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A number of factors have to be considered for implementing an accurate attenuation correction (AC) in a combined MR-PET scanner. In this work, some of these challenges were investigated and an AC method based entirely on the MR data obtained with a single dedicated sequence was developed and used for neurological studies performed with the MR-PET human brain scanner prototype.
The focus was on the bone/air segmentation problem, the bone linear attenuation coefficient selection and the RF coil positioning. The impact of these factors on the PET data quantification was studied in simulations and experimental measurements performed on the combined MR-PET scanner. A novel dual-echo ultra-short echo time (DUTE) MR sequence was proposed for head imaging. Simultaneous MR-PET data were acquired and the PET images reconstructed using the proposed MR-DUTE-based AC method were compared with the PET images reconstructed using a CT-based AC.
Our data suggest that incorrectly accounting for the bone tissue attenuation can lead to large underestimations (>20%) of the radiotracer concentration in the cortex. Assigning a linear attenuation coefficient of 0.143 or 0.151 cm−1 to bone tissue appears to give the best trade-off between bias and variability in the resulting images. Not identifying the internal air cavities introduces large overestimations (>20%) in adjacent structures. Based on these results, the segmented CT AC method was established as the “silver standard” for the segmented MR-based AC method. Particular to an integrated MR-PET scanner, ignoring the RF coil attenuation can cause large underestimations (i.e. up to 50%) in the reconstructed images. Furthermore, the coil location in the PET field of view has to be accurately known. Good quality bone/air segmentation can be performed using the DUTE data. The PET images obtained using the MR-DUTE- and CT-based AC methods compare favorably in most of the brain structures.
An MR-DUTE-based AC method was implemented considering all these factors and our preliminary results suggest that this method could potentially be as accurate as the segmented CT method and it could be used for quantitative neurological MR-PET studies.
There has recently been great interest in combining PET and MRI, and a number of integrated scanners capable of simultaneous acquisition have been developed and successfully tested for small animal (1-5) and human (6) imaging. Improved PET data quantification is expected to be one of the main advantages of MR-PET and an accurate attenuation correction (AC) method is necessary for obtaining a precise quantitative measure of the radiotracer concentration. However, due to the limited space available inside the MR scanner bore, the MR-compatible PET inserts developed to date are not equipped with a transmission system, which makes the implementation and validation of an MR-based AC method necessary. This is however not a trivial task as there are several factors that have to be considered in order to implement an accurate AC. Because the MR signal reflects tissue proton densities (and tissue relaxation times) and not electron density, the MR images are not directly related to the tissue linear attenuation coefficients (LACs). However, attenuation maps (μ-maps) have been previously estimated from segmented MR images (7). Image segmentation is a mature research domain and many algorithms have been developed to segment images obtained using different imaging modalities, including MRI (8-10). These tasks range from those with a small number of tissue classes such as gray/white matter (GM/WM) being segmented, to more complex ones in which specific head structures are identified. Using these techniques, it is relatively straightforward to identify water based structures. The more challenging task consists of differentiating the bone tissue from air-filled spaces, since they both appear dark (i.e. no signal) on the MR images obtained using conventional sequences. Bone is especially relevant as a photon attenuating medium, being the tissue with the highest LAC and thus susceptible to introduce large bias in the adjacent GM structures when misclassified as air or soft tissue.
In this work, the challenges and requirements for implementing an MR-based AC methodfor the Siemens MR-PET brain prototype were investigated. In particular, the need for identifying the bone for accurate PET data quantification in brain structures was studied in simulation using segmented MR and CT images. As a potential solution to the problem of discriminating air and bone in MR, preliminary results using a new dual-echo ultra-short echo time (DUTE) sequence for imaging the bone and discriminating it from the air-filled cavities are presented. Particular to the integrated MR-PET scanner, the challenges related to the RF coil attenuation were also investigated. Proof-of-principle MR-DUTE-based AC was performed on the combined scanner in human subjects. Our results suggest that it is possible to develop an AC method which could be as accurate as the one provided by the segmented CT AC method using the proposed MRI DUTE sequences.
