Patent Description:
Non-alcoholic fatty liver disease (NAFLD), a range of diseases characterized by steatosis, is associated with metabolic syndrome, diabetes, and obesity (Ekstedt et al. , <NUM>; Ertle et al. , <NUM>) and can lead to advanced fibrosis, cirrhosis, and hepatocellular carcinoma (Ekstedt et al. , <NUM>; Wattacheril et al. Non-alcoholic steatohepatitis, a more serious form of NAFLD, is now the single most common cause of liver disease in developed countries (Sanyal, <NUM>; Misra et al. , <NUM>) and is associated with high rates of morbidity and mortality. The evaluation and grading of hepatocellular fat in patients with NAFLD usually requires a liver biopsy and histology. However, as liver biopsy is an expensive, invasive, and painful procedure that is sensitive to sampling variability (Hubscher, <NUM>; Wieckowska et al. <NUM>), imaging modalities, including magnetic resonance spectroscopy (MRS) and MRI, are frequently being adopted to determine proton density fat fraction (PDFF). PDFF-MRS is a noninvasive and accurate method for quantification of hepatic fat content, but it has proven difficult to implement in clinical practice owing to its expense and dependence on specific expertise. Further, the method only gives a local estimate of the liver fat content (Reeder et al. Multi-echo MRI-determined PDFF imaging provides non-local, quantitative, standardized measurements of hepatic fat that is reproducible and correlates closely with MRS (Noureddin <NUM>; Kamg <NUM>), liver biopsy (Tang et al. , <NUM>) and ex vivo measurements (Bannas et al.

Traditional quantitative fat-fraction analysis using the two point Dixon (2PD) technique, which is based on acquisition of out-of-phase and in-phase images, has been shown to be useful to evaluate hepatic fat (Dixon, <NUM>; Glover, <NUM>; Qayyum et al. , <NUM>; Pilleul et al. , <NUM>; Reeder et al. , <NUM>; Reeder & Sirlin, <NUM>). The disadvantage of 2PD in relation to multi-echo Dixon is that T2* has to be determined in a separate experiment. But, in essence, both dual and multi echo Dixon shares the same confounding factors that influence MRI signal intensity, including T<NUM>* decay, spectral complexity of hepatic fat, and T<NUM>-saturation bias (Reeder et al. , <NUM>; Chebrolu et al. While the first three factors can be reduced through signal modeling, T<NUM>-saturation bias is commonly avoided using a low flip angle since the alternative, long repetition times, results in unfeasible breath-hold times. However, a low flip angle is associated with a low signal-to-noise-ratio (SNR), leading to reduced sensitivity in liver PDFF imaging (Johnson et al. This complicates the trade-off between image voxel size, breath-hold length, and SNR. Furthermore, as the SNR decreases, proper coil positioning becomes more important, especially in obese patients, and this makes clinical translation of the technique more difficult.

By increasing the flip angle, and thereby transforming the acquisition into a T<NUM>-saturated state, the trade-off between voxel size, breath-hold length, and desired level of SNR becomes less critical (Kühn et al. However, the T<NUM> weighting causes a bias in the measured signals unless corrected (Fleysher et al. It is possible to adaptively measure and compensate for the T<NUM> bias using a more complex experiment, as has been demonstrated by Kuhn and colleagues (Kühn et al. Applying such correction to three-dimensional (3D) acquisitions is straightforward if the T<NUM> values of the fat and water tissue are well characterized. Nevertheless, the specific characteristics of the MR pulse sequence implementation and uncertainty in quantification of the T<NUM> of water and fat may influence the validity of the signal equations and lead to residual T<NUM> bias.

