Quantitative MR Measures of Intrarenal Perfusion in the Assessment of Transplanted Kidneys

Rationale and Objectives

The purpose of this study was to evaluate prospectively a gadolinium-based perfusion technique for intrarenal blood flow in transplanted kidneys and to determine if magnetic resonance imaging (MRI) measurements of intrarenal perfusion could be used to differentiate between normal-functioning kidney allografts and allografts with acute tubular necrosis (ATN) or acute rejection.

Materials and Methods

Twenty-one subjects were enrolled within 4 months of receiving a kidney transplant. A biopsy was performed on subjects to diagnose each allograft as having either ATN or acute rejection. A group of subjects with normal functioning transplants was also enrolled in our study. MRI perfusion images were acquired on a 1.5 T MRI system within 48 hours after biopsy using an echo planar, T2∗-weighted sequence, and an injection of gadodiamide contrast agent administered at a dose of 0.1 mmol/kg. Scan parameters were: repetition time/echo time/flip = 1000 ms/30 ms/60°, field of view = 340 × 340 mm, matrix = 128 × 64, slice thickness = 10 mm, and temporal resolution = 1.0 seconds. Cortical and medullary blood flow values were calculated.

Results

Medullary blood flow values were significantly ( P = .02) lower in allografts undergoing acute rejection (121 ± 41 mL/100 g/min) compared to normal-functioning allografts (221 ± 96 mL/100 g/min) and those with ATN (247 ± 124 mL/100 g/min). Cortical blood flow values were also significantly ( P = .03) reduced in allografts with acute rejection (243 ± 116 mL/100 g/min) compared to those with normal function (413 ± 116 mL/100 g/min).

Conclusions

Preliminary results indicate that MRI perfusion techniques may provide a means of determining noninvasively the viability of renal allografts, potentially alleviating the need for biopsy in some patients.

Kidney transplantation has become the preferred method of kidney replacement therapy since the first successful transplant more than 50 years ago . Although advances in surgical techniques and immunosuppressive therapy have resulted in 1-year graft survival rates exceeding 90%, graft dysfunction in the early posttransplant period remains a serious clinical problem and an important factor in determining the ultimate fate of the allograft . Graft dysfunction in the early posttransplant period results from a variety of causes, including cyclosporine toxicity, infection, vascular compromise, ureteral obstruction, acute tubular necrosis (ATN), and acute rejection . The compounding effects of acute rejection plus delayed graft function (defined as the need for dialysis during the first week posttransplantation) in the transplant course are devastating. Ojo et al. noted that individuals with acute rejection and delayed graft function had a 5-year graft survival of only 35% . Thus, early dysfunction can dramatically influence long-term graft outcomes.

Clinically, a distinction can be made between many of the causes of allograft dysfunction (eg, cyclosporine toxicity can be distinguished from infectious causes on the basis of clinical assessment and laboratory testing). Also, magnetic resonance imaging (MRI) or ultrasound can be used to determine if vascular compromise and ureteral obstruction are present. However, it is difficult to differentiate ATN from acute rejection, because both have similar radiographic and laboratory findings. Biopsy is the only means of differentiating between these two types of dysfunction, yet it has the disadvantages of potential complications and sampling error.

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Materials and methods

Subjects

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Table 1

Subject Demographics, Serum Markers, and Blood Flow Values Measured by MRI for Subjects with Normal-Functioning Transplanted Kidneys and Transplanted Kidneys Undergoing ATN or Acute Rejection

Kidney Transplants Normal Functioning ATN Rejection_n_ 5 4 8 Deceased donor 4 4 7 Living related donor 1 0 2 Age (y) 43 ± 10 49 ± 11 49 ± 12 Creatinine (mg/dL) 1.6 ± 0.4 3.5 ± 2.0 4.1 ± 2.0 Hematocrit (%) 36 ± 6 32 ± 3 31 ± 6 Mean medullary blood flow (mL/100 g/min) 221 ± 96 247 ± 124 121 ± 41 Mean cortical blood flow (mL/100 g/min) 413 ± 116 377 ± 152 243 ± 116

ATN: acute tubular necrosis; MRI, magnetic resonance imaging.

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MRI Perfusion Technique

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MRI Perfusion Calculations

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Statistical Analysis

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Results

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Table 2

Pearson Correlation Coefficients, and P Values if Significant, for Serum Markers and MRI-Measured Blood Flow for 17 Renal Transplant Subjects

Creatinine (mg/dL) Hematocrit (%) Mean Medullary Blood Flow (mL/100 g/min) Mean Cortical Blood Flow (mL/100 g/min) Creatinine (mg/dL)r = 1.0r = −0.1r = −0.6; P = .007r = −0.5; P = .04 Hematocrit (%)r = 1.0r = 0.1r = 0.2 Mean medullary blood flow (mL/100 g/min)r = 1.0r = 0.7; P = .003 Mean cortical blood flow (mL/100 g/min)r = 1.0

MRI, magnetic resonance imaging.

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Discussion

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Appendix

Theory

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Cm(t)=−kTEln(S(t)So), C

(

)

−

(

)

where S 0 is the signal intensity prior to the arrival of contrast material, TE is the echo time of the MR imaging sequence, and k is dependent on the R2∗ relaxivity of the contrast agent. C m (t) was calculated according to Eq. (1) . C m (t) was defined for both a major artery (renal or aorta), C a (t), as well as the tissue of the transplanted kidney, C tiss (t). C a (t) is also referred to as the AIF.

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[Ctiss(t)]={[H(t)]⊗[Ca(t)]}. [

tiss

(

)

]

{

[

(

)

]

⊗

[

(

)

]

}

For this study, C tiss (t) was fit by the iterative method described below. An initial guess, H 0 (t), for H(t) was made using the lag-normal model (46):

H(t)=2Cσπ√∫t−tlag0e−(−tc)2/σ2∗e−(t−tlag−)/γd H

(

)

∫

−

lag

−

(

−

)

∗

−

(

−

lag

−

)

where C , t c , σ, γ, and t lag are free variables. C a (t) was then convolved with H 0 (t) to yield C ‘ tiss (t), where the prime indicates that this is an intermediate approximation. A chi-squared quality of fit value was calculated for C’ tiss (t) with respect to the measured tissue data, C m (t) ( Figure 1 c). The five parameters defining H 0 (t) were then systematically modified, to produce H ‘ (t), where again the prime indicates an intermediate approximation. The process was iterated until a minimum for the chi-squared value was found using a Nelder-Mead simplex search algorithm. The H’(t), and C’ tiss (t) associated with the minimum chi-squared value were defined as the final H(t) and C tiss (t) ( Fig. 1 c).

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MTT=∫H(t)dtHmax, MTT

∫

(

)

max

where H max denotes the maximum value of H(t). The regional renal blood volume (rRBV) was calculated from C tiss (t) and C a (t) by:

rRBV=κ∫0tCtiss(t)dtρ∫0tCa(t)dt, rRBV

∫

tiss

(

)

∫

(

)

where κ is a function of the measured hematocrit (taken to equal 0.73 in the present work) and ρ is the density of the kidney (taken to equal 1.04 g/mL in the present work). Finally, in accordance with the central volume principle, the quantitative renal blood flow was calculated by:

RBF=rRBVMTT. RBF

rRBV

MTT

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Quantitative MR Measures of Intrarenal Perfusion in the Assessment of Transplanted Kidneys

Rationale and Objectives

Materials and Methods

Results

Conclusions

Materials and methods

Subjects

MRI Perfusion Technique

MRI Perfusion Calculations

Statistical Analysis

Results

Discussion

Appendix

Theory

References

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