CT perfusion of the liver: principles and applications in oncology

Abstract

With the introduction of molecularly targeted chemotherapeutics, there is an increasing need for defining new response criteria for therapeutic success because use of morphologic imaging alone may not fully assess tumor response. Computed tomographic (CT) perfusion imaging of the liver provides functional information about the microcirculation of normal parenchyma and focal liver lesions and is a promising technique for assessing the efficacy of various anticancer treatments. CT perfusion also shows promising results for diagnosing primary or metastatic tumors, for predicting early response to anticancer treatments, and for monitoring tumor recurrence after therapy. Many of the limitations of early CT perfusion studies performed in the liver, such as limited coverage, motion artifacts, and high radiation dose of CT, are being addressed by recent technical advances. These include a wide area detector with or without volumetric spiral or shuttle modes, motion correction algorithms, and new CT reconstruction technologies such as iterative algorithms. Although several issues related to perfusion imaging-such as paucity of large multicenter trials, limited accessibility of perfusion software, and lack of standardization in methods-remain unsolved, CT perfusion has now reached technical maturity, allowing for its use in assessing tumor vascularity in larger-scale prospective clinical trials. In this review, basic principles, current acquisition protocols, and pharmacokinetic models used for CT perfusion imaging of the liver are described. Various oncologic applications of CT perfusion of the liver are discussed and current challenges, as well as possible solutions, for CT perfusion are presented.

Figure 1:

Images from CT perfusion examination of HCC in a 58-year-old man. An unenhanced image and a series of dynamic images following intravenous administration of contrast agent are shown. Numbers in left upper corner represent time from injection of contrast agent in seconds.

Figure 2a:

(a) After motion correction, ROI within proximal abdominal aorta is drawn to obtain arterial input function (red circle, top left image). For liver perfusion imaging, a second ROI is drawn within the portal vein to determine portal venous input function addressing unique character of dual blood supply in liver (blue circle, top right image). A third ROI is drawn over spleen, which allows separation of arterial and portal venous blood flow in the liver (green, top right image). Time-intensity curves are generated from abdominal aorta (red), portal vein (blue), and spleen (green). Yellow curve represents time-intensity curve obtained from ROI drawn over normal liver tissue. (b) Perfusion software used in this example automatically generates color-coded perfusion maps of entire liver representing blood flow (BF), blood volume (BV), permeability, hepatic arterial perfusion (HAP), portal venous perfusion (PVP), and hepatic perfusion index (HPI). Note increased blood flow, blood volume, HAP, and HPI (solid arrows) and decreased permeability and PVP (open arrows) in hepatic nodule can be appreciated by visual inspection. (c) After drawing additional ROIs over metastasis (arrow) and normal liver tissue (arrowhead), quantitative perfusion parameters are displayed. In this example, HPI of nodule (ROI 1) significantly increased to 86.48% compared with adjacent normal liver parenchyma (14.49%; ROI 2) (yellow box in lower right image). (d) Wedge resection confirmed 1.5-cm well-differentiated HCC (arrows). (Image courtesy of J. M. Lee.)

Figure 2b:

(a) After motion correction, ROI within proximal abdominal aorta is drawn to obtain arterial input function (red circle, top left image). For liver perfusion imaging, a second ROI is drawn within the portal vein to determine portal venous input function addressing unique character of dual blood supply in liver (blue circle, top right image). A third ROI is drawn over spleen, which allows separation of arterial and portal venous blood flow in the liver (green, top right image). Time-intensity curves are generated from abdominal aorta (red), portal vein (blue), and spleen (green). Yellow curve represents time-intensity curve obtained from ROI drawn over normal liver tissue. (b) Perfusion software used in this example automatically generates color-coded perfusion maps of entire liver representing blood flow (BF), blood volume (BV), permeability, hepatic arterial perfusion (HAP), portal venous perfusion (PVP), and hepatic perfusion index (HPI). Note increased blood flow, blood volume, HAP, and HPI (solid arrows) and decreased permeability and PVP (open arrows) in hepatic nodule can be appreciated by visual inspection. (c) After drawing additional ROIs over metastasis (arrow) and normal liver tissue (arrowhead), quantitative perfusion parameters are displayed. In this example, HPI of nodule (ROI 1) significantly increased to 86.48% compared with adjacent normal liver parenchyma (14.49%; ROI 2) (yellow box in lower right image). (d) Wedge resection confirmed 1.5-cm well-differentiated HCC (arrows). (Image courtesy of J. M. Lee.)

