MONTE CARLO BASED SPECT ACTIVITY QUANTIFICATION AND TUMOR DOSIMETRY FOR 177 LU-DOTATATE TREATMENTS

Transcription

1 SAHLGRENSKA ACADEMY MONTE CARLO BASED SPECT ACTIVITY QUANTIFICATION AND TUMOR DOSIMETRY FOR 177 LU-DOTATATE TREATMENTS Ida Marin Essay/Thesis: Program and/or course: Level: Semester/year: Supervisors: Examiner: Report no: 30 hp Medical Physics Programme Second Cycle At/2016 Peter Bernhardt, Johanna Svensson, Emma Wikberg Magnus Båth

2 Abstract Essay/Thesis: Program and/or course: Level: Semester/year: Supervisors: Examiner: Report No: Keywords: 30 hp Medical Physics Programme Second Cycle At/2016 Peter Bernhardt, Johanna Svensson, Emma Wikberg Magnus Båth Neuroendocrine tumors, radionuclide therapy, dosimetry Purpose: Theory: The purpose of this work was to compare three image reconstruction algorithms for single photon emission computed tomography (SPECT), to quantify the absorbed dose to neuroendocrine tumors treated with lutetium 177-DOTATATE ( 177 Lu- DOTATATE) and to investigate the dose response relationship. Peptide receptor radionuclide therapy (PRRT) using the radiopharmaceutical 177 Lu- DOTATATE has shown to be an effective palliative treatment for neuroendocrine tumors. 177 Lu emits primarily electrons, but also photons, which enable gamma camera imaging. Dosimetric calculations are generally performed for risk organs using planar gamma camera images. Estimations of the absorbed dose in tumors are normally not made. By producing such estimations, a tumor dose response relationship can be achieved, which could aid in the selection of patients suited for 177 Lu-DOTATATE treatment. Method: Results: Three SPECT image reconstruction algorithms: iterative reconstruction with attenuation correction (IRAC), IRAC with a resolution recovery correction (IRACRR) and Sahlgrenska Academy reconstruction code (SARec) were compared using phantom measurements. Neuroendocrine tumors were identified and measured by a radiologist according to the response evaluation criteria in solid tumors (RECIST). Planar imaging was performed at day 0, 1, 2 and 7 after 177 Lu-DOTATATE infusion and SPECT at day 1. Out of 51 patients evaluated by the radiologist, 24 were included in this study. Tumors were segmented manually in single photon emission computed tomography/computed tomography (SPECT/CT) and planar gamma camera images. Activity in tumors was calculated in SPECT at day 1. The conjugate view formula was used in planar images for determining the kinetics of the radionuclide by acquiring a time-activity curve, which was adjusted to match activity in SPECT at day 1. Absorbed dose was calculated assuming local energy deposition by electrons. SARec showed a higher signal to background ratio than the other algorithms. The mean tumor absorbed dose and specific mean tumor absorbed dose (mean tumor absorbed dose per injected activity) were 65 Gy and 2.5 mgy/mbq, respectively. The absorbed dose obtained with SARec was in average 36% higher than that obtained with IRAC. No significant correlation between absorbed tumor dose and tumor response could be established.

3 Table of content Introduction... 1 Neuroendocrine tumors Lu-DOTATATE treatment... 1 Gamma camera imaging... 2 Monte Carlo method for image reconstruction... 3 Activity quantification and dose estimation... 3 Aims... 5 Materials and method... 6 Phantom measurements... 6 Patient imaging and SPECT reconstruction... 8 Tumor segmentation... 8 Dosimetry... 9 Tumor response analysis Results Discussion Conclusion Acknowledgements Reference list... 22

