Techniques for measuring renal transit time

European Journal of

Nuclear Medicine Original article

Techniques for measuring renal transit time Charles D. Russell, M. Japanwalla, Salma Khan, Johnny W. Scott, Eva V. Dubovsky

1 Division of Nuclear Medicine, University of Alabama at Birmingham Medical Center, Birmingham, Alabama, USA 2 Nuclear Medicine Service, V.A. Medical Center- Birmingham, Birmingham, Alabama, USA

Received 25 June and in revised form 7 August 1995

Abstract. A variety of techniques have been used for quantitative estimation of renal transit time. We compared different indices of transit time in a group of 30 patients having baseline and ACE inhibitor technetium- 99m mercaptoacetyltriglycine (MAG3) renography prior to arteriography: peak time, mean transit time, and the ratio of background-subtracted counts at 20 min to those at 3 min. Each index was calculated from whole-kidney ROI, cortical ROI, and cortical factor (by factor analysis). The strongest correlations between angiographic percent of stenosis and transit time index were observed for the peak time (Spearman p=0.469, n=53, P <0.005) and for the R20/3 (again p=0.469, n=53, P <0.005) using the whole-kidney ROI and using only the baseline data without captopril. (Spearman's p is simply the correlation coefficient calculated from rank in list, which al- lows for nonlinear correlation.) Thus simple indices of transit time (whole-kidney peak time and R20/3) correlated as well with the observed pathology as did more complicated methods that required deconvolution, factor analysis, or selection of a cortical ROI.

Key words: Factor analysis - Technetium-99m mercaptoacetyltriglycine - Transit time - Deconvolution - Kid- ney

Eur J Nucl Med (1995) 22:1372-1378

Introduction

The prolonged renal transit time that results from functionally significant renal artery stenosis (RAS) has been used as a diagnostic test ever since it was first described by Dore and Taplin in 1963 [1]. Taplin regarded the sec- ond phase of the renogram, in his classic three-phase description, as a measure of radiotracer transit time. Other measures of transit time have since been introduced, such as the mean transit time obtained by deconvolution

Correspondence to: C.D. Russell, Division of Nuclear Medicine, University of Alabama Hospital, 619-19th Street South, Birming- harn, AL 35233, USA

analysis. Some investigators calculate transit time from a cortical or parenchymal region of interest (ROI) rather than from the whole kidney. In the present paper we compare alternative methods of measuring transit time in a series of 30 patients undergoing baseline and postcaptopril renography followed by contrast angiography.

Material and methods

Thirty hypertensive adults, of whom three had renal transplants, were recruited from patients referred for renal arteriography. Prior to arteriography, both baseline and postcaptopril gamma camera renograms were performed using a single-day protocol, The baseline study employed 0.5-1.5 mCi (18-52 MBq) of technetium- 99m mercaptoacetyltriglycine (MAG3), with the patient oft all an- giotensin-converting enzyme (ACE) inhibitors for the preceding 48 h. Orte to two hours after the initial low dose of the 99mTc- MAG 3, 50 mg of oral captopril was administered, and l h after captopril administration the postcaptopril renogram was performed with a higher dose of 5-10 mCi (185-370 MBq) of 99mTc- MAG 3. Eight ounces of water was administered orally 10 min pri- or to each injection of 99mTc-MAG» and another 8 ounces of water with the oral captopril dose. The subjects were imaged posteri- orly in the supine position, except for the three subjects with transplanted kidneys, who were imaged anteriorly. A low-energy all-purpose collimator was used. Data were acquired at a high frame rate and reframed for the present paper into 60 20-s frames. Single-kidney ERPF was measured by combining conventional split function with a single-sample 99mTc-MAG3 clearance [2, 3]. Seventeen patients later underwent surgery or angioplasty, of whom three were cured of hypertension and five improved. The 30 patients had among them 54 functioning kidneys. For one subject (two kidneys), the computer acquisition failed for the first minutes of the postcaptopril study; for another patient (one kidney) the first minutes of the precaptopril study were lost because of improper positioning; for a third (two kidneys) the postcaptopril study was lost because of uncorrectable patient motion. This left a total of 53 baseline and 50 postcaptopril renograms for com- paring methods of calculating transit time.

