Special Applications of Parallel Imaging 5

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D. Atkinson, PhD

Department of Medical Physics, University College London, London, WC1E 6BT, UK

C O N T E N T S

5.1 Introduction 63 5.2 Applications Enhanced by

Parallel Imaging Speed-up 63

5.3 Parallel Imaging for Artefact Reduction 63 5.3.1 The Need for Artefact Correction 63 5.3.2 Artefact Causes 64

5.3.3 Phased-Array Combination 64 5.3.4 Averaging 64

5.3.5 Detect−Reject 64

5.3.6 Detect−Correct; SMASH Navigators 66 5.3.7 Coil-Based Artefact Reduction 66 5.3.8 Consistency-Based Artefact Removal 67 5.4 Discussion and Future 68

5.5 Conclusion 70

References 70

Special Applications of Parallel Imaging 5

David Atkinson

5.1

Introduction

As described in the previous chapters, the phrase

“parallel imaging” refers to the simultaneous, or parallel, acquisition of data from multiple coils. These coils are located at different spatial positions and pro- vide different, but connected, information about the patient. The most common use of parallel imaging is to enable image reconstruction following an acquisition that has been speeded up by skipping phase- encode lines. This chapter brieﬂ y highlights applications where a conventional speed-up can reduce artefacts and then examine applications where the extra information from multiple coils can be used in more novel ways to reduce artefacts.

5.2

Applications Enhanced by Parallel Imaging Speed-up

In general, the signal-to-noise ratio (SNR) of an image is improved by imaging for longer. Conversely, when parallel imaging is used to reduce acquisition times, there is an inherent reduction in image SNR as discussed in Chapters 3 and 4. This assumes that the signal is constant, but in some applications there is a rapid decay of signal and it is advantageous to acquire data quickly. Examples include hyperpolarized gas imaging where the signal decays rapidly once the gas comes into contact with tissue and the ultra-short TE imaging of very short-T2 musculoskeletal tissue.

Another example is techniques using echo trains, such as echo-planar imaging (EPI), where the acquisition window is relatively long. In these cases, T2 effects cause a reduction of signal during the acquisition window leading to a loss of SNR and an image blurring. Parallel imaging shortens the echo-train length, reducing the signal-decay problem. There is a further beneﬁ t to shortening echo trains because k-space is now covered faster in the phase-encoded direction.

When the magnetic ﬁ eld deviates from the ideal, such as in the presence of susceptibility effects, shortening the phase-encoded time gives less time for erroneous phase to build up. In the image, this is seen as reduced susceptibility artefacts and reduced distortions.

5.3

Parallel Imaging for Artefact Reduction

5.3.1

The Need for Artefact Correction

Shortened scan times can also help to reduce the chance of motion corrupting an image. In practice, the reduction in scan time is restricted to a factor

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Artefact Causes

To form images, the raw MR data (k-space) is divided into blocks and each block is Fourier transformed to give an image. A block may correspond to a single slice in 2D multi-slice imaging or a whole volume in true 3D imaging. Separate averages and dynamic frames are composed of separate blocks. The Fourier transform process means that every single pixel in an image has a contribution from all points in the cor- responding block of k-space (cf. chap. 1). A change in the patient (e.g. movement or blood in-ﬂ ow effects) at anytime during k-space acquisition has the potential to affect every pixel in the image. Typically artefacts appear as a blurring of the image and ghosting in the phase-encoded direction(s).

There are essentially three strategies for reducing or eliminating these problems: acquiring and combining data in a more benign way; rejecting cor- rupted data; or correcting the data before forming the ﬁ nal image.

5.3.3

Phased-Array Combination

Prior to the emergence of speed-up techniques such as SMASH and SENSE, multiple coil phased-arrays were used to enhance image SNR (Roemer et al.

1990). A ﬁ nal image is formed from a combination of the individual coil images. In applications where ghosts have a periodicity which is both small and known, it has been shown that ghosts can be can- celled using images formed from certain coil combi- nations (Kellman and McVeigh 2001). An example application with low-periodicity ghosts is non-inter- leaved multi-shot EPI imaging.

When knowledge of the coil sensitivities is used in image reconstruction, ghosts that are widely separated from their source may be reduced in intensity. Kellman

parallel imaging, one fully sampled acquisition can be replaced by two scans each speeded-up by a factor of two. There is no increase in scan time, but now there are two images that can be averaged. The ghosts in the resultant image may be more benign than if just one fully sampled data set had been acquired (Larkman et al. 2004). Figure 5.1 demonstrates the potential beneﬁ ts of averaging. A free-breathing sub- ject was imaged with one fully sampled (R=1) scan, and then in the same total time, two speeded-up scans with reduction factors of 2 (R=2). The artefacts after averaging the two R=2 scans are more benign than in the single R=1 scan. This approach has the advantage that it can be applied on any MR imaging system that is capable of parallel imaging and does not require any additional reconstruction or post-processing software.