4 Introduction Neuroendocrine tumors Neuroendocrine tumors (NETs) originate from neuroendocrine cells, which are dispersed in the body. Thus, the primary sites of NETs are numerous, where the most common are the respiratory tract, gastrointestinal canal and pancreas (1). NETs may be divided into functioning or non-functioning i.e. be clinically symptomatic due to hormone elevation or silent (2). NETs are often well differentiated and slow growing. However, approximately 50% of patients have advanced disease at diagnosis and most of these patients will die from the disease (3). Therefore, the treatment is most often of palliative intent. If feasible, surgery is the treatment of choice for patients with localized or loco-regional disease. For disseminated disease, systemic treatment is adequate. Although, due to the indolent nature of many NETs, systemic treatments have been challenging to develop. In most NETs, an overexpression of somatostatin receptors (SSTRs) is present (1). Somatostatin analogues are therefore suited for use in NET treatment, and have shown to be effective in symptom relief for functioning tumors. In addition, somatostatin analogues have an anti-proliferative effect. Somatostatin analogues are also used bound to radionuclides, enabling therapy with peptide receptor radionuclide therapy (PRRT) and imaging with somatostatin receptor scintigraphy (4). 177 Lu-DOTATATE treatment There are several radiopharmaceuticals used for PRRT, one of which is 177 Lu-DOTATATE, where the radionuclide 177 Lu is bound by the chelator DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid) to the somatostatin analogue octreotate. Octreotate binds to the somatostatin receptors on the cell surface and the 177 Lu-DOTATATE complex is then internalized into the cell by means of endocytosis. The radionuclide is thereby accumulated in tumors. Treatment with 177 Lu-DOTATATE has shown to be effective in terms of tumor regression, progression free survival and overall survival, while side effects are limited. Improvement in quality of life has also been reported (5). 177 Lu decays to hafnium 177 ( 177 Hf) by β - -emission with a half-life of 6.7 days. The maximum energy of emitted β - -particles is 498 kev and the maximum range in tissue is 2 mm. The mean energy of emitted β - -particles, Auger electrons and conversion electrons is 147 kev. 177 Lu also emits γ-photons, including two γ-photon energies of 113 (6.2%) kev and 208 (10.4%) kev which can be used for gamma camera imaging (6). 177 Lu- DOTATATE treatment is most often delivered in several fractions at 6-10 week intervals. An intravenous infusion containing the radiopharmaceutical is administered to the patient. In most clinical protocols 3.7 or 7.4 GBq per fraction is used (1). The main organs at risk are the kidneys and bone marrow and these organs restrict the activity of the radionuclide that can safely be given. The number of fractions delivered to a patient is determined by the accumulated absorbed dose to the risk organs. An estimation of the absorbed dose to the kidneys is therefore conducted following each fraction and 1

5 blood counts monitored as a surrogate for bone marrow dosimetry (1). Absorbed dose to tumors in 177 Lu-DOTATATE treatment is normally not estimated. To the author s knowledge, few studies have quantified absorbed dose to tumors treated with 177 Lu- DOTATATE and only one study (7) has evaluated the tumor dose response. Knowledge of the relationship between absorbed dose and tumor response would facilitate in the assessment of expected profit for individual patients and consequently help to select patients for whom 177 Lu-DOTATATE treatment is suitable. To produce estimations of the absorbed doses to organs and tumors in 177 Lu-DOTATATE treatments, knowledge of biokinetics of the radionuclide and activity concentration is required. By acquiring images of the patient at different time points after 177 Lu-DOTATATE infusion, the biokinetics may be followed and activity quantification performed (8). Time points and techniques for gamma camera imaging in clinical practice may be chosen in several ways. 1, 24, 48 plus one later time point hours post infusion (p.i.) is often used for planar imaging, i.e. day 0, 1, 2 and 4-7 post infusion. On day 1, SPECT imaging of the patient is often performed (1). Gamma camera imaging The gamma camera detects photons emitted from radionuclides within a patient. The basic components are a crystal where the photons interact, photomultiplier tubes which collect the signal and the collimator, which attenuates most photons, except those directed perpendicularly to the detector within a certain solid angle. The collimator is required for resolving the spatial distribution of the radionuclide. In planar imaging, only anterior and posterior projections are acquired, whereas in single photon emission computed tomography (SPECT) a 3D representation is achieved by acquiring projections from many angles and reconstructing the collected signal (sinogram) using a reconstruction algorithm (9). There are many different analytical and iterative reconstruction algorithms. The concept of iterative algorithms is to find a distribution that would generate the measured projections by using successive estimates. The current estimate is forward projected and compared to the measured projections. A correction of the current estimate is made, resulting in a new estimate. This is iterated for a preset number of iterations or until the error is minimized (10). Gamma camera images are degraded due to discrepancies between true and measured activity distribution. Image degrading factors include physical factors such as attenuation, scatter, limited resolution due to collimator hole separation, partial volume effect and noise. They also include patient factors, such as movement and anatomy and technical factors including the choice of image reconstruction algorithm in the case of SPECT imaging. Some of the image degrading factors can be corrected for (11). An overview of image degrading factors is shown in figure 1. 2

