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Perfusion CT for the assessment of tumour vascularity: which protocol?

Division of Clinical and Laboratory Investigation, Brighton & Sussex Medical School, University of Sussex, Falmer, Brighton BN1 9PX, UK

	Abstract

Top Abstract Introduction Image acquisition protocols Data processing Summary References

 
Perfusion CT is a technique that can be readily incorporated into the existing CT protocols that continue to provide the mainstay for anatomical imaging in oncology to provide an in vivo marker of tumour angiogenesis. By capturing physiological information reflecting the tumour vasculature, perfusion CT can be useful for diagnosis, risk-stratification and therapeutic monitoring. However, a wide range of perfusion CT techniques have evolved and the various commercial implementations advocate different acquisition protocols and processing methods. Acquisition choices include first pass studies or delayed imaging, temporal resolution versus image noise, and single location sequences or multiple spiral acquisitions. Data processing may be semi-quantitative or, using either compartmental analysis or deconvolution, produce results that are quantified in absolute physiological terms such as perfusion, blood volume and permeability. This article discusses the advantages and disadvantages of the more common CT perfusion protocols and offers proposals that could allow for easier comparison between studies employing different techniques.

	Introduction

Top Abstract Introduction Image acquisition protocols Data processing Summary References

 
The recent availability of commercial software that enables existing CT systems to capture physiological parameters reflecting the vasculature within tumours and other tissues has heralded the arrival of perfusion CT into the clinical arena. The introduction of multidetector CT systems (MDCT) has stimulated further interest in perfusion CT techniques and the future implementation of perfusion CT on integrated positron emission tomography (PET)/CT systems will enable tissue vascularity and glucose metabolism to be evaluated at the same sitting, potentially providing new insights into the pathophysiology of cerebral ischaemia and tumour aggression [1].

Within cancer imaging, perfusion CT techniques are readily incorporated into the existing CT protocols that continue to provide the mainstay for anatomical imaging in oncology. By providing an in vivo marker of angiogenesis, perfusion CT can be used for diagnosis, risk-stratification and therapeutic monitoring [2, 3]. For example, quantification of contrast enhancement and perfusion, as an adjunct to a conventional CT examination, can aid characterization of indeterminate pulmonary nodules and may lessen the numbers of patients requiring FDG-PET, thereby saving on healthcare expenditure [4, 5]. Perfusion CT can reveal occult hepatic metastases and other tumour sites undetected by conventional CT [6–9]. Applications of perfusion CT in risk stratification include estimation of tumour grade in cerebral glioma and lymphoma and the prediction of response of head and neck cancer to radiotherapy [10–12]. More recently, CT measurements of hepatic perfusion amongst patients with colon cancer have been shown to provide risk-stratification that is superior to the Dukes' classification [13]. Perfusion CT can also be used to measure the physiological response of tumours to drug treatment or radiotherapy [11, 14–17].

An effective imaging strategy for assessing tumour vascularity would also be of value to monitor "anti-angiogenesis" drugs that aim to halt cancer progression by suppressing the tumour blood supply. Early clinical trials have indicated that conventional imaging strategies that use tumour size or other structural criteria may not suitable for monitoring the effects of anti-angiogenesis drugs [18]. Furthermore, existing functional imaging techniques that are not directed at the vascular system, such as FDG-PET, may also be inappropriate because anti-angiogenesis drugs may induce uncoupling of tumour perfusion and other aspects of tumour physiology [19, 20]. In view of the wide availability and low cost of CT combined with its ease of quantification as compared with contrast-enhanced MRI techniques, perfusion CT is potentially well suited to monitoring tumour response to anti-angiogenesis agents [21].

CT measurements of perfusion have been shown to be reproducible and have been validated against a range of reference methods [22–32]. The increasing number of publications reporting a correlation between contrast enhancement parameters and histological measurements of angiogenesis, such as microvessel density (MVD), validates the use of perfusion CT as a marker of angiogenesis [33–35]. However, a number of distinct perfusion CT techniques have been developed and the various commercial implementations of perfusion CT use different analysis methodologies, advocate a range of image acquisition parameters and suggest various rates of contrast medium administration. To date, no consensus has emerged as to which perfusion CT technique is optimal for the assessment of tumour vascularity. This paper reviews the more common protocols for perfusion CT, highlighting their advantages and disadvantages, and presents for discussion, proposals that could facilitate comparison between different techniques.