It is possible to process the two R=2 images sepa- rately. In theory they could be automatically assessed and one rejected if it was severely artefacted. An alternative is to manipulate the images before averaging to make the data consistent and in the following example, this is done by image registration.

Using parallel imaging, the time to acquire a single block of raw data may be shortened sufﬁ ciently to make breath-holding practical. Kellman et al. (2005) have recently demonstrated this in cardiac imaging and have used image registration to align images acquired in separate breath holds before they were combined. Figure 5.2 shows how the raw data block for a fully sampled acquisition might extend beyond a breath-hold, but with parallel imaging, it may be possible to ﬁ t acquisitions within breath-holds.

5.3.5

Detect−Reject

Parallel imaging can be used to detect short-lived data inconsistencies and to reconstruct the remain-

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Fig. 5.1. Potential beneﬁ ts from averaging two speeded-up scans compared with one fully sampled acquisition. Rectangles indicate blocks of raw data used to form images and their length represents the imaging time. Image examples were acquired on a free- breathing volunteer. Top: fully sampled; bottom: average of two faster scans reconstructed using parallel imaging. (Courtesy of D.

Larkman, Hammersmith Hospital, Imperial College London)

Fig. 5.2. Upper graph indicates respiratory motion with B denoting a breath-hold. Signiﬁ cant motion occurs at times shown shaded. Parallel imaging enables data acquisition time to ﬁ t into a breath-hold. Note that the breath-hold positions vary; hence, the need for image registration before image combination.

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lar sampling. Images were reconstructed from the irregular k-space by using the generalised SMASH approach (Bydder et al. 2002a).

5.3.6

Detect−Correct; SMASH Navigators

The previously described data rejection works well only if the artefact cause is localised in time. Further- more, corrupt data is discarded, whereas potentially it could be ﬁ xed and included in the ﬁ nal reconstructed image. The SMASH navigator method (Bydder et al. 2003) was developed with the aim of stepping through k-space and correcting artefacts. In conventional SMASH parallel imaging, the coil sensitivities are used to predict k-space lines that have been skipped in the acquisition. Each missing line is estimated from its neighbouring acquired line. In the SMASH navigator method, the data is fully sampled

cally a linear array coil), and, errors in the motion determination may propagate as successive lines are processed.

5.3.7

Coil-Based Artefact Reduction

Rather than stepping through k-space line by line, Atkinson et al. (2004) considered the whole k-space in one optimisation scheme. They also used the generalised SMASH method since this does not suffer the same restrictions on coil geometries as conventional SMASH. As outlined in Fig. 5.5, one image per coil was reconstructed from fully sampled data and an additional image ‘F’ computed using all the coil data simultaneously. In the absence of noise and artefacts, all these images should be identical and corrected for the different spatial coil sensitivities, which are known from a prior coil calibration scan. When in-

Fig. 5.3. The detect–reject method. K-space damaged by short motions is indicated with an open circle. PI parallel image recon- struction

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ﬂ ow or motion corrupts the data, these images differ and this can be detected by subtracting the images.

Using a physical model of the artefact cause (e.g.

rigid-body motion for imaging the head, or intensity changes in the aorta for axial abdominal imaging), the underlying data can be corrected. The artefact needs to be parameterised; for example, the effect of ﬂ owing blood was characterised by a complex multi- plicative factor over the pixels containing the vessel.

In order to ﬁ nd the values of these parameters, trial values are applied to the data and the images compared in an optimisation scheme. When the images are most similar, the artefact is at a minimum. One key step is that in the optimisation, the parameters are actually applied to the coil sensitivities and not to the object because the latter is unknown. This enables the multiple images (labelled 1,2,...F in Fig. 5.5) to be computed and then compared.

Fig. 5.5. The coil-based artefact reduction method of Atkinson et al. (2004). The algorithm starts with the measured data from each coil. The \Ac denotes generalised SMASH reconstruction with coil sensitivity data c modiﬁ ed by A, the current estimate of the artefact cause. Image F is formed using data from all coils. This illustration assumes non-accelerated scans, but in principle the method can also be applied to accelerated scans provided that the number of coils exceeds the speed-up factor.

Fig. 5.4. The SMASH navigator method. Line 1 of the measured data is used with SMASH to predict line 2. This is compared with the actually measured line 2. Differences consistent with motion are found and applied to the measured line 2. The sequence continues to the last line.

