Radial sliding-window magnetic resonance angiography (MRA) with highly-constrained projection...

Radial Sliding-Window Magnetic ResonanceAngiography (MRA) With Highly-Constrained ProjectionReconstruction (HYPR)

Hyun J. Jeong,1* Ty A. Cashen,1 Michael C. Hurley,2 Christopher Eddleman,3

Christopher Getch,3 H. Hunt Batjer,3 and Timothy J. Carroll1,2

Sufficient temporal resolution is required to image the dynam-ics of blood flow, which may be critical for accurate diagnosisand treatment of various intracranial vascular diseases, such asarteriovenous malformations (AVMs) and aneurysms. Highly-constrained projection reconstruction (HYPR) has recently be-come a technique of interest for high-speed contrast-enhancedmagnetic resonance angiography (CE-MRA). HYPR provideshigh frame rates by preferential weighting of radial projectionswhile maintaining signal-to-noise ratio (SNR) by using a highSNR composite. An analysis was done to quantify the effects ofHYPR on SNR, contrast-to-noise ratio (CNR), and temporal blurcompared to the previously developed radial sliding-windowtechnique using standard filtered backprojection or regriddingmethods. Computer simulations were performed to study theeffects of HYPR processing on image error and the temporalinformation. Additionally, in vivo imaging was done on patientswith angiographically confirmed AVMs to measure the effectsof alteration of various HYPR parameters on SNR as well as thefidelity of the temporal information. The images were scored byan interventional radiologist in a blinded read and were com-pared with X-ray digital subtraction angiography (DSA). It wasfound that with the right choice of parameters, modest im-provements in both SNR and dynamic information can beachieved as compared to radial sliding-window MRA. MagnReson Med 61:1103–1113, 2009. © 2009 Wiley-Liss, Inc.

Key words: HYPR; radial imaging; MRA; time-resolved; arterio-venous malformation; AVM

Contrast-enhanced MR angiography (CE-MRA) (1) hasbeen a successful clinical imaging modality. The success-ful translation of CE-MRA into the day-to-day diagnosis ofvascular disease depends on the work of several individ-uals and groups (2–14). In CE-MRA, high signal-to-noiseratio (SNR) and spatial resolution are desired to visualizesmall arteries. At the same time, sufficient temporal reso-lution is required to image the dynamics of the blood flow,which may be critical for accurate diagnosis and treatmentof various vascular diseases, such as arteriovenous malfor-mations (AVMs) and aneurysms.

Despite the success of CE-MRA in many vascular beds,X-ray digital subtraction angiography (DSA) remains thecurrent clinical standard for time-resolved angiography ofthe intracranial vasculature, although it is invasive andhas ionizing radiation and nephrotoxic contrast agents.X-ray DSA has spatial and temporal resolution superior toMRA, typically with a frame rate of three to six frames persecond. Such temporal resolution is required to image anumber of neurovascular conditions, including intracra-nial AVMs (IAVMs), which affect the lives of up to 300,000people between the ages of 20 and 40 years in the UnitedStates alone (15). The treatment of IAVMs is complex; itmust be tailored (16,17) to each patient and monitoredwith periodic angiograms. One approach, radioemboliza-tion using gamma knife surgery, must be planned using anX-ray angiogram acquired at peak nidal enhancement priorto venous opacification. The demands of a high frame rateand high spatial resolution present a challenge to currentmethods of performing CE-MRA.

There have been many improvements to CE-MRA inincreasing the temporal resolution. To accelerate the speedof acquisition, partial Fourier (13,18) can be used, whereonly parts of the k-space are acquired. Further accelerationcan be achieved by parallel imaging (19–23), where mul-tiple receiver coils are used in parallel to each acquiredifferent information. Cashen et al. (24) proposed radiallysampling (25,26) of k-space with a sliding window recon-struction to increase the frame rate of CE-MRA. Based onthe early work of Riederer et al. (2), this technique recon-structs intermediate frames between repeated measure-ments so that time-resolved series of images can be up-dated as often as the time it takes to acquire one radialprojection. In comparison to X-ray, the MRA still has ahigher artifact level and inferior SNR, and suffers fromtemporal blurring due to the lengthy acquisition time, orbroad “temporal footprint,” of the acquisition of 3D datasets.