	Image acquisition protocols

Top Abstract Introduction Image acquisition protocols Data processing Summary References

 
Perfusion CT techniques typically require a baseline image acquisition without contrast enhancement followed by a series of images acquired over time after an intravenous bolus of conventional iodinated contrast material. The resulting temporal changes in contrast enhancement, often displayed as time–attenuation curves (Figure 1), are subsequently analysed to quantify a range of parameters that reflect the functional status of the vascular system. Pixel-by-pixel analysis can produce quantitative parametric images with high spatial resolution. As contrast agents exhibit two-compartment pharmacokinetics with intravascular and extravascular components (Figure 1), there are essentially two categories of physiological data that are accessible by quantifying contrast enhancement on CT. The intravascular phase of enhancement can evaluate perfusion, i.e. blood flow per unit volume or mass of tissue, and relative blood volume, parameters that are generally increased in malignant tissue reflecting the greater MVD found in tumours. The extravascular phase can also be used to evaluate vascular permeability exploiting the fact that tumour blood vessels are abnormally permeable to circulating molecules, including contrast media [21, 36].

View larger version (18K): [in this window] [in a new window]   Figure 1. Time–attenuation curves from tumours are determined by the contrast material in the intravascular and extravascular compartments. During the first pass, the contrast material is predominantly intravascular and contrast enhancement reflects perfusion and blood volume. Delayed enhancement is determined by the passage of contrast material into and out of the extravascular space, as determined by rate constants k1 and k2.

A range of factors may affect the choice of whether to adopt a first pass study or delayed imaging (or both). The high image frequency required for first pass studies may restrict the volume of tissue that can be assessed even using MDCT or table toggling techniques [37]. Whilst volume acquisitions are readily feasible for delayed permeability studies, such protocols are associated with increased radiation burden. In the chest and abdomen, longer delayed protocols are more prone to image mis-registration from respiratory or other motion. In some organs, the anticipated physiological differences between tumour and normal tissues may influence the choice of protocol. For example, delayed imaging for permeability measurement may be more suitable for the study of brain tumours because the vascular permeability of cerebral gliomas is characteristically much higher than the almost impermeable intact blood–brain barrier whilst tumour perfusion and blood volume values may closely approximate those of normal cerebral cortex. When assessing tumour response to anti-angiogenesis agents, the mechanism of action of a specific agent may favour measurement of one particular physiological parameter and hence select for a particular enhancement phase. For example, agents that block vascular endothelial growth factor (VEGF) may produce greatest effect upon vascular permeability.

Temporal resolution or reduced image noise?
The radiation burden associated with a perfusion CT acquisition protocol is determined by the number of images in the sequence and the tube current (mAs) used for each image. A greater number of images results in more data points on the time–attenuation curve, and therefore higher quality perfusion measurements. Similarly, a larger tube current results in less photon noise within each image and hence, greater certainty in the attenuation measurement at each time point. Image noise can also be reduced by using thicker image slices and lower resolution reconstruction filters but at the expense of spatial resolution. In the context of oncology, the radiation exposure associated with perfusion CT is small compared with the radiotherapy dose that many patients will receive. Nevertheless, there remains a need to limit the radiation burden associated with perfusion CT studies and thus a balance must be made between a greater number of images acquired within a given time, i.e. temporal resolution, and the degree of image photon noise that is accepted in each image. Protocols 1 and 2 in Table 1 illustrate this balance. Protocol 1 uses thinner slices for spatial resolution and a high scan frequency for temporal resolution but accepts greater image noise by using a lower mAs. As a rule of thumb, the tube current for a protocol of this type should be a third to half that used for a conventional diagnostic image. Protocol 2 minimizes image noise by using a higher mAs and a greater slice thickness but the lower imaging frequency reduces temporal resolution.