The method has the advantages that ghosts and artefacts do not need to be localised in the image, the artefact energy is put back in the correct place in the image (rather than data discarded) and there is no require- ment for the artefact cause to be short-lived; however, a physical model of the artefact cause is required and in the case of ﬂ ow, the user needs to specify the location of the artery causing the problem. Figure 5.6 presents results from this method on an abdominal image cor- rupted by ﬂ ow artefacts from the aorta.

5.3.8

Consistency-Based Artefact Removal

Winkelmann et al. 2005 have recently proposed a scheme that can determine the location of ghosts and the source of ﬂ ow-type artefacts. A standard SENSE

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reconstruction is performed to give one image. This is used with the coil sensitivities to predict the measured data. A consistency check between this predic- tion and the actually measured data reveals image locations that are overlaid by ghosts. An analysis of consistency enables the location of the artefact source to be determined. With this information, an extended SENSE reconstruction is performed in which both the underlying object and the overlying ghost are treated as unknowns. This is performed only in regions iden- tiﬁ ed as being overlaid by ghosts. An outline of the technique is presented in Fig. 5.7 and an example of abdominal image correction presented in Fig. 5.8. The advantages of this method are that it automatically

determines the location of the artefact source and no physical model of the cause is required. The disadvan- tages are that some data is discarded and the method is applicable only in situations where ghosts are spatially both localised and separated.

5.4

Discussion and Future

There now exist a number of schemes that use parallel imaging to reduce artefacts through the rejec-

Fig. 5.6. Breath-hold image through the abdomen of a volunteer. Left: the original data shows a ﬂ ow artefact arising from the aorta. Middle: the image corrected after using the method of Atkinson et al. (2004). Right: for comparison, the same slice imaged with saturation bands (not feasible in practice for multi-slice imaging)

Fig. 5.7. The consistency-based artefact removal method of Winkelmann et al. (2005). The algorithm starts with the measured data from each coil. Image F is formed using a SENSE reconstruction and data from all coils. Image F is multiplied by the known coil sensitivities and the result is compared with the originally acquired coil data. Artefacts can then be removed using an extended SENSE reconstruction algorithm. A non-accelerated scan is shown for illustration and, in principle, the method can also be applied to accelerated scans provided the number of coils exceeds the speed-up factor.

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tion or correction of data. The next stage will be for researchers to bring together their various advantages into a more uniﬁ ed scheme. As the underlying methods contain many similarities, hopefully this should be possible.

There is a current trend in MR scanner hardware towards greater numbers of receiver channels and coils (cf. Chapters 13 and 44). These are prima- rily intended for acquisition speed-up but the extra

ﬂ exibility should allow for the inclusion of coils designed to make artefact correction most effective.

We are likely to see the advantages of the different techniques described above brought together to pro- vide a method that requires minimal user interven- tion and is applicable to a wide range of artefacts. In general motion is non-rigid and takes place in three dimensions. Recent work (Batchelor at al. 2005) has shown how to handle non-rigid motion and develop-

Fig. 5.8a–d. Ghost artefact removal using the extended SENSE reconstruction of Winkelmann et al. (2005). The conventional SENSE reconstruction shows a ghosting artefacts of a volunteer’s aorta that are indicated by the arrows. c The consistency check of this SENSE reconstruction allows an identiﬁ cation of the ghosts and triggers an extended reconstruction to remove the artefacts (b). A ﬁ nal consistency check is shown in d. (Courtesy of R. Winkelmann, Philips Medical Systems)

c a b

d

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5.5

Conclusion

In conclusion, the speed-up made possible by parallel imaging has added benefi ts when signal decay is a limitation, such as in the imaging of hyperpolarized gases or short-T2 tissues and when the decay over the acquisition window is signifi cant. These applications can benefi t from the vendors’ implementations of parallel imaging without further modifi cations.

Research on the explicit use of multiple coils to detect artefacts and reject or correct the data has begun to appear in the literature. With the increas- ing number of numerical tools these bring, and the improvements in scanner and computer hardware, we can look forward to clinical applications of parallel imaging in the ﬁ eld of artefact correction.

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sion in imaging of myocardial infarction using B1-weighted phased-array combined phase-sensitive inversion recovery.

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Kellman P, Larson AC, Hsu LY et al. (2005) Motion-corrected free-breathing delayed enhancement imaging of myocardial infarction. Magn Reson Med 53:194–200

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Winkelmann R, Börnert P, Dössel O (2005) Ghost artefact removal using a parallel imaging approach. Magn Reson Med in press