As compared to the rather challenging dynamic CT perfusion protocol, a single acquisition after contrast administration and adenosine stress may represent a promising alternative option to define myocardial contrast distribution at a predefined point in time at a relatively low level of radiation exposure. Given the high image quality of the latest high-pitch acquisitions, potentially resulting in radiation exposure of less than 1 mSv, it can be assumed that multiple scans, performed at different timing, may extend the small diagnostic window for delayed contrast distribution using such single-shot techniques. However, static imaging at one given time point—be it by single-source/single-energy or by dual-source/dual-energy techniques—will only allow for depiction of iodine distribution in the myocardium at a given time. Hence, the analysis of myocardial attenuation acquired at a single phase of arterial contrast enhancement without continuous time-resolved image acquisition enables detection of myocardial blood pool deficits but does not allow quantification of perfusion (George et al. 2006, 2007; Ko et al. 2012a, b; Blankstein et al. 2009).

As a perspective for these kinds of acquisition protocols, using the specific spectral characteristics of iodine and myocardium when exposed to different X-ray energy levels, Dual-energy acquisitions have been suggested as an alternative to quantify myocardial iodine concentration (Ruzsics et al. 2009a, b). It remains unclear at which point in time the acquisition should be triggered, as the inflow of contrast is clearly dependent on many factors. Using the data derived from dynamic, time-resolved CT perfusion datasets, Bischoff and colleagues have specifically defined the most valid acquisition time for the detection of ischemia, comparing opacification of ischemic versus nonischemic myocardium over time (Bischoff et al. 2012). Under pharmacological stress using adenosine, a maximum mean HU difference between ischemic and nonischemic myocardium (ranging from 17.7 to 22.5 HU) was observed 24–32 s after injection of contrast medium. Still, the optimal time point for a single-shot acquisition will certainly still be dependent on contrast injection, cardiac output, and scan parameters.

The main advantage of time-resolved dynamic CT imaging of the myocardial perfusion yields the promise of absolute perfusion quantification. As outlined above, first-pass acquisitions are limited either to the pure visual assessment of hypo-enhanced myocardial areas and/or density/iodine uptake measurements, and do not allow an exact quantification of myocardial perfusion. The typical quantitative parameters that can be calculated from a dynamic time-density curve as acquired by a dynamic perfusion protocol are the MBF and the myocardial blood volume (MBV) (see Fig. 2). The MBF scale units are ml/min/g, and the MBV scale units are ml/g. As 3D dynamic acquisition of the heart has recently become possible, the question must be raised, is absolute quantification of myocardial perfusion by CT needed at all, given the fact that these protocols are technically more challenging and less radiation dose effective as compared to single-phase protocols? Ultimately, the goal for cardiac perfusion imaging is to improve diagnostic accuracy for identifying flow-obstructing stenosis compared with CTA and CT perfusion alone and to provide information on the hemodynamic functional relevance of detected lesions.

In a clinical setting, the hemodynamic significance of a given coronary artery stenosis should be defined with invasive fractional flow reserve (FFR) measurements using a flow wire, representing the currently accepted standard for assessing the hemodynamic significance of coronary artery stenotic lesions (Tonino et al. 2009; Serruys et al. 1997). While CT provides a useful noninvasive technique to evaluate coronary artery anatomy, the functional significance of many coronary artery CT findings is often unclear and may lead to increased downstream test utilization (e.g., radionuclide perfusion imaging, stress MR imaging, or FFR) to help guide treatment decisions (Tonino et al. 2009; Shaw et al. 1999; Blankstein et al. 2010). Specifically, Meijboom et al. (2008) showed on the one hand that the diagnostic accuracy of a quantitative assessment of coronary artery stenosis with CT was highly correlated with angiographic findings. However, for the detection of hemodynamically significant coronary artery stenosis, as assessed with FFR, CT angiography only had a sensitivity of approximately 50 %. Thus, the combination of morphologic imaging with perfusion CT could ameliorate this diagnostic insufficiency of morphologic CT imaging alone (Bamberg et al. 2011).

Still, until dynamic myocardial CT perfusion imaging can be introduced to clinical routine, a number of questions need to be answered; such as, can a single perfusion scan under stress conditions differentiate between nonviable myocardium and reversible ischemia? Recent publications on dynamic cardiac CT perfusion imaging have acquired data at stress only and have not included additional dynamic perfusion imaging at rest, as protocols are designed to minimize the total radiation exposure. Acquiring two dynamic acquisitions, radiation dose would often exceed 20 mSv equivalent dose per patient. A solution to this missing rest perfusion dataset could be to use the dedicated coronary CT angiographic acquisition under resting conditions to assess first-pass myocardial perfusion. In this acquisition, a perfusion deficit would typically reflect nonviable myocardium or a high-grade stenosis inducing perfusion defects even at resting conditions. The second stress perfusion dynamic dataset would allow for identification of perfusion defects induced by significant stenosis not leading to myocardial iodine filling defects under resting conditions. Further, focused research will be necessary to determine which optimized combination of CT protocols will allow for differentiation between infarcted and ischemic myocardium by using either first-pass “static,” dynamic rest perfusion imaging, or delayed enhancement acquisitions (Vliegenthart et al. 2012).