A prototype MR-compatible PET scanner designed to fit inside the Siemens MAGNETOM Trio a TIM system 3T human MR scanner (Siemens Healthcare Inc.) was recently installed at MGH. This PET scanner – called BrainPET – uses magnetic field insensitive avalanche photodiodes as scintillation photon detectors. The gantry physical inner and outer diameters are 35 and 60 cm, respectively. The transaxial field of view (FOV) is 32 cm and the axial FOV is 19.25 cm. Emission data are acquired in list mode format, sorted in the line of response (LOR) space and compressed axially in the sinogram space for fast reconstruction (11). This axial compression (span=9 and maximum ring difference=67) generates 1399 sinograms, each consisting of 256 radial elements and 192 angular projections. The images are reconstructed with the ordinary Poisson ordered subsets expectation maximization (OP-OSEM) algorithm from prompts, variance reduced random coincidences (12), detector sensitivity (obtained from a plane source), scatter (13) and attenuation (the latter two accounting for head - different models are investigated in this work - and RF coil). The reconstructed volume consists of 153 slices with 256×256 pixels (1.25×1.25×1.25 mm3). A “PET-friendly” CP transmit/8-channel receive RF coil was specifically designed for this scanner by positioning most of the attenuating components (i.e. capacitors, etc) outside the PET FOV.
Simulations were used to study the factors affecting the accuracy of the AC in the combined MR-PET scanner. In particular, the focus was on (i) the bone/air segmentation problem, (ii) the bone tissue LAC selection, and (iii) the RF coil positioning. Tools were developed for generating noiseless emission data with attenuation and for reconstructing the attenuation corrected images. The emission data were generated in image space assuming uniform distribution in structures of interest identified from segmented MR datasets (i.e. GM and WM structures) or assuming uniform distribution in a cylindrical phantom. These volumes were then forward projected to obtain the “true” emission sinograms. “True” and modeled μ-maps were created in a similar way and were forward projected (and exponentiated) to generate the attenuation correction factors (ACFs). To obtain the different attenuated emission data, first the “true” emission sinogram was “attenuated” using the “true” AC sinogram. The modeled μ-maps were then used to correct the attenuated emission data. Other factors (i.e. noise, scatter, detector sensitivity, gaps between detector blocks etc) were not included in these simulations. The relative changes (RC) with respect to the “true” AC method were calculated for all the voxels included in the initial emission volume using the equation: RC=100×(Cmodeled-Ctrue)/Ctrue, where Cmodeled and Ctrue were the values measured from the volumes reconstructed using a particular model or the “true” AC method, respectively. Small, moderate, large and severe RCs (i.e. under- as well as overestimation) were defined as 0-5%, 5-10%, 10-20% and >20%, respectively. RC images were used for the qualitative and quantitative analysis of the AC inaccuracy in individual slices. For obtaining a global assessment of the effects, histograms of the RCs for all the voxels included in the initial emission volume were also analyzed in each case. Bias and variability were defined as the deviation from zero (i.e. no RC) of the maximum value observed in the histogram and as the full width at half maximum value, respectively.
Co-registered high resolution MR and CT datasets acquired in human subjects were used to segment the brain structures (8) and the bone tissue. The PET emission data were created from the segmented MR data assuming a 4:1 GM:WM uptake ratio, and no uptake in any of the other structures (i.e. CSF, scalp, muscles, fat, etc) (Fig. 1).
Four models of μ-maps were created from the segmented MR and CT data: two MR-based (MRfirst and MRsecond) and two CT-based (CTsegmented and CTscaled) (Fig. 1). The first MR-based model (MRfirst) was obtained by binary segmenting the MR data based on empirically determined threshold (e.g. 10% of the maximum intensity), reslicing the MR volume so that the MR voxel size matches the PET voxel size and assigning a LAC corresponding to water at 511 keV (i.e. 0.096 cm−1) to all non-zero voxels. As a next step (MRsecond), the μ-map obtained in the first step was processed by applying a morphological closing operation (14) so that all the gaps present in the images (i.e. the voxels corresponding to bone and air cavities) were classified as soft tissue. For the first CT-based method (CTsegmented), the CT dataset was segmented into bone (300 to 2000 Hounsfield Units (HUs) (15)), soft tissue (−200 to 300 HU) and air cavities (<−200 HU) and uniform LACs were assumed for these three tissue classes (i.e. 0.151, 0.096 and 0 cm−1, respectively). For the second CT-based method (CTscaled), the HUs were converted to LACs at 511 keV using the standard PET/CT hybrid scaling method (i.e. all the voxels above and below 300 HU were scaled using 0.405 and 0.496 as scaling factors, respectively (15)). Four models of ACs (i.e. MRfirst, MRsecond, CTsegmented and CTscaled) were obtained from these maps. The “true” AC sinogram was derived from the scaled CT data (the current “gold standard”).