Figure 2c:

(a) After motion correction, ROI within proximal abdominal aorta is drawn to obtain arterial input function (red circle, top left image). For liver perfusion imaging, a second ROI is drawn within the portal vein to determine portal venous input function addressing unique character of dual blood supply in liver (blue circle, top right image). A third ROI is drawn over spleen, which allows separation of arterial and portal venous blood flow in the liver (green, top right image). Time-intensity curves are generated from abdominal aorta (red), portal vein (blue), and spleen (green). Yellow curve represents time-intensity curve obtained from ROI drawn over normal liver tissue. (b) Perfusion software used in this example automatically generates color-coded perfusion maps of entire liver representing blood flow (BF), blood volume (BV), permeability, hepatic arterial perfusion (HAP), portal venous perfusion (PVP), and hepatic perfusion index (HPI). Note increased blood flow, blood volume, HAP, and HPI (solid arrows) and decreased permeability and PVP (open arrows) in hepatic nodule can be appreciated by visual inspection. (c) After drawing additional ROIs over metastasis (arrow) and normal liver tissue (arrowhead), quantitative perfusion parameters are displayed. In this example, HPI of nodule (ROI 1) significantly increased to 86.48% compared with adjacent normal liver parenchyma (14.49%; ROI 2) (yellow box in lower right image). (d) Wedge resection confirmed 1.5-cm well-differentiated HCC (arrows). (Image courtesy of J. M. Lee.)

Figure 2d:

(a) After motion correction, ROI within proximal abdominal aorta is drawn to obtain arterial input function (red circle, top left image). For liver perfusion imaging, a second ROI is drawn within the portal vein to determine portal venous input function addressing unique character of dual blood supply in liver (blue circle, top right image). A third ROI is drawn over spleen, which allows separation of arterial and portal venous blood flow in the liver (green, top right image). Time-intensity curves are generated from abdominal aorta (red), portal vein (blue), and spleen (green). Yellow curve represents time-intensity curve obtained from ROI drawn over normal liver tissue. (b) Perfusion software used in this example automatically generates color-coded perfusion maps of entire liver representing blood flow (BF), blood volume (BV), permeability, hepatic arterial perfusion (HAP), portal venous perfusion (PVP), and hepatic perfusion index (HPI). Note increased blood flow, blood volume, HAP, and HPI (solid arrows) and decreased permeability and PVP (open arrows) in hepatic nodule can be appreciated by visual inspection. (c) After drawing additional ROIs over metastasis (arrow) and normal liver tissue (arrowhead), quantitative perfusion parameters are displayed. In this example, HPI of nodule (ROI 1) significantly increased to 86.48% compared with adjacent normal liver parenchyma (14.49%; ROI 2) (yellow box in lower right image). (d) Wedge resection confirmed 1.5-cm well-differentiated HCC (arrows). (Image courtesy of J. M. Lee.)

Figure 3:

Plot of time-intensity curve of spleen and liver from CT perfusion study using maximum slope method. Diagram shows how maximum slope for arterial perfusion (S_A) and portal perfusion (S_P) are derived. Time to peak splenic enhancement (arrow) indicates end of arterial phase and beginning of portal venous phase of liver perfusion, which is used for separating arterial and portal venous phases. Maximal slope (S_A or S_P) of liver time-intensity curve in each phase is divided by peak aortic and portal enhancement to calculate both hepatic arterial and portal perfusion, respectively. (Reprinted, with permission, from reference .)