Time-activity curves were generated from whole-kidney ROI, from cortical ROI two pixels (1.3 cm) wide, and from parenchymal factors defined by factor analysis [4]. An interpolative background subtraction previously described for factor analysis [4], based on a rectangular ROI surrounding each kidney, was used for all calculations. As input function for deconvolution, an estimate of the blood time-activity curve was obtained from an ROI over

European Journal of Nuclear Medicine Vol. 22, No. 12, December 1995 - © Springer-Verlag 1995

the left ventricle or, in the case of transplanted kidneys, from the abdominal aorta. As usual for such studies, though a debatable practice, no background correction was applied to the input function.

Factor analysis. The techniques of factor analysis and principal component analysis (both loosely called factor analysis in the nuclear medicine literature) can be used to separate overlapping structures such as renal parenchyma and collecting system [5-10]. Not only can separate images be obtained, but also separate time- activity curves for parenchyma and collecting system. We obtained parenchymal time-activity curves free of collecting system activity as previously described, except for using 55 frames rather than 20 as before [4]. Factor analysis can separate cortex from collecting system only if the collecting system is visualized at some point in the study. When the collecting system is not visualized, collecting system activity cannot be a significant source of error in the cortical time-activity curve, and for such kidneys the cortical ROI time-activity curve was used in place of the factor curve [4]. For each patient conventional images were inspected first, and if the collecting system was not visualized then factor analysis was not attempted. After factor analysis, the factor images were inspected to verify that images indeed resembled parenchyma and collecting system and that the factor time-activity curves were physiological. For one patient, factor analysis failed on the postcaptopril examination because of excessive and uncorrectable patient motion. In this instance, factor analysis yielded two nearly identical whole-kidney images, rather than one parenchymal and one collecting system image. Patient motion was veri- fied by cine display of the original acquisition.

Deconvolution. Time-activity curves, whether from whole-kidney ROI, cortical ROI, or renal parenchyma (by factor analysis), were deconvoluted by a previously described modification of the Kuruc method [4], using the first 55 20-s frames. This method has the advantage of not requiring an arbitrary smoothing factor; it only imposes the constraint of monotonicity, which for the kidney must hold on physiological grounds. For some abnormal kidneys, transit was still incomplete at 55 frames; to rank these for rank correlation, they were assigned the maximum transit time of 55 frames (18.3 rain).

R e s u l t s

Figure 1 shows the factor analysis images o f a normal transplanted kidney. The baseline study was performed with 1.5 mCi (55 MBq) o f 99mTc-MAG3 and the postcaptopril study with 10 mCi (370 MBq). The difference in count ing statistics led to a visible difference in image quality between the two studies, but parenchyma and collecting system were well separated on both. For each of the two image components , pa renchyma and collecting system, factor analysis yields a separate t ime-activity curve. The parenchymal curves for this subject are shown in Fig. 2 after deconvolution by the activity in ROI over the abdominal aorta. The postcaptopril curve shows the expected shape, simulating bolus injection in- to the renal artery without recirculation. Thus an initial spike for arteriovenous transit is fol lowed by a plateau for transit through the nephron f rom glomerulus to collecting system. The baseline curve only crudely approxi-

Baseline

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P a r e n c h y m a

Captopril Pelvis

P a r e n c h y m a Pelvis Fig. 1. Normally functioning transplanted kidney: factor images. Baseline (1.5 mCi, 56MBq) and postcaptopril (10mCi, 370 MBq) studies. Higher quality of postcaptopril images is due to higher dose

30 ,

0

~ e 2 0

8 g ~ E lO ,

m

. . . . . . . . . baseline

"",,_ . . . . . . ~_ .

captopril

I I "k'k'"~l 0 I I

0 1 2 3 4 5

minutes Fig. 2. Normally functioning transplanted kidney: deconvoluted parenchymal factor time-activity curve for normally functioning transplanted kidney (baseline and after captopril). Poor quality of baseline is due to low counts over aortic ROI with 1.5 mCi (56 MBq) dose

mates the expected shape due to the poor count rate over the abdominal aorta achieved with the 1.5 mCi (56 MBq) baseline dose.