Assuming that bone/air segmentation can be performed (either using the DUTE sequences proposed later or using an atlas based method (16)), LACs have to be assigned to all tissue classes identified. However, the value of the bone tissue LAC is still subject of debate, the published values ranging from 0.136 to 0.180 cm−1 (7).
Starting from the μ-map derived from the segmented CT dataset described above, six models of μ-maps were created assuming the following LACs for bone tissue: 0.136, 0.143, 0.151, 0.157, 0.171 and 0.180 cm−1 (7).
Because the probability of both annihilation photons reaching the PET detectors depends on the total attenuation along each particular LOR, another factor that could affect the PET quantification in a combined MR-PET scanner is the attenuation in the RF coil located between the patient and the PET detectors. These effects were studied using a simulated 16 cm diameter uniform phantom. The selection of the scaling factors was based on the analysis of the data obtained from CT and from two transmission scans of the RF coils (at 662 and 511 keV on the Siemens HRRT and HR+, respectively). The complete μ-map was obtained by assuming a constant 0.096 cm−1 LAC throughout the phantom volume and adding it to the coil μ-map. Because the coil is actually larger than the reconstructed PET FOV, the forward projection was performed on an extended FOV larger than the coil diameter. The central 256 radial bins of the attenuation sinograms were subsequently cropped for each angle to obtain the AC sinograms required by the data processing. Images were reconstructed from the data including and ignoring the RF coil μ-map. Furthermore, to study the effect of coil mispositioning on the PET quantification, the coil μ-map was translated 1-5 mm along the X and Y axes and rotated 1-5° about the Z axis.
Experimental measurements were performed to study some of these factors using the standard data acquisition, processing and reconstruction methods on the combined MR-PET scanner. The purpose of these experimental measurements was not to replicate the simulations, but rather to provide potential solutions in each case, for this particular scanner. All the human studies were approved by the Institutional Review Board at MGH.
UTE sequences have been recently proposed and developed for imaging cortical bone and other connective tissues of relevance for musculoskeletal system imaging (17, 18). These sequences hold the potential of being an elegant solution to the MRI bone/air segmentation problem and for deriving the μ-maps (19). These sequences are used to image tissues with short T2* relaxation time, such as bone (T2* in the range of 0.05-2 ms). In principle, the bone tissue could be identified if data were acquired at two echo times such that the signal from the bone is present in the first echo (UTE1) and not in the second echo (UTE2) dataset, while the signals from other tissue classes (i.e. soft tissue, air) are similar in both cases. These sequences were tested trying to identify the parameters that are most useful for bone/air discrimination.
To minimize the acquisition time and to overcome the potential image artifacts introduced by patient motion when using sequential UTE, new DUTE sequences were proposed and implemented for collecting the signal from both echoes during the same acquisition. The following parameters were chosen for these studies: TE 0.07/2.46 ms, TR 200 ms, FA 10°, radial projections 32,000, bandwidth 1532 Hz/Px, FOV 320 mm, base resolution 192, acquisition time 3:20 min:sec. As proof-of-principle, a human skull was first imaged using this DUTE sequence and compared with the results from the CT (120 kV/50 mA, 20 s acquisition time). Next, human volunteers were scanned inside the BrainPET.
To estimate the bone tissue LAC, 325 CT datasets acquired at MGH were analyzed. All the subjects were scanned on the same scanner (GE Medical Systems) using the same acquisition protocol (i.e. 2.5 mm DX bone axials, 120 kVp/250 mA). For each dataset, the average values and standard deviations for all the voxels in the 300-2000 HU range were computed and these data were analyzed as a function of gender and age.
The position of the RF coil with respect to the BrainPET scanner was determined by placing fiducial markers filled with a solution of FDG at precise locations on the coil and acquiring PET data. To test the reproducibility of the coil positioning with respect to the PET scanner, the whole setup was repeatedly repositioned inside the scanner. In addition, the PET insert was removed and repositioned inside the magnet (as done routinely at our institution), maintaining the coil in the same location. PET data were acquired in each case and the positions of the fiducial markers in the reconstructed images were compared.
