BJR
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
British Journal of Radiology (2004) 77, 728-734
© 2004 British Institute of Radiology
doi: 10.1259/bjr/41168942
This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Nakashige, A
Right arrow Articles by Ito, K
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Nakashige, A
Right arrow Articles by Ito, K

Full Paper

Quantitative measurement of hepatic portal perfusion by multidetector row CT with compensation for respiratory misregistration

A Nakashige, MD 1 J Horiguchi, MD 2 A Tamura, MD 3 T Asahara, MD 4 F Shimamoto, MD, PhD 5 and K Ito, MD 1

1 Department of Radiology, Division of Medical Intelligence and Informatics, Programs for Applied Biomedicine, Graduate School of Biomedical Sciences, 2 Department of Radiology, School of Medicine, Hiroshima University, 1-2-3, Kasumi-cho, Minami-ku, Hiroshima 734-8551, 3 Department of Radiology, Kure City Medical Association Hospital, 15–24, Asahi-cho, Kure 737-0056, 4 Department of Surgery, Division of Frontier Medical Science, Programs for Biomedical Research, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi-cho, Minami-ku, Hiroshima 734-8551 and 5 Department of Pathology, School of Health Sciences, Hiroshima Women's University, 1-1-71, Ujina-Higashi, Minami-ku, Hiroshima, 734-8554, Japan


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 References
 
Our purpose was to determine whether hepatic portal perfusion assessed by multidetector row CT using compensation for respiratory misregistration can predict the severity of chronic liver disease. We carried out dynamic CT in 43 patients (chronic hepatitis: n=9; cirrhosis: n=24; normal liver: n=10). In this series, 20 patients had liver tumours. The CT protocol was designed to avoid respiratory artefacts and included two interscan breathing periods during the study. To compensate for respiratory misregistration, image sets in the same z-axis position were acquired from four-slice data on each scan, and the portal perfusion calculations were made according to the maximum slope method. Portal perfusion was compared with and without compensation for respiratory misregistration, and the different types of hepatic disease. In the liver tumour patients in particular, portal perfusion was compared with the degree of hepatic fibrosis in the liver sections. Portal perfusion in the patients without compensation for respiratory misregistration (1.10 ml min–1ml–1) was higher than that of those with compensation (0.99 ml min–1ml–1; p=0.036). Hepatic portal perfusion of patients with chronic hepatitis (0.97 ml min–1ml–1) and liver cirrhosis (0.88 ml min–1ml–1) was less than that of patients with normal liver (1.32 ml min–1ml–1; p=0.03, 0.001). Moderate correlation was seen between portal perfusion and the percentage of fibrosis in patients with liver tumours (r=0.55). Hepatic portal perfusion obtained by multidetector row dynamic CT using compensation for respiratory misregistration has the potential to improve non-invasive assessment of the degree of chronic liver disease.


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 References
 
Various imaging techniques, such as xenon-enhanced CT [1, 2], isotope scintigraphy [3–5], and Doppler ultrasound [6–10], have been applied in the evaluation of hepatic perfusion. Positron emission tomography (PET) using oxygen-15 labelled water is regarded as the gold standard in this area [4, 5], however its prevalence and clinical application are limited due to high cost. Other techniques have not been accepted as the standard modality because of low spatial resolution or poor reproducibility [1, 3, 6, 8, 9].

Since dynamic first-pass analysis of CT images obtained with the use of iodinated contrast agents was described by Miles et al [11, 12] and Blomley et al [13], many studies have been conducted using this same method [14–24]. Several authors have, however, highlighted the problem of motion artefacts associated with the single-slice CT method [14, 17, 19]. A large number of respiratory motion artefacts, which considerably influence quantification; these are unavoidable due to patients having to breathe quietly during the long examination period. To minimize such motion artefacts, we designed a multislice protocol using a multidetector CT with several breath holds and with data collected in the inspiration phase. With this method, the same single level is examined throughout the scan period by retrospective selection of image sets in the same z-axis position.