An alternative approach to dynamic imaging was re-cently proposed by Mistretta et al. (27). Highly-con-strained projection reconstruction (HYPR) can achievehigh frame rates by reconstructing sparse k-space datacombined with a high-SNR composite image (27,28).HYPR requires the data to be sparse in space and there isongoing debate as to the fidelity of the dynamic informa-tion (29) owing to the broad temporal footprint of thecomposite image. The interaction between the sparsity ofthe image data, the temporal footprint of the compositeimage, and the fidelity of the dynamic information is anarea of active research (27,29).

1Department of Biomedical Engineering, Northwestern University, Chicago,Illinois, USA.2Department of Radiology, Feinberg School of Medicine, Northwestern Uni-versity, Chicago, Illinois, USA.3Department of Neurosurgery, Feinberg School of Medicine, NorthwesternUniversity, Chicago, Illinois, USA.This work was supported by National Institutes of Health (NHLBI) R01088437.*Correspondence to: Hyun Jeong, 737 N. Michigan Ave., Suite 1600, Chi-cago, IL 60611. E-mail: h-jeong@northwestern.eduReceived 16 January 2008; revised 1 October 2008; accepted 19 October2008.DOI 10.1002/mrm.21888Published online in Wiley InterScience (www.interscience.wiley.com).

Magnetic Resonance in Medicine 61:1103–1113 (2009)

© 2009 Wiley-Liss, Inc. 1103

We hypothesized that HYPR processing yields signifi-cant improvements in SNR compared to radial slidingwindow reconstruction without HYPR processing. We alsohypothesized that the portrayal of dynamic information inHYPR depends upon specific aspects of the HYPR dataselection. We evaluated our approach using compositeimages with broad and narrow temporal footprints as wellas time frames of varying sizes. Our studies were per-formed using computer simulations and angiographicallyconfirmed intracranial AVM patients. Furthermore, MRAimages were clinically evaluated by a radiologist throughdirect comparison with X-ray angiography in patients withintracranial AVMs.

MATERIALS AND METHODS

We performed the simulations, acquisitions, and HYPRprocessing based on a pulse sequence that uses a radialk-space trajectory in-plane and Cartesian samplingthrough-plane, also known as the “stack of stars” samplingscheme (24). We coupled this acquisition with a slidingwindow reconstruction to allow frame rates comparable tointracranial X-ray angiography.

HYPR

The original HYPR image reconstruction algorithm devel-oped by Mistretta et al. (27) uses the following formula:

IHYPR�x,y,z� �1

NP� C�x,y,z� � �� P�r,�,��

PC�r,�,��� . [1]

The HYPR image, IHYPR(x,y,z), is calculated by multiply-ing the time-averaged composite C(x,y.z) with an imagereconstructed using the ratio of unfiltered backprojections(BPs) of the current time frame P(r,�,�) and correspondingunfiltered backprojections of the composite imagePC(r,�,�). P(r,�,�) usually consists of limited projections, asmall subset of the full number of projections covering180°. Then, the image intensity is scaled by dividing thelimited number of projections, NP (a subset of full N�

projections), used to reconstruct each time frame. Huangand Wright (29) have proposed the following correction tothe original formula (Eq. [1]):

IHYPR�x,y,z� �1

NP� C�x,y,z� �

�P�r,�,��

�PC�r,�,��. [2]

With the correction, IHYPR(x,y,z) converges to the com-posite image C(x,y,z) when the number of limited projec-tions, NP, approaches the number of projections used tomake the composite, since �P(r,�,�)/�PC(r,�,�) � 1.

The corrected formula (Eq. [2]) was used in this study.However, in our study, to maximize the SNR in HYPRimages, we signal-averaged the composite, C(x,y,z), overmultiple measurements of the 3D volume where contrastagent was present (static composite). The composite wasalso formed by time averaging a number of projectionsbefore and after the current frame (sliding composite).Figure 1 illustrates the two composites. In either case,�P(r,�,�)/�PC(r,�,�) does not equal one, and for NP equal to

the number of projections in the composite, the HYPRimage is not the same as the composite image. The com-posite, which also has N� projections, is time averages ofmultiple repetitions containing N� projections.

The NP indicates the number of in-plane projections.With a 3D or multislice acquisition, the NP includes all theprojections with the same angle in that 3D volume. Hence,throughout the remainder of this work, it should be un-derstood that when NP is stated as being a specific numberof projections, the actual number of projections includedin the image is NP times the number of slice or partitionencodings.

Simulations

Computer simulations were performed in order to quantifythe effects of HYPR processing on temporal profiles ofcontrast bolus, as well as image artifacts. By doing com-puter simulations we were able compare how each recon-struction method alters the truth images. Unlike in vivoimaging, the truth images can be user-defined. This allowsa known input to HYPR processing, enabling direct com-parison of the effects of HYPR on the truth images.

