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Departmental imaging requirements Essay Introduction Diagnosing, staging, and re-staging of cancer, as well as the monitoring and planning of cancer treatment, has traditionally relied on anatomic imaging like computed tomography (CT) and magnetic resonance imaging (MRI). Spatially accurate medical imaging is an essential tool in three dimensional conformal radiation therapy (3DCRT) and intensity-modulated radiation therapy (IMRT) treatment planning. CT imaging is the standard imaging modality for image based radiation treatment planning (RTP). CT images provide anatomical information on the size and location of tumors in the body. They also provide electron density information for heterogeneity-based patient dose calculation. The major limitation of the CT imaging process is soft tissue contrast, which is overcome by using contrast agents or using another anatomical imaging modality like MRI. One of the disadvantages of anatomical imaging techniques like CT and MRI is its inability to characterize the tumor. Tumors need to be characterized whether they are benign or malignant and if malignant it would be helpful to know whether the proliferation is slow or fast. Necrotic, scar, and inflammatory tissue often cannot be differentiated from malignancy based on anatomic imaging alone. Anatomical imaging has high sensitivity for detection of structural changes, but a low specificity for further characterization of these abnormalities. Single photon emission computed tomography and positron emission tomography (PET) are imaging techniques that provide information on physiology rather than anatomy. These modalities have been used for evaluation of tumor metabolism, differentiation between tumor reoccurrence and radiation necrosis, detection of hypoxic areas of the tumor, and other functional imaging. Radiation treatment planning requires an accurate location of the tumor and the normal tissue and also knowledge of the size of the tumor for contouring the treatment volume. Although PET provides necessary functional information for RTP, it has a few limitations. The spatial resolution of PET is too poor to give accurate quantitative information. The greatest limitation in using PET for RTP is its lack of anatomical information. This limitation of PET is overcome by evaluating PET and CT images together. Fused PET and CT images give better diagnostic evaluation than PET or CT images used alone (Bar-Shalom et al, 2003; Cohade Wahl, 2003). But fusion of PET and CT images are meaningful only when they are correctly spatially registered. Hence a proper spatial registration is required for accurate delineation of tumor volume. The necessity of accurate spatial registration of fused images requires different fusion techniques for different image datasets. Software fusion and hardware fusion are the two different approaches considered by the scientific community (Townsend et al, 2003; Townsend et al, 2002). Software fusion approaches use different transformation algorithms to fuse different modality images acquired at different times. The transformation algorithms are classified as rigid and non-rigid transformation algorithms. They are based on whether they fuse images of rigid-body (e. g. , head) or non rigid (e. g. , abdomen) objects (Patton, 2001; Yap, 2002). Although software fusion gives better diagnostic information than using separate images, physicians may not rely on the information if the fused images were acquired at different times. Also the chances of a change in patient position are high for image acquisition done at different times. The hardware approach of image fusion is headed towards designing a single imaging system to acquire simultaneously the different image modalities required. Hardware fusion is partially achieved by construction of a hybrid PET/CT scanner (Beyer et al, 2000; Townsend et al, 2004) which acquires different modalities sequentially. These hybrid scanners are two separate scanners enabled to operate in sequence one after another to acquire the different image modality datasets in a single imaging session. Although hybrid scanners do not give a true hardware fusion and have not proven to be a better fusion technique scientifically (Kalabbers et al, 2002), they have gained popularity for image acquisition in a single session. Due to reduced scan time and patient motion, PET/CT is considered reliable among the oncology community. These hybrid PET/CT scanners, due to reduced scan time and reliable registration of PET and CT datasets, are becoming common in RTP. A PET image fused with a CT image can be used in treatment planning to eliminate geographic misses of the tumor and escalation of dose to the hypermetabolic aspects of a tumor. Fused images improve the accuracy in staging of lymph nodes. Although the use of PET/CT in RTP is growing at a fast pace, little research has been done in the direction of validating the PET/CT datasets for RTP. Discussion CT images describe the electronic density distribution of cross sections of the patient anatomy. CT systems provide gray scale display of linear attenuation coefficients that closely relate to the density of the tissue. CT imaging evolved from conventional