Progression of hepatic fibrosis correlates with the severity of chronic liver disease. Vascular resistance in the sinusoid of the liver increases reducing portal perfusion [25]. In previous studies, portal perfusion measured on single-slice CT images correlated with the clinical and biological degrees of chronic liver disease [16, 19]. In a search of the medical literature, we found no published reports comparing portal perfusion with the degree of hepatic fibrosis determined histopathologically.

In this study, we determine whether or not CT portal perfusion correlates with the degree of hepatic fibrosis determined clinically and histologically.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 References
 
Patients
From August 2001 to August 2002, 56 patients underwent multidetector row dynamic CT examination of the liver at our institution. The study was approved by the ethics committee of our institution. Informed consent was obtained from all patients. Patients were informed that radiation exposure would be 8.32 mGy x 25 scans at a particular dose–length product (see "Radiation Exposure" below). This was fully explained in comparison with the doses in previously published single-slice perfusion CT (6.81 mGy x 25 and 6.61 mGy x 40) [14, 19] and 5-phase liver tumour protocol that is standard practice in our institution (78.4 mGy x 5) (see "Radiation Exposure" below).

Perfusion CT was carried out in 56 patients. The data from 13 patients were excluded from the study because of metastatic liver tumour (n=4), excessive body movement (n=3), heart failure (n=2), use of oxygen during perfusion study (n=2), severe arterioportal shunt (n=1) and portal tumour thrombus (n=1). The reason why the patients with metastatic liver tumour were excluded is that there is published evidence that existence of occult and overt metastasis decreases portal blood flow of the whole liver as well as that of the tumour segment [20–24, 26–28]. Use of oxygen may also potentially alter hepatic perfusion [29]. The remaining 43 patients (27 men and 16 women; mean age 57.0 years old, range 38–74 years) had chronic hepatitis (n=9), cirrhosis (n=24) or were normal volunteers (n=10).

Chronic viral hepatitis was B virus-related (n=1), C virus-related (n=7), and B+C virus-related (n=1). Cirrhosis was hepatitis B virus-related (n=4), and hepatitis C virus-related (n=20), and the Child-Pugh classification system was used to grade each patient's condition as A (n=17), B (n=4), or C (n=3). The diagnoses of chronic hepatitis and cirrhosis were confirmed by liver sections (n=20) and needle biopsy (n=3), and were based on history, laboratory tests and clinical features such as portal hypertension in the other patients. There were no patients who underwent both needle biopsy and surgery. In the 10 normal liver patients, absence of parenchymal liver disease was confirmed by history, physical examination, laboratory screening, and Doppler ultrasound of the liver.

Targeting the liver tumour patients, we also compared portal perfusion with the degree of hepatic fibrosis in liver sections. This series consisted of the liver tumour patients (12 men and 8 women; mean age 63.2 years, range 45–74 years) undergoing surgery for hepatocellular carcinoma (mean tumour size 28 mm, range 14–65 mm).

Data acquisition
The scanner used was a multidetector row CT scanner (LightSpeed Qx/i, GE Medical Systems, Milwaukee, WI). Patients fasted for 6 h and rested calmly for 30 min before CT examination. After a preliminary unenhanced scan covering the hepatic hilum, the four-slice level was chosen and included the spleen and the portal trunk/main portal branches. The dynamic study was performed with the following parameters: 4 slice x 5 mm collimation, 80 kV, 200 mA, and gantry rotation speed 0.8 s. A non-ionic iodinated contrast agent (Iopamiron 370 mgI ml–1; Schering, Berlin, Germany) was injected via a 20-gauge needle placed in the antecubital vein using a pump injector (flow rate 5 ml s–1; total amount 30 ml). The first CT scan was an unenhanced baseline scan. Contrast-enhanced scanning was then performed every 2 s between 7 s and 40 s after the start of the contrast injection. Scanning was then performed every 7 s between 53 s and 74 s, and between 87 s and 115 s. A total of 100 images were acquired in a series of 25 scans. All imaging was done in inspiration. The patients were instructed to breathe during the 41 s to 52 s and 75 s to 86 s periods. Prior to imaging, rehearsal of this breath holding technique was performed.

