System and method for time-resolved, three-dimensional angiography with flow information

A system and method are provided for generating time resolved series of angiographic volume data having flow information integrated therewith. The method includes generating a series of 3D time-resolved vascular volumes from time resolved x-ray projection data and calculating blood velocity in the vascular volumes x-ray projection data to determine a rate of change of calculated contrast material arrival time at positions along the vascular volumes. The method also includes displaying the 3D time-resolved vascular volumes with a graphical indication of blood velocity in the vascular volumes.

CROSS-REFERENCE TO RELATED APPLICATIONS

BACKGROUND

The present disclosure is directed to angiography and, in particular, the disclosure relates to a system and method for producing time-resolved, three-dimensional (3D) angiographic images, as referred to as four dimensional (4D) angiographic images including flow information generated from x-ray data.

Since the introduction of angiography beginning with the direct carotid artery punctures of Moniz in 1927, there have been ongoing attempts to develop angiographic techniques that provide diagnostic images of the vasculature, while simultaneously reducing the invasiveness associated with the procedure. In the late 1970's, a technique known as digital subtraction angiography (DSA) was developed based on real-time digital processing equipment. Due to steady advancements in both hardware and software, DSA can now provide depictions of the vasculature in both 2D and rotational 3D formats. Three-dimensional digital subtraction angiography (3D-DSA) has become an important component in the diagnosis and management of people with a large variety of central nervous system vascular diseases.

In recent years competition for traditional DSA has emerged in the form of computed tomography angiography (CTA) and magnetic resonance angiography (MRA). CTA provides higher spatial resolution, but is not time-resolved unless the imaging volume is severely limited. The images are not isotropic and secondary reconstruction yields degraded spatial resolution. CTA is also limited as a standalone diagnostic modality by artifacts caused by bone at the skull base and the contamination of arterial images with opacified venous structures. Further, CTA provides no functionality for guiding or monitoring minimally-invasive endovascular interventions. Significant advances have been made in both the spatial and the temporal resolution qualities of MRA. Currently, gadolinium-enhanced time-resolved MRA (TRICKS) is widely viewed as a dominant clinical standard for time-resolved MRA. TRICKS enables voxel sizes of about 10 mm3and a temporal resolution of approximately 10 seconds. Advancements such as HYBRID highly constrained projection reconstruction (HYPR) MRA techniques, which violate the Nyquist theorem by factors approaching 1000, can provide images with sub-millimeter isotropic resolution at frame times just under 1 second. Nonetheless, the spatial and temporal resolution of MRA are not adequate for all imaging situations and its costs are considerable. Furthermore, the spatial and temporal resolution is substantially below other methods, such as DSA.

The recently-introduced, four-dimensional (4D) DSA techniques can use rotational DSA C-arm imaging systems controlled with respect to a particular injection timing so that there is time dependence in the acquired reconstructed 4D volumes. As described in U.S. Pat. No. 8,643,642, which is incorporated herein by reference, a 3D DSA volume can be used as a constraining volume to generate a new 3D volume contains the temporal information of each projection. As in 3D DSA, a mask rotation without contrast is followed by a second rotation in which contrast is injected. The process creates a series of time resolved 3D angiographic volumes that can be updated, for example, every 1/30 of a second.

Thus, the above-described systems and methods have improved over time and, thereby, provided clinicians with an improving ability to visualize the anatomy of the vessels being studied. Of course, vessels are dynamic and functional structures and the specifics of the anatomy is used by the clinician to deduce information about the dynamic and functional nature of the vessels. Put another way, with ever increasing spatial and temporal resolution, the clinician has been provided with clearer and more accurate information about the geometry (i.e., anatomy) of the vessels. Unfortunately, the equally important dynamics of blood flow through the vasculature still depends upon the qualitative assessment gained from visualization of a contrast bolus as it passes through the vessels. As such, while the deductions made by the clinician about the structural dynamics and function of the vessel (i.e. anatomy) have correspondingly improved, even the best deductions about the circulatory dynamics (e.g. blood flow and velocity) are still qualitative and thus inherently limited.

Therefore, it would be desirable to have a system and method for providing information about the function or dynamic performance of the vasculature as well as its anatomy to a clinician performing an angiographic study.