planar radiographs. In planar X-ray film imaging the three dimensional anatomy of the patient is reduced to a two dimensional attenuation projection image and the depth information of the structures are lost. In CT imaging several attenuation projection images for a volume of tissue are acquired at different angles. These sets of projection images are reconstructed by filtered back projection algorithm to generate two dimensional attenuation cross-section of anatomy of the patient. The attenuation measurement for a CT detector element is given by Equation 1 and Equation 2. Equation 1 represents attenuation measurement for homogenous object and Equation 2 represent attenuation measurement for inhomogeneous (heterogeneous) objects. _ P(x) =1n [I0] = ? x ? (1) __ _ I x _ _ _ P(x) = 1n [I0] = x d x ? (2) __ L _ I x In the above equation P(x) is the measured projection data for attenuation along the x direction. Io is the intensity of the x-ray beam measured without the patient in the way for that detector element. This is also known as a blank scan. I (x) is the measured intensity after attenuation by the patient.? (x) is the measured attenuation coefficient as a function of location in the patient. A CT scanner positions a rotating x-ray tube and detector on opposite sides of the patient to acquire projection images. Early CT scanners used pencil beams of x-rays and a combination of translation and rotation motion to acquire projection images (Bushberg et al, 1994). Modern CT scanners have a stationary or rotating detector array with a rotating fan beam x-ray tube. There are also two types of scanning: axial and helical CT scanning. In axial scanning the patient is moved step by step acquiring sets of projection images for each slice. In helical scanning the patient table moves continuously while the x-ray tube acquires a series of projection images. The projection images are acquired for a helical path around the patient. In helical scanning to reconstruct a cross-sectional planar image, the helical data is interpolated to give axial plane projection data before reconstruction. By removing the time to index the table between slices the total scan time of the patient is reduced. Also reconstruction can be done for any slice thickness after acquiring the data. This helical scanning is available in most of the current CT scanners. The reconstructed CT image is a two dimensional matrix of numbers, with each pixel corresponding to a spatial location in the image and in the patient. Usually the matrix is 512 pixels wide and 512 pixels tall covering a 50 cm x 50 cm field of view. The numeric value in each pixel represents the attenuation coefficient as a gray level in the CT image. These numbers are called Hounsfield units or CT numbers. The reconstruction process generates a matrix of Hounsfield units which give the linear attenuation values normalized to the attenuation of water. This normalization is given by Equation 3. CT Number (HU) = 1000 (? pixel ? water) ____________ ?water CT number gives an indication of the type of tissue. Water has a CT number of zero. Negative CT numbers are typical for air spaces, lung tissues and fatty tissue. Values of ? pixel greater than ? water correspond to other soft tissues and bone. Radiologists occasionally make critical diagnostic decisions based on CT number of particular regions of interest. Also attenuation values given by CT numbers are used to calculate the dose delivered to the tumor in RTP. CT number is an important parameter in CT images which must be frequently checked for accuracy. Positron emission tomography (PET) imaging generates images that depict the distribution of positron emitting radionuclide in the patient body. PET imaging often uses the F-18 fluorodeoxyglucose (FDG) radioactive tracer to track increased glucose metabolic activity of tumor cells and to provide images of the whole body distribution of FDG. When the positron is emitted by the radioactive tracer it annihilates with an electron to generate two 511 kev photons emitted in nearly opposite directions. These photons interact with the ring of detector elements surrounding the patient. If both the emitted photons are detected then the point of annihilation lies on the line joining the points of detection. This line joining the points of detection is known as the line of response (LOR). The circuit used by the scanner to record the detector interactions occurring at the same time is called coincidence circuitry. This whole process is called annihilation coincidence detection. Thus a PET scanner uses annihilation coincidence detection instead of mechanical collimation like gamma cameras to acquire projections of activity distribution in the patient. Projections acquired at different angles are reconstructed using iterative algorithms to generate cross-sectional images of activity distribution. The annihilation coincidence detection process allows many false events to be acquired. Corrections are necessary for these false events before the projections are reconstructed. The total events acquired are classified as trues, random and scatter. A true coincidence is simultaneous interactions occurring in the detectors resulting from emissions occurring in the