Data analysis
To compensate for respiratory misregistration (i.e. interimage disagreement between z-axis levels due to differences in respiratory depth), images at the same anatomical level (portal trunk/main portal branches and the spleen) were collected by visual selection of one of four slices from each of the 25 scans.

For each breath hold session, the numbers of patients and scans requiring respiratory compensation was recorded. The number of scans requiring respiratory compensation was defined by the number of images where a different slice level from the original was selected.

A time–attenuation curve was created on a workstation (AZ -700W, Anzai Medical Co., Ltd. Tokyo, Japan) with software that had been developed for brain perfusion. Regions of interests (ROIs) were drawn on each image in the portal trunk/main portal branches, right hepatic lobe and spleen. A ROI was drawn in a non-tumour segment of the right hepatic lobe (Figure 1Go). The ROI at the portal vein was set to include as much of the portal vein as possible to minimize the influence of laminar flow, without lying too close to the edge to minimize volume averaging. Attenuation on the unenhanced first scan (baseline scan) was subtracted from each measurement on the contrast enhanced CT series for each organ, thereby providing relative measures of contrast enhancement on subsequent scans. Hepatic portal perfusion was calculated using the maximum slope method previously described by Blomley et al [13] and Bader et al [14] as follows: assuming that arrival times and the distribution of transit times in the liver are similar to those of the spleen, a model curve of pure arterial hepatic enhancement was computed by multiplying the baseline subtracted splenic time–attenuation curve by the ratio of the maximum arterial liver gradient (prior to peak splenic enhancement) and the maximum splenic gradient. This hepatic time–attenuation curve was then subtracted from the original hepatic time–attenuation curve, resulting in a pure portal venous curve (Figure 2Go). The hepatic portal perfusion (ml min–1ml–1) was calculated by dividing the maximum gradient of this pure portal venous hepatic curve by the peak portal trunk CT number increase.



View larger version (134K):
[in this window]
[in a new window]
  		Figure 1. Typical placement of regions of interest (ROIs) for the measurement of CT values. ROIs in the right lobe of the liver, spleen, and portal trunk are drawn as large as possible, avoiding large vessels. ROIs in the liver and spleen have margins not less than 5 mm from the organ surfaces; this avoids partial volume effects and artefacts from the ribs. An enhanced area in segment 7 indicates hepatocellular carcinoma (arrow).

 


View larger version (13K):
[in this window]
[in a new window]
  		Figure 2. An example of the time–attenuation curve in portal perfusion measurement. Time–attenuation curve of relative enhancement ({Delta}HU) of the liver, spleen (a), and subtracted liver (a pure portal venous curve separate from its arterial component) (b). Gs, the peak splenic gradient; Gl, the peak gradient of original hepatic time–attenuation curve; Gp, the peak gradient of subtracted hepatic time–attenuation curve in the portal venous phase.

 
The time required for the whole analytic process after CT performance was about 40 min.

Collagen quantification
For the assessment of hepatic fibrosis in the liver tumour patients, three tissue sections were taken randomly from three different areas of each surgical specimen (partial hepatectomy, n=15; segmentectomy, n=5). The sections were then stained with Sirius Red/Fast Green stain [30, 31]. Collagen quantification using a colorimetric method was first described by Jimenez et al [30]. In our study, we modified the technique so that we could easily quantify collagen density on the computer (magnification ratio x 25). Percentages of fibrotic areas within the total area were determined (Adobe Photoshop 5.0J; Adobe Systems Inc., San Jose, CA). The mean fibrotic area (percentage) of the three sections was used for statistical analysis (Figure 3Go).



View larger version (89K):
[in this window]
[in a new window]
  		Figure 3. Quantification of hepatic fibrosis. (a) Photomicrograph of a liver section from a cirrhosis patient shows regenerating nodules of various sizes encircled by fibrotic septa (blackish areas) including the portal triads. The fibrotic area is 24.6% of the total area. (b) Photomicrograph of a liver section from a normal liver patient shows minimal deposition of collagen around the portal triads. The fibrotic area is 5.2% of the total area. (Sirius Red/Fast Green staining; original magnification, scale=1 mm).

 
Quantification of fibrosis was done by an investigator who was unaware of the results of either the CT or the operation.