SUMMARY

The present disclosure overcomes the aforementioned drawbacks by providing a system and method for integrating functional and/or dynamic flow information with high-quality anatomical angiographic images. In particular, a system and method is provided that can integrate flow information with a time-resolved angiographic study, including 4D DSA studies. In one configuration, velocity information or flow information is coupled with 4D DSA images to provide time-resolved, anatomical angiographic images that include flow or velocity and velocity-derived information.

In accordance with one aspect of the disclosure, a system is provided for generating time resolved series of angiographic volume data having velocity or velocity-derived information integrated therewith. The system includes an image processing system configured to receive angiographic volume data acquired from a subject having received a dose of a contrast agent using an imaging system, process the angiographic volume data to generate angiographic volume images, and process the angiographic volume data to derive flow information associated with vessels in the angiographic volume images. The system also includes a display configured to display the angiographic volume images of the subject including the flow information coupled with at least a portion of the vessels in the angiographic volume images.

In accordance with another aspect of the disclosure, a method is provided for generating time resolved series of angiographic volume data having flow information integrated therewith. The method includes generating a series of 3D time-resolved vascular volumes from time resolved x-ray projection data and calculating blood velocity in the vascular volumes x-ray projection data to determine a rate of change of calculated contrast material arrival time at positions along the vascular volumes. The method further includes displaying the 3D time-resolved vascular volumes with a graphical indication of blood velocity in the vascular volumes.

DETAILED DESCRIPTION

Referring toFIG. 1, a system10is illustrated for creating time-resolved angiographic images having flow or velocity information. In particular, the system10includes an angiographic imaging system12. As will be described, the angiographic imaging system12can be used to acquire data, which can be conceptualized as including time-resolved angiographic data14and flow or velocity data16. The time-resolved angiographic data14and flow or velocity data16can be processed and provided to a clinician via a display18. As will be further described, the information may be provided to the clinician using multiple displays including a first display18and a secondary display20or multiple additional displays. As will also be described, the process of deriving velocity or flow data can be performed partially or in whole using an image processing system, which may include a graphics processing unit (GPU) or other processor, including a central processing unit (CPU).

Referring now toFIGS. 2A and 2B, an example of the angiographic imaging system12may include an x-ray imaging system30. The x-ray imaging system30is illustrated as a so-called “C-arm” imaging system; however, other geometries may be used to acquired x-ray angiographic images. For example, any of a variety of x-ray imaging systems capable of acquiring data to create a 4D-DSA image may be used, including systems that acquire time-resolved 2D images using a single plane x-ray system.

The imaging system30, as illustrated, may be generally designed for use in connection with interventional procedures. The imaging system30is characterized by a gantry32forming a C-arm that carries an x-ray source assembly34on one of its ends and an x-ray detector array assembly36at its other end. The gantry32enables the x-ray source assembly34and detector array assembly36to be oriented in different positions and angles around a patient disposed on a table38, while enabling a physician access to the patient.

The gantry includes a support base40, which may include an L-shaped pedestal that has a horizontal leg42that extends beneath the table38and a vertical leg44that extends upward at the end of the horizontal leg42that is spaced from of the table38. A support arm46is rotatably fastened to the upper end of vertical leg44for rotation about a horizontal pivot axis48. The pivot axis48is aligned with the centerline of the table38and the support arm46extends radially outward from the pivot axis48to support a drive assembly50on its outer end. The C-arm gantry32is slidably fastened to the drive assembly50and is coupled to a drive motor (not shown) that slides the C-arm gantry32to revolve it about a C-axis52, as indicated by arrows54. The pivot axis48and C-axis52intersect each other at an isocenter56that is located above the table408and they are perpendicular to each other.

The x-ray source assembly34is mounted to one end of the C-arm gantry32and the detector array assembly36is mounted to its other end. As will be discussed in more detail below, the x-ray source assembly34includes an x-ray source (not shown) that emits a beam of x-rays, which are directed at the detector array assembly36. Both assemblies34and36extend radially inward to the pivot axis38such that the center ray of this cone beam passes through the system isocenter56. The center ray of the x-ray beam can, thus, be rotated about the system isocenter56around either the pivot axis38, the C-axis52, or both during the acquisition of x-ray attenuation data from a subject placed on the table38.