Another question to be solved is which cut-off value of a quantitative parameter, such as MBF, should be used to optimize the differentiation of normal myocardium from myocardium with reduced perfusion. In a recent publication by Bamberg et al., an MBF of 75 mL/100 mL/min was determined as an ideal cut point, on the basis of maximization of the area under the curve (ROC analysis, detection of significant coronary artery disease as defined by FFR) as a criterion (Bamberg et al. 2011). However, this factor may need further validation across different patient populations. For instance, in subjects with an average MBF higher than this threshold level, findings could be incorrectly categorized as having a nonhemodynamically significant stenosis, even if the stenosis might be significant, i.e., leading to false-negative results. This fact could potentially lead to a decreased negative predictive value (NPV) for dynamic stress perfusion imaging. Thus, the optimal MBF threshold might be depending on the individual overall myocardial perfusion and should be adapted individually.

The inherent advantages of dynamic CT perfusion imaging over static imaging or established imaging modalities, such as scintigraphic techniques, are obvious. On the one hand, dynamic CT perfusion imaging might still be helpful in patients suffering from a multi-vessel CAD, as quantitative parameter maps comprising the whole myocardium in a 3D dataset should help to correctly identify disease by low MBF values in several vessel territories. Here, static single-shot techniques displaying only the relative iodine distribution in the myocardium might result in false-negative findings, as iodine contents in several areas of the myocardium could be homogeneously decreased in a multi-vessel CAD. Furthermore, smaller perfusion defects due to small vessel disease have been reported to be visible on dynamic CT perfusion datasets but cannot be seen scintigraphically or in imaging of the large vessels (with either catheter angiography or CTA) (Schuijf et al. 2006; De Bruyne et al. 2001). This high spatial resolution of CT perfusion is an inherent advantage, however, in a scientific setting comparing CT perfusion to other established imaging techniques, this fact may cause a real CT perfusion defect to appear as a false-positive result. E.g., although SPECT imaging is well established for the evaluation of myocardial perfusion, it may not detect subendocardial, nontransmural perfusion abnormalities because of the limited spatial resolution of approximately 10 mm, and some of the apparent “false-positive” CT perfusion studies may show perfusion abnormalities that may be below the resolution of nuclear MPI (Kido et al. 2008).

In conclusion, investigators who perform further research will need to determine the added value of “dynamic” imaging with MBF quantification to “static” imaging (i.e., a single set of images obtained during early myocardial perfusion) and to determine whether the added radiation exposure associated with dynamic imaging can be offset by improved detection of ischemia, e.g., in patients suffering from multi-vessel CAD.

Several publications have reported on the diagnostic value and technical optimization of dynamic cardiac perfusion CT (see Table 2). In the first animal work involving five pigs with an induced 80 % LAD stenosis and using a dual-source CT with shuttle mode for dynamic perfusion imaging, Mahnken et al. showed the possibility of quantifying perfusion values and found significant differences between ischemic (related to an 80 % coronary area stenosis) and nonischemic myocardium (74.0 ± 21.9 versus 117.4 ± 18 mL/100 mL/min, p < 0.002) (Mahnken et al. 2010). If a scan situation in patients is to be simulated using an animal model, given their biological characteristics, the porcine model is known to be most similar to humans (Swindle et al. 1988). The weight of a pig’s heart is in a similar range, and injection of contrast material can be performed at a comparable rate to that used in humans, resulting in a similar opacification pattern of the left ventricle and myocardium. Accordingly, Bamberg et al. performed another in vivo animal study including seven pigs (Bamberg et al. 2012). Using different degrees of flow wire–adjusted induced coronary stenosis and different fluorescent colors, Bamberg et al. were able to simulate different perfusion conditions, both under rest and stress, demonstrating that a dynamic acquisition and derivation of MBF can depict differences in the tributary coronary artery territory similar to the distribution of microspheres in the corresponding histopathological correlation (i.e., 72 vs. 53 % for 75 % stenosis, rest versus stress) (see Fig. 3). The derived quantitative perfusion values were in line with early research on cardiac MRI (Patel et al. 2010).