Statistical analysis
Results were compared between portal perfusion with and without compensation for respiratory misregistration by means of Wilcoxon signed rank sum test. Moreover, portal perfusion was compared between normal liver, chronic hepatitis and cirrhosis patients, and grades of Child-Pugh classification by means of Fisher's Protected Least Significant Difference test. Portal perfusion in liver cirrhosis patients was compared between those with and those without a primary tumour by means of the two-tailed Student's t-test for unpaired data.

In the liver tumour patients in particular, linear regression analysis was used to identify statistical correlation between portal perfusion and fibrosis. Data are given as mean±standard deviation (SD). Statistical significance was defined as a p-value of less than 0.05.

Radiation exposure
Prior to patient scanning, we measured radiation exposure relating to the perfusion study protocols and the diagnostic 5-phase liver tumour protocol that are standard in our institution. We used a 10 cm pencil-shaped ionization chamber in a cylindrical phantom (diameter, 32 cm) to determine the CT dose index at the centre and at four locations in the periphery of the phantom (Radiation Monitor Controller, model 9015, Radocal Corp., Monrovia, CA).

The weighted CT dose index was determined as two-thirds of the mean peripheral CT dose index and one-third of the central CT dose index. The dose–length product was calculated as the weighted CT dose index x collimation x the number of rotations. These were performed with the parameters of the present study (80 kV, 200 mA) with those from previously published studies (120 kV, 125 mA and 120 kV, 100 mA) [14, 19] and the diagnostic 5-phase protocol that are standard in our institution.


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 References
 
Slice compensation for respiratory misregistration was necessary (range 1–13 scans, median 6.0), and successfully performed in all patients as illustrated in Figure 4Go.



View larger version (125K):
[in this window]
[in a new window]
  		Figure 4. Five representative scan images from the serial data are shown. Images at almost the same anatomical level can be selected by virtue of multirow detector CT. The advantage of this technique is emphasised in patients with small spleen (like this case).

 
The results summarizing the number of patients and scans requiring compensation are shown in Table 1Go. Portal perfusion in the patients without compensation (1.10±0.27 ml min–1ml–1) was significantly higher than that of those with compensation (0.99±0.31 ml min–1ml–1; p=0.036).


View this table:
[in this window]
[in a new window]
  		Table 1. Summary of the number of patients and scans requiring compensation for respiratory misregistration

 
Portal perfusion was 1.32±0.34 ml min–1ml–1 in normal-liver patients, 0.97±0.33 ml min–1ml–1 in chronic hepatitis patients and 0.88±0.39 ml min–1ml–1 in cirrhosis patients. The difference between the chronic hepatitis and cirrhosis patients did not reach statistical significance (p=0.58). In contrast, perfusion in the chronic hepatitis patients and the cirrhosis patients was significantly less than that in the normal-liver patients (p=0.03, 0.001, respectively; Figure 5Goa). Portal perfusion was 0.95±0.43 ml min–1ml–1 in Child A patients, 0.79±0.07 ml min–1ml–1 in Child B patients, and 0.46±0.09 ml min–1ml–1 in Child C patients. Portal perfusion in the Child C patients was significantly less than that in the Child A patients (p=0.045; Figure 5Gob). Portal perfusion did not differ significantly between the non-liver tumour group and the liver tumour group (p=0.953; Figure 6Go).



View larger version (17K):
[in this window]
[in a new window]
  		Figure 5. Distribution of portal perfusion values between hepatic diseases. Box plots in which the boundary of the box closest to zero indicates the 25th percentile, line within the box marks the median, and the boundary of the box farthest from zero indicates the 75th percentile. Error bars below and above the boxes indicate 10th and 90th percentiles, respectively. Outliers are represented as individual dots. Graphs show box plots of portal perfusion (a), and in patients with cirrhosis (the Child-Pugh classification system) (b).

 


View larger version (24K):
[in this window]
[in a new window]
  		Figure 6. Box-whisker plot showing distribution of portal perfusion values in liver cirrhosis patients with and without primary tumour.