6 Figure 1: Formation of a measured activity distribution from a true activity distribution in SPECT. Some image degrading factors are corrected for in the reconstruction (12). Monte Carlo method for image reconstruction Ordered subset expectation maximization (OSEM) is a frequently used reconstruction method where the forward projection step presumes a perfect ray projection or an attenuated perfect ray projection. By ignoring other image degrading factors, the image does not accurately represent the activity distribution. Some new algorithms use attenuation correction and implement collimator response in the forward projections. A more comprehensive characterization of the forward projection formation can be made by simulation of the radiation transport using the Monte Carlo (MC) method. The drawback of the MC method is the long simulation time due to the immense amount of computations. Simulation times can be reduced by limiting the amount of computations by use of variance reduction techniques. Also, writing the code for GPU (graphic processing unit) instead of the more commonly used CPU (central processing unit), permitting a more effective parallelization of the code, substantially reduces simulation times. Monte Carlo implementation in the forward projection of OSEM reconstruction algorithms thus has the potential for improving image quality in research and clinical settings (9). Activity quantification and dose estimation For activity quantification in planar images, the conjugate view method is often used. Activity is calculated by: A = C A C P e µt t 2 µ, (1) k(1 e µt ) e µt 2 3

7 where T is the body thickness in the anteroposterior direction, t is the thickness of the organ, µ is the effective attenuation coefficient, k is the sensitivity factor of the imaging system and C A C P is the geometrical mean of the background corrected counts in the anterior and posterior images. Activity as a function of time may then be plotted and a curve fitted. By integrating the curve, the time-integrated activity (Ã) is obtained and absorbed dose (D) may be calculated by: D = Ã φ M, (2) where is the energy emitted per disintegration by the radionuclide, φ is the absorbed fraction of the energy and M is the organ mass (1). Activity quantification in planar images has an inherent shortcoming in that activity in the organ or tumor of interest cannot be distinguished from activity in overlapping tissues. The 3D activity distribution is projected into a 2D representation and depth information is hence lost. With the tomographic technique SPECT, however, all three dimensions are resolved (13). Activity quantification in SPECT can be used to adjust the time-activity curve obtained from planar images. This is thus a dosimetry method where both 2D and 3D imaging contribute in the estimation of the absorbed dose. 4

8 Aims The aims of this work were to: compare three SPECT image reconstruction algorithms to determine which is best suited for activity quantification. quantify the absorbed dose to neuroendocrine tumors treated with 177 Lu-DOTATATE and to investigate the dose response relationship. 5