Fat-referenced lipid quantification allows fat quantification in T<NUM>-weighted Dixon imaging, and was originally introduced by Hu and colleagues and Dahlqvist Leinhard and colleagues (Hu and Nayak, <NUM>; Dahlqvist Leinhard et al. This quantification method calibrates the observed signal intensities of the water and fat images using the lipid signal in pure adipose tissue. This transforms the Dixon images into a common intensity scale where a value of <NUM> in the fat image corresponds to an adipose tissue concentration of <NUM> %. The invariability to the T1 weighting has been shown by Peterson et al. (Peterson et al. In that study, intramuscular adipose tissue quantification using the fat-referenced technique was validated against conventional low-flip-angle PDFF estimation with a very high agreement between the methods. Recently, Andersson and colleagues further validated the fat-referenced technique in whole-body imaging at both <NUM> T and <NUM> T for bias field estimation in skeletal muscle and liver tissue (Andersson et al. Further, Heba and colleagues determined that the accuracy of magnitude-based MRI for estimating hepatic PDFF using MRS as a reference was unchanged when using different numbers of echoes and was unaffected by possible subject-based confounders (Heba et al.

Further references are found in <NPL>; <NPL>; and <NPL>.

As discussed above, there is a need for a framework for calculation of PDFF of for instance a liver. It is an object of the present invention to provide such framework for calculation of PDFF based on T1-weighted two-point and multipoint Dixon imaging according to the appended independent claims. Embodiments of the present invention are provided by the accompanying dependent claims. The present invention thereby provides technology for accurate estimation of organ PDFF using fat-referenced Dixon imaging, by direct use of fat estimates obtained following fat-referencing. This is especially relevant for estimation of liver PDFF for diagnosis and treatment of deceases as discussed above.

By calculating a PDFF with T<NUM>* relaxation correction it may be meant that in the method according to the present invention the reconstruction of fat and water images may, or may not, be corrected for T<NUM>* effects and/or spectral dispersion effects due to characteristics of the lipid spectrum, or the reconstruction.

The PDFF calculation apparatus configured to perform the PDFF calculations may be constituted by a computer comprising the necessary computer executable program and provided with the necessary input for the calculations.

The present invention and its embodiments provide that accurate proton density fat fraction (PDFF) estimation may be achieved in T<NUM>-weighted fat- and water-separated imaging using the presented framework based on fat-referenced fat quantification. The present invention provides that two-point Dixon (2PD) magnetic resonance imaging (MRI) using simplistic reconstruction without a multispectral lipid model may be used for accurate liver PDFF estimation using fixed T2* correction. This may further be improved by taking the individual T2* values of the liver water signal into account. But this is also applicable to other organs in the human body.

The fat-referenced quantification technique shows much lower sensitivity to T2* effects in 2PD PDFF calculations compared to the 2PD fat fraction technique. This lowered sensitivity to T2* relaxation is achieved because the fat referenced calculations do not include the water signal in the denominator.

The present invention provides that PDFF may be accurately estimated using T1 saturation corrected 10PD acquisitions using the suggested approach. Limits of agreement of ± <NUM> % for liver PDFF acquired with different sequences, in different breath holds, and with different acquisition coils in the datasets fulfilling strict quality control and ± <NUM>% in the analysis including all datasets may be achieved with the present invention, which are lower than what is commonly observed using other state of the art implementations.

The findings of using the present invention may be compared with those of the recent study by Heba and colleagues, in their retrospective analysis of <NUM> adults with non-alcoholic fatty liver disease (NAFLD), where liver PDFF was estimated using unenhanced <NUM>. 0T MRI, using right liver lobe magnetic resonance spectroscopy (MRS) as a reference (Heba et al. In this previous study, PDFF MRI findings were in close agreement with magnetic resonance spectroscopy (MRS), with the two-echo method based on fat fraction measurement with spectral correction but without T2* correction being least accurate. (Heba et al.

The present invention provides an alternative way to compensate for effects caused by the hepatic lipid spectrum. Correction of the lipid spectrum based on the acquired data, is normally a complex process, especially as the analysis also involves estimation of lipid T<NUM>* and water T<NUM>* relaxation (Qayyum et al. <NUM>; Reeder et al. <NUM>; Hu et al. Here, no assumptions have been made about the details of the lipid spectrum model. Using the methods described herein, the only basic assumptions made are that in-phase and opposite-phase imaging creates a highly specific contrast for fat and water, and that the effects on the observed lipid signal caused by the lipid spectrum are similar in both the reference adipose tissue and in the liver tissue.