 
In the liver tumour patients, moderate correlation was seen between portal perfusion and fibrosis (r=0.55, p=0.014; Figure 7Go).



View larger version (15K):
[in this window]
[in a new window]
  		Figure 7. Relationship between portal perfusion and fibrotic area in the liver tumour patients. The data points denote hepatic perfusion values in ml min–1ml–1.

 
For our protocol parameters (slice collimation of 4 x 5 mm, 80 kV, 200 mA, 25 scans), the weighted CT dose index was 4.16 mGy, and the dose–length product was 8.32 mGy x 25. For the protocol parameters in previously published reports [14, 19] (slice collimation of 1 x 8–10 mm, 120 kV, 125–100 mA), the weighted CT dose index was 6.81 mGy and 8.26 mGy, and the dose–length product was 6.81 mGy x 25 and 6.61 mGy x 40. For the 5-phase diagnostic liver protocol which is standard in our institution (slice collimation of 4 x 5 mm, 120 kV, 200 mA, 5 scans), the weighted CT dose index was 20.4 mGy, and the dose–length product was 78.4 mGy x 5.


   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
References
 
Quantification of hepatic perfusion on dynamic CT, introduced by Miles et al [12] in 1993, has allowed for separate evaluations of arterial and portal perfusion of the liver. To date, different methodologies, such as the maximum slope method [11–17, 20, 22–24] and the dual-input one-compartmental model [18, 19, 21] have been described. We used the maximum slope method because its calculation is straightforward.

Respiration-related motion artefacts are a significant problem in conventional quantification of hepatic perfusion with single-slice CT. Bader et al [17] reported that severe motion artefacts impairing ROI measurements occurred on more than one slice in 8 out of 25 patients. They pointed out that irregularity of the time-attenuation curve by motion artefacts caused an increase in the maximum gradient of the organ, thus leading to overestimation of the perfusion value. In most previous hepatic perfusion studies, patients were advised to hold their breath as long as possible and to breathe as calmly and shallowly as possible when they could no longer hold their breath [14, 17, 19]. Motion artefacts, however, are inevitable with this technique. Our protocol, which was designed to avoid respiratory artefacts, included two interscan breathing periods during the contrast study. With this method, respiratory misregistration is compensated for by retrospective selection of image sets in the same z-axis position. Although respiratory misregistration often occurred, compensation was possible in all cases by selection of images in almost the same z-axis position. In addition to this, multilevel datasets have another advantage in that they allow areas of the liver to be studied where there is no slice of portal vein visible (Figure 4Go). Although we carried out the axial scan according to the studies in the previous reports, helical acquisition of volume data is theoretically useful for the compensation of respiratory misregistration because image reconstruction at an arbitrary level is possible.

Tsushima et al [16] and Beers et al [19] reported that portal perfusion in cirrhosis, measured by CT, decreases in correlation with the degree of hepatic dysfunction, determined on the basis of clinical (e.g. the Child-Pugh classification system [19]) and biological (e.g. prothrombin ratio [16]) data. Results of our study agreed with results of these previous studies. Portal perfusion in the chronic hepatitis patients and the cirrhosis patients was significantly less than that in the normal-liver patients. Portal perfusion had a tendency to decrease in parallel with the severity of chronic liver disease, and this agrees with the results of Beers et al [19].

Hepatic fibrosis, a common response to chronic inflammatory conditions such as viral hepatitis, leads eventually to cirrhosis. Normally, collagen constitutes 4% of the liver protein. In cirrhosis, it increases to 15–30% because of excessive deposition within the space of Disse and defenestration of the basal laminae, sinusoids and hepatic vein. This results in a considerable increase in vascular resistance, with subsequent increase in portal vein pressure from the normal 6–10 mmHg to 20–30 mmHg. Thus, portal perfusion decreases in patients with cirrhosis [25, 32]. The correlation between portal perfusion and the degree of fibrosis we found in our study also supports this hypothesis and suggests portal perfusion holds potential for predicting the degree of chronic liver disease. CT perfusion may have a clinical application technique in predicting the degree of disease in patients with abnormal coagulation who are in the high risk category for undergoing needle biopsy.