Figure 4a:

(a) Schematic diagrams show key features of single-input, dual-input, single-compartment, and dual-compartment models. Single-input model (top) assumes vascular supply to hepatic lesions is mainly from hepatic artery, although normal liver is supplied from both hepatic artery and portal vein. Dual-input model (middle) adopts physiologic status of liver which is supplied by low-pressure portal vein (75%) and supplemented by high-pressure hepatic artery (25%). Using single-compartment model (top and middle), only vascular compartment is considered. Dual-compartment model (bottom) assumes dynamic distribution of contrast agent between two compartments. Using a dual-compartment model, kinetic properties such as permeability surface area product (PS) can be quantified. (b) Behavior of normal liver can be approximated by a single-compartment model because the space of Disse (equivalent to interstitial space of other organs) communicates freely with sinusoids through fenestrae. However, in disease states such as liver cirrhosis, deposition of collagen impedes free exchange of contrast material between the two spaces, requiring use of dual-compartment model.

Figure 4b:

(a) Schematic diagrams show key features of single-input, dual-input, single-compartment, and dual-compartment models. Single-input model (top) assumes vascular supply to hepatic lesions is mainly from hepatic artery, although normal liver is supplied from both hepatic artery and portal vein. Dual-input model (middle) adopts physiologic status of liver which is supplied by low-pressure portal vein (75%) and supplemented by high-pressure hepatic artery (25%). Using single-compartment model (top and middle), only vascular compartment is considered. Dual-compartment model (bottom) assumes dynamic distribution of contrast agent between two compartments. Using a dual-compartment model, kinetic properties such as permeability surface area product (PS) can be quantified. (b) Behavior of normal liver can be approximated by a single-compartment model because the space of Disse (equivalent to interstitial space of other organs) communicates freely with sinusoids through fenestrae. However, in disease states such as liver cirrhosis, deposition of collagen impedes free exchange of contrast material between the two spaces, requiring use of dual-compartment model.

Figure 5a:

Representative example of HCC shows typical findings at perfusion CT. (a) Arterial phase image (top left) shows 3.2-cm hyperenhancing mass (open arrow) in segment VIII of liver. Color-coded CT perfusion maps show increased blood flow (BF) (top middle), blood volume (BV) (top right), HAP (bottom left), and HPI (bottom right) of tumor (solid arrows) compared with adjacent normal hepatic parenchyma. Note marked decrease in PVP of tumor (arrowhead, bottom middle). (b) Photograph of gross specimen after liver transplantation confirms encapsulated, well-differentiated HCC (arrows). (Image courtesy of J. M. Lee.)

Figure 5b:

Representative example of HCC shows typical findings at perfusion CT. (a) Arterial phase image (top left) shows 3.2-cm hyperenhancing mass (open arrow) in segment VIII of liver. Color-coded CT perfusion maps show increased blood flow (BF) (top middle), blood volume (BV) (top right), HAP (bottom left), and HPI (bottom right) of tumor (solid arrows) compared with adjacent normal hepatic parenchyma. Note marked decrease in PVP of tumor (arrowhead, bottom middle). (b) Photograph of gross specimen after liver transplantation confirms encapsulated, well-differentiated HCC (arrows). (Image courtesy of J. M. Lee.)

Figure 6:

Whole-liver quantitative color mapping CT improves detection of a small, 1.3-cm HCC (arrow) in segment VIII, missed at triple-phase CT on unenhanced (top left), arterial (top right), and portal venous phase (bottom left) images. Note several hypervascular foci without clear associated washout on portal venous phase image. In contrast, on color map image of AEF (similar to HPI on CT perfusion images) (bottom right) obtained from triple-phase CT, HCC is well depicted with increased AEF. HCC was pathologically proven after liver transplantation (not shown). (Images courtesy of K. W. Kim and J. M. Lee.)