All simulations were performed using MATLAB(V2006b; The Mathworks, Natick, MA, USA). A simulationmodel was adopted from a previous study (24) that mod-eled a bolus of contrast agent using a 3D step functionmoving in a straight line across the field of view (FOV)over time (Fig. 2). This geometry was chosen because thefunctions can be modeled as a product of three separablerect functions in the x, y, and z dimensions, and can beanalytically transformed back and forth in k-space andimage space so that k-space sampling can be done on thetrue k-space representation of the truth images. The imagespace function for the bolus moving from left to right (L-R)can be represented as follows:

i�x,y,z,t� � rect�xw�rect� y

vt� �y0 � vt��rect�z

w�. [3]

Equation [3], when transformed into rectangulark-space, becomes

I�kx,ky,kz,t� � sin c�wkx� sin c�vtky�e�j2�y0vt�ky sin c�wkz�,

[4]

where w is the thickness of the vessel, v is the velocity ofmoving bolus flowing through a blood vessel, and t is timein seconds. This effectively models a step function movingin the y direction. The cylindrical representation can beobtained by substituting kx � kr sin (k�) and kx � kr-

cos (k�). The analytical function in k-space (product ofsinc functions) was sampled radially with real imagingparameters. Gaussian noise with standard deviation (SD)equal to the maximum value of I(kx,ky,kz,t) divided by 300was added to the k-space simulation data to yield a max-imum SNR of 300 for each projection. The truth imageswere defined as the inverse Fourier transform of the k-space function for a case where t is a discrete variable foreach measurement volume, which simulates an infinitelyshort acquisition time.

1104 Jeong et al.

Simulations were performed with two 4-pixel-thick bo-lus models with one bolus moving from left to right at7 mm/s in a 250 mm � 250 mm � 32 mm pixel FOV, andthe other moving in the opposite direction at the samespeed. The simulated data sampling was based on ourclinical 3D radial MRA sequence with the following pa-rameters: TR � 3 ms, NSlices � 16, slice thickness � 2 mm,number of angles (N�) � 192, NReadout � 192, and NRepetitions �8. The data were reconstructed using conventional filteredBP with sliding window with full 192 projections, as wellas using HYPR with projections NP � 6, 16, 32, 64, and192, which includes all the partitions for those projec-tions. A sliding window factor of 32 was used for allreconstructions, so that 32 intermediate images were ob-

tained between each volume repetition. The static com-posite for the HYPR reconstruction was obtained by aver-aging all eight repetitions reconstructed with filtered BP offull 192 projections without sliding window. The slidingcomposite was formed by filtered BP of the time average of192 projections behind the first frame and 96 projectionsafter the current frame.

To quantify the artifact level of the images, recon-structed image error was measured as a function of thereconstructed time frame throughout the bolus passage.Simulations done without added Gaussian noise wereused for the artifact level calculation to calculate pureartifact without noise. Image errors of the HYPR processedimages were calculated using a modification of an estab-lished method (30). The formula used to calculate imageerror is:

Image Error � 100 � ��x,y

�Itruth�x,y� � IHYPR�x,y� 2

�x,y

�Itruth�x,y� 2 . [5]

For HYPR, additional energy is introduced to the imagefrom the composite. Therefore, subtraction from the truthmust be done after appropriate normalization. For ourstudies, all images were normalized so that the total energyfor a single image is one. Then �x,y�Itruth�x,y� 2 � 1, and Eq.[5] reduces to:

Image Error � 100 � � �x,y

�Itruth�x,y� � IHYPR�x,y� 2. [6]

FIG. 1. Timing diagram of HYPR with sliding and static composites. The sliding composite slides along each set of limited projections, whilethe static composite stays fixed for each frame. The radial projections (dotted vertical lines) indicated by �N, where N is the pseudorandomacquisition order.

FIG. 2. Simulation truth image and ROIs that represent artery (solid)and vein (dotted). The length of these objects increases over time inthe direction of the arrows to simulate arterial and venous filling.

Radial Sliding Window MRA With HYPR 1105

The average and SD of image errors were calculated foreach number of projections used. A paired Student’s t-testwas performed to compare the static and sliding compos-ites.

Regions of interest (ROIs) were selected in the simula-tion images and intensity was plotted over time on imageswith added noise. The chosen ROIs are shown on Fig. 2.The ROIs were chosen in such a way that one represents anarterial time course and the other represents a venous timecourse. The plots were normalized so that the maximumvalue of the intravascular signal was one. SNR was alsocalculated by averaging the value inside the ROIs anddividing by the SD of the values inside an ROI covering aregion of the simulation where no signal should bepresent.

