Organ Dose estimates for Radio-Isotope Therapy treatment planning purposes

Transcription

1 GE Healthcare Organ Dose estimates for Radio-Isotope Therapy treatment planning purposes Dosimetry toolkit package White Paper

2 Content Introduction... 3 Dosimetry Toolkit... 4 Purpose... 4 Input... 4 Content... 4 Output... 4 SPECT Reconstruction... 4 Quantitative Reconstruction... 5 Registration... 5 Organs definition... 6 Segmentation operations... 6 ROI/VOI tools:... 6 System sensitivity calibration standard dose syringe... 7 Time Activity Curves and fitting... 8 Saving results... 8 Algorithms and clinical examples used... 9 Registration Algorithm... 9 Registration stages in Multi WB SPECT/CT scenario... 9 Registration stages in Hybrid scenario... 9 Segmentation algorithms...10 Threshold criterion for NM image...10 Threshold criterion for CT image...10 Segmentation Propagation...11 Inner segmentation...11 Coronal Overlap (Hybrid scenario)...11 System suggested organs Segmentation:...12 Lungs segmentation...12 Liver segmentation...12 All bone cavities segmentation...13 Additional tools for Non-contiguous volumes...13 System sensitivity calibration standard dose syringe...14 References...15

3 Introduction Radio Isotope therapy is defined as a radiation therapy that uses administered radiopharmaceutical to transfer radiation energy to a pathological target tissue in order to achieve a destructive tissue effect. The destructive tissue effect depends on the amount of transferred energy to the tissue, i.e the absorbed radiation dose, which is measured in units of gray (Gy). It is essential to be able to calculate the absorbed radiation dose to a targeted tissue at any stage of treatment in order to enable safe and effective therapy planning and monitoring. Note: The term dose refers to the radiation dose in the SI unit grays. It should not be confused with the frequently used dose to describe the administered activity in GBq or mci. Targeted Radio Isotope Therapy is used today for single focal lesion of tumors: breast, lung, prostate, etc. The aim in the near future is to have personalized therapy based on patients genetic or protein profiles. Targeted Radio Isotope therapies will be prescribed based on molecular signatures. Individual patient dosimetry may support the following goals: To determine minimum effective and maximum tolerated absorbed doses per individual patient To monitor tumor response and organ toxicity during treatment To predict tumor response and normal organ toxicity based on pre-therapy dosimetry To compare the dose response results of different therapy strategies Radiation in radionuclide therapy is directed to the target tissue by the radiopharmaceutical. This dynamic metabolic process creates a complex spatial and temporal radiation distribution with biochemical and physical variations over time. Pharmacokinetics as well as biological or physical bystander effects (on non-target organs) influence the amount of the radiation dose to the target. In contrast to externalbeam radiation therapy, which uses a controlled and time limited radiation, radionuclide therapy delivers a low and continuously decreasing dose rate. Therefore the calculation of the accumulated radiation dose for radionuclide therapy is complex. Nuclear Medicine (NM) imaging is used to measure the activity distribution over time. The calculation of the absorbed dose in each organ is based on these scans. Due to the limited spatial resolution of the NM scans, the dosimetry calculations are always an approximation. Radiation doses to target organs are usually calculated using the MIRD formalism by commercially available software such as MIRDOSE3 or OLINDA/EXM. These models are based on assumptions about anatomy (standard man and woman) and radiopharmaceutical distribution (uniformity of uptake in source and target) that are not necessarily valid in individual patients. Nevertheless they provide a practical and standardized model for clinical practice [1]. The aim of the dosimetry toolkit is to simplify the procedure for quantifying radiopharmaceutical dose, reduce the processing time and improve accuracy of results, compared to the manual tools currently used. The Dosimetry toolkit creates an essential input to other radiotherapy planning SW.