Single location or repeated spiral acquisition?
An important constraint produced by adopting a high image frequency is that there is insufficient time for table movement during image acquisition and therefore the extent of tissue studied in the craniocaudal (Z) direction is limited to the width of the detector, i.e. up to 2 cm on current MDCT systems. Thus single location sequences will be restricted to two adjacent 10 mm slices or four adjacent 5 mm slices. Thinner slices are possible but will increase image noise. To achieve greater volume coverage, repeated spiral acquisitions can be performed but with a much reduced image frequency, for example every 20 s. Protocol 3 in Figure 1 is an example of such an acquisition technique. Such a low image frequency precludes calculation of tissue perfusion or blood volume but can be used for assessing vascular permeability using Patlak analysis (see below). Furthermore, the introduction of MDCT with its rapid imaging capability means that a whole organ could now be image at or near its time of peak enhancement and the resulting images used to determine standardized perfusion values or standardized enhancement values (SPV and SEV, respectively, see below) over a large tissue volume [38]. Correct timing could be achieved by using a single-location timing sequence following a small test-bolus of contrast medium to determine the time of peak tissue enhancement (analogous to the timing sequences often used during CT angiography to determine the time of peak vascular enhancement). Data from this timing sequence could also be processed to obtain absolute perfusion data within the chosen slice level. However, in general, repeated spiral acquisitions have been used for semi-quantitative analysis but with successful clinical application, for example in the characterization of pulmonary nodules [5, 39, 40] and detection of hepatic micrometastases [6–9].

Which phase of respiration?
Respiratory motion is a major cause of image mis-registration during a dynamic image sequence for perfusion CT in the chest and abdomen, leading to errors in perfusion values and artefacts on parametric images. Yet, there has been no consensus amongst perfusion CT protocols for as to whether image series should be acquired with suspended respiration or quiet breathing. Suspended respiration is unlikely to be appropriate for image series longer than 45–60 s, unless acquisition pauses are created to allow the patient to take a breath, as might be the case for multiple spiral protocols. Unfortunately, patients who have suspended their breathing will often slowly exhale during the image series leading to motion artefacts. Good quality perfusion images can be obtained using quiet respiration but the patient must be warned to avoid the temptation to take a deep breath when experiencing the "hot flush" commonly associated with a rapid bolus of iodinated contrast medium. Quiet respiration will also be appropriate when using a combined PET/CT system to co-register CT perfusion and PET images, as suspended respiration is not possible for the PET data acquisition which takes several minutes. In the future, methods for respiratory gating of CT images may reduce motion artefacts in perfusion CT.

Contrast medium bolus
The shape of the bolus of contrast medium is an important consideration for perfusion CT. When using compartmental analysis to obtain perfusion values, a short sharp bolus is necessary because the validity of the method requires that peak arterial concentration occurs prior to the time of maximal increase in tissue enhancement. Thus, compartmental analysis methods need a relative small bolus (40–50 ml) administered with higher injection rates of between 5 ml s^–1 and 10 ml s^–1. Rates higher than 10 m s^–1 are unlikely to produce further benefit due to buffering in the venous system. Deconvolution analysis can tolerate lower injection rates but higher rates may still be beneficial by maximizing tissue enhancement and so improve the signal-to-noise ratio. Superior tissue enhancement and favourable bolus geometry are also more readily achievable when higher concentrations of contrast media (370 mg ml^–1 or greater) are used.

	Data processing

Top Abstract Introduction Image acquisition protocols Data processing Summary References

 
Semi-quantitative analysis or absolute quantification?
Semi-quantitative parameters such as peak enhancement or enhancement rate are readily obtained from tumour time–attenuation curves and do reflect tumour vascularity to some degree. However, the tumour enhancement pattern is also fundamentally dependent upon the arterial time–attenuation curve, known as the "input function". The input function is, in turn, determined by the cardiac output and central blood volume, factors that may vary considerably between individual patients and even between repeated studies on the same patient, for instance after therapy. Thus, it is not possible to determine the extent to which observed differences in semi-quantitative parameters such as peak enhancement are due to variations in overall cardiovascular function rather than differences in tumour vascularity. This uncertainty may be particularly important when evaluating antivascular tumour agents, many of which are known to alter cardiovascular function. Despite this constraint, peak enhancement measures have been shown to correlate with MVD in lung and renal cancer [33–35].