According to a first aspect of the invention, a computer-implemented method of calculating a proton density fat fraction, PDFF, from awater and fat separated magnetic resonance imaging, MRI, data obtained using T1-weighted multipoint Dixon imaging based on fat-referenced lipid quantification in a region of interest (ROI) and using determination of a reference tissue is provided, as defined in claim <NUM>.

The method comprises the step of determining: <MAT> wherein.

In an embodiment, the PDFF may be determined from a fat-referenced two-point Dixon analysis without previous correction for T<NUM>* relaxation effects, and the water signal in the reference tissue, Wref, may be low such that a resulting value when Wref is multiplied with a resulting T<NUM>* relaxation effect provides an approximation that the water signal in the ROI equals an observed water signal in the ROI, W2PD, being a reconstruction of the water signal from the MRI data in the ROI using the fat-referenced two-point Dixon analysis, providing the PDFF to be calculated using <MAT> wherein βf = <NUM>,.

In one embodiment, the T<NUM>* relaxation effect value may be determined in a separate experiment.

In another embodiment, the T<NUM>* relaxation effect value may be set as a constant based on a population mean.

According to a second aspect of the invention, a proton density fat fraction, PDFF, calculation apparatus configured to perform calculation of a PDFF according to any of the embodiments above. As seen in <FIG>, the PDFF calculation apparatus <NUM> may receive input from a MRI source <NUM>. The MRI source <NUM> may provide water and fat separated MR data for the ROI and the reference tissue. The PDFF calculation apparatus <NUM> may be a computer configured to perform the calculations according to any of the embodiments above.

The invention will in the following be described in more detail with reference to the enclosed drawings, wherein:.

The present invention will be described more fully hereinafter according to preferred embodiments of the invention as well as examples outside the scope of the present invention.

In spoiled gradient echo water-fat separated image reconstruction after taking T<NUM>* and lipid spectrum effects into account, the water (W) and fat (F) signals can be represented by the following equations: <MAT> and <MAT> where Wunsat and Funsat are the unsaturated water and fat signals, and sw and sf are the water- and fat-saturation factors that are dependent on the local flip angle α, the repetition time TR and the tissue dependent T<NUM> values, T1w and T1f, for water and fat. Note that the exact value of α is unknown as it is dependent on prescan performance and on the characteristics of the radiofrequency pulse profile.

To quantify the fat content of a tissue, the unsaturated Funsat is insufficient as it is dependent on a range of unknown factors, besides the number of fat protons. Proton density fat fraction (PDFF) is a quantitative fat-content technique that is invariant to these unknown factors. In PDFF imaging Funsat is calibrated using a unsaturated in-phase signal reference, IPunsat = Funsat + Wunsat, e.g. PDFF is defined as: <MAT>.

Because the multiplicative factors are identical in Funsat and IPunsat, PDFF is the fraction of MRI visible fat protons in relation to the sum of MRI visible fat and water protons. Furthermore, as PDFF is based on the unsaturated MRI signals, the acquisition parameters must be set such that sw « sf, e.g. by choosing a low flip angle. Alternatively, additional images need to be collected to determine the ratio between and sw and sf.

An alternative quantitative technique is fat-referenced MRI where F is calibrated using a fat signal Fref (Romu et al. , <NUM>; Dahlqvist Leinhard et al. The benefit is that this measurement is invariant to the water and fat saturations given that Fref is affected by the same sf as F. However, the fat-referenced signal corresponds to the number of fat protons in the measurement point relative to the number of fat protons in the reference, and is thus not identical to PDFF. To translate the fat-reference signal to PDFF, assume that there exists an in-phase reference, R, which saturates with a fat saturation factor, sf,R, e.g.: <MAT>.

Then, the PDFF equation can be expressed as: <MAT> where the factor <MAT> corrects for any difference in saturation between the measured fat signal and the reference. Also note that if the saturation of R is similar to that of the fat signal, then βf ≈ <NUM>, as is assumed in the present invention.