Radiation exposure is one of the most important issues in the use of CT for investigating perfusion, especially with multislice acquisition. 2 cm coverage of the z-axis doubles the dose of radiation used for conventional 1 cm coverage. Hepatic CT perfusion has been performed with parameters such as 120 kV, 125 mA [14], and 120 kV, 100 mA [19]. To reduce patient's exposure to radiation, Wintermark et al [33] used the parameters 80 kV and 200 mA in cerebral perfusion CT. They concluded that the use of these parameters resulted in increased contrast enhancement and improved perfusion analysis. Accordingly, we used these parameters. The weighted CT dose index measured by a phantom on the parameters (80 kV, 200 mA) was reduced about 39% to 50% compared with that reported for parameters used in previous studies (120 kV, 125 mA [14], and 120 kV, 100 mA [19]).

Some researchers have pointed out that the diagnosis of cirrhosis by needle biopsy has a false-negative rate ranging from 9.3% to 51% if the sample is less than 5 mm long and if there is macronodular cirrhosis [34, 35]. For this reason, patients undergoing surgery for hepatocellular carcinoma were selected as candidates in our study because sampling tissues obtained from surgical specimens are larger than those obtained by needle biopsy. In our study, the ROIs of the liver parenchyma in patients with hepatocellular carcinoma were drawn in a non-tumour segment in order to avoid direct mechanical compression of portal radicles and the influence of arteriovenous shunt. In our study, portal perfusion did not differ significantly between the non-liver tumour group and the liver tumour group.

There were some limitations in our study. First, the overall sample size was small. Second, we did not use a conventionally accepted histological scoring system such as Ishak [36], although we believe percentage of fibrosis reflects the severity of hepatic disease as mentioned above.

In conclusion, our protocol with interscan breathing during multislice acquisition overcomes the problem of motion artefacts on dynamic perfusion CT images of the liver. Portal perfusion measured using this method correlated with the degree of chronic liver disease as well as the amount of fibrosis. Portal perfusion measured by multidetector row dynamic CT using compensation for respiratory misregistration therefore has the potential to improve non-invasive assessment of the degree of fibrosis associated with chronic liver disease.


   Footnotes
 
Supported in part by a grant from the Japan Society for the Promotion of Science. Back

Received for publication August 7, 2003. Revision received March 3, 2004. Accepted for publication June 1, 2004.