4 Dosimetry Toolkit Purpose Dosimetry Toolkit uses multi WB SPECT/CT and/or WB planar datasets for quantifying changes in radiopharmaceutical uptake over time and calculating residence time per organ for Radio-Isotope Therapy treatment planning purposes. The purpose of the Dosimetry Toolkit is to define and report the patient organs volume, activity and residence time of radiopharmaceutical concentration within patient organs. These results are based on consecutive patient scans and can be used as input for Radio-Isotope Therapy planning applications (such as OLINDA or similar). The organs of interest in this respect are large organs (more than 50cc), such as liver, lungs, kidneys, spleen, and heart. Note: This toolkit is intended to replace tedious manual tools for organs definition and activity calculations, in order to enable improved processing workflow and productivity. The accuracy of the dosimetry toolkit results depends heavily on user provided quantitative input: injected activity, organ definition, system sensitivity and reconstruction parameters. The user is encouraged to verify the dosimetry toolkit results vs. the existing tools used by the facility. This verification should be performed prior to the clinical use of the dosimetry Toolkit. Input The toolkit supports the following types of input: Series of whole body SPECT/CT scans Series of Whole Body Planar scans with single SPECT/CT (Hybrid scenario) Series of planar WB (for which Volume results can not be provided) A single time sample of SPECT/CT can be used to determine the organs volume and activity. Content The Dosimetry toolkit includes tools that can help the user to perform in a quick and convenient way the following tasks: Reconstruction of all raw SPECT/CT data, including accurate SPECT/CT registration adjustment and quality control, along with patient motion detection and correction, attenuation, scatter and collimator blurring corrections. Image reconstruction is performed using iterative algorithm (OSEM). Registrations of all the scans to one common reference with semi-automatic/manual tools that enable the user to perform local organ registration Segmentation of the different organs (such as heart, liver, lungs, kidneys, spleen, bone marrow), with semi-automatic/ manual tools to differentiate between overlapping organs Create time activity curves for each of the organs Curve fitting Calculate imaging agent Residence time in each organ Output The toolkit can export: Organs volumes Organs activities Organs imaging agent time activity curves Organs imaging agent residence time Individual or combined organ masked volumes as datasets. These datasets can be exported in Dicom format All numerical values (volumes, activities, residence time) can be exported to MS Excel file Time and Percentage of injected dose can be saved in format suitable for input to OLINDA, avoiding the need to type all the results in OLINDA interface SPECT Reconstruction The quality and quantitative accuracy of SPECT reconstructed images are affected by noise in the projection data, resolution degradation caused by the collimator-detector response (CDR) function of the imaging system, photon attenuation, and scatter in the patient s body. Recently, there has been significant progress in the development of model-based corrective SPECT image reconstruction methods that include correction for these image quality-degrading factors. The corrective image reconstruction package (also referred to as Resolution Recovery) developed by Johns Hopkins University (JHU) provides improved SPECT images by including physical models of the imaging process into iterative image reconstruction algorithms. In particular, it provides accurate 3D models of the collimator-detector response functions for a variety of clinical applications. This results in a significant