Recently, it has been shown that peak enhancement measures can be easily converted into a more directly physiological parameter simply by using the patient's weight and the dose of contrast medium administered to calculate the SPV, defined as the ratio of tumour perfusion to mean whole body perfusion [38]. Mean whole body perfusion is defined by the cardiac output divided by the patient's weight. The derivation of the SPV is based on the Fick principle and is conceptually analogous to the standardized uptake value (SUV) widely used in PET. Indeed, a preliminary study found a correlation between SPV and SUV in lung nodules and proposed a threshold SPV of 1.5 as distinguishing between benign and malignant lesions [38].

The fact the SPV effectively normalizes tumour perfusion to the patient's cardiac output and weight means that this parameter may be more directly related to tumour vascularity than absolute perfusion measurements. A simple reduction in cardiac output but no change in tumour vascularity or MVD will lead to reduced tumour perfusion whilst the SPV, being normalized to cardiac output, will remain unchanged. The normalization for cardiac function offered by the SPV may provide an explanation for the fact that closely related peak enhancement measures correlate with tumour MVD and also indicates that SPV measurements may be particularly useful in monitoring anti-cancer drugs known to alter cardiac output. By incorporating fewer calculation steps, peak enhancement and therefore SPV, measurements are also less prone to interobserver error than the more complex perfusion analyses [28].

Compartmental analysis or deconvolution?
Compartmental analysis and deconvolution represent the two most widely used analysis methods for determination of perfusion and other vascular physiological parameters from dynamic CT data. A detailed description of these methodologies is available elsewhere [3, 21]. Compartmental analysis for first pass studies is based on the Fick principle and effectively considers the intravascular and extravascular spaces as a single compartment, a concept that is valid for times points prior to the moment when contrast medium appears in the draining veins of the tissue of interest. Perfusion is calculated either from the maximal slope of the tissue concentration–time curve or from its peak height, normalized to the arterial input function [3, 21]. A two compartment model is used for measurements of capillary permeability and blood volume. Two compartmental analysis can be simplified by assuming the back flux of contrast medium from extravascular to intravascular compartments to be negligible for the first 1 to 2 min, a technique known as Patlak analysis [3, 21].

Deconvolution uses arterial and tissue time–attenuation curves to calculate the impulse residue function (IRF) for the tissue of interest, where the IRF is a theoretical tissue curve that would be obtained from an instantaneous arterial input [3, 21]. For ease of calculation, the IRF is usually constrained in its shape to comprise a plateau followed by a single exponential decay. The height of the flow corrected IRF will give the tissue perfusion and the area under the curve will determine the relative blood volume. This approach can also be extended to include a measurement of capillary permeability by use of a distributed parameter model [3].

Compartmental analysis and deconvolution methods have both been validated against other reference standards such as microspheres in animals or O¹⁵-water PET in humans [22–31]. Indeed, preliminary studies showing a strong correlation between the results obtained by processing the images data sets with both techniques suggest that the two analysis methods are broadly equivalent (Griffiths MR, 2001, pers. comm.). However, there are differences between the techniques in their validation, theoretical assumptions and susceptibility to noise and motion.

Validation of the deconvolution has largely been confined to the brain [23, 24, 30, 31] whereas compartmental analysis as been validated in a wider range of organs [22, 25–29]. Compartmental analysis for perfusion imaging is constrained by its main assumption, that the bolus of contrast medium must be retained in the organ of interest at the time of measurement. With large boluses or for organs with rapid vascular transit, it is possible for this assumption to be breached resulting in underestimation of the perfusion value. On the other hand, deconvolution makes assumptions about the shape of the IRF. Whilst appropriate for most tissues, the assumption of a plateau and single exponential wash-out may not be valid for organs such as the spleen that have a complex microcirculation with slow and fast circulation pathways [41], and the kidney where the prescribed IRF shape does not allow for the transfer of contrast material between cortex and medulla down the urinary tubular system.