Four subjects were scanned for these proof-of-principle studies. In all these cases the head μ-map was derived by analyzing the data obtained with the DUTE sequence. Rather than using the simple difference between DUTE1 and DUTE2, the following steps were followed for improving the identification of the three classes (i.e. soft tissue, bone and air) and generating the μ-maps:
A mask that included the voxels from all classes was obtained by applying a morphological closing operation to the DUTE2 data. This step was required for excluding the voxels outside the subject's head (e.g. RF coil). Subsequently, an opening operation was applied to exclude voxels at the air-tissue interface where susceptibility artifacts were present.
The original DUTE1 and DUTE2 volumes were first divided by the corresponding smoothed volumes obtained after applying a 3D Gaussian low-pass filter with a 20 mm radius kernel. In this way, the image inhomogeneities due to coil non-uniform sensitivity were removed. The resulting datasets were combined using the transformation (DUTE1-DUTE2)/(DUTE22) tuned to enhance the bone tissue voxels. Specifically, the numerator allows the selection of the voxels with the largest signal change (i.e. bone versus other classes), while the denominator allows the selection of the voxels with the lowest signal in the DUTE2 images (i.e. bone and air). Bone LAC (0.151 cm−1) was assigned to all the voxels above an empirically determined threshold from the transformed images (i.e. 0.012).
A similar procedure as in the case of the bone tissue was used for segmenting the voxels corresponding to air cavities starting from the low-pass filtered data combined using the transformation (DUTE1+DUTE2)/(DUTE12) and using an empirically determined threshold from the transformed images (i.e. 0.14).
Soft tissue LAC was assigned to all the voxels included in the mask that were not identified as bone or air.
An identical data processing workflow and the same thresholds were used for deriving the DUTE-segmented μ-maps in all 4 subjects. CT data were also available and were used to generate the CT-derived μ-maps (CTscaled and CTsegmented methods). The CT and MR-PET studies were performed less than one month apart and no surgical procedures that would alter the skull/brain morphology were performed between the scans. Since the MR and PET volumes were already co-registered, the CT volume was co-registered to the MR volume.
In all the cases, the complete μ-maps were obtained by adding the DUTE- or CT-segmented head μ-maps to the RF coil μ-map. The PET data reconstructed using the ACFs obtained from these μ-maps were qualitatively and quantitatively compared.
The air/bone tissue segmentation problem is approached differently in the two approximate MR-based AC methods: in the MRfirst model the bone tissue is misclassified as air, while in the MRsecond model the voxels corresponding to bone tissue and some thin air cavities are misclassified as water. Both methods introduce biases in the reconstructed images and representative RC images (and histograms) are shown in Figure 2A (and Suppl. Fig. 1A). The MRfirst method leads to moderate overall underestimation of the activity and to large underestimation in structures adjacent to bone. The MRsecond method leads to a moderate overall underestimation in most brain structures but also to a severe overestimation in structures adjacent to real air cavities filled by the morphological filter. The CTsegmented model estimates accurately (within 5% compared to the CTscaled model) the activity concentrations in most of the brain structures.
Representative RC images (and histograms) for three of the six AC models studied are shown in Figure 2B (and Suppl. Fig. 1B). Overall moderate under- and overestimations were observed for the lower and higher LAC values, especially in structures adjacent to bone. The LACs that resulted in the smallest image bias and smallest variability were 0.171 and 0.136 cm−1, respectively. However, the former had the largest variability and the latter had the largest bias. Assigning LAC of 0.143 and 0.151 cm−1 resulted in a better trade-off between bias and variability.
A CT of the RF coil and RC images (and histograms) for representative cases studied are shown in Figure 3 (and Suppl. Fig. 1C). Large underestimations were observed in the data of the simulated phantom reconstructed ignoring the coil μ-map. The results of the coil μ-map displacement studies suggest that for this particular coil, translations of 1-2 mm in the X or Y directions, or rotations of 1-2° about the Z axis can be tolerated without introducing significant changes in the reconstructed PET images (i.e. < 5% change for all the cases). Similar to the previous case, larger variability and no changes in bias were observed for larger displacements. Displacements larger than 5 mm or 5° are not possible in the case of the 8-channel coil that was custom built to fit tightly inside the BrainPET scanner.