5 enhancement in image quality and potential improvement in diagnostic properties. Clinical application of these techniques are available and can be applied to reduce SPECT acquisition time with equal or better image characteristics when compared to standard reconstructions [5]. Collimatordetector blur is the main factor affecting the resolution and noise properties of nuclear medicine images. In reconstructed SPECT images, these characteristics are strongly affected by the applied reconstruction algorithm and its parameters. Collimator Detector Response (CDR) consists of four main components: intrinsic response (system without collimator), the geometric, septal penetration and septal scatter components of the collimator parameters. The geometric component of CDR results from photons passing through the collimator holes without interacting with the collimator itself. The septal penetration component describes the portion of the CDR where photons reaching the detector after passing through one or more septa do not scatter. The scatter component refers to photons that scatter in the septa and are detected. For the majority of clinically used collimator designs, the last two components are generally significant for only mediumand high-energy imaging. For each combination of acquisition system, radiopharmaceutical and particular acquisition protocol, the collimator detector response function provides the probability that a photon emitted from any point of the imaged object will contribute to a pixel of the resulting image. By including an accurate model of the collimator-detector response function in an iterative SPECT reconstruction algorithm, the blurring effect may be included in the iterative reconstruction process, resulting in improved spatial resolution. Current estimate Update Update coefficients Project Physics of the imaging prosess Back project Figure: Scheme of iterative reconstruction process. Estimated projection Compare Error Projection Measured projection (Date) A CDR compensation technique was developed at the University of North Carolina-Chapel Hill (UNC) and JHU [6,7,8]. The effect of CDR compensation on reconstructed images has been studied from qualitative [9], quantitative [10,11,12], and clinical task-based [13, 14, 15] perspectives. It has been found that CDR corrected images demonstrate not only improved resolution and signal-to-noise ratio, but also lower noise variability in reconstructed images. Significant improvement of the resolution properties requires more iterations of the reconstruction method with compensation than are usually recommended for the same data when no compensation is applied. On the other hand, image noise level tends to amplify with the number of iterations. Taking into account the differences in the iterative reconstruction process, with and without CDR, application-specific optimization of acquisition and processing parameters is essential to successful utilization of the corrective reconstruction method [14, 15, 16,17]. Quantitative Reconstruction Usually the reconstruction used for visualization purposes is done with limited number of iterations and with postfiltering. For more accurate quantitative results the number of iterations should be increased and postfiltering should be avoided. The resulting image is quantitatively more accurate, but also noisier and less suitable for visualization purposes. The reconstruction parameters (number of iterations, number of subsets) for accurate quantitation should be optimized per each radiopharmaceutical, collimator and clinical protocol (scan time, number of views etc ). For example, for quantitative reconstruction of In111 scans with MEGP collimator, it is recommended to use 30 iterations with 5 subsets [18,19]. In order to optimize reconstruction parameters per specific Isotope/ Collimator/Camera combination, the user should use phantom studies of Jaszczak and/or anthropomorphic phantom(s). In order to improve quantitative accuracy, reconstruction should include corrections for patient motion, attenuation, scatter and collimator blurring. Registration The application includes two stages of registration: WB registration and organ registration. First all the WB scans (SPECT/CT or WB planar) are registered, to achieve optimal registration of the complete patient body (or all the scanned parts of it) between all scans. Although the whole body, registered as rigid body, may be properly registered, internal organs may be slightly mis-registered due to local shifts/ rotation. Once the scans are registered, the user can correct location and shape of each Organ VOI (SPECT/CT) or ROI (Planar) on each of the images, if needed.

6 Organs definition Note: The following is applicable to multiple SPECT/CT or Hybrid Scenarios Only. Defining an Organ on Multiple Planar WBs scenario is freehand ROI drawing based on the operator experience. Organs are defined using the CT and NM slices interchangeably, using threshold-based algorithms. Some organs may have an initial automated proposal or semiautomatic proposal; some organs are defined manually. The defined organ is superimposed over CT and NM slices. All organs are saved in a mask volume, each with a new color. The user must confirm that organs are accurately defined. In some cases the system may suggest initial segmentation for specific organs, nevertheless the responsibility always lies on the user to confirm that the organ volume is delineated correctly. Results are more accurate for large volumes (more than 50cc). For smaller volumes (of organs or lesions), accuracy of activity quantitation is degraded due to partial volume effects that are inherent because of the limited gamma camera spatial resolution. The discrepancy between NM and CT resolution causes organ s activity to spill over the organs contours as depicted in the CT image. In order to include all the organ s activity within the VOI, the organ VOI definition may be expanded slightly beyond the organ s limits as depicted in the CT image. For organs that are defined based on the CT image, the user may consider slight dilatation of the organ VOI beyond the CT limits in order to get more accurate organ s activity estimation (note that in this case the volume measurement is less accurate). To project the SPECT organs accurately over the WB planar images, their frontal outline is created. Some organs may overlap, mostly with the lungs. Overlapped areas may be removed when projected over the planar images. Computation of organ activities in the planar images is extrapolated by the ratio of used/removed volumes. Segmentation operations are based on the underlying data and use threshold operators to segment the data. The selected viewport, CT or NM, is the basis for the threshold segmentation. For both NM and CT images, the segmentation process starts at a user selected seed point and propagates in 2D/3D (ROI or VOI). The segmentation propagates when some or all the adjacent voxels withstand the threshold criterion. The propagation is stopped when none of the adjacent voxels comply with the threshold criterion. ROI/VOI tools: ROI tools are working on a single slice (2D). VOI tools act similar to the ROI tools, extended to 3D. The VOI tools cover large volumes without the need to go over slice by slice, however the visualization and ability to control their expansion are more complex as the screen display is 2D. Segmentation ROI/VOI Tools available: Draw automatic ROI/VOI starting at a user selected seed point, Draw semi automatic ROI/VOI starting at a seed point ( growing ROI/VOI ), Draw manual ROI/VOI starting at a seed point (acts as a pencil), Erase automatic ROI/VOI starting at a seed point, Erase semi automatic ROI/VOI starting at a seed point, Erase manual ROI/VOI starting at a seed point (acts as an eraser). Dilate Region/Volume voxels Erode Region/Volume voxels Open Bridges Close Gaps Auto NM segmentation Segmentation operations The application segments the WB SPECT/CT image volume into different organs this means that each organ is enclosed by a VOI. This segmentation is done based on the CT and NM images and serves as first approximation of the organs volumes. The user has various tools to correct/improve this automated segmentation prior to confirming the organ segmentation.