As mentioned above, the x-ray source assembly34contains an x-ray source that emits a beam of x-rays when energized. The center ray passes through the system isocenter56and impinges on a two-dimensional flat panel digital detector housed in the detector assembly36. Each detector element produces an electrical signal that represents the intensity of an impinging x-ray and, hence, the attenuation of the x-ray as it passes through the patient. During a scan, the x-ray source and detector array are rotated about the system isocenter56to acquire x-ray attenuation projection data from different angles. By way of example, the detector array is able to acquire thirty projections, or views, per second. Generally, the numbers of projections acquired per second is the limiting factor that determines how many views can be acquired for a prescribed scan path and speed. Accordingly, as will be described, this system or others can be used to acquire data that can be used to crate 4D DSA image data sets that may provide 3D angiographic volumes at the rate of, for example, 30 per second. As will be further described, such 4D DSA images may be augmented with flow or velocity information.

Referring particularly toFIG. 2B, the rotation of the assemblies34and36and the operation of the x-ray source are governed by a control system58of the imaging system30. The control system58includes an x-ray controller60that provides power and timing signals to the x-ray source. A data acquisition system (DAS)62in the control system58samples data from detector elements in the detector array assembly36and passes the data to an image reconstructor64. The image reconstructor64, receives digitized x-ray data from the DAS62and performs image reconstruction. The image reconstructed by the image reconstructor64is applied as an input to a computer66, which stores the image in a mass storage device68or processes the image further.

The control system58also includes pivot motor controller70and a C-axis motor controller72. In response to motion commands from the computer66, the motor controllers70and72provide power to motors in the imaging system30that produce the rotations about the pivot axis38and C-axis52, respectively. A program executed by the computer66generates motion commands to the motor controllers70and72to move the assemblies34and36in a prescribed scan path.

The computer66also receives commands and scanning parameters from an operator via a console74that has a keyboard and other manually operable controls. An associated display76or displays allows the operator to observe the reconstructed image and other data from the computer66. The operator supplied commands are used by the computer66under the direction of stored programs to provide control signals and information to the DAS62, the x-ray controller60, and the motor controllers70and72. In addition, the computer66operates a table motor controller78, which controls the patient table408to position the patient with respect to the system isocenter56.

The above-described system can be used to acquire raw angiographic data that can then be processed to generate a time-resolved 3D angiographic image in the form of a 4D DSA image. Referring toFIG. 3, a process for creating a 4D DSA image begins at process block80with the acquisition of image data from a region-of-interest in a subject using a medical imaging system, such as a CT system or a single-plane, biplane, or rotational x-ray systems. At process block82, a time-series of 2D images is generated from at least a portion of the acquired image data. While the time-series of 2D images can have a high temporal and spatial resolution and may include images acquired at different angles around the subject, it generally cannot provide a sophisticated 3D depiction of the subject. At process block84, a 3D image of the subject is reconstructed from the acquired image data. Though individual projections used to reconstruct this 3D image may themselves convey some degree of temporal information, the reconstructed 3D image itself is substantially free of temporal resolution. For brevity, the 3D image substantially without temporal resolution and the time-series of 2D images may simply be referred to as the “3D image” and “2D images,” respectively.

At process block86, the time-series of 2D images and the static 3D image are selectively combined so that the temporal information included in the 2D images is imparted into the 3D image. This results in the production of a time-resolved 3D image of the subject with high temporal and spatial resolution. While the selective combination process varies based on the medical imaging system used and the nature of the acquired image data, it generally involves the steps of (1) registering the 2D images to the 3D image, (2) projecting the attenuation value of the pixels in the 2D images into the 3D image, and (3) weighting the 3D image with the projected values for each individual frame of the time-series of 2D images. It is contemplated that the temporal weighting in step (3) generally involves multiplying the projected pixel values with the 3D image. These three steps, which can be referred to as “multiplicative projection processing” (MPP), may be accompanied by additional steps to improve image quality or reduce the prevalence of errors and artifacts. For example, the intensity values of pixels and voxels in the 2D images and 3D image produced at process blocks82and84may quantify an x-ray attenuation level at a given location in the subject. These attenuation levels may not be preserved when multiplying the 3D image with projected pixel values. Accordingly, more accurate indications of the attenuation levels may be restored using the intensity value at each voxel in the time-resolved 3D image, for example, by taking the n-th root, if (n−1) different sets of 2D images are used, to weight the 3D image.

