Pharmacokinetic maps were obtained with the commercial software Tissue 4D (Siemens Healthcare, Erlangen, Germany). After an automated step of motion correction of the VIBE sequences at variable FAs with the dynamic VIBE sequence, the Toft model [41 ] was chosen for pharmacokinetic parameter calculations. The arterial input function (AIF) used for the analysis was set to “intermediate”, on the basis of population-based AIFs built in Tissue 4D. Finally, 3D maps of Ktrans, Kep, ve, and iAUC were obtained [23 (link)].
Tissue 4d
Manufactured by Siemens
Sourced in Germany
The Tissue 4D is a specialized lab equipment designed for the analysis of tissue samples. It utilizes advanced imaging technology to capture comprehensive spatial and temporal data of tissue structures. The core function of the Tissue 4D is to enable researchers to study the dynamic behaviors and interactions within complex tissue environments.
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15 protocols using tissue 4d
1
Pharmacokinetic Mapping with Tissue 4D
2
Breast MRI Perfusion Analysis Protocol
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Data processing was performed by 2 independent radiologists with more than 5 years of breast MRI experience. They were both blinded to the patients’ clinical history and histopathological results. The T1 mapping and 35-phase dynamic series were transferred to a workstation and subsequently processed with commercial software Tissue 4D (Siemens Healthcare). After motion correction and registration were automatically performed, a series of ROIs was manually drawn on the continuous levels for each lesion, avoiding visible necrosis, vessels, calcifications, and cystic appearing areas by referring to T2-weighted images. The pharmacokinetic parameters were analyzed based on the Tofts model. The arterial input function was set to “intermediate” type (population arterial input function) which is built in Tissue 4D. The perfusion parameters including Ktrans, Kep, and Ve for the whole enhanced lesion were then generated for each voxel defined by the ROIs.
3
Dynamic Contrast-Enhanced MRI Analysis
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Pharmacokinetic maps were obtained with the commercial software Tissue 4D (Siemens Healthcare, Erlangen, Germany). After an automated step of motion correction of the VIBE sequences at variable FAs with the dynamic VIBE sequence, the Toft model [63 ] was chosen for the pharmacokinetic parameters calculation. The arterial input function (AIF) used for the analysis was set to “intermediate,” on the basis of population-based AIFs built in Tissue 4D. Finally, 3D maps of ktrans, kep, ve, and iAUC were obtained.
In addition to these quantitative maps, from the fitting of the VIBE signal at variable FAs also the relaxation rate R1 (inverse of relaxation time T1, used in the generation of pharmacokinetic parameters) was obtained, by an in-house software developed in Matlab (The MathWorks Inc., Natick, MA) and saved for feature extraction.
In addition to these quantitative maps, from the fitting of the VIBE signal at variable FAs also the relaxation rate R1 (inverse of relaxation time T1, used in the generation of pharmacokinetic parameters) was obtained, by an in-house software developed in Matlab (The MathWorks Inc., Natick, MA) and saved for feature extraction.
4
DCE-MRI Evaluation of CKD-516 Perfusion
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As in most of the earlier DCE-MRI trials for vascular targeting agents, we selected perfusion parameters including voxel-wise perfusion maps of volume transfer coefficient (Ktrans) and initial area under the gadolinium concentration-time curve until 60 seconds (iAUC) to evaluate the perfusion changes induced by CKD-516 (10 (link)). Parameteric maps of Ktrans and iAUC were generated from DCE-MRI using a post-processing software program (Tissue4D, Siemens Medical Solutions, Erlangen, Germany) based on the Tofts model (Fig. 2 ) (15 (link)).
5
Tumor Perfusion Analysis with DCE-MRI
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The DCE-MRI parameters of Ktrans, Kep and Ve were calculated with the standard Tofts double-compartment model in the post-perfusion treatment software TISSUE 4D (Siemens). The region of interest was delineated in the solid part of the tumor with obvious enhancement, avoiding necrotic tissue, large vessels, bone and esophageal cavity.
6
Evaluating Tumor Perfusion Response to VXM01 Treatment
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Example 4
Tumor perfusion was evaluated by contrast media transit time (Ktrans) during dynamic contrast enhanced magnetic resonance imaging (DCEMRI) to characterize treatment response. Dynamic Contrast-Enhanced T1—weighted imaging was performed. DCE—MRI was assessed on a 1.5 Tesla System (Magnetom Aera, Siemens, Erlangen, Germany) on day 0, 38 and 3 months after treatment. Dynamic contrast—enhanced MR-imaging was performed with VIBE (volumetric interpolated breath-hold) sequences. For that purpose, a dose of 8 ml Gadovist was injected.
For every examination, regions of interest were manually drawn within the tumor-tissue followed by pixel-by-pixel analysis using a Siemens software package (Tissue 4D). ROI-modeling was based on the Tofts model with assumed T10 (1000 ms) and Parker AIF. For the estimation of tumor-perfusion Ktrans was regarded as primary endpoint.