Representative slices obtained using the single echo UTE sequences are shown in Figure 4A. Bone signal intensity changes significantly when the data are acquired with a TE=0.07 ms compared to the data acquired with a TE=2.46 ms, while the air regions show no significant change. The acquisition times for these sequences were 4.5 minutes for a single average. Since the signals from the short and long T2* components were acquired separately, the total acquisition time was 9 minutes. Our goal is to spend minimal time acquiring the data used for deriving the μ-map so that other MR sequences can be run simultaneously with the PET data acquisition. A solution to this problem is the DUTE sequence proposed. This has the advantage of eliminating potential motion artifacts that could occur when using sequential UTE sequences. This is particularly relevant as the segmentation accuracy depends on the relationship between the two signals for each voxel. Figure 4B shows 3D reconstructions of the skull obtained from the DUTE1 (left) and from the CT data (right). These images were registered closely within the resolution of the DUTE data suggesting negligible distortions compared with CT for PET AC purpose. Images obtained in human subjects using these sequences are presented in the next section.
Small variations with age and gender were noted in the average values determined from the analysis of the 325 CT datasets, but the differences observed were not statistically significant (Table 1). The overall average value was 1220 HU (SD=24 HU). Converting this average value to LAC at 511 keV using the same scaling factor for bone tissue as the one used in the simulations, a 0.143 cm−1 LAC was obtained. Interestingly, this was also one of the two values that provided the best trade-off between bias and variability in our simulations.
The displacements measured after the RF coil and the PET insert were repeatedly repositioned inside the scanner were smaller than 1 mm along and 1° about all the axes. Based on the results of our simulations, these displacements are not expected to cause major changes in the reconstructed images. However, problems may arise when dedicated (flexible) surface coil are used and the DUTE sequences might be useful for determining their location. As Figure 4B (left) demonstrates, in cases where the coil position or shape (in the case of flexible coils) is unknown, the DUTE1 data might be used for localization.
The μ-maps generated from the segmented CT and DUTE data (Suppl. Fig. 2A) are shown in Figure 5A for a representative subject (left and right, respectively) and in Supplemental Figure 3A-C for the other 3 subjects. These images demonstrated a good overall agreement: most of the soft and bone tissue, maxilar sinus and mastoid air cavities being correctly segmented. The skull appeared slightly thinner in the DUTE-based segmentation and this is explained by the relatively poor spatial resolution of the DUTE data (i.e. 1.67 mm). The segmentation method failed to identify some parts of the nasal air cavities, these voxels being misclassified as bone. Similarly, a small fraction of the CSF voxels (which had very low signal in the original data) was misclassified as bone or air. Adding the information derived from another sequence (e.g. T2 SPACE, MPRAGE) could be a solution to these problems but it would increase the complexity of the method. Instead, the focus will be on improving the DUTE sequence itself (e.g. higher spatial resolution, bandwidth, number of radial projections, etc) and refining the segmentation, the goal being to develop a method that relies solely on the DUTE data.
Images from the PET data corrected using the ACFs derived from the CT- and DUTE-segmented μ-maps are shown in Figure 5B. These data suggest that the visual interpretation of the PET images would not be impaired by using the DUTE-based AC method as currently implemented. However, as observed from the RC images (using the segmented CT as a reference), moderate to severe under- as well as overestimations were still present in the data and they corresponded to the misclassified voxels in the μ-map (Fig. 5B).
Representative PET images reconstructed ignoring and including the coil attenuation are shown in Figure 6. In addition to the severe underestimation of the activity (Fig. 6, middle column), axial artifacts can be observed in the images (see Fig. 6 sagittal). These artifacts disappeared after accounting for the coil attenuation (see Fig. 6, right column).
A number of factors have to be considered when implementing an MR-based PET AC method for quantitative imaging with the BrainPET.