The 2D images and 3D image produced at process blocks82and84, respectively, can be produced using DSA techniques. That is, 2D images depicting the subject's vasculature can be produced by reconstructing image data acquired as a bolus of contrast passes through the vasculature and subtracting out a pre-contrast, or “mask,” image acquired before the administration of contrast agent. Likewise, a 3D image of the same vascular structures can be produced by reconstructing image data acquired as contrast agent occupies the vasculature and subtracting out a mask image to remove signal associated with non-vascular structures. The time-resolved 3D image produced by combining the DSA images depicts the subject's vascular structures with both excellent spatial and excellent temporal resolution and may thus be referred to as a 4D-DSA image. As used herein, this time-resolved 3D image may also be referred to as a 4D image, a 4D angiographic image, or a 4D DSA image. The 4D-DSA images can be displayed as “pure” arterial, pure venous, or composite arterial and venous images and can be fully rotated during each state of the filling of the vasculature, thereby enabling greatly simplified interpretation of vascular dynamics. The spatial resolution of these 4D-DSA images may be on the order of 5123pixels at about 30 frames per second. This represents an increase over traditional 3D-DSA frame rates by a factor between 150 and 600, without any significant image quality penalty being incurred. Further discussion of 4D DSA techniques may be found in U.S. Pat. No. 6,643,642, which is incorporated herein by reference in its entirety. Also, U.S. Pat. No. 8,768,031 is incorporated herein by reference, which extends the 4D DSA imaging process to use time-independent 3D rotational DSA volumes. Furthermore, US Published Patent Application US2013/0046176, which describes the use of dual-energy x-ray imaging with 4D DSA, is incorporated herein by reference.

Referring toFIG. 4, a more specific implementation of the above-described process can be employed to produce a 4D-DSA image of a subject using a single-plane x-ray system in combination with a rotational x-ray system or CT system. In this case, the process begins at process block90, when time-resolved image data from a ROI in the subject is acquired using the single-plane system following the administration of a contrast agent to the subject. Using the above-discussed DSA techniques, a time-series of 2D-DSA images at selected angles about the ROI is generated at process block92. These 2D-DSA images depict the contrast agent passing through and enhancing arterial structures in the ROI. The 2D-DSA images are substantially free of signal from non-vascular structures, as well as signal from venous structures can be excluded due to the high temporal resolution of the 2D acquisition. A 3D-DSA image is reconstructed at process block96from the acquired image data. Specifically, the projections acquired at process block90may be log subtracted from those acquired in a non-contrast mask sweep. Typically, vascular structures in the 3D-DSA image are substantially opacified due to the use of contrast agent and the time necessary for data acquisition.

Referring now toFIGS. 4 and 5, the images produced thus far can be selectively combined with the steps indicated generally at98to produce a 4D-DSA image with the detailed 3D resolution of the 3D-DSA image and the temporal resolution of the time-series of 2D-DSA images. In the exemplary depiction of the selective combination process provided inFIG. 5, a single frame of the time-series of 2D-DSA images112includes two image regions having arterial signal114, while the 3D-DSA image116includes both arterial signal118and venous signal120and122. At process block100ofFIG. 4, a frame of the 2D-DSA image112is registered to the 3D-DSA image116at the selected angle and, at process block102, the values of the pixels in the 2D-DSA frame are projected along a line passing through each respective pixel in a direction perpendicular to the plane of the 2D-DSA frame. The projection of pixels with arterial signal114into the 3D-DSA image is indicated generally at124. For simplicity, the projection of pixels in the 2D-DSA frame with no contrast is not shown. At process block104ofFIG. 4, the 3D-DSA image116is weighted by the values projected from the 2D-DSA frame112to produce the 4D-DSA image126. This may include multiplying the projected values with the voxels of the 3D image that they intersect. The weighting process results in the preservation of the arterial signal118and the exclusion, or “zeroing-out,” of undesired venous signal122in the 4D-DSA image. In addition, the intensity value of the arterial signal114in the 2D-DSA frame is imparted into the 3D arterial signal volume118, thereby allowing the changes in arterial signal over time captured by the 2D-DSA images to be characterized in the 4D-DSA image. At decision block106ofFIG. 4, if all of the frames have yet to be processed, the process moves to the next frame of the time-series of 2D-DSA images at process block108and repeats the selective combination process generally designated at98. This cycle continues until, at decision block106, it is determined that a 4D-DSA image has been generated for all relevant time frames. The 4D-DSA image can thus be delivered at process block110.