9 Materials and method 51 patients with NETs received 177 Lu-DOTATATE therapy at Sahlgrenska University Hospital between 2006 and Out of these, 24 patients were included in this study. The cause of the exclusion of 27 patients was predominantly lack of evaluable tumors or lack of diagnostic CT or SPECT/CT images. All patients had progressive disease at start and tumors overexpressing SSTRs. Patients received a 177 Lu-DOTATATE infusion of 7.5 GBq ( ) GBq during 30 minutes 2-5 times with approximately 8 week intervals. Tumors for evaluation were chosen according to the response evaluation criteria in solid tumors (RECIST). This study was approved by the Regional Ethics Review Board in Gothenburg. Phantom measurements Phantom measurements were used to select image reconstruction parameters for patient imaging. For accurate activity quantification in patient images, system sensitivity values and correction factors for partial volume effect are required. Sensitivity values for SPECT and recovery coefficients for partial volume effect correction were obtained by phantom measurements. Recovery coefficients are the measured activity concentration quotients between an object and the surrounding material divided by the true activity concentration quotients. A recovery coefficient of 1 represents an ideal image rendering of the activity concentration ratio. For phantom imaging, a Discovery 670 Pro (General Electric Medical Systems, Milwaukee, WI, USA) gamma camera and computed tomography (CT) system was used with an energy window of 20% set over the 208 kev photon peak. 120 projections were collected, each during 720 s. The phantom used was the Jaszczak SPECT imaging phantom, which is cylindrical with six hollow spheres with inner diameters of 10, 12, 16, 20, 25 and 31 mm (figure 2). For algorithm comparison and determination of recovery coefficients, the phantom and spheres were filled with a solution of 177 Lu and distilled water with an activity concentration ratio of 25 between the hot spheres and background. The activity concentrations in the hot sphere and background solutions were measured using a Wallac 1480 Wizard 3 (Perkin Elmer, Waltham, MA, USA) automatic gamma counter. Spherical volumes of interest (VOIs) were drawn in each hot sphere, with a VOI diameter equal to the inner diameter of the corresponding sphere. For each hot sphere, 20 background VOIs of identical shape and size were distributed in the background volume of the phantom. For sensitivity estimation, a second phantom measurement was made, where the spheres were removed and a large cylindrical VOI was placed in the homogenous solution in the phantom. The activity injected into the phantom was measured using a Capintec CRC-15R dose calibrator (Scanflex Medicals AB, Täby, Sweden). Sensitivity factors for SPECT were estimated for the gamma cameras and reconstruction methods used in the patient images. This was done by determining SPECT sensitivity factors for the gamma camera used for phantom imaging (S SPECT,pha ) and presuming a linear relationship between SPECT and planar imaging. Planar sensitivity factors are routinely measured by medical physicists in the clinic and were thus known for all gamma cameras. SPECT sensitivity factors for the patient imaging cameras (S SPECT,pat ) were calculated as S SPECT,pat = S SPECT,pha S plan,pha S plan,pat (3) 6

10 where S plan,pat and S plan,pha are the planar sensitivity factors for patient imaging cameras and the phantom imaging camera respectively. Figure 2: The Jaszczak SPECT imaging phantom. A comparison was made between three image OSEM reconstruction algorithms: iterative reconstruction with attenuation correction (IRAC), IRAC with a resolution recovery correction (IRACRR) and Sahlgrenska Academy reconstruction code (SARec). IRACRR is developed by General Electric (GE) and is considered a state-of-the-art OSEM algorithm which includes scatter correction (9). SARec is an in-house developed reconstruction algorithm using Monte Carlo simulation in the forward projection step of the iterative reconstruction. Signal to background ratio (SBR) was calculated for all sphere sizes and 1 to 9 iterations by: SBR = S B T where S is the mean voxel value in the hot spheres, B is the mean of the 20 background VOI mean voxel values and T is the true activity concentration ratio as measured in the Wallac gamma counter. Recovery coefficients (RCs) are the ratios of measured to true activity concentration ratio and were used for correction of the partial volume effect. RC is identical with SBR and were thus retrieved in the same manner. Recovery coefficients were determined for all sphere sizes and for reconstruction algorithms SARec (with Butterworth filter of order 2 and a cutoff frequency of 0.07 cycles/mm) and IRAC. Due to the difficulties in visually assessing tumor size in unfiltered SARec patient images, Butterworth filtration was used for SARec reconstruction of patient images and this filtration was therefore also used in the determination of recovery coefficients. A two-parameter sigmoid curve (equation 5) was fitted to the recovery coefficient values for SARec and IRAC. This was done in software MATLAB (Mathworks, Natick, MA, USA). (4) 7