Figure 7a:

Images show perfusion CT improving detection of liver metastases from rectal cancer and identifying early treatment response after chemotherapy. (a) On arterial phase CT image (top left), two ill-defined, enhancing lesions (arrows) are in the left hepatic lobe. On color-coded CT perfusion maps, both metastases (arrows) show increased blood flow (BF) (top middle), blood volume (BV) (top right), permeability (bottom right), and HPI (bottom right) compared with adjacent normal hepatic parenchyma. Note, additional tiny metastatic focus (arrowhead) is clearly visualized on perfusion maps with increased blood volume and HPI but not well visualized on arterial phase CT image (top left). (b) CT perfusion images obtained after one cycle of chemotherapy show marked decrease in blood flow, blood volume, and HPI of the tumor (arrow). (c) PET/CT image before chemotherapy shows strong FDG uptake in both dominant metastases (arrows). Posttreatment PET/CT image (one cycle) also showed marked decrease of FDG uptake, indicating good treatment response.

Figure 7b:

Images show perfusion CT improving detection of liver metastases from rectal cancer and identifying early treatment response after chemotherapy. (a) On arterial phase CT image (top left), two ill-defined, enhancing lesions (arrows) are in the left hepatic lobe. On color-coded CT perfusion maps, both metastases (arrows) show increased blood flow (BF) (top middle), blood volume (BV) (top right), permeability (bottom right), and HPI (bottom right) compared with adjacent normal hepatic parenchyma. Note, additional tiny metastatic focus (arrowhead) is clearly visualized on perfusion maps with increased blood volume and HPI but not well visualized on arterial phase CT image (top left). (b) CT perfusion images obtained after one cycle of chemotherapy show marked decrease in blood flow, blood volume, and HPI of the tumor (arrow). (c) PET/CT image before chemotherapy shows strong FDG uptake in both dominant metastases (arrows). Posttreatment PET/CT image (one cycle) also showed marked decrease of FDG uptake, indicating good treatment response.

Figure 7c:

Images show perfusion CT improving detection of liver metastases from rectal cancer and identifying early treatment response after chemotherapy. (a) On arterial phase CT image (top left), two ill-defined, enhancing lesions (arrows) are in the left hepatic lobe. On color-coded CT perfusion maps, both metastases (arrows) show increased blood flow (BF) (top middle), blood volume (BV) (top right), permeability (bottom right), and HPI (bottom right) compared with adjacent normal hepatic parenchyma. Note, additional tiny metastatic focus (arrowhead) is clearly visualized on perfusion maps with increased blood volume and HPI but not well visualized on arterial phase CT image (top left). (b) CT perfusion images obtained after one cycle of chemotherapy show marked decrease in blood flow, blood volume, and HPI of the tumor (arrow). (c) PET/CT image before chemotherapy shows strong FDG uptake in both dominant metastases (arrows). Posttreatment PET/CT image (one cycle) also showed marked decrease of FDG uptake, indicating good treatment response.

Figure 8a:

Images show perfusion CT enabling prediction of early treatment response after chemotherapy for liver metastases from sigmoid colon cancer. (a) Baseline CT perfusion image of liver before chemotherapy shows large, 6.2-cm low-attenuating mass (arrow) at dome of liver. Liver metastases show decreased blood flow (BF) (top middle), blood volume (BV) (top right), and permeability (bottom left) and increased HPI (bottom middle) compared with adjacent normal parenchyma. (b) On perfusion CT scan after one cycle cytotoxic chemotherapy with capecitabine and oxaloplatin, tumor does not show change in size or attenuation (top left), indicating no response to chemotherapy based on RECIST. However, substantial decrease in perfusion parameters suggests tumor response. (c) Three cycles of chemotherapy later, CT findings confirmed partial response based on RECIST with a marked decrease (6.2 to 2.9 cm) in tumor size. MIP = maximum intensity projection.

Figure 8b:

Images show perfusion CT enabling prediction of early treatment response after chemotherapy for liver metastases from sigmoid colon cancer. (a) Baseline CT perfusion image of liver before chemotherapy shows large, 6.2-cm low-attenuating mass (arrow) at dome of liver. Liver metastases show decreased blood flow (BF) (top middle), blood volume (BV) (top right), and permeability (bottom left) and increased HPI (bottom middle) compared with adjacent normal parenchyma. (b) On perfusion CT scan after one cycle cytotoxic chemotherapy with capecitabine and oxaloplatin, tumor does not show change in size or attenuation (top left), indicating no response to chemotherapy based on RECIST. However, substantial decrease in perfusion parameters suggests tumor response. (c) Three cycles of chemotherapy later, CT findings confirmed partial response based on RECIST with a marked decrease (6.2 to 2.9 cm) in tumor size. MIP = maximum intensity projection.