The venous signal120preserved in the 4D-DSA image126illustrates a potential challenge when generating 4D images using only a single time-series of 2D images acquired at a single angle. That is, signal from desired structures, such as the arterial signal114in this example, can inadvertently be deposited in 3D voxels representing undesired structures, such as the venous region120in this example. The unwanted structures can thus be preserved in the 4D image as “shadow artifacts” when their signal lies along the projected values of a desired structure in a dimension inadequately characterized by the time-series of 2D images. This can result, for example, in a 4D-DSA image in which desired arterial structures are obscured by undesired venous structures for some time frames. However, this will cause a temporary anomaly in the contrast versus time course for the vein. If the time frames of the 4D-DSA image are analyzed, this anomaly can be recognized as inconsistent with the general waveform of the vein and the vein can be suppressed in the time frame where the projected arterial signal is strong. Accordingly, temporal parameters such as mean transit time (MTT) or time-to-fractional-peak can be calculated for each voxel and this information can be used to clean up shadow artifacts. To assist an operator in identifying shadow artifacts and temporal irregularities, the temporal parameters can be color-coded and superimposed on the 4D-DSA image delivered at process block110ofFIG. 4. The temporal parameters can also be exploited to infer information related to potential perfusion abnormalities in the absence of direct perfusion information from parenchymal signal. Further still and as will be described in detail, velocity information can be used to discern arterial structures or venous structures and distinguish or discriminate between the two.

The acquisition of contrast enhanced image data can be performed following the administration of contrast agent to the subject via either IV or IA injection. When scanning a local area, IA injections allow high image quality and temporal resolution as well as reduced contrast agent dose. However, IV injections are often more suitable for scanning larger regions where multiple IA injections at different locations and different arteries would otherwise be required.

Regardless of whether the contrast agent is introduced as an IV or IA injection, the present disclosure provides systems and methods for utilizing information about the time of arrival (TOA) of the contrast agent to provide flow or velocity data along with the 4D DSA images. Thus, while the above-described process generates time-resolved 3D image or 4D DSA images, as will be described, the above described process can be augmented to provide 4D DSA images with flow or velocity information.

The basic 4D DSA process described above can be augmented to provide the ability for quantification of blood velocity or flow. That is, as will be described, the present disclosure provides systems and methods to incorporate quantitative information into 4D DSA through the development of a TOA display. For example, referring toFIG. 1, the display18,20, can show the 4D DSA images where the voxels in each 4D DSA volume are color-coded with the time at which the voxel intensity crosses a preselected fraction of the threshold for that voxel. The color-coded voxel is also modulated by the voxel intensity, thus, reflecting both time and iodine concentration.

Referring toFIG. 3, the above-described 4D DSA process can be augmented to track contrast curves at process block130. This information, as will be described, will allow the above-described 4D DSA frames to be augmented so that the voxels will reflect both iodine concentration and user selected temporal parameters, such as time of arrival, blood flow velocity, and flow. To do so, at process block132, the centerline of each vessel in the 3D images is determined. The centerline of the vessels can be found using, as a non-limiting example, a process such as described in Eric Schrauben, Anders Wåhlin, Khalid Ambarki, Jan Malm, Oliver Wieben, and Anders Eklund. Automated 4D Flow Whole Vessel Segmentation and Quantification using Centerline Extraction. ISMRM 2014 abstract, which is incorporated herein by reference in its entirety. Skeletonization on a binary 3D DSA volume can be performed according to, as a non-limiting example, a thinning procedure suitable for elongated objects such as blood vessels, resulting in a one-voxel wide vessel centerline representation.

At process block134, a TOA curve is generated for each voxel. In one form, a threshold may be compared to the arrival time curve at any position in a vessel. However, such a process is subject to noise. Instead the TOA curve may be calculated by taking the first temporal moment of the contrast curve normalized by an integral over the contrast curve:

where x is the vector 3D position along the centerline and C is time dependent and spatially dependent contrast curve. Thus, TOA is calculated by using the first moment of the frame time weighted by the contrast arrival curve and normalized by the integral of the contrast arrival curve. This process generates a temporal parameter with good SNR for each voxel reflecting the advancement of the bolus and does so using the statistical information from the entire contrast curve rather than the value at a specific time.