The compartmental analysis method has been successfully applied and validated for the measurement of hepatic perfusion, including separate estimation of arterial and portal components [22, 28, 43–45]. However, simple use of the maximal rate of portal phase enhancement and an arterial rather than portal venous input function will result in underestimation of portal perfusion. Methods that correct for this underestimation have been proposed but may not be readily applicable to the whole liver and there is currently no consensus as to the optimal analysis for hepatic perfusion CT. A deconvolution method for measurement of hepatic arterial and portal perfusion has been proposed but remains to be fully validated in humans [6].

Vascular permeability values are specific to the size and charge of the tracer molecule used. For example, MR contrast agents will produce different results from CT techniques. Thus, validation of both compartmental and deconvolution approaches to estimating capillary permeability with CT has proved difficult. CT based measurements of the vascular permeability within the renal medulla have been shown to suffer significant artefacts [45].

By including the complete time series of images in the calculation, the deconvolution method can tolerate greater image noise and is therefore particularly well suited to measuring lower levels of perfusion (e.g. ;20 ml min^–1 100 ml^–1). The ability to measure low perfusion values accurately may be particularly important in studies after therapy. For instance, a threshold as low as 20 ml min^–1 ml^–1 has been proposed as distinguishing active and inactive lymphoma [11]. However, deconvolution analysis includes all the acquired images for its calculations and therefore there are greater opportunities for image mis-registration due to motion of the patient. Compartmental analysis for determination of vascular permeability similarly uses multiple images from the time-series. However, the compartmental method for perfusion measurement requires accurate registration of effectively only three images: the baseline image and the images immediately before and after the time of maximal rate of tissue enhancement. Patient motion at other times will be of little significance. Yet, in practice, the time of maximal tissue enhancement tends to correspond to the "hot flush" sensation commonly experienced by patients after rapid injection of contrast media, and reaction to this hot flush, particularly if unexpected, is a common cause for movement of the patient.

	Summary

Top Abstract Introduction Image acquisition protocols Data processing Summary References

 
From the above review, it can be seen that there is a bewildering array of acquisition and processing variables for perfusion CT and consensus guidelines are needed to alleviate the current confusion surrounding the technique. However, any consensus guidelines will initially need to be sufficiently flexible to allow for variation in technique until further data are available to help solve the many unanswered questions that remain. Meantime, the following proposals are offered for discussion:

1. Contrast medium boluses should be as short as practicable. Although not essential for deconvolution analysis, all analysis methods will benefit from rapid injection of lower volumes of high concentration contrast media.
   
2. Simple measurements of tissue enhancement should also be expressed as SEV or SPV [38]. The calculation is simple; requiring only a record of the patient's weight and the dose of contrast agent given along with a simple calibration for the CT system used, and will allow for comparison of results obtained using different acquisition techniques. It would be useful for CT manufacturers to develop software for calculation of SEV and SPV from regions of interest and pixel-by-pixel to generate parametric images. Such software would be directly analogous to SUV software currently implemented on PET systems.
   
3. Image sequences intended for either compartmental analysis or deconvolution should also be used to determine SPV. Peak enhancement data for SPV determination is inherently available from data sequences intended for either analysis method, further promoting comparability between studies.
   
4. The deconvolution method is to be preferred for measuring tumour perfusion when values are anticipated to be low, either before or after treatment. The lower susceptibility to image noise will improve measurement confidence at low perfusion levels.
   
5. Compartmental analysis is to be preferred for organs with complex circulatory pathways until further validation of the deconvolution method is available for such organs.
   

Perhaps most importantly, perfusion CT protocols should be easy to use and readily understood by radiographers, radiologists, clinicians and research scientists. Disparities in current acquisition and processing protocols promote confusion and impede the acceptance of the technique. Furthermore, simple protocols are not only liable to produce more reproducible results but are also more likely lead to the wider acceptance of perfusion CT and so bring its many benefits to the imaging of patients with cancer.

	References

Top Abstract Introduction Image acquisition protocols Data processing Summary References

 

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