AVM Patient Studies

We tested the simulated results by scanning four patientswith angiographically confirmed AVMs. The imaging pro-tocols were approved by the institutional review board(IRB).

Sequence

Images were acquired with a Siemens 3T Trio scanner(Siemens Medical Solutions, Erlangen, Germany) and a 3Dspoiled gradient-echo sequence with a radial in-plane andFourier through-plane trajectory. The partition loop wasinside the projection loop. Signal reception was providedby a receive-only 12-channel head coil. Typical imagingparameters were: FOV � 220 mm � 220 mm, partitionthickness � 2.5 mm, TR � 2.7–3.0 ms, TE � 1.3 ms, N� �128 or 192, NReadout � 192, NSlices � 20–30, readout/slicepartial Fourier factors � 75%/75%, receiver bandwidth �1300 Hz/pixel, and flip angle � 15°. Images were acquiredin a sagittal plane with z-phase encoding in the L-R direc-tion. The resulting acquisition was 1.15 mm � 1.15 �2.5 mm. With the parameters above, the resulting in ac-quisition times were between 7 and 13 s per repetition.The number of repetitions was varied so that the totalimaging time was close to 90 s. To minimize the coherenceof undersampling artifacts, the radial projections were ac-quired in a pseudorandom order instead of linearly in-creasing the angle from 0° to 180° using the scheme de-scribed (24).

A single dose (0.1 mmol/kg) of Gd-DTPA (Magnevist;Berlex, Wayne, NJ, USA) was administered for each scanwith a power injector (Spectris Solaris; MEDRAD, Indi-anola, PA, USA). The images were acquired in a sagittaland/or coronal plane. For sagittal scans, the z encodingwas done in the L-R direction, and for coronal scans, the zencoding was done in the anterior–posterior direction.When images were taken from both sagittal and coronalplanes, the order of acquisition was randomized to reducethe effect of residual gadolinium in the first scan.

Reconstruction

Raw data were obtained from the scanner for reconstruc-tion using MATLAB. A Fourier transform was performedin the z and r dimensions. The first measurement volume,acquired prior to contrast agent administration, was then

subtracted from the later frames. Then filtered BP andHYPR processing was performed using both static andsliding composites. Similarly to the reconstruction donewith simulation data, a sliding window factor of 32 wasused. This resulted in 32 intermediate frames betweenactual measurements and reconstructed frame rate of 3.2frames/s. The sliding composite was set by averaging pro-jections measured 10 s (192 projections) behind and 5 s (96projections) ahead of the current frame. Finally, maximumintensity projections (MIPs) were obtained from the 3Dvolume images.

Analysis

ROIs were drawn on the MIPs to cover the common carotidand the superior sagittal sinus on sagittal images to serveas representative regions of arterial and venous enhance-ment, respectively. To calculate the SNR, the values insidethe ROIs were averaged and divided by the SD of an ROIdrawn in the air.

Temporal profiles were plotted by taking the average ofvalues inside each ROI as a function of time. The overlapintegral was used to quantify the separation of arterial andvenous phases. In a sense, it evaluates the degree of venouscontamination. The overlap integral is defined as:

OI � �n�1

N

min �A�n ,V�n �, [7]

where A[n] and V[n] are temporal profiles normalized sothat their maximum value is one. It simply calculates thearea of the region of overlap between arterial and venouscurves in the temporal profiles. A lower overlap integralcorresponds to better separation between arterial and ve-nous phases.

The quality of the MRA images of the intracranial AVMswas scored by an interventional radiologist blinded to thereconstruction parameters. The clinician was given recon-struction sets that varied in NP and composite type in arandom order. The image quality was given scores (1 �nondiagnostic, 2 � poor, 3 � good, and 4 � excellent) inthe following categories: visualization of feeders, visual-ization of nidus, visualization of draining veins, SNR/artifacts, and quality of dynamic information. The scoreswere given relative to the X-ray DSA images, the standardof reference.