   References
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

  1. Gur D, Good WF, Herbert DL, Yonas H, Wozney P, Van Thiel DH, et al. Blood flow mapping in the human liver by the xenon/CT methods. J Comput Assist Tomogr 1985;9:147–50.
  2. Sase S, Monden M, Oka H, Dono K, Fukuta T, Shibata I. Hepatic blood flow measurements with arterial and portal blood flow mapping in the human liver by means of Xenon CT. J Comput Assist Tomogr 2002;26:243–9.[CrossRef][Medline]
  3. Martin-Comin J, Mora J, Figueras J, Puchal R, Jaurrieta E, Badosa F, et al. Calculation of portal contribution to hepatic blood flow with 99mTc-microcolloids. A noninvasive method to diagnose liver graft rejection. J Nucl Med 1988;29:1776–80.[Abstract/Free Full Text]
  4. Iwasa M, Nakamura K, Nakagawa T, Watanabe S, Katoh H, Kinosada Y, et al. Single photon emission computed tomography to determine effective hepatic blood flow and intrahepatic shunting. Hepatology 1995;21:359–65.[CrossRef][Medline]
  5. Ziegler SI, Haberkorn U, Byrne H, Tong C, Kaja S, Richolt JA, et al. Measurement of liver blood flow using oxygen-15 labelled water and dynamic positron emission tomography: limitation of model description. Eur J Nucl Med 1996;23:169–77.[CrossRef][Medline]
  6. Ohnishi K, Saito M, Nakayama T, Iida S, Nomura F, Koen H, et al. Portal venous hemodynamics in chronic liver disease: effects of posture change and exercise. Radiology 1985;155:757–61.[Abstract/Free Full Text]
  7. Walsh KM, Leen E, MacSween RN, Morris AJ. Hepatic blood flow changes in chronic hepatitis C measured by duplex Doppler color sonography: relationship to histological features. Dig Dis Sci 1998;43:2584–90.[CrossRef][Medline]
  8. Leen E, Goldberg JA, Angerson WJ, McArdle CS. Potential role of doppler perfusion index in selection of patients with colorectal cancer for adjuvant chemotherapy. Lancet 2000;355:34–7.[CrossRef][Medline]
  9. Leen E, Goldberg JA, Robertson J, Angerson WJ, Sutherland GR, Cooke TG, et al. Early detection of occult colorectal hepatic metastases using duplex colour Doppler sonography. Br J Surg 1993;80:1249–51.[Medline]
  10. Blomley MJ, Lim AK, Harvey CJ, Patel N, Eckersley RJ, Basilico R, et al. Liver microbubble transit time compared with histology and Child-Pugh score in diffuse liver disease: a cross sectional study. Gut 2003;52:1188–93.[Abstract/Free Full Text]
  11. Miles KA. Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 1991;64:409–12.[Abstract/Free Full Text]
  12. Miles KA, Hayball MP, Dixon AK. Functional images of hepatic perfusion obtained with dynamic CT. Radiology 1993;188:405–11.[Abstract/Free Full Text]
  13. Blomley MJ, Coulden R, Dawson P, Kormano M, Donlan P, Bufkin C, et al. Liver perfusion studied with ultrafast CT. J Comput Assist Tomogr 1995;19:424–33.[Medline]
  14. Bader TR, Herneth AM, Blaicher W, Steininger R, Mühlbacher F, Lechner G, et al. Hepatic perfusion after liver transplantation: noninvasive measurement with dynamic single-section CT. Radiology 1998;209:129–34.[Abstract/Free Full Text]
  15. Tsushima Y, Unno Y, Koizumi J, Kusano S. Measurement of human hepatic and splenic perfusion using dynamic computed tomography: a preliminary report. Comput Methods Programs Biomed 1998;57:143–6.[CrossRef][Medline]
  16. Tsushima Y, Blomley JK, Kusano S, Endo K. The portal component of hepatic perfusion measured by dynamic CT: an indicator of hepatic parenchymal damage. Dig Dis Sci 1999;44:1632–8.[CrossRef][Medline]
  17. Bader TR, Grabenwöger F, Prokesch RW, Krause W. Measurement of hepatic perfusion with dynamic computed tomography, assessment of normal values and comparison of two methods to compensate for motion artifacts. Invest Radiol 2000;35:539–47.[CrossRef][Medline]
  18. Materne R, Van Beers BE, Smith AM, Leconte I, Jamart J, Dehoux JP, et al. Non-invasive quantification of liver perfusion with dynamic computed tomography and a dual-input one-compartmental model. Clin Sci 2000;99:517–25.[Medline]
  19. Van Beers BE, Leconte I, Materne R, Smith AM, Jamart J, Horsmans Y. Hepatic perfusion parameters in chronic liver disease: dynamic CT measurements correlated with disease severity. AJR Am J Roentgenol 2001;176:667–73.[Abstract/Free Full Text]
