Material Supplementary 4.DC1http://www.jimmunol.org/content/suppl/2010/06/16/jimmunol.090387

This paper presents a review of imaging techniques and of their utility in system biology. During the last decade systems biology has matured into a distinct field and imaging has been increasingly used to enable the interplay of experimental and theoretical biology. In this review, we describe and compare the roles of microscopy, ultrasound, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and molecular probes such as quantum dots and nanoshells in systems biology. As a unified application area among these different imaging techniques, examples in cancer targeting are highlighted.

The purpose of this study was to determine the accuracy and sources of error in estimating single-kidney glomerular filtration rate (GFR) derived from low-dose gadolinium-enhanced T1-weighted MR renography. To analyze imaging data, MR signal intensity curves were converted to concentration vs. time curves, and a three-compartment, six-parameter model of the vascular-nephron system was used to analyze measured aortic, cortical, and medullary enhancement curves. Reliability of the parameter estimates was evaluated by sensitivity analysis and by Monte Carlo analyses of model solutions to which random noise had been added. The dominant sensitivity of the medullary enhancement curve to GFR 1-4 min after tracer injection was supported by a low coefficient of variation in model-fit GFR values (4%) when measured data were subjected to 5% noise. These analyses also showed the minimal effects of bolus dispersion in the aorta on parameter reliability. Single-kidney GFR from MR renography analyzed by the three-compartment model (4.0-71.4 ml/min) agreed well with reference measurements from (99m)Tc-DTPA clearance and scintigraphy (r = 0.84, P < 0.001). Bland-Altman analysis showed an average difference of 11.9 ml/min (95% confidence interval = 5.8-17.9 ml/min) between model and reference values. We conclude that a nephron-based multicompartmental model can be used to derive clinically useful estimates of single-kidney GFR from low-dose MR renography.

For time-resolved acquisitions with k-space undersampling, a simulation method was developed for selecting imaging parameters based on minimization of errors in signal intensity versus time and physiologic parameters derived from tracer kinetic analysis. Optimization was performed for time-resolved angiography with stochastic trajectories (TWIST) algorithm applied to contrast-enhanced MR renography. A realistic 4D phantom comprised of aorta and two kidneys, one healthy and one diseased, was created with ideal tissue time-enhancement pattern generated using a three-compartment model with fixed parameters, including glomerular filtration rate (GFR) and renal plasma flow (RPF). TWIST acquisitions with different combinations of sampled central and peripheral k-space portions were applied to this phantom. Acquisition performance was assessed by the difference between simulated signal intensity ( Key words: time-resolved MRI; dynamic contrast-enhanced MRI; MR renography; optimal sampling; TWIST Dynamic contrast-enhanced MR imaging (DCE MRI) plays an important role in many applications, such as perfusion imaging in oncology (1), MR angiography (2), and MR renography (MRR) (3,4). Among the key requirements of DCE MRI is achieving sufficiently high temporal resolution without sacrificing spatial resolution and anatomic coverage. Strategies for achieving both high temporal and spatial resolution often employ k-space undersampling, such as keyhole imaging (5), blocked regional interpolation scheme for k-space (BRISK) (6), continuous update with random encoding (CURE) (7), time-resolved imaging of contrast kinetics (TRICKS) (8,9), and k-t Broad-use Linear Acquisition Speed-up Technique (k-t BLAST) (10). The resulting image artifacts and spatial resolution depend on the size of the frequently updated portion of k-space (the "center") and on the nature and extent of undersampling of the periphery. A large central portion of k-space is likely to produce high-quality images but lower temporal resolution. On the other hand, undersampling of the peripheral k-space regions can result in ringing artifacts, which not only impair postprocessing steps, such as image segmentation, but may also obscure visualization and characterization of smaller structures. Furthermore, undersampling may distort enhancement curves, especially when the signal is changing rapidly, for example, during firstpass perfusion, and can affect the accuracy of kinetic modeling parameters.Despite increasing use of fast acquisition techniques and DCE MRI in diagnostic radiology, few studies have explored the problem of balancing the temporal and spatial properties of the acquisition protocol. A number of studies have evaluated the minimum temporal resolution required for accurate derivation of parameters using tracer kinetic modeling from dynamic data (11); however, there is no general methodology to guide the selection of optimal imaging parameters necessary to achieve proper temporal resolution as well as good-quality images. In humans, the main obstacle to opti...