Mean changes in tumor perfusion were −9% in the VXM01 group (n=26) vs. +18% in the placebo group (n=11). A greater than 33% drop in tumor perfusion was detected in 35% of evaluable VXM01 treated patients vs. 10% in the placebo group. The strongest responders were further analyzed in a subgroup analysis. Maximum average effects were detected at the d38 time point. The effects of various doses of VXM01 on tumor perfusion are graphically depicted in
7
Quantitative Liver DCE-MRI Analysis
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Image analysis was performed by two radiologists in consensus (L.Z., with 10 years of experience in abdominal MR imaging, and, W.Z., with 2 years of experience). Both radiologists were blinded to the histopathologic results.
Quantitative parameters including Ktrans, Kep, Ve as well as semi-quantitative parameter iAUC of the liver parenchyma were estimated from DCE-MR images using a commercial postprocessing software based on a modified Tofts model (Tissue 4D, Siemens Medical Solutions) installed at an image processing workstation (Syngo MMWP, Siemens Medical Solutions). The arterial input function (AIF) was measured at abdominal aorta and the venous input function (VIF) was measured at the main portal vein. Three circular regions of interest (ROI) were drawn by hand in the left lateral, right lateral, and medium lateral liver lobes to measure mean values. Care was taken to avoid large vessels, moving artifacts and any focal lesion.
The RE was calculated according to the following formula: RE = (PostSI − PreSI)/PreSI (23). PostSI is the liver signal intensity 15, 20 or 25 minutes after venous administration of contrast agents, respectively. PreSI is precontrast signal intensity of the liver. The ROIs were drawn as described above and the ROIs of each four phases were placed in identical anatomic positions for evaluations.
Quantitative parameters including Ktrans, Kep, Ve as well as semi-quantitative parameter iAUC of the liver parenchyma were estimated from DCE-MR images using a commercial postprocessing software based on a modified Tofts model (Tissue 4D, Siemens Medical Solutions) installed at an image processing workstation (Syngo MMWP, Siemens Medical Solutions). The arterial input function (AIF) was measured at abdominal aorta and the venous input function (VIF) was measured at the main portal vein. Three circular regions of interest (ROI) were drawn by hand in the left lateral, right lateral, and medium lateral liver lobes to measure mean values. Care was taken to avoid large vessels, moving artifacts and any focal lesion.
The RE was calculated according to the following formula: RE = (PostSI − PreSI)/PreSI (23). PostSI is the liver signal intensity 15, 20 or 25 minutes after venous administration of contrast agents, respectively. PreSI is precontrast signal intensity of the liver. The ROIs were drawn as described above and the ROIs of each four phases were placed in identical anatomic positions for evaluations.
8
Non-Rigid Motion Correction for DCE-MRI
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From the DCE-MRI images obtained in a moving mode, we generated motion-corrected images using a dedicated software (Tissue 4D; Siemens Healthcare, Erlangen, Germany). We conducted motion correction with a non-rigid registration technique that aligned the dynamic data set to a defined reference volume by transforming each individual pixel so that the image pixels were displayed in the same geometrical position for both data sets. The advantage of the non-rigid registration is that local changes caused by for example, breathing, can be mapped correctly without having to correct organ or bone positions that are not affected by the local motion (14 ). Consequently, we could acquire three image sets (i.e., static images, moving images, and motion-corrected images) of each sequence (CAIPIRINHA-VIBE, Radial-VIBE, and c-VIBE) and use them for data analysis.
9
Quantitative Nasopharyngeal Tumor Imaging
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The ADC values were measured on ADC maps by drawing the regions of interest (ROI) on the nasopharyngeal tumors. The DCE-MRI images were transferred to a post-processing workstation and were analyzed using a dedicated software application (TISSUE 4D, Siemens Medical Systems, Erlangen, Germany). After the motion correction and registration of the pre- and post-contrast acquisitions, T1 mapping was generated automatically. The ROIs were manually drawn onto the primary nasopharyngeal tumor images. The software allows for the implementation of a population-based approach for the determination of the arterial input function (AIF) that was scaled based on the gadolinium dose and modeled using the bi-exponential method proposed by Tofts and Kermode [13 (link)]. The following pharmacokinetic parameters were subsequently calculated: the volume transfer constant (Ktrans), rate constant (Kep), extravascular extracellular volume fraction (Ve), and initial area under the curve (iAUC).
10
Multimodal Imaging Quantification of Tumors
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Images were analysed in consensus, by a nuclear medicine physician (AS) with 33 years of experience and by a radiologist with 17 years of experience in MR, on a dedicated workstation (SyngoVia, Siemens Healthcare). The automated PET encircling function from the mMR-general layout workflow was used to include the entire FDG avid tumour and obtain the SUVmax. For measuring the ADCmean, the same layout workflow was used; the entire tumour was manually encircled on the DWI and ADC images. Spatial correlation with subtracted MR perfusion images was employed to improve lesion identification, when necessary. For the MR perfusion analysis, a Tofts-based commercially available software (Tissue4D, Siemens Healthcare) was employed. The entire tumour was manually encircled on the earliest contrast-enhanced subtracted image set that best demonstrated the cancer against the adjacent normal tissues. Then, the following measurements were obtained: mean volume transfer coefficient (Ktrans), mean flux rate constant (Kep), mean extracellular volume ratio (Ve), and the initial area under the curve (iAUC).
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