibitionGoogle Scholar
- Elkington AG, He T, Gatehouse PD, Prasad SK, Firmin DN, Pennell DJ: Optimization of the arterial input function for myocardial perfusion cardiovascular magnetic resonance. J Magn Reson Imaging. 2005, 21: 354-9. 10.1002/jmri.20294.View ArticlePubMedGoogle Scholar
- Kostler H, Ritter C, Lipp M, Beer M, Hahn D, Sandstede J: Prebolus quantitative MR heart perfusion imaging. Magn Reson Med. 2004, 52: 296-9. 10.1002/mrm.20160.View ArticlePubMedGoogle Scholar
- Parker GJ, Roberts C, Macdonald A, Buonaccorsi GA, Cheung S, Buckley DL, Jackson A, Watson Y, Davies K, Jayson GC: Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med. 2006, 56: 993-1000. 10.1002/mrm.21066.View ArticlePubMedGoogle Scholar
- Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, Larsson HB, Lee TY, Mayr NA, Parker GJ, Port RE, Taylor J, Weisskoff RM:Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999, 10: 223-232. 10.1002/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S.View ArticlePubMedGoogle Scholar
- Virmani R, Burke AP, Farb A, Kolodgie FD: Pathology of the vulnerable plaque. J Am Coll Cardiol. 2006, 47: C13-8. 10.1016/j.jacc.2005.10.065.View ArticlePubMedGoogle Scholar
- Luque A, Slevin M, Turu MM, Juan-Babot O, Badimon L, Krupinski J: CD105 positive neovessels are prevalent in early stage carotid lesions, and correlate with the grade in more advanced carotid and coronary plaques. J Angiogenes Res. 2009, 1: 6-10.1186/2040-2384-1-6.PubMed CentralView ArticlePubMedGoogle Scholar
- Silvera SS, Aidi HE, Rudd JH, Mani V, Yang L, Farkouh M, Fuster V, Fayad ZA: Multimodality imaging of atherosclerotic plaque activity and composition using FDG-PET/CT and MRI in carotid and femoral arteries. Atherosclerosis. 2009, 207: 139-43. 10.1016/j.atherosclerosis.2009.04.023.PubMed CentralView ArticlePubMedGoogle Scholar
- Kerwin WS, Cai J, Yuan C: Noise and motion correction in dynamic contrast-enhanced MRI for analysis of atherosclerotic lesions. Magn Reson Med. 2002, 47: 1211-7. 10.1002/mrm.10161.View ArticlePubMedGoogle Scholar
- Murase K: Efficient method for calculating kinetic parameters using T1-weighted dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Med. 2004, 51: 858-62. 10.1002/mrm.20022.View ArticlePubMedGoogle Scholar
- Calcagno C, Vucic E, Mani V, Goldschlager G, Fayad ZA: Reproducibility of black blood dynamic contrast-enhanced magnetic resonance imaging in aortic plaques of atherosclerotic rabbits. J Magn Reson Imaging. 2010, 32: 191-8. 10.1002/jmri.22225.PubMed CentralView ArticlePubMedGoogle Scholar
- Kerwin WS, O’Brien KD, Ferguson MS, Polissar N, Hatsukami TS, Yuan C: Inflammation in carotid atherosclerotic plaque: a dynamic contrast-enhanced MR imaging study. Radiology. 2006, 241: 459-68. 10.1148/radiol.2412051336.PubMed CentralView ArticlePubMedGoogle Scholar
- Kerwin WS, Oikawa M, Yuan C, Jarvik GP, Hatsukami TS: MR imaging of adventitial vasa vasorum in carotid atherosclerosis. Magn Reson Med. 2008, 59: 507-14. 10.1002/mrm.21532.View ArticlePubMedGoogle Scholar
- Yankeelov TE, Luci JJ, Lepage M, Li R, Debusk L, Lin PC, Price RR, Gore JC: Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. Magn Reson Imaging. 2005, 23: 519-29. 10.1016/j.mri.2005.02.013.View ArticlePubMedGoogle Scholar
- Virmani R, Ladich ER, Burke AP, Kolodgie FD: Histopathology of carotid atherosclerotic disease. Neurosurgery. 2006, 59: S219-27. 10.1227/00006123-200608000-00001. discussion S213-213View ArticlePubMedGoogle Scholar
- Fuster V, Fayad ZA, Moreno PR, Poon M, Corti R, Badimon JJ: Atherothrombosis and high-risk plaque: part II: approaches by noninvasive computed tomographic/magnetic resonance imaging. J Am Coll Cardiol. 2005, 46: 1209-18. 10.1016/j.jacc.2005.03.075.View ArticlePubMedGoogle Scholar
- Fuster V, Moreno PR, Fayad ZA, Corti R, Badimon JJ: Atherothrombosis and high-risk plaque: part I: evolving concepts. J Am Coll Cardiol. 2005, 46: 937-54. 10.1016/j.jacc.2005.03.074.View ArticlePubMedGoogle Scholar
- Chen H, Cai J, Zhao X, Underhill H, Ota H, Oikawa M, Dong L, Yuan C, Kerwin WS: Localized measurement of atherosclerotic plaque inflammatory burden with dynamic contrast-enhanced MRI. Magn Reson Med. 2010, 64: 567-73.PubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.