In fat-referenced lipid quantification, a signal reference is acquired from regions of pure adipose tissue within the subject and interpolated over the complete image volume (Romu et al. , <NUM>; Dahlqvist Leinhard et al. To convert the fat-referenced signal to PDFF, let Fref represent the fat signal of the reference tissue, and set the saturation of R to the saturation level of Fref, i.e. sf,ref=sf,R. Thus, the PDFF of the reference tissue is equal to Fref · R-<NUM>, so <MAT>, and eq. <NUM> describing PDFF in the measurement point can therefore be reformulated as (see <FIG>): <MAT> where <MAT> is the fat-referenced signal, e.g. the raw fat signal calibrated by the interpolated fat reference signal.

The consequence of Eq. <NUM> is that the calibrated fat signal in the fat-referenced analysis can be converted to PDFF by adjusting for the PDFF in the adipose reference tissue and for any difference in fat saturation relative to the reference. Furthermore, if the fat saturation is similar to the reference, in accordance with the present invention, then the fat-referenced PDFF can be computed as: <MAT>.

In 2PD analysis, using simplistic reconstruction of the fat and water image components after phase-sensitive reconstruction of the OP image, the observed fat signal is given by <MAT> where <MAT> is a function of the fat T<NUM>*-relaxation, <MAT>, the spectral dispersion of fat, d, and the echo times Top and Tip. Similarly, <MAT> describes the crosstalk caused by the water signal as a function of <MAT> and the echo times Top and Tip. Similarly, the observed water signal is given by <MAT> <MAT>.

Solving for the PDFF in Eq. <NUM>, with the corresponding signal estimated using two-point Dixon imaging, gives <MAT>.

Furthermore, since <MAT> in adipose tissue and assuming similar T<NUM> * effects F2PD and F2PD,ref, i.e. <MAT>, Eq. <NUM> can be approximated to: <MAT> where <MAT> and PDFFref are the only unknowns.

Two different implementations for PDFF quantification in T<NUM>-saturated Dixon imaging can be used, the first one corresponding to an embodiment of the invention, while the second one corresponds to an example outside the scope of the present invention.

Implementation <NUM> (embodiment of the invention) Fat-referenced Dixon imaging with correction for effects of T<NUM>* relaxation and adipose tissue water concentration.

Assuming T<NUM>-saturated 2PD, such that the PDFF is given by Eq. <NUM>. Furthermore, the values of <MAT> and PDFFref in Eq. <NUM> can either be determined on an individual level in a separate experiment, or assumed to be constant and set to a population mean.

Implementation <NUM> (example outside the scope of the present invention). Water-referenced T<NUM>*-corrected Dixon imaging with T<NUM>-saturation correction based on fat-referenced Dixon imaging.

If the saturation ratio between fat and water, βw = sf/sw, is known, the PDFF from a T<NUM>-saturated Dixon acquisition, corrected for T<NUM>* and spectral dispersion effects, is given by <MAT>.

The saturation ratio βw can then be determined based on a separate PDFF experiment such as the fat referenced PDFF2PD, by minimizing the following expression with respect to βw, <MAT> which minimizes the observed differences between PDFF in the water-referenced acquisition and PDFF2PD from the fat-referenced T<NUM>*-corrected 2PD acquisition.

Claim 1:
Computer-implemented method of calculating a proton density fat fraction, PDFF, from water, W, and fat, F, separated magnetic resonance imaging, MRI data obtained using T1-weighted multipoint Dixon imaging based on fat-referenced lipid quantification in a region of interest (ROI) and using determination of a reference tissue, characterized in that the method comprises the step of determining PDFF as: <MAT> wherein
F is the fat signal in the ROI provided from the MRI data,
and R is defined as a quota between Fref and PDFFref such that the method comprises the step of determining PDFF as: <MAT> wherein
Fref is the fat signal in the reference tissue; wherein T1 relaxation of the fat signal in the reference tissue and in the ROI is assumed to be equal; and
PDFFref is the PDFF of the reference tissue provided by a separate experiment of the reference tissue or by a predetermined constant.