11 1 RC(d) = 1 + ( a (5) d )b In equation 5, d is the sphere diameter, a and b are fit coefficients. Patient imaging and SPECT reconstruction All gamma camera patient imaging was performed during therapy of each patient, i.e. between the years 2006 and Diagnostic CT images were acquired before and after treatment and used for evaluation of tumor response. Planar gamma camera images of patients after 177 Lu-DOTATATE infusion were acquired at 2, 24, 48 and 168 h post infusion. At 24 h post infusion imaging with SPECT and low resolution CT for fusion was also performed. The gamma cameras Millennium VG (GE Medical systems, Milwaukee, WI, USA), Infinia Hawkeye 4 (GE Healthcare, Milwaukee, WI, USA) and Irix (Philips Healthcare, Amsterdam, The Netherlands) were used for patient imaging. Based on results from the phantom imaging as described in a subsequent section, SPECT images were reconstructed with SARec and a Butterworth filter of order 2 and a cutoff frequency of 0.07 cycles/mm was applied. The numbers of iterations and subsets were 6 and 10, respectively, for SARec and 2 and 10, respectively, for clinical IRAC. IRACRR reconstruction was not available for the gamma cameras used for patient imaging. Tumor segmentation Tumors were identified and measured by an experienced radiologist by visual inspection of diagnostic CT images. The tumors were chosen and measured according to the response evaluation criteria in solid tumors (RECIST)(14). The largest diameter of the tumor in the transversal plane was measured. VOIs and ROIs were drawn manually in SPECT/CT and planar images with guidance from the measurements in the diagnostic CT images. Planar images were produced by calculating the geometrical mean of the anterior and posterior images. ROIs were drawn in these geometrical mean images by visually matching uptake in the coronal view of the SPECT image with uptake in the geometric mean image from day 1 p.i. For other time points, the ROI from day 1 p.i. was moved to match the uptake. Circular background ROIs were used to correct for activity in overlapping tissues (figure 3E). Tumor segmentation is visualized in figure 3. All segmentation was performed by one observer. The precision of the tumor segmentation in SPECT within the observer was evaluated by determining an intra user coefficient of variation in the absorbed dose. This was done by segmenting 6 tumors 5 times for the first treatment cycle and calculating the absorbed tumor dose for each segmentation. For 17 tumors in 7 patients, the absorbed tumor dose obtained using manual segmentation in SPECT was compared with the absorbed dose obtained using a 42% threshold (42% threshold was used in a similar study by Ilan et al. (7)) of the maximum voxel value in the tumor for segmentation. In this comparison, only the first treatment cycle was used. 8

12 Figure 3: Manually drawn VOI in SPECT/CT in A) transversal view B) sagittal view and C) coronal view. D) Diagnostic CT with measurement of the largest diameter of the tumor. E) Tumor ROI and background ROI in a planar geometrical mean image. Dosimetry Dosimetric calculations were made for 52 tumors in 24 patients. In 5 patients absorbed doses to tumors as calculated using SARec and IRAC images were compared. Tumor dosimetry was performed in SPECT and planar gamma camera images. Planar images were used for biokinetic assessment. Relative activity quantification in planar images was performed using the conjugate view method (equation 1). One point, [0,0], was added by assuming zero activity at time point zero p.i. A biexponential curve (equation 6) was fitted using the software MATLAB to obtain a time-activity curve. A(t) = a e (b t) + c e (d t) (6) Absolute quantification of activity was made in SPECT images and used for correcting the timeactivity curve. This was done by calculating the activity ratio between activity obtained in SPECT at 24 hours p.i. and activity obtained by the conjugate view method at 24 hours p.i. and multiplying the curve with this ratio (figure 4): 9

13 Figure 4: Illustration of a time-activity curve and correction by use of SPECT. Sensitivity factors for the gamma cameras were used in the translation from count distribution to activity distribution. In addition, for SPECT images the count distribution was corrected for partial volume effect by use of equation 5, which requires an object diameter. For this purpose, the volume of the tumor was defined as the drawn VOI volume and approximated with a sphere of equal volume. The absorbed dose was calculated using equation 2. The emitted energy for electrons alone was used and the absorbed fraction was approximated to 1, due to the short maximum range of 2 mm for electrons originating from 177 Lu. Tumor response analysis Out of the 52 tumors in 24 patients used for dosimetric evaluation, 50 tumors in 23 patients were used in the dose response relationship evaluation. The cause of this was missing size information in 2 of the tumors. Tumor response was evaluated by calculation of the best response (BR): BR = d B d S d B 100 (7) In this equation, d B is the largest tumor diameter at baseline and d S is the smallest measurement of the largest tumor diameter in CT scans after treatment. The accumulated mean absorbed dose to tumors until the date of best response was calculated and a dose response relationship was evaluated using linear regression analysis. For tumor evaluation using the RECIST guidelines, the sum of the largest tumor diameters within each patient are used. Best response was therefore also calculated in this manner: BR Σ = Σd B Σd S Σd B 100 (8) For this dose response analysis, the mean tumor absorbed dose of all tumors used within each patient was calculated. 10