For each point x, the inverse of the slope of TOA vs. x, namely dx/dt provides a local estimate of the average blood speed over the cardiac cycle. Thus, local velocity is calculated as the inverse of the slope calculated.

Referring toFIGS. 6A and 6B, TOA curves are illustrated. In particular,FIG. 6Aillustrates a TOA verses position in an artery andFIG. 6Billustrates TOA versus position in a vein. As illustrated, the gradient of the TOA versus position curve provides velocity.FIG. 6Cis a composite image showing a color velocity representation in the artery131and vein133corresponding to the two graphs135,137. The first scale135provides a color-coded velocity scale and the second scale137provides a grey-scale display of arrival time in the non-color-coded vessels segments139. Furthermore, the processing can be constrained to a limited or selected region of interest (ROI).

At process block136, to fit the slope of the TOA values determined by Equation 1, the order of points along a given vessel in which velocity is to be measured may be determined. For example, referring toFIG. 7, a schematic illustration of a vessel200is provided. To determine the order of points along the vessel200at process block136ofFIG. 3, a marching cubes algorithm may be used. In doing so, a series of marching cubes202,204are used to find the order and position of points206along a centerline208of the vessel200. As a non-limiting example, the vessel200may be the carotid artery. Vessel branch endpoints and junction points can be automatically identified and labeled within the vascular tree to produce a unique branch identification for each vessel.

Alternatively, a wider range of vessels and associated velocities may be processed automatically using a rotating mask process, which may rotate in 3D, such that the rate of change of TOA values along the centerline208can be used to estimate velocity. Alternatively, the complete centerline data may be used to determine the local direction of the centerline208in order to calculate the local TOA gradient of the TOA, such as described above with respect toFIGS. 6A and 6Bat each point206. This rotating mask algorithm uses rotating binary masks210to determine the direction of the centerline210by positioning a plurality of masks212along a potential centerline214and calculating a probability measure for each pixel that a given mask212is aligned with the potential centerline214. The mask212is rotated in 3D to find the orientation that has the highest correlation with the local centerline208. This process controls against the need to trace the centerline208through bifurcations because the rotating mask212can be used to automatically track centerlines through bifurcations. Thus, velocity can be calculated at each point206in the vascular volume208without having to proceed step by step through the vascular tree.

Regardless of the particular algorithm utilized, determining the centerline inside complex structures, such as arteriovenous malformations (AVMs) and aneurysms, can be difficult. Even if the centerline or centerlines of a particular complex structure cannot be adequately determined, the above-described systems and methods can be used to determine centerlines and velocities of the vessels entering and exiting these complex structure. When a region where the determination of the centerline may be unreliable, the above-described systems and methods can insert the grey scale 4D DSA information into the color display or otherwise indicate that the flow information is not available in that complex structure. Nevertheless, the clinician's needs are met by providing the flow information entering and exiting the complex structure.

Referring again toFIG. 3, at process block138, the derived velocity and/or direction information can be used to generate a velocity volume. The velocity volume may be created using a color lookup table or other memory-storage mechanism. The lookup table or other mechanism may store velocity values or ranges and associated color codings. As such, the derived velocity information can be compared to the color lookup table or other memory-storage mechanism to generate flow/velocity volumes that are color coded. At process block140, the flow/velocity volume, and associated color information, is combined with the 4D DSA images. More particularly, at process block140, the volume may be modulated by the 4D DSA intensity values at each point in time. For example, the arrival volume may be subjected to a color preserving multiplication by each of the time resolved 4D DSA time frame volumes. As such, the color-coded voxel is also modulated by the voxel intensity to, thereby, reflect both time and iodine concentration.

An example image is provided inFIG. 8. In particular,FIG. 8shows two frames300,302from a movie of a color-coded time of arrival display of a 4D DSA showing an AVM. Note that the second frame302, but for the present disclosure, would have required a dose-prohibitive, x-ray path passing directly from head to foot. This view is available because of the 3D nature of the 4D DSA time frame.

Thus, as described above, systems and methods are provided to obtain quantitative flow information using analysis of 4D-DSA flow curves. The result is the ability to display quantitative information created by providing a color coded display of the 4D DSA frames so that the voxels reflect both iodine concentration and user selected temporal parameters, such as time of arrival, blood flow velocity, and flow.