RESULTS

Simulations

Figure 3 shows five sets of reconstructed time frames fromthe simulation of bolus dynamics with each frame sepa-rated by 12 frames (3.6 s). Figure 3a represents truth im-ages. Figure 3b and c show HYPR reconstruction usingonly six projections with sliding and static composites,respectively. Similarly, Fig. 3d and e were reconstructedusing 192 projections with sliding and static composites,respectively. Figure 3f is a filtered BP with 192 projectionswithout HYPR, which has been included for reference.HYPR reconstruction with only six projections results in

1106 Jeong et al.

misrepresentation of the simulated bolus (see arrows inFig. 3b and c). The first frame of Fig. 3c, where the lengthof the bolus should be the shortest, shows a longer bolusprofile than the later frames. While the sliding compositeimage (Fig. 3b) exhibits a better representation of the trueextent of the simulated bolus, the first frame still shows anartifact that makes the bolus look longer. The artifacts inthe direction of movement for HYPR images, which appearrandomly in consecutive frames not included in the fig-ures, are reduced as more projections are used, as shownin Fig. 3d (also see Table 1), but the front end of the bolus

is spatially elongated for every frame, more severely for thestatic composite. When 192 projections are used for HYPR,the sliding composite images more closely resemble thefiltered BP without HYPR, as well as truth images, thanstatic composite images.

Signal vs. time curves derived from the arterial signal(solid circle) and the venous signal (dotted circle) arepresented in Fig. 4. Curves for static and sliding compos-ites are presented separately to compare the effects ofusing the two different composites. Note that images re-constructed with as few as six projections (Fig. 4c and h)

FIG. 3. Comparison of HYPR reconstruction using small and large numbers of projections with static and sliding composites. In each setof images the time frame increases from left to right, with frames separated by 1.5 s in time. a: Truth. b: Sliding composite HYPR (NP �6). c: Static composite HYPR (NP � 6). d: Sliding composite HYPR (NP � 192). e: Static composite HYPR (NP � 192). f: Filtered BP withoutHYPR reconstruction. Arrows indicate misrepresentation of the bolus model.

Table 1Image Error and SNR Over Time for Varying Number of Projections in HYPR*

Number ofprojections

Average image error SD image error Average SNR

Staticcomposite

Slidingcomposite

Staticcomposite

Slidingcomposite

Staticcomposite

Slidingcomposite

6 34.6 34.3 8.88 6.64 24.2 15.516 32.1 31.6 4.66 2.96 24.4 15.432 33 30.9 3.97 2.94 25.1 16.864 35.2 31.6 3.19 3.27 25.7 18.1192 44.3 40.6 6.15 5.19 26.9 22.1No HYPR 43.8 4.99 14.4

*Average image error over time of the simulation images for varying number of projections in HYPR (NP � 6-192) with static and slidingcomposites, and 192-projection filtered backprojection (FB). Bold values indicate minimum values, which occur at 32 projections with asliding composite. Eight repetitions with 16-slice encodes were simulated with an acquisition time of 9.2 s per repetition. The staticcomposite was obtained by averaging all eight repetitions reconstructed with filtered backprojection of full 192 projections without slidingwindow. The sliding composite was formed by filtered backprojection of the time-average of 192 projections behind the first frame and 96projections after the current frame.

Radial Sliding Window MRA With HYPR 1107

have severe fluctuations in signal, and the time coursecannot be determined. As more projections are used toreconstruct the images, the fidelity of the time course isimproved but blurred, and the frame-to-frame fluctuationsare reduced (Fig. 4g and l).

The mean and SD of the image error as a function of thenumber of projections are reported in Table 1. In bothsliding and static composites, using very few (six) or many(192) projections results in a higher image error. The imageerror is at the minimum when 16 projections are used witha static composite or when 32 projections are used with asliding composite. The SD of image error, which shows thefluctuations of image error from one frame to the next, is atthe minimum when 32 projections are used with a slidingcomposite or when 64 projections are used with a staticcomposite. Overall, a 32-projection image with a slidingcomposite results in the lowest average image error andSD. The paired Student’s t-test showed that the slidingcomposite results in significantly (P � 0.05) lower imageerror than the static composite.

The average SNR measured over time on an ROI drawnon the top bolus model showed an increasing trend asmore projections were used. The SNR values for staticcomposite images were greater than those of the slidingcomposite images of the corresponding number of projec-tions.

AVM Patient Studies

The patient study showed results similar to those observedin simulations. Figure 5 shows representative sagittal pro-jections of an angiographically confirmed AVM (Fig. 5d).Images were reconstructed using a sliding composite withNP � 6, 32, and 192 projections for Fig. 5a–c, respectively.These images demonstrate the trade-off between the num-bers of projections used in the reconstruction. As moreprojections are used, the temporal footprint becomesbroader, resulting in undesired artifactual increase in sig-nal intensity in the superior sagittal sinus prior to thearrival of contrast agent. Using fewer projections shows anincrease in streak artifacts as shown in Fig. 5a.