7 System sensitivity calibration standard dose syringe System sensitivity should be measured for each combination of camera, collimator and radioisotope used. Accurate measurement of the patient injected activity dose in a calibrated dosimeter is required. Time of dosimeter measurement must be recorded to allow accurate decay correction. In order to accurately measure system sensitivity and compensate for variations in system sensitivity between scans, a standard dose syringe can be used. A syringe with small amount of dose is placed near to and outside the patient during the sequence of acquisitions. The initial syringe activity should be measured by an external dosimeter (time of measurement should be recorded) and should also be imaged in a separate scan on the camera. The same syringe (with its decaying activity) should be used in all patient scans. The user fills a dialog (see below) with patient and syringe information. The standard dose syringe images are used to calibrate the patient counts acquired, based on the known half-life of the isotope used. If there is no syringe image the application activates a dialog to specify the system sensitivity by the user. Commercially available OLINDA software package can be used for calculation of NM radiopharmaceuticals internal absorbed doses in organs and tumors.

8 Time Activity Curves and fitting Time activity curve is created for each of the organs defined. These curves are fitted to an exponential function of the form: y = A e BX Usually exponential fit is done by taking the logarithm of the function and looking for the parameters that give the least square fit. This fit gives greater weights to small y values so, in order to weight the points equally, it is often better to minimize the function n y i (ln y i -a-bx i ) 2 i =1 See reference [4] The equal weight exponential fit is used in Dosimetry Toolkit. Saving results The picture below shows the final report The results are saved on the Xeleris workstation. The user is able to save segmented data and organ names, as part of a results series. Data recorded at the Xeleris database is DICOM compatible. It can be exported, backed up and retrieved, and is transferable between Xeleris 3 Systems. A template for MS Excel spread sheet is provided for the numerical results. Time and Percentage of injected dose can be saved in format suitable for input to OLINDA, avoiding the need to type all the results in OLINDA interface. Commercially available OLINDA software package can be used for calculation of NM radiopharmaceuticals internal absorbed doses in organs and tumors.

9 Algorithms and clinical examples used Registration Algorithm The registration is performed automatically with a rigid registration algorithm that utilizes the NLM Insight Segmentation & Registration Toolkit (ITK). Tools for manual adjustment of the scans are provided, for small modifications of the automatic registration results, in order to get the optimal registration. Automatic Image registration is an iterative process that is performed on coarse resampled image with a gradual increase in resolution, in a stepwise manner to determine the final translation and rotation parameters. This stepwise algorithm results in improved registration (over one step registration ) both in terms of reducing processing time, in its convergence to accurate true solution and the robustness of the solution. The automatic registration is based on the following components: The transformation of the image voxel positions to new 3D coordinates (x, y, z position). For rigid registration this will include translation and rotation. A cost function-a metric depicting the adequacy of the registration. The optimizer-determines how to update the transformation parameters after each iteration, based on the change in values of the cost function. Regular Step Gradient Descent optimizer and least mean Squares cost function are used in all the steps. In the first coarse steps the transformation include translation operation only, while in the later finer steps the transformation include both translation and rotation operators. Registration stages in Multi WB SPECT/CT scenario In the Multi WB SPECT/CT scenario that includes a Series of Whole Body SPECT/CT scans, there are two stages of registration: WB Registration of all SPECT/CT sets to a common reference this stage is performed before organs are defined Organs local registration on each of the WB SPECT/CT images WB registration After all the NM & CT scans were reconstructed (using standard Volumetrix MI UI) to create set of 3D whole-body images of the patient in various time points, all the scans are registered to one common reference. The reference image is the CT image of the latest scan, while the moving images (the images that are moved in order to be registered to the reference) are the CT images of all the other scans. Organ registration After all organs have been defined on the earliest SPECT/CT image, the organs masks are copied onto all the other SPECT/CT images (sets) and the user is required to check organs positions on each set. These sets are already rigidly registered to a common reference. The program loads the user selected set of registered NM & CT images. Organs defined on the first set are copied onto this set. The user has to review and adjust each organ in the loaded set, if needed. The same operation is repeated for all SPECT/CT sets. Registration stages in Hybrid scenario In the hybrid scenario that includes a Series of Whole Body Planar scans with single SPECT/CT, there are three stages of registration: Registration of the SPECT scan with the nearest in time planar WB scan. This stage is performed before organs are defined Registration of all WBs to common planar reference (used in the previous stage) this stage is performed after organs are defined Organs local registration on each of the WB planar images For all these registration stages, the application suggests initial registration and the user can confirm or modify the registration.