Figure 3 shows a patient with RAS. Marked parenchymal retention is seen on the right side after captopril, which is characteristic for RAS when captopril renography is performed with a tubular agent such as ortho-iodohippurate (OIH) or 99mTc-MAG3. Figure 4 shows factor images for this patient. Note that factor analysis sep- arates parenchyma and collecting system well on the baseline study, even though the dose in this patient was

European Journal of Nuclear Medicine Vol. 22, No. 12, December 1995

Baseline

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@'!~i!i~ ~ ~~ii~ii~~~~i~i~i~~~~ : ~~~,~~~ ~ ~ ~~,~~ ~,~:,~~~ ~~ ~~ !~il ~~~ ~,,~~;,i~%1~;~'!~;~i~i~i,~;~?, ~

Captopril

] @ ¸ !il ¸ ¸

F ~ | j A ~

t p w

- ~ o ; t I

Fig. 3. Right-sided RAS: 3-min dynamic frames. Note parenchymal retention after captopril on the side of stenosis

only 0.52 mCi (18 MBq). [The program is successful even with 0.3 mCi (11 MBq) doses of iodine-131 OIH in patients with good renal function.] After captopril, however, the factor images for the right (stenotic) kidney show no separation of parenchyma from collecting system; both factor images show parenchyma. The rea- son factor analysis failed in this case ean be seen by ref- erence to Fig. 3; note that the collecting system was not visualized on conventional images either. When no collecting system activity can be detected by the eye, none is detected by the factor analysis program. In such cases, however, interference by collecting system activity is not a problem so that either whole-kidney or cortical ROI can be used for the parenchymal time-activity curve. De- convoluted time-activity curves are shown in Fig. 5. For the left kidney, the parenchymal time-activity curve from factor analysis was used. For the right kidney, which had

no collecting system activity, either the cortical RO1 or the whole-kidney time-activity curve could be used; both are shown. Note that the deconvoluted time-activity curve for the normal left kidney has the expected vascu- lar spike followed by a plateau with tubular transit of about 5 min. The stenotic right kidney has markedly prolonged transit, longer than the duration of the study, so that the curve never returns to baseline.

To illustrate the three different methods of generating time-activity curves (parenchymal factor, whole-kidney ROI, or cortical ROI), return to Fig. 3 and examine the left (normal) kidney after captopril. Spastic emptying of the left renal pelvis can be seen between the fourth and fifth images of the dynamic sequence. This occurs occa- sionally and has no known clinical significance; here it provides a convenient example of the distinction between parenchymal and collecting system activity. The time-activity curves for this kidney are shown in Fig. 6. The spastic emptying of the collecting system is clearly reflected by the time-activity curve from the whole-kidney ROI, but not by the curve from the cortical ROI. Fäctor analysis eliminated most of the collecting system activity and has better statistics than the cortical ROI. The factor analysis is only approximate; to show its faults, which are minor, the factor curve is displayed without smoothing.

From these three different time-activity curves, three different transit time parameters were calculated, for a total of nine combinations. The three transit time parameters were: peak time, mean transit time (MTT), and the ratio of counts at 20 min to those at 3 min (R20/3). Each of the nine different measurements was compared with the angiographic estimate of percent stenosis, with results shown in Table 1. The correlation was expressed by means of the Spearman rank correlation coefficient [11]. The rank correlation coefficient is calculated simply by ranking each measurement in numerical order, then re- placing each measurement by its rank before calculating the usual (Pearson product-moment) correlation coefficient. This converts a curved relationship to a straight line, and has the advantages of: (1) not assuming a linear relationship between the test variables, and (2) not requiring an arbitrary threshold (e.g., 50% stenosis) to separate positive from negative arteriograms. A related sta- tistic, Kendall's r, is better suited to the calculation of confidence limits and is reported in Table 2. Note that ranges for testing for the existence of a significant correlation differ from those for testing the difference between two observed correlations [11].

To estimate normal ranges for these transit time parameters, the mean and standard deviafion were calculated for 14 kidneys that proved in retrospect to be normal, of which three were from the present series and 11 from a previous series [4]. Table 3 shows the results.