Figure 6 shows the corresponding arterial and venoussignal vs. time curves. Images that were reconstructedusing a small number of projections (NP � 6 in Fig. 6b, NP

� 16 in Fig. 6c) resulted in temporal profiles with largeframe-to-frame fluctuations. As more projections wereadded, the profiles became smoother, but the plots showedtemporal blurring, characterized by a broader profile indi-cating early and prolonged contrast enhancement.

The results comparing SNR, CNR, and overlap integralare shown in Table 2. The results of the regular filtered BPare shown to provide a reference. There are several trendsin the table. In almost all cases, the mean SNR and meanCNR values are higher in HYPR-processed images with thestatic composite than with the sliding composite. The

FIG. 4. Signal curves for different image reconstructions base sim-ulations. a: Truth. b: Filtered BP with NP � 192. c–g: Static com-posite (NP � 6, 16, 32, 64, 192). h–l: Sliding composite (NP � 6, 16,32, 64, 192). [Color figure can be viewed in the online issue, whichis available at www.interscience.wiley.com.]

1108 Jeong et al.

overlap integral is also highest for static composite images.These data also demonstrate a trend of increasing SNR asthe number of projections is increased. The highest meanCNR values were seen for NP values between 16 and 64.Note that in some extreme cases with static compositeHYPR using 192 projections, the CNR values resulted inzero, meaning that the artery and vein were enhancedsimultaneously. The trend of increasing overlap integral asnumber of projections is increased is observed in bothsliding and static composites. HYPR images with NP be-tween 16 and 64 showed improvement in CNR, SNR, andoverlap integral values over filtered BP without HYPR.

Figure 7 shows the plot of qualitative scores of imagequality evaluated blindly by a radiologist. For the slidingcomposite HYPR, the dynamic information is best for thesix-projection images, and the score decreases as moreprojections are used. For the static composite, however,using small number of projections actually lowers thedynamic information score. The worst dynamic informa-tion was recorded for 192-projection HYPR with the staticcomposite. The SNR/artifact scores are the lowest for six-projection HYPR images. The scores are better for imageswith the number of projections greater than 6, but in gen-eral do not depend on the composite type or number ofprojections. The scores of the best HYPR images were forNP between 16 and 64. While SNR/artifact scores weresimilar for sliding and static composites for NP � 16–64,the sliding composite scored better on dynamic informa-tion. The scores for visualization of feeders, nidus, anddraining veins did not vary significantly for the differentnumber of projections or the composite types, or for regu-lar filtered BP with sliding window.

DISCUSSION

We have found that by using HYPR with a sliding com-posite, SNR is increased without significant changes intemporal profile compared to standard filtered BP. A staticcomposite yields even higher SNR but temporal informa-tion is blurred. Thus, the size of the window for the com-posite is a trade-off between temporal information andSNR. Using a smaller number of projections shows benefitsof decreasing temporal blur, indicated by a smaller overlapintegral, but at the same time it causes large projection-dependent fluctuations in temporal profiles, manifested byflashing of the dynamic series of the images. Using the fullnumber of projections yields a very smooth profile, but thelong temporal footprint results in temporal blurring, asshown in AVM patient plots. We found the optimal imag-ing parameter to be NP � 32 with a sliding composite,which resulted in the lowest image error while achievinghigher SNR and CNR than filtered BP with acceptabletemporal dynamics. Through radiologist readings, system-atic improvement in SNR and dynamic information forHYPR images from conventional filtered BP images wasseen for HYPR images with 16–64 projections with slidingcomposites, though the improvements were small.

Large fluctuations in the temporal profiles and imageerror from HYPR images reconstructed from fewer projec-tions can be attributed to the different set of angles used forreconstruction of each frame. If one of the few projectionsused in reconstruction happens to be close to being per-pendicular to the direction of the bolus, the reconstructedimage of it will be a line of bolus that extends from one endto the other, which is not a good representation of theimage. And since there are only a small number of projec-

FIG. 5. AVM patient images re-constructed with a sliding com-posite image: (a) NP � 6 slidingcomposite HYPR, (b) NP � 32sliding composite HYPR, (c) NP

� 192 sliding composite HYPR,and (d) X-ray angiogram. Dottedarrows indicate radial streak ar-tifacts with a small NP. Solid ar-rows indicate varying degree ofearly venous phase due to dif-ferent numbers of projections.