10 Registration of SPECT image with its conjugate WB planar image When the automatic registration is completed, the registered images are displayed in the order they were acquired. Manual adjustment of registration is supported via a screen based user interface to match every pair of WB planar images, for small modifications of the automatic registration, in order to get optimal match. Organ specific registration between consecutive scans After all the WB planar images are registered to a common reference, the user is able to manually correct residual local (organ specific) mis-registration. Segmentation algorithms Threshold criterion for NM image The reference image is the summed coronal slices of the SPECT image while the moving image is the nearest in time WB planar image. This WB scan serves as a common reference for all the other WB scans. Manual adjustment of registration is supported via a screen based user interface, for small modifications of the automatic registration, in order to get optimal match of SPECT 3D scan (single or multi fields of view) with the Wholebody 2D image. Registration of all WBs to common planar reference The threshold value is defined by the counts at the seed point multiplied by the NM Threshold (in the range of 0-1). All the voxels with counts above the threshold pass the threshold criterion. Threshold criterion for CT image The CT range in Hounsfield Units (HU) is divided into 3 ranges: Lungs values all values below a predefined, customizable value (default=-400). If the seed point is in the lungs range, all the voxels in the lungs range (HU <-400) pass the threshold criterion. Bones values all values above a predefined, customizable value (default=200). If the seed point is in the Bones range, all the voxels in the Bones range (HU > 200) pass the threshold criterion. Soft tissue values all values between the Lungs and the Bones. If the seed point is in the soft tissue range (-400 < HU < 200), the threshold criterion is defined as follows: The reference value is equal to HU Value at the Seed point , and the CT percents from the reference value define the threshold (in units of HU+1000). All the voxels with HU values in the range of the reference value +/-CT percent pass the threshold criterion. After all organs are segmented and defined on the WB SPECT/CT image, the 3D organs masks are projected onto the conjugate WB planar image. All the WB planar images have to be registered in order to project the organ ROIs at their correct location over the scans at all times. Initial registration of all WBs to common planar reference is performed automatically, without user intervention. The same WB scan that was already registered to the SPECT image (the WB planar image nearest to the SPECT image) is used as a reference for the other WB planar scans to register to. Example: for a seed of HU=100 with a CT percent threshold of 8%, all voxels in the range of 12HU to 188HU are searched, as 12 =( )*(1-0.08)-1000, 188 =( )*(1+0.08)-1000