14 Results The phantom measurements resulted in SBRs for reconstructions algorithms IRAC, IRACRR and SARec, and recovery coefficients for SARec and IRAC. Relative SBRs are shown in figure 5. SBR increased for each iteration, but reached a plateau at iteration 4 for IRAC and at various iterations for the various sphere diameters for IRACRR and SARec. Figure 5: SBR vs. iterations for IRAC (top left), IRACRR (top right) and SARec (bottom left). SBR vs. sphere diameter for SARec, filtered SARec, IRAC and IRACRR (bottom right). An SBR of 1 represents an ideal image rendering of the activity concentration ratio between hot spheres and background. SPECT images reconstructed with IRAC and SARec from the same sinogram are shown in figure 6. Figure 6: A SPECT/CT transversal slice of a patient with NETs. Left: IRAC reconstruction. Right: SARec reconstruction. 11

15 The sensitivity factors acquired by phantom measurements were 1.81 cts/bq and cts/bq for Millennium VG with SARec and IRAC reconstruction, respectively, and 1.58 cts/bq and cts/bq for Infinia with SARec and IRAC reconstruction, respectively. These values are based on a 30 minutes long SPECT imaging session, which was used for patient imaging. Recovery coefficients for filtered SARec and IRAC and corresponding curve fits are shown in figures 7 and 8 respectively. R 2 -values for the curve fits were and , respectively. Figure 7: Recovery coefficients vs. sphere diameter in filtered SARec and a two-parameter sigmoid fit. Figure 8: Recovery coefficients vs. sphere diameter in IRAC and a two-parameter sigmoid fit. Absorbed doses to tumors for 2-5 cycles of treatment are shown in figure 9. The mean absorbed dose and specific mean absorbed dose after 2-5 cycles of treatment were 65 Gy (range ) and 2.5 mgy/mbq (range ), respectively. The median specific absorbed dose was 2.0 mgy/mbq. The 12

16 mean intra user coefficient of variation in absorbed dose due to manual segmentation in SPECT was 11% (range 5%-22%). Figure 9: Absorbed doses to tumors after 2-5 cycles of treatments. A) Absorbed doses B) Specific absorbed doses Absorbed doses to tumors as calculated using SARec and IRAC images are shown in figure 10. The absorbed dose acquired with SARec was a mean of 1.36 (range ) times higher than that acquired with IRAC. 13

17 Figure 10: Absorbed dose to tumors per treatment cycle as calculated using IRAC vs. SARec. Best response versus tumor absorbed dose is shown in figure 11. Linear regression analysis of the best response versus absorbed dose resulted in a R 2 -value equal to and a p-value of Hence no significant absorbed dose response correlation could be established. The result from grouping the tumors in accordance with RECIST criteria within individual patients is shown in figure 12 as BR Σ versus mean absorbed tumor dose for each patient. Linear regression resulted in a R 2 -value equal to 0.12 and a p-value of

18 Figure 11: Best response vs. absorbed dose to tumors at the time of best response. Figure 12: Best response of all tumors within each patient versus the mean absorbed dose of the tumors. The number of tumors used for each data point is listed to the right. 15

19 The ratio between the absorbed dose obtained using a 42% threshold for segmentation and the absorbed dose obtained using manual segmentation for the first cycle of treatment is shown in figure 13. The mean and median ratios were 4.7 and 2.0, respectively (range ). Figure 13: Ratio between dose obtained using a 42% threshold and dose obtained using manual segmentation. 16