Radial Sliding Window MRA With HYPR 1109

tions, this projection has more weight in determining thereconstructed image than with a larger number of projec-tions. Moreover, since angular ordering is scheduled sothat there are periodically angles that are perpendicular or

close to perpendicular to the path of the bolus, periodicspikes can be seen. As we increase the number of projec-tions, the weight of the “bad projection” in determiningthe reconstructed image decreases, and the fluctuation am-

FIG. 6. Temporal profile of ROIs (artery: red; vein:blue) of an AVM: (a) filtered BP, (b–e) static com-posite (Np � 6, 16, 64,192), and (f–i) sliding com-posite (Np � 6, 16, 64, 192).

1110 Jeong et al.

plitude decreases. Huang and Wright (29) previouslyshowed in simulations the effects of HYPR processing inthe temporal profiles of objects and cross-talk betweenmultiple objects. However, the simulations were limited tostationary objects with intensities varying over time; inclinical situations, the bolus of contrast agent physicallymoves along the direction of the blood vessels.

SNR and CNR benefits of HYPR were observed in bothsimulations and AVM patient studies. In simulations, av-erage SNR values over time were greater for all HYPRreconstructions than conventional filtered BP with 192projections, which was expected since the static and slid-ing composites contain about 8 and 2.5 times more data,respectively, than filtered BP. It was also found that theSNR increased as more projections were used. In the AVMpatient studies, we found that SNR and CNR increased inevery case with the right choice of NP, which ranged be-tween 16 and 64. The images with static composites havehigher SNR than sliding composite HYPR and regular fil-tered BP images without any HYPR processing, because

the static composite is signal averaged over several mea-surements with high signal. However, the static compositecannot maintain the temporal profile as well as the slidingcomposite, as shown by early venous enhancement andprolonged arterial enhancement in the patient images. Ifthe composite includes information that is too far ahead intime, it introduces signals that should not appear in thecurrent time frame. Figure 6 shows that arterial signalsstay higher in static composites than sliding compositesbecause the static composite’s temporal footprint coversmore of the past information than the sliding composite.The sliding composite reduces this effect by limiting thepast temporal information included in the composite.Therefore, deciding what kind of composite to use is atrade-off between temporal information and SNR.

In some data sets, CNR was actually lowest when thehighest number of projections is used. Temporal blurringthat results from a large number of projections makes thebolus profile broader, decreasing the contrast between ar-tery and vein, even though the SNR is higher due to less

Table 2Table of Maximum CNR, Maximum SNR, and Overlap Integral (OI) of AVM Patients*

NP

Mean CNR Mean SNR Overlap integral

Staticcomposite

Slidingcomposite

Staticcomposite

Slidingcomposite

Staticcomposite

Slidingcomposite

Patient 14 29.25 49.17 29.48 51.01 18.00 20.2616 42.29 67.93 43.23 71.63 22.84 25.1732 47.49 68.87 48.31 76.22 24.34 26.9864 53.73 73.22 55.15 79.86 27.33 30.66128 53.00 62.66 60.62 83.26 34.27 38.90No HYPR 42.98 43.83 31.54

Patient 24 22.71 179.50 39.74 217.13 20.48 25.1116 33.22 233.97 57.24 284.96 26.40 32.7932 39.71 252.51 65.10 307.69 28.20 35.1264 43.83 257.20 73.31 317.97 31.95 40.01128 33.18 225.80 78.28 293.71 42.83 53.66No HYPR 24.20 63.68 39.68

Patient 36 7.24 13.24 31.31 40.39 32.09 33.9616 7.23 14.38 34.34 52.16 50.90 59.1332 7.54 51.03 41.09 147.06 38.80 41.6964 8.49 30.56 48.21 150.81 40.15 43.86192 7.15 0.00 58.99 151.08 48.26 54.59

No HYPR 11.37 47.82 44.28Patient 4

6 23.72 41.63 39.04 79.11 34.32 41.8616 30.17 48.04 47.53 89.37 39.21 48.8232 35.46 49.51 52.99 91.95 39.97 50.2464 41.41 46.76 59.98 91.14 41.24 52.32192 10.77 31.36 67.81 78.25 43.84 64.17No HYPR 7.45 49.08 38.42

Patient 56 15.51 19.32 27.65 33.51 34.27 40.1916 18.56 22.31 36.91 46.44 36.53 42.7832 21.04 21.65 43.04 51.52 36.62 43.0364 23.29 19.88 48.55 56.68 36.26 42.65192 7.64 0.00 59.57 63.21 34.15 38.63No HYPR 13.26 33.96 38.92

*Table of mean CNR, mean SNR, and overlap integral (OI) of AVM patient images reconstructed with HYPR using sliding or staticcomposites with varying number of projections (NP � 6-192) and 192-projection filtered backprojection (no HYPR).