For the two parameters showing best correlation, whole-kidney R20/3 and peak time, we also looked at the effeet of renal function. Using 50% stenosis as diagnostic threshold and whole-kidney postcaptopril R20/3


Baseline

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L. Parenchyma L. Pelvis R. Parenchyma

Captopril

L. Parenchyma L. Pelvis R. Parenchyma

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=~o 0.6 ~ ~ 0.4' "SE E ~ 0 . 2 m

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. . . . . . . . . right cortical

\ \ ~ ' ~ . . . . . right whole , , . ~ ~~~ ~~~_. kidney

:ù ::-::i:-::-:::, :-:- 0 =

0 5 10 15 20

Time, min

Fig. 5. Right-sided RAS: deconvoluted parenchymal transit, after captopril. For the stenotic kidney, transit is incomplete at 20 min, and the cortical ROI and whole-kidney ROI lead to equivalent results

. . . . . . . whole kidney 1 0 0 0 0 0

• . . " " ' " " - ' . . . . . . cortical ROI E 8 0 0 0 0 . . - " ' . . . . . . . ,

"- 6 0 0 0 0 " ' , nchyma == 40000

o 2 0 0 0 0 (.,1 /

0 I ! I i

0 5 10 15 2 0

minutes

Fig. 6. Normal kidney with pelvic spasm, time-activity curves. Ef- fect of pelvic spasm on whole-kidney curve is absent from cortical ROI and largely absent from parenchymal fäctor. (Compare conventional images, Fig. 3, left kidney postcaptopril)

or peak time as predictor, logistic regression showed that single-kidney ERPF had no independent predictive value (P >0.32 for R20/3, P >0.61 for peak time, Wald test) [12], so that taking renal function into account had no effect on the relationship between the transit time pa-

R. Pelvis

R, Pelvis

Fig. 4. Right-sided RAS: factor images. Note separation of parenchyma and collecting system achieved with 0.52 mCi (19 MBq) baseline dose. Factor analysis failed on the stenotic side postcaptopril, since the collecting system was not visualized (cf. Fig. 3)

rameter and the probability of stenosis. Specifically, the value of the transit time parameter that corresponded to 50% probability of 50% or more stenosis was independent of single-kidney ERPE To test the effect of renal function on the dispersion, a cross-product term was introduced. It was found that the variation of stenosis probability with change in transit time parameter was likewise independent of single-kidney ERPF (P >0.83 for R20/3, P >0.71 for peak time).

Discussion

In 1963, only 3 years after Taplin had introduced the ra- diopharmaceutical 131I-OIH, he also described the characteristic prolongation of tubular transit time that occurs with RAS [1]. Taplin regarded the peak time as a measure of renal transit time, an interpretation that was later confirmed by direct injection of tracer into the renal artery [13]. More sophisticated (and presumably more ac- curate) methods of measuring transit time were subse- quently developed, based first on model fitting [14, 15] and later on deconvolution [16, 17]. As the gamma camera replaced probe renography, it became possible to separate parenchymal from collecting system activity and to correct for tissue background, presumably im- proving still further the estimates of tubular transit time. Using the gamma camera with deconvolution analysis, Gruenewald and co-workers showed direct corre]ation between transit time and RAS [18]. Overviews of this field can be found in reviews by Britton and Nimmon and by Russell [19, 20].

Recent workers have preferred the transit time for renal parenchyma to that for whole kidney, since it is presumably less sensitive to variations in hydration state (because the concentration of tubular urine varies less


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Table 1. Correlation of transit time measurements with % stenosis. Spearman rank correlation coefficient, % stenosis versus transit time parameter before and after captopril. For n=50, a Spearman rank correlation coefficient >0.28 is statistically significant (P <0.05)

Peak R20/3 MTT

Before captopril (n=53 kidneys) Whole Cortex Factor

After captopril (n=50) Whole Cortex Factor

% Change with captopriI (n=49) Whole Cortex Factor

0.47 0.47 0.36 0.33 0.42 0.33 0.36 0.16 (n=49) 0.27

0.39 0.45 0.44 (n=49) 0.36 0.44 0,34 0.36 0.14 (n=45) 0,31

0.04 -0.04 0.12 (n=48) 0.05 -0.07 0.04

-0,10 -0,08 (n=42) 0.02

Peak R20/3 MTT Table 2, Correlation of transit time measurements with % stenosis. Kendall ~ sta- tistic with standard deviation, % stenosis versus transit time parameter before and after captopril. For n=50, a Kendall ~: >0.22 is statistically significant (P <0.05)

Before captopril (n=53 kidneys) Whole Cortex Factor

After captopril (n=50) Whole Cortex Factor

% Change with captopril (n=49) Whole Cortex Factor

0.35+.18 0.35_+.18 0.27+_.19 0.26+_.19 0.31+_.18 0.25+.19 0.29_+.19 0.11_+.20 (n=49) 0.19_+.19