20 Discussion The SARec reconstruction code showed higher signal to background ratio than IRAC and IRACRR for all sphere sizes. For reconstruction of patient SPECT images, only IRAC and SARec were available. Due to higher SBR for SARec than IRAC, SARec was chosen for image reconstruction in patients. IRACRR was included for comparative purposes only, due to its state-of-the-art status. In the choice of number of iterations used in patient imaging, there was a trade-off between SBR and reconstruction time. 6 iterations gave an SBR that was considered sufficiently high. The mean and median specific absorbed doses were 2.5 mgy/mbq and 2.0 mgy/mbq, respectively. There was a large variation in absorbed dose (range mgy/mbq) both between patients and between tumors within the same patient. These variations might be related to differences in somatostatin receptor density, binding affinity and varying degrees of vascularization and necrosis (15, 16). Three other studies have determined absorbed dose to tumors and resulted in the values presented in Table 1. Gupta et al. and Wehrmann et al. used only planar imaging for dosimetry, whereas Garkavij et al. used both planar and SPECT imaging. The results in these studies show higher absorbed doses than the present study. This may be due to the use of a threshold when segmenting the tumors in SPECT. In Garkavij et al. tumor VOIs were drawn manually and a 50% threshold of the maximum value was then applied to avoid inclusion of voxels affected by partial volume effects. Thresholding is discussed in a subsequent section. Table 1: Specific absorbed dose to neuroendocrine tumors in three studies. All doses are in units of mgy/mbq. Reference Mean Median Range Gupta et al. (15) Wehrmann et al. (17) 9.7 Garkavij et al. (18) The SARec absorbed dose was a mean of 1.36 (range ) times higher than that using IRAC. The ratio for patient 48 tumor 3 deviated from the other tumors. The cause of this deviation is not known and needs to be further investigated. In the present study, no correlation between absorbed dose and tumor response could be established. The tumor response may be affected in large extent by other factors than radiation dose. NETs form a heterogeneous group of tumors and there may be a large variation in proliferation rate, radiosensitivity and perhaps other biological factors. Stratification by diagnosis or proliferation index could be advantageous in reducing the impact of biological variation. However, the small number of patients included in the present study was not sufficient for stratifying the data. As mentioned previously, one other study by Ilan et al. (7) have investigated the dose response relationship for 177 Lu-DOTATATE. Only pancreatic NETs were included in the present study and a 42% threshold was used for segmentation. A correlation between absorbed dose and best response was established when using a 2- parameter sigmoid fit on the form y = 100/(1 + (a/d)^b ). Only tumors larger than 2.2 cm in diameter were used. 17

21 The dosimetric calculations in the present work are affected by uncertainties in estimation of sensitivity factors for the gamma camera, acquisition of recovery coefficients, tumor segmentation and time-activity curve fitting. Uncertainties affecting the best response analysis are present in tumor identification and measurement as well as the imaging time points, which may not coincide with the true best response time point. In a study (19) by Gustafsson et al. uncertainty propagation in renal dosimetry in 177 Lu-PRRT has been investigated. The result was a standard deviation in the absorbed dose of approximately 6%. That result may to some extent be applicable to the present study, but there are a few important differences suggesting a higher value than that achieved by Gustafsson et al. These differences include the segmentation, which is assumed to vary more for tumors than kidneys due to the large variability in tumor size and shape, and the imaging scheme, where SPECT imaging is done day 0, 1, 4 and 7. Also, the time-activity curve fitting differs, which is linear day 0-1 and monoexponential day 1-7 for the Gustafsson study. Tumor segmentation is an important part of activity quantification and thus dosimetry. For accurate determination of the activity concentration within a tumor, the VOI must coincide with the tumor volume. Contouring the tumors in SPECT/CT presents difficulties due to the poor image quality of the CT and tumors are often not identifiable. The CT used for fusion with SPECT is often performed without contrast agents and using a smaller amount of radiation than used in diagnostic CTs. A method for tumor segmentation in SPECT is thus desirable. Frequently used methods are manual segmentation of the tumor volume and thresholding. The drawback of manual segmentation is the subjectiveness, which limits the reproducibility. In this work the intra user coefficient of variation due to manual segmentation was 11% and is thought to be the largest source of uncertainty affecting dosimetry. The poor precision is presumably due to a large impact in mean dose when including or excluding voxels in small tumors as well as the difficulties in separating voxels containing tumor tissue from those containing normal tissue when contouring a tumor with inhomogeneous uptake. When using thresholding, a VOI is defined which contains the tumor. A threshold is set equal to a certain percentage of the maximum voxel value within this VOI. By including only voxels with values above the threshold, a new, smaller VOI is formed. The optimal threshold value for this purpose (i.e. the value that produces the VOI that best matches the tumor) has been investigated by Erdi et al (20). The drawback of this method is that the optimal threshold value depends on tumor size, shape and contrast level. To successfully use this method, a priori knowledge of these properties is thus required. Size and shape may be established by use of CT or magnetic resonance imaging (MRI). In accordance with the RECIST criteria obeyed in the present study, only the largest diameter of the tumor in the transversal plane was measured. Size and shape were hence not known. An alternate method would be to abandon the RECIST criteria and measure the tumors also in the sagittal and frontal plane, which would provide a more accurate characterization of the tumor size and response as well as a better premise for automated segmentation. Although, this would limit the comparability of the results with other studies, since RECIST is widely used. In Erdi et al. the optimal threshold has been obtained by phantom measurements using a homogenous concentration of the radionuclide in spherical volumes. Tumor uptake may however be inhomogeneous and the use of thresholding could therefore result in a VOI containing only the high uptake volume of the tumor, which would generate an erroneous value of the mean tumor dose. On the other hand, for inhomogeneous tumors, the mean absorbed dose may not be the most relevant quantity. The results from the comparison between automated segmentation with a 42% threshold and manual segmentation obtained in the present work, show a median absorbed dose ratio of 2.0 (range ). No value was below 1 and this result suggests that higher absorbed doses are obtained when using a 42% threshold. 18