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A three-compartment model is proposed for analyzing magnetic resonance renography (MRR) and computed tomography renography (CTR) data to derive clinically useful parameters such as glomerular filtration rate (GFR) and renal plasma flow (RPF). The model fits the convolution of the measured input and the predefined impulse retention functions to the measured tissue curves. A MRR study of 10 patients showed that relative root mean square errors by the model were significantly lower than errors for a previously reported three-compartmental model (11.6% ؎ 4.9 vs 15.5% ؎ 4.1; P < 0.001). GFR estimates correlated well with reference values by 99m Tc-DTPA scintigraphy (correlation coefficient r ‫؍‬ 0.82), and for RPF, r ‫؍‬ 0.80. Parameter-sensitivity analysis and Monte Carlo simulation indicated that model parameters could be reliably identified. Key words: computed tomography; glomerular filtration rate; impulse retention function; magnetic resonance renography; renal plasma flow MR renography (MRR) and computed tomography renography (CTR) are increasingly used for noninvasive measurement of single-kidney function (1-7). These dynamic imaging techniques record the transit of a tracer, such as Gd-DTPA or iodinated contrast agents, from the aorta through the renal system. Tracer activity versus time curves can then be derived for intrarenal regions such as renal cortex, medulla, and collecting system. Design of an appropriate physiologic model is an essential part of accurate quantification of renal function (1,2).Several models have been proposed to estimate glomerular filtration rate (GFR) from MRR (3-6) and CTR (7). Baumann and Rudin (3) computed the GFR from the medullary uptake of the tracer using the cortical concentration as the input function. Another method (4) used a PatlakRutland plot to estimate GFR from the clearance of the tracer from the vascular compartment. This approach used whole-kidney concentration, obviating the need for regional segmentation of the kidneys. Both of these methods ignored the outflow of the tracer, and the results can be biased by improper selection of the "upslope" interval. Annet et al. (5) extended these techniques to account for tracer leaving the nephron space, thus enabling fitting of the model to measured data over a longer time period. All of these models assume instantaneous mixing of tracer within every compartment.More recently, models have been proposed with the aim of extending physiologic measures beyond GFR. Krier et al. (7) represented the cortex and medulla curves as extended gamma-variate functions with parameters shown to yield renal plasma flow (RPF) and tubular transit times in addition to GFR. GFR and RPF measures were validated against the reference values in pig model using CT renography. Hermoye et al. (8) determined RPF and GFR in rabbits from the cortical impulse response function by numerical deconvolution of renal cortical enhancement curves. The impulse response function exhibited three sequential peaks presumed to reflect the contrast in glomeruli, pro...

There are discrepancy between MR findings and clinical presentations. The compressed cervical cord in patients of the spondylotic myelopathy may be normal on conventional MRI when it is at the earlier stage or even if patients had severe symptoms. Therefore, it is necessary to take a developed MR technique-diffusion tensor imaging (DTI)-to detect the intramedullary lesions. Prospective MR and DTI were performed in 53 patients with cervical compressive myelopathy and twenty healthy volunteers. DTI was performed along six non-collinear directions with single-shot spin echo echo-planar imaging (EPI) sequence. Intramedullary apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values were measured in four segments (C2/3, C3/4, C4/5, C5/6) for volunteers, in lesions (or the compressed cord) and normal cord for patients. DTI original images were processed to produce color DTI maps. In the volunteers' group, cervical cord exhibited blue on the color DTI map. FA values between four segments had a significant difference (P \ 0.01), with the highest FA value (0.85 ± 0.03) at C2/3 level. However, ADC value between them had no significant difference (P [ 0.05). For patients, only 24 cases showed hyperintense on T2-weighted image, while 39 cases shown patchy green signal on color DTI maps. ADC and FA values between lesions or the compressed cord and normal spinal cord of patients had a significant difference (both P \ 0.01). FA value at C2/3 cord is the highest of other segments and it gradually decreases towards the caudal direction. Using single-shot spin echo EPI sequence and six non-collinear diffusion directions with b value of 400 s mm -2 , DTI can clearly show the intramedullary microstructure and more lesions than conventional MRI.

MRI can clearly demonstrate shape, margin, internal components and surrounding tissues. Different subtypes of retroperitoneal liposarcoma exhibited varying MRI features, depending on tumor histological components. MRI should be an ideal method for diagnosing retroperitoneal liposarcoma.