11 Segmentation Propagation The segmentation process starts at the seed point. All neighbors within a specified square/cube are checked. Voxels that comply with the threshold criterion are entered into the cache and added to organ VOI. When all neighbors of current voxel were tested, the current voxel is removed from the cache. This process continues for the neighbors of all the voxels within the cache and stops when the cache is empty. This process is referred to as outer segmentation (the default). The square/cube size to each side of the current voxel is defined by the Neighbors parameter. The default value for outer segmentation neighbors is 1 so neighbors cube size is 3*3*3 voxels (3 voxels include the current voxel and one neighbor at each side). The picture below demonstrates the segmentation propagation on an NM image. The segmentation process starts at the seed point (the left image) and is built up according to the threshold criterion as shown on the images to the right. Manual mode-holding and moving left mouse button ( drag ), starts to paint the segmentation manually, with the current setting of pen size. (0 5 pixels to each side of the pointer tip). This mode also holds when one moves the mouse while the Semi Automatic iteration is in place, as described above. Inner segmentation Holding the [Shift] key prior to starting the automatic and semiautomatic modes activates an inner search. This continues the propagation only when the full square/cube around the voxel is passing the threshold criterion (as defined above). To stop the propagation it is enough to have one voxel within the cube that does not meet the threshold criterion. The default value for inner neighbors is 3 (defining 7*7*7 cube). A practical example: Using outer segmentation by [Alt]+Clicking on a point inside the Left lung in a CT slice (left image) will look for all points below 400HU (the default lung threshold) and continue as long as one of neighbors (a 3*3*3 cube) is less than-400. This catches the trachea as part of the lung as shown on the center image below. Using the inner segmentation technique ([Alt]+[Shift]+Clicking on a point inside the Left lung), the resultant segmentation avoids the trachea as shown on the right image below. There are 3 modes for Segmentation Propagation: Semi-Automatic mode-holding left mouse button continuously, without moving it, starts the semi-automatic segmentation propagation, as shown above. The process stops on one of the following: -- when the segmentation ceases to find new points withstanding the segmentation criterion (empty cache). An audible note will notify the end of the process. -- the user releases the mouse button. -- the user starts to move the mouse. Automatic mode-holding the [Alt] key while clicking the left mouse button, starts the automatic segmentation propagation. The process stops when no new points withstanding the segmentation criterion are found (empty cache). During the propagation the user does not see the progress of the segmentation. The automatic segmentation result is shown only when it is completed (an hourglass cursor indicates the process). Coronal Overlap (Hybrid scenario) In the case of Hybrid Scenario the 3D organs masks are projected to the WB planar 2D image. In this case overlap between projected 2D organs may occur. The toolkit enables handling this overlap. The 3D presentation menu includes the [coronal overlap] menu entry. When selected, the 3D image is projected to the anterior view and all the regions, which contain more than one organ, are painted in white. The areas in white do not represent the depth, contradictory to the other surface rendered parts of this image. They symbolize the fact that there are overlapped organs along this coronal line. Hovering over these areas print the organs involved and the Volume/counts of the overlapped volumes.

12 A voxel within lung is searched. The algorithm looks for a sequence of 6 cms of continuous lung voxels all with HU<- 200, that follows a sequence of 4 cms of tissue voxels over 200HU. The next lung voxel that follows the lung sequence is determined as the seed point. 8 vectors, starting at the seed point, along the 8 diagonals, must hit a soft tissue before reaching the edge of the image. If any vector hits the border, this seed is not within the lung and a new seed is searched for. By default, the volumes of these overlapped areas are removed from the planar coronal ROIs. The counts (activities) and volume of the remaining partial organs are extrapolated by the ratio of the known full volume to the partial volume, assuming that the organ has uniformly distributed activity. As this may not be the case, the user has to determine which organ this sub volume belongs to. System suggested organs Segmentation: The system can suggest automatic segmentation for several organs: Lungs Liver All Bone Cavities (bone marrow + spinal cord) In order to initiate the automatic segmentation the workspace must be empty (no voxel has been segmented yet). The user can request automatic segmentation for the 3 organs above by selecting the organ from the Organ Pick list and click [Confirm]. The organ is not declared automatically, allowing the user to edit the VOI before confirming it by pressing Confirm button again. Additional tools for non contiguous volumes (soft tissue, fat, soft tissue + fat etc ) are available. Lungs segmentation The system suggested lung segmentation is based on the clear lung boundaries on the CT image. Use of the standard semiautomatic or automatic propagation segmentation will usually include the trachea and other air cavities. In order to avoid the air cavities outside the lungs, the automatic lung segmentation works as follows: Once such a seed point is found, an inwards segmentation below 200 is performed with 3 neighbors. The segmentation propagation collects only voxels having the full 7*7*7 voxels (15.4 mm3) cube around them passing the threshold criterion of HU<-200. As the mainstem bronchial diameter is less than 15mm (see reference [2,3]), an inward segmentation with neighborhood of 15.4mm will not include the mainstem bronchial. Once such inner part of the lung is found, dilation of 3 voxels below 200HU fills back the missing boundaries. This is first done for right lung and then for the left. The picture below displays automatic lung detection Coronal, sagittal, transaxial and 3D presentation. Liver segmentation Liver is considered to have uniform HU values and slow edges. It is also assumed that the boundaries of the liver and other surrounding soft tissues are well defined. As there are many soft tissue organs that fill these criteria, the user should triangulate at the center of the liver prior to call for the automatic segmentation. This defines both the seed and the mean HU to look for. An inwards segmentation with 4 neighbors is applied, defining a neighborhood of 9*9*9 voxels (19.8mm 3 ) cube. As the boundaries depend on patient motion during the CT scan, e.g. heavy strikes initiated from breathing, Liver Heart