Radial Sliding Window MRA With HYPR 1111

undersampling artifacts. Low CNR is also observed insome cases with the fewest projections. In this case, lowerSNR from the streak artifacts from angular undersamplingdominates over the sharper bolus profile from a smallnumber of projections. Overall, the highest CNR occursbetween 16 and 64 projection HYPR images.

In this work we analyzed the effects of HYPR on artifactand noise separately. While the number of projectionscontrols fluctuations in signal and radial undersamplingartifacts and temporal blur, the length of the compositecontrols the SNR as well as temporal blur. In our study, 32was determined to be the minimum number of projectionsthat results in an acceptable level of signal fluctuationsand radial streak artifacts, which probably will not dependon other imaging parameters. However, the effects of thecomposite length can be simplified to SNR vs. acquisitiontime. It is a well-known fact that SNR is proportional to thesquare root of imaging time. Likewise, a longer composite,with more signal-averaged repetitions, yields images withhigher SNR. This result agrees with previous studies (27–29). Therefore, the optimal length of the composite willdepend on other parameters that affect acquisition time,such as TR, N�, and NSlices. The difference between HYPRand a simple long-acquisition MRA with signal averagingis that the long temporal footprint of HYPR is weightedand distributed to limited projection images, recoveringsome temporal information while producing artifacts char-acterized by fluctuations and flashing of signals.

Aside from the quantitative conclusions from the data,the qualitative effects of HYPR, seen through the eyes of aclinician, seem less pronounced. The results from the ra-diologist’s reading agree with the quantitative analysis onthe trends observed with quantitative SNR and image errormeasurements: more projections improve SNR and artifactlevels, and less projections result in better dynamic infor-mation. However, from the fact that the conventional fil-tered BP method with sliding window scored not too farbelow the best HYPR methods qualitatively, it is still ques-tionable whether the SNR gains and better dynamic infor-mation from HYPR are significant enough to convince aradiologist of its diagnostic power, especially consideringthe processing overhead involved with HYPR.

This study had some limitations. True SNR calculationsfor HYPR images, which are still under debate, are rather

complicated and involve knowledge of the number of vas-cular pixels, number of projections used, and number offrames, as well as the SNR of the composite (27,29,31).However, we were interested in how the HYPR imagescompare against non-HYPR-processed images using a met-ric that is not calculated differently for HYPR. Therefore,we used an operational definition of SNR, the averagevalue inside the ROI divided by the SD of air, which stillresults in SNR values that can be used for comparison.Another limitation, which is intrinsic to the technique, isthat there is still a broad temporal footprint. Even withlimited projections, processing with HYPR adds the longtemporal footprint of the composite. A possible remedy forthis problem is using parallel imaging in either the radial(32) or through-plane direction (33) in the sequence toreduce the acquisition time required for one frame. Fi-nally, the computational time of the HYPR algorithm is nottrivial; reconstruction of a full time series with one NP

value takes about 10 h to reconstruct using MATLAB on anaverage personal computer.

CONCLUSIONS

Effects of HYPR processing with static and sliding com-posites on MRA images were studied in simulations andpatients with intracranial AVMs. From the quantitativeanalysis, it was determined that the static compositeboosts the SNR of standard filtered BP images, but tempo-ral information is lost. The sliding composite, which has asmaller window of averaged frames, yields images withlower SNR than the static composite, but the sliding com-posite maintains temporal information better than thestatic composite. We have also shown that a smaller num-ber of projections resulted in fluctuations in signal andimage error. As the number of projections increased, fluc-tuations were reduced, but signal was blurred in time.However, in the radiologist’s readings, improvementsmade by HYPR were more subtle. The optimal parameterthat improves SNR while maintaining the temporal dy-namics compared to standard filtered BP was determinedto be NP � 32 with a sliding composite.

ACKNOWLEDGMENT

This work was supported by the NIH/NHLBI R01 088437.

FIG. 7. Image quality was scored by a radiologist in two categories qualitatively: dynamic information and SNR/artifact levels. “Slid.” and“Stat.” indicate sliding and static composites, respectively. *In two cases, 128 projections were acquired in each repetition, and NP � 4 and128 was used instead of NP � 6 and 192.

1112 Jeong et al.

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