22 As SPECT is used for quantitative activity assessment and planar imaging merely determines the kinetics, the source of uncertainty in planar imaging is the placement of the tumor ROI, which should be the same at every time point, as well as the content in the background VOI, which should reflect the kinetics of the tissue overlapping the tumor. The translation from time-integrated activity to absorbed dose is defined by the energy of the particles emitted by the radionuclide and the transport and energy deposition of these particles. In this work, energy deposition of photons was omitted. The electron energy was assumed to be locally deposited, i.e. the electron energy released in a decay is assumed to be absorbed in the same voxel as the decay occurred. Other ways of determining the absorbed dose distribution from the activity distribution have been purposed. A study by Ljungberg et al. (21) evaluates two methods, a local deposition method and Monte Carlo simulation of energy transport. A 3D point dose kernel is proposed as a third option. In the case of 177 Lu, the advantage of using Monte Carlo simulation for this purpose was found to be marginal, since the SPECT spatial resolution is of the same order as the maximum electron range. In other words, the uncertainty in the activity distribution prohibits accurate determination of the dose distribution. Therefore, when estimating dose from 177 Lu in SPECT images, assuming local deposition is considered sufficient. Another assumption made in this work is that the density of the voxel volume where the energy is deposited is equal to that of water. For a more exact determination of the absorbed dose, the voxel density may be determined using a CT-derived mass density image (19). When the dose distribution within a VOI is acquired by any of the methods described above, a dose volume histogram (DVH) may be determined. A DVH reveals any heterogeneity in the dose distribution present, which enables evaluation of how well the mean absorbed dose represents the distribution. Calculations of DVHs have not been attempted in this work, but would be an interesting future prospect. However, it should be noted that the aforementioned poor spatial resolution still prevents high accuracy of such a calculation. 19

23 Conclusion The SPECT reconstruction algorithm SARec showed higher SBR than IRAC and IRACRR in all iterations and sphere diameters evaluated. The absorbed tumor doses obtained in this study were lower than those obtained in other studies. The cause of this is thought to be the segmentation method. Segmentation by thresholding gave higher absorbed doses than manual segmentation. The absorbed tumor dose obtained with SARec reconstructed SPECT images was in average 36% higher than that obtained with IRAC reconstructed images. No correlation between absorbed dose to tumors and tumor response could be established in this study. 20

24 Acknowledgements I would like to thank my supervisors Peter Bernhardt, Johanna Svensson and Emma Wikberg for your guidance. You have enhanced my interest in research and nuclear medicine by your dedication to the area and interest in my project. I would also like to thank Tobias Rydén for helping me with SARec and PhONSAi. Thanks to Jens Hemmingsson, who have answered a lot of my questions and been of great help with the phantom measurements. Thanks to Linn Hagmarker, who have helped me with MATLAB and biokinetics. I also wish to thank Rebecca Hermann for providing treatment images and Rauni Rossi Norrlund for providing CT images. Finally, I would like to thank the PhONSA group as a whole for your positive and including attitude towards me. 21

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