0.29+-.19 0.34_+.19 0.32+.19 (n=49) 0.25_+.19 0.32_+.19 0.25_+.19 0.26+-.19 0.09+.21 (n=45) 0.23-+.19

0.01_+.20 -0.05+.20 0.08-+.20 (n=48) 0.03+.20 -0.06+.20 0.03_+.20

-0.07+-.20 -0.06+_.22 (n=42) 0.01+.20

Table 3. Normal transit times: mean and standard deviation for 14 normal kidneys

Peak (min) R20/3 (min) MTT (min)

Whole 3.91+1.43 0.35+0.33 3.22__.0.97 Cortex 3.24+1.25 0.31_+0.32 2.62_+0,73 Factor 2 .76_+0 .48 (0.18+0.07) 2.04_+0.37

than pelvic urine) and also less sensitive to dilatation and peristalsis of the collecting system [4, 21-24]. The parenchymal time-activity curve can be obtained from a manually drawn parenchymal ROI or from factor analysis. Various functional imaging techniques have been proposed to help the computer operator select an appro- priate ROI [4, 25-27]. With factor analysis, selection of parenchyma is automatic and does not require operator- dependent ROI selection, In principle, this better separation of parenchyma from collecting system should yield bettet results.

Table 1 shows the expected correlations between transit time parameters and RAS. These correlations are strongly significant even without captopril. Unexpected, however, was the finding that the simplest transit time

parameters - whole-kidney peak time and whole-kidney R20/3 - yielded the best correlation.

Table 1 shows that MTT, calculated by deconvolution, correlated significantly with the angiographic findings. The differences between MTT and simpler methods were not statistically significant, so that these data justify using MTT for clinical diagnosis. On the other hand, there is no evidence in this small seiles that calculating MTT is worth the extra effort, since simpler methods arë as effective.

Also unexpected was finding as strong a correlation in the baseline studies as after captopril. A possible ex- planation was that improved sensitivity was balanced by a loss in specificity. To test this hypothesis, receiver op- erating characteristic (ROC) curves were generated. Fig- ure 7 shows ROC curves for R20/3 at baseline and after captopril, The administration of captopril shifted the R20/3 curve slightly toward poorer specificity (right- ward) and little if any toward greater sensitivity (up- ward). The ROC curves for the other transit time parameters were similar bnt lower (less diagnostic).

Table 1 shows that the parenchymal time-activity curve obtained by factor analysis did not lead to useful values of R20/3. This is probably due to cumulative errors in calculating the parenchymal activity at 20 min by


0.8

~'~ /5"-4- "y 0.6 'Im œ

0.4 ~-~ ~ .... ~'"" captopril

0.2 - - - identity

0 I I I I I

0 0.2 0.4 0.6 0.8 1

1 - specificity Fig. 7. ROC curves for whole-kidney R20/3 (baseline and after captopril). The line of identity is also shown, which represents ab- sence of correlation between test and diagnosis. The arteriogram was regarded as positive for stenosis _>50%

fäctor analysis. Normally the residual renal activity at 20 min is quite small, so that small numerical errors be- come relatively important.

The change induced by captopril administration was not orten by itself of diagnostic value, as Table 1 shows. Captopril interferes with the autoregulatory control mechanism for glomerular filtration rate, causing a com- pensated kidney to decompensate. I f the kidney has al- ready decompensated, there may be no further change with captopril. When the abnormal captopril response is bilateral, the test is frequently found to be a false positive [28].

As Britton has emphasized [29], renal transit is not uniform but occurs with a statistical range of transit times. One might therefore expect that the peak time would be influenced primarily by those tracer molecules with fast transit, that R20/3 would be influenced primarily by those with slow transit, and that MTT would fall in between. Which index best revealed functionally significant RAS would thus depend on whether that abnor- mality was better characterized by fast-transit or by slow-transit components. However, the differences in Ta- ble 1 are not large enough to make this distinction.

In conclusion, when different measures of transit time were compared in a population referred for evaluation of possible RVH but otherwise unselected, the simplest measures (whole-kidney peak time and whole-kidney R20/3) worked as well as more sophisticated methods such as deconvolution, factor analysis, or use of a corti- cäl ROI. These findings do not exclude the possibility that a larger study might show a significant difference, or that the more sophisticated methods might be prefera- ble in selected patients (e.g., patients with obstruction, dilated collecting system, or chronic parenchymal disease).

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Techniques for measuring renal transit time

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