Figure 8c:

Images show perfusion CT enabling prediction of early treatment response after chemotherapy for liver metastases from sigmoid colon cancer. (a) Baseline CT perfusion image of liver before chemotherapy shows large, 6.2-cm low-attenuating mass (arrow) at dome of liver. Liver metastases show decreased blood flow (BF) (top middle), blood volume (BV) (top right), and permeability (bottom left) and increased HPI (bottom middle) compared with adjacent normal parenchyma. (b) On perfusion CT scan after one cycle cytotoxic chemotherapy with capecitabine and oxaloplatin, tumor does not show change in size or attenuation (top left), indicating no response to chemotherapy based on RECIST. However, substantial decrease in perfusion parameters suggests tumor response. (c) Three cycles of chemotherapy later, CT findings confirmed partial response based on RECIST with a marked decrease (6.2 to 2.9 cm) in tumor size. MIP = maximum intensity projection.

Figure 9a:

Images show perfusion CT facilitating diagnosis of local recurrence after TACE for HCC. (a) On precontrast (top left), arterial (top right), portal venous (bottom left), and delayed (bottom right) phase perfusion CT images 30 days after treatment, a densely lipiodol-laden nodule (arrow) is in right lobe of liver. No definite enhancing viable tumor focus is seen on conventional CT scans. (b) Color maps of perfusion CT, however, show area (arrows) of increased blood flow (BF) (top left), blood volume (BV) (top middle), permeability (top right), HAP (bottom left), and HPI (bottom right) and decreased PVP (bottom middle), indicating recurred/residual HCC at leading edge of tumor (arrowheads). (c) Arterial phase image (left) obtained 1 month after perfusion CT shows increased size of enhancing viable tumor (arrow) around faint lipiodol residuals (arrowhead), with washout on portal venous phase image (right), confirming recurrent/residual disease.

Figure 9b:

Images show perfusion CT facilitating diagnosis of local recurrence after TACE for HCC. (a) On precontrast (top left), arterial (top right), portal venous (bottom left), and delayed (bottom right) phase perfusion CT images 30 days after treatment, a densely lipiodol-laden nodule (arrow) is in right lobe of liver. No definite enhancing viable tumor focus is seen on conventional CT scans. (b) Color maps of perfusion CT, however, show area (arrows) of increased blood flow (BF) (top left), blood volume (BV) (top middle), permeability (top right), HAP (bottom left), and HPI (bottom right) and decreased PVP (bottom middle), indicating recurred/residual HCC at leading edge of tumor (arrowheads). (c) Arterial phase image (left) obtained 1 month after perfusion CT shows increased size of enhancing viable tumor (arrow) around faint lipiodol residuals (arrowhead), with washout on portal venous phase image (right), confirming recurrent/residual disease.

Figure 9c:

Images show perfusion CT facilitating diagnosis of local recurrence after TACE for HCC. (a) On precontrast (top left), arterial (top right), portal venous (bottom left), and delayed (bottom right) phase perfusion CT images 30 days after treatment, a densely lipiodol-laden nodule (arrow) is in right lobe of liver. No definite enhancing viable tumor focus is seen on conventional CT scans. (b) Color maps of perfusion CT, however, show area (arrows) of increased blood flow (BF) (top left), blood volume (BV) (top middle), permeability (top right), HAP (bottom left), and HPI (bottom right) and decreased PVP (bottom middle), indicating recurred/residual HCC at leading edge of tumor (arrowheads). (c) Arterial phase image (left) obtained 1 month after perfusion CT shows increased size of enhancing viable tumor (arrow) around faint lipiodol residuals (arrowhead), with washout on portal venous phase image (right), confirming recurrent/residual disease.