13 it requires a bit of experience to define a good seed point to get accurate liver delineation as in the picture below Inverse of the result above gives the bone cavities voxels. and not erroneous liver segmentation that includes other organs (see picture below) The following slices show both marrow in the sternum and spinal cord. All bone cavities segmentation Automatic segmentation of all bone cavities can be used to define bone marrow The automatic search for bone cavities is performed as follows: The segmentation is performed on the CT image. The segmentation does not separate between Red and Yellow Marrow tissues. The quality of the detected cavities depends mostly on the thickness of the CT slices. Thick slices, such as the above (4.26mm) will interpolate axial cavities, affecting both the HU values and the axial resolution. Additional tools for Non-contiguous volumes Tools are available for enabling non-standard segmentation in cases where the volume of interest is not contiguous. At first stage all the voxels above 100HU are marked regardless of continuity. Adding to these voxels additional voxels by starting a segmentation from a seed point at the top left corner, to include all the voxels having HU below 100HU (i.e. Soft tissue and air) with requirement for Continuity. Full Tissue segmentation enables segmentation of spatially non-contiguous voxels within selected density (HU) range. It is activated on the CT image by holding the [Alt]+[F]. The HU range is defined by the HU value of the seed point and the CT percent threshold. The segmentation marks all voxels that comply with the threshold criterion based on the seed point and the CT percent. There is no requirement for continuity of voxels. Left image shows bone segmentation-seed point selected on bone, bone CT threshold criterion is used. Center image shows soft tissue segmentation. Seed point selected on soft tissue, soft tissue CT threshold criterion is used. Right image shows fat segmentation. Seed point selected on fat, soft tissue CT threshold criterion is used. Bone tissue fat Previously found voxels (HU>100) block segmentation propagation from penetrating the bone. The new segmentation includes all voxels except bone cavities.

14 Further segmentation is available by adding to the current segments: Below an example of adding fat to soft tissue: First performing soft tissue segmentation (as described above) then add fat by holding the [Alt]+[F] while clicking on fat voxel. System sensitivity calibration standard dose syringe The user fills a dialog with patient and standard dose syringe information. The standard dose syringe images are used to calibrate the patient counts acquired, based on the known half-life of the isotope used. If there is no calibration image the application activates a dialog to specify the system sensitivity by the user. For Hybrid and WB scenarios If a standard dose syringe is scanned within the patient WB planar scans, the application detects its location over all the geometrical mean images, and place ROIs over them. The syringe activity (counts per pixel) is assumed to be much higher than patient activity. The user can review and edit these ROIs as needed. In order to avoid decrease of patient image color dynamic range, the system finds the maximal count outside the standard dose and uses it as initial window level. Note that each of the patient scans are displayed with a different window level, thus the display does not reflect the relative intensity between scans. Using the standard dose permits longer late scans to get clearer organs silhouettes while maintaining accurate dose calculations. Multi SPECT/CT scenario A separate standard dose syringe image may be acquired. The user has to enter the syringe activity dose used for the calibration image as part of the patient activity Dialog. Calibration scan should be acquired as a static dual head image of the standard dose syringe. The application detects the source using 1% of its maximum value as the threshold, and the average counts of the 2 images are used to compute the Calibration factor.

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