Assuming that the RF coil attenuation is properly accounted for, the next factor that could have the largest impact on PET data quantification, based on the results of our simulations, is the bone/air segmentation. As demonstrated by our simulations, the MRfirst method that was used in our initial MR-PET studies was clearly inaccurate because it ignored the bone attenuation. The MRsecond method partially accounted for this, misclassifying bone as water; however, some voxels corresponding to air filled cavities were also treated similarly and, as a result, bias was introduced in the reconstructed images. Nevertheless, the MRsecond method is likely better than most calculated AC methods (e.g. ellipse, contour fitting in image or sinogram space(20-23)). These methods have not been designed to segment the internal air cavities (e.g. nasal, oral, mastoid, frontal sinus). This is relevant for most current generation PET scanners using calculated AC, including BrainPET, because the subject's head is not in the traditional AC-PC orientation and a large fraction of the LORs are passing through the air cavities. Although not specifically studied in our simulations, more severe overestimations of the activity in the brain regions adjacent to air cavities would likely be observed in the case of the calculated methods. The under- and overestimation effects could even cancel each other in regions adjacent to mixed air/bone structures (i.e. frontal sinus, mastoid bone), making the interpretation of the images even more difficult.
The scaled CT method (15) is considered the “gold standard” for deriving the μ-map in clinical PET/CT scanners. Currently, the CTsegmented AC method is only used for selective segmentation of the voxels corresponding to contrast agents and metallic implants. More importantly, this method is likely the “silver standard” for segmented MR-based AC methods. The results of the simulations presented demonstrate that an accurate delineation of the bone is necessary for brain studies using combined MR-PET scanners and a method to precisely identify the bone is essential, due to the proximity of the GM to the skull. While atlas based methods could also be used (16, 24, 25), in patients with modified bone anatomy (i.e., glioblastoma patients) the DUTE approach (possibly incorporating some atlas-based information) may be superior.
Mis-registration between the emission volume and the μ-map is a common source of artifacts for all AC methods and it is usually caused by subject motion. Although MR and PET are acquired simultaneously, multiple MR sequences are generally used and motion may occur during the acquisition. These effects can be reduced on an integrated MR-PET scanner. First, the hardware co-registration is likely to be superior to the software co-registration of the separately acquired datasets. More importantly, the temporal correlation of the PET and MR signals allows one to use the MR signal for tracking the motion of the object and for correcting the PET data retrospectively (26). There are other factors that could affect the spatial co-registration. Any deformations present in the MR data (due to MR distortions caused by gradient nonlinearities) have to be characterized and corrected for. Similar to CT imaging, dental implants could also introduce artifacts in the MR-based segmentation. However, instead of showing as high intensity streak artifacts as in CT, they are more localized and appear as round signal voids in the MR images. Of relevance to the DUTE method proposed, these artifacts are reduced in the DUTE1 images.
Knowing the exact position (i.e. within a few millimeters) of the RF coil with respect to the magnet isocenter is not essential for MR imaging, however, this becomes relevant in a combined MR-PET scanner. Our data demonstrate that the coil attenuation cannot be ignored. Our coil has been redesigned to fulfill minimum attenuation requirement.
Finally, PET data accuracy may be affected by reconstruction related artifacts. Of particular interest in the case of the BrainPET prototype are the transaxial gaps between the block detectors as they can concentrate any data inconsistencies due to imperfect normalization, attenuation and scatter correction. This explains the axial artifacts observed when the RF coil attenuation and scatter were neglected, as illustrated in Fig. 6. The characterization of these inconsistencies is however outside the scope of the present paper.
Attenuation correction is a mandatory step not only for obtaining quantitative data, but also for performing meaningful qualitative image interpretation in PET studies. In this work, segmented CT was proposed as the “silver standard” for segmented MR-based AC. For accurate AC in neurological studies performed using the integrated MR-PET scanner, three compartments must be identified: water-based structures (i.e. WM, GM, CSF), bone tissue (i.e. skull) and air-filled cavities. The most challenging task – the discrimination of bone tissue from air cavities – can be achieved using the proposed DUTE sequence. In addition, the attenuation of the RF coil has to be accounted for. An MR-DUTE-based AC method implemented considering all these aspects could in principle provide an estimation of the radiotracer concentration in a particular voxel as accurately as the “silver standard”. Implementing an accurate MR-based AC is essential for the wide acceptance of this new imaging modality and it will allow us to take advantage of the augmented quantitative capabilities of the combined MR-PET scanner.
The authors gratefully acknowledge Larry Byars from Siemens for many extremely useful discussions and for his invaluable contribution to the MR-PET project. This work was partly supported by NIH grant 1R01CA137254-01A1.