  20. Fuentes MA, Keith CJ, Griffiths M, Durbridge G, Miles KA. Hepatic haemodynamics: interrelationships between contrast enhancement and perfusion on CT and Doppler perfusion indices. Br J Radiol 2002;75:17–23.[Abstract/Free Full Text]
  21. Cuenod CA, Leconte I, Siauve N, Resten A, Dromain C, Poulet B, et al. Early changes in liver perfusion caused by occult metastases in rats: detection with quantitative CT. Radiology 2001;218:556–61.[Abstract/Free Full Text]
  22. Tsushima Y, Blomley MJ, Yokoyama H, Kusano S, Endo K. Does the presence of distant and local malignancy alter parenchymal perfusion in apparently disease-free areas of the liver? Dig Dis Sci 2001;46:2113–9.[CrossRef][Medline]
  23. Leggett DA, Kelley BB, Bunce IH, Miles KA. Colorectal cancer: diagnostic potential of CT measurements of hepatic perfusion and implications for contrast enhancement protocols. Radiology 1997;205:716–20.[Abstract/Free Full Text]
  24. Miles KA, Leggett DA, Kelley BB, Hayball MP, Sinnatamby R, Bunce I. In vivo assessment of neovascularization of liver metastases using perfusion CT. Br J Radiol 1998;71:276–81.[Abstract]
  25. Orrego H, Blendis LM, Crossley IR, Medline A, Macdonald A, Ritchie S, et al. Correlation of intrahepatic pressure with collagen in the Disse space and hepatomegaly in humans and in the rat. Gastroenterology 1981;80:546–56.[Medline]
  26. Ridge JA, Bading JR, Gelbard AS, Benua RS, Daly JM. Perfusion of colorectal hepatic metastases. Relative distribution of flow from the hepatic artery and portal vein. Cancer 1987;59:1547–53.[CrossRef][Medline]
  27. Platt JF, Francis IR, Ellis JH, Reige KA. Liver metastases: early detection based on abnormal contrast material enhancement at dual-phase helical CT. Radiology 1997;205:49–53.[Abstract/Free Full Text]
  28. Sheafor DH, Killius JS, Paulson EK, DeLong DM, Foti AM, Nelson RC. Hepatic parenchymal enhancement during triple-phase helical CT: can it be used to predict which patients with breast cancer will develop hepatic metastases? Radiology 2000;214:875–80.[Abstract/Free Full Text]
  29. Cray SH, Crawford MW, Khayyam N, Carmichael FJ. Effects of hypoxia and isoflurane on liver blood flow: the role of adenosine. Br J Anaesth 2001;86:425–7.[Abstract/Free Full Text]
  30. Jimenez W, Pares A, Caballeria J, Heredia D, Bruguera M, Torres M, et al. Measurement of fibrosis in needle liver biopsies: evaluation of a colorimetric method. Hepatology 1985;5:815–8.[CrossRef][Medline]
  31. Peterson TC, Hodgson P, Fernandez-Salguero P, Neumeister M, Gonzalez FJ. Hepatic fibrosis and cytochrome P450: experimental models of fibrosis compared to AHR knockout mice. Hepatol Res 2000;17:112–25.[CrossRef][Medline]
  32. Craig JR. Liver. In: Kissane JM, editor. Anderson's pathology (9th edn). Princeton, NJ: C.V. Mosby, 1990:1199-320.
  33. Wintermark M, Maeder P, Verdun FR, Thiran JP, Valley JF, Schnyder P, et al. Using 80kVp versus 120kVp in perfusion CT measurement of regional cerebral blood flow. AJNR Am J Neuroradiol 2000;21:1881–4.[Abstract/Free Full Text]
  34. Pagliaro L, Rinaldi F, Craxì A, Di Piazza S, Filippazzo G, Gatto G, et al. Percutaneous blind biopsy versus laparoscopy with guided biopsy in diagnosis of cirrhosis: a prospective, randomized trial. Dig Dis Sci 1983;28:39–43.[CrossRef][Medline]
  35. Zaitoun AM, AI Mardini H, Awad S, Ukabam S, Makadisi S, Record CO. Quantitative assessment of fibrosis and steatosis in the liver biopsies from patients with chronic hepatitis C. J Clin Pathol 2001;54:461–5.[Abstract/Free Full Text]
  36. Ishak K, Baptista A, Bianchi L, Callea F, De Groote J, Gudat F, et al. Histological grading and staging of chronic hepatitis. J Hepatol 1995;22:696–9.[CrossRef][Medline]



This article has been cited by other articles:


Home page
 Eur Respir JHome page
A. Vonk-Noordegraaf, S. A. van Wolferen, J. T. Marcus, A. Boonstra, P. E. Postmus, J. W. L. Peeters, and A. J. Peacock
Noninvasive assessment and monitoring of the pulmonary circulation
Eur. Respir. J., April 1, 2005; 25(4): 758 - 766.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Nakashige, A
Right arrow Articles by Ito, K
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Nakashige, A
Right arrow Articles by Ito, K

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
BJR DMFR IMAGING  ALL BIR JOURNALS