same nuclear transformation. Random coincidences occur when emissions from different nuclear transformations interact in coincidence with the surrounding detectors. Scatter coincidence occurs when one or both photons from annihilation is scattered in the patient body and interact with the detector to give a false LOR. The acquired annihilation events need to be corrected for random and scatter events. Random coincidence events along any LOR may be directly measured using the delayed coincidence method (Levin, 2003). The delayed coincidence method uses two coincidence circuits. The first circuit measures both true and random coincidence events. The second circuit has a delay of several hundred microseconds inserted into the coincidence window, so all true coincidences are thrown out of coincidence. The counts measured in the second circuit are subtracted from the first to give true counts. Scatter correction is done for the projection data by model-based scatter estimation (Levin, 2003). The scatter correction factor is estimated by mathematical models and applied to the projection data before reconstruction. Image fusion was initially achieved by software fusion of anatomical and functional images. Software fusion was generally successful with brain and rigid body volumes. It encountered significant difficulties when fusing images of the rest of the body. Alignment algorithms fail to converge the two image sets due to problems of patient movement or discrepancies in patient positioning between two scans. Also involuntary movements of internal organs arise when patient are imaged on different scanners and at different times. Dual modality PET/CT imaging is a combination of imaging technologies helping to acquire accurately aligned anatomical and functional images in the same scanning session. Also an additional advantage of the combined PET/CT scanner is the use of CT images for attenuation correction. CT images can be scaled in energy and used to correct the PET data for attenuation effects (Kinahan et al, 2003; Kinahan et al, 1998). Dual-modality PET/CT was first built at the University of Pittsburgh in collaboration with CTI (Knoxville, TN) and Siemens Medical Solutions (Hoffman Estates, IL), combining separate PET and CT scanning devices into one device. The PET/CT prototype consisted of a rotating partial ring PET system and a single slice CT scanner mounted on the same rotating support. The CT scanner combined with PET often uses helical scanning CT to enable fast patient throughput, but new scanners with both helical and axial scanning are available now. The CT data is usually acquired first, followed by PET acquisition. There are typically two separate acquisition processing units for CT and PET, and an integrated display workstation. The acquired CT and PET datasets are sending to the reconstruction processing unit for reconstruction. Reconstructed images are fused in the fusion workstation. CT and PET images can also be separately viewed in the workstation. The protocol for PET/CT imaging starts with patient preparation. 5 – 15 mCi of FDG is injected into the patient 45 – 60 min before the start of image acquisition. After 45 min, the glucose circulates through the body; the patient gets ready for image acquisition by emptying the bladder. The patient is positioned on the table for an initial topogram. The topogram is used to select the scan range for PET/CT image acquisition. The scan range is selected as a number of bed positions. Once the image acquisition region is selected in the topogram, the helical CT scan is done first; it takes around 30 sec to acquire one bed position. After completion of the CT portion, the scanner bed is moved to the PET starting position and the emission scan is started. The emission scan duration per bed position varies with the detector technology used. With conventional bismuth germinate oxyorthosilicate (BGO) system, acquisition times will range from 5 to 8 minutes per bed position. The new lutetium oxyorthosilicate (LSO) technology reduces emission scans to 3 to 5 minutes per bed position (Humm et al, 2003). The CT data are used to perform attenuation correction. Image reconstruction is completed a few minutes after the PET image acquisition is completed. Since the CT data is used for attenuation correction, the total scan duration for a PET/CT scanner is shorter than that for stand-alone PET scanner, because the CT acquisition is much faster than a conventional PET transmission acquisition. Conclusion. To conclude, Positron Emission Tomography/Computerized Tomography (PET/CT) is an imaging test that produces high resolution pictures of the body’s biological functions and anatomic structures. These images show body metabolism and other functions rather than simply the gross anatomy and structure revealed by a standard CT or MRI scan. This is important because functional changes are often present before obvious structural changes in tissues are evident. PET/CT imaging can uncover abnormalities that might otherwise go undetected. For example, PET/CT can determine the presence and extent of tumors unseen by other imaging techniques, or detect Alzheimer’s disease one to two years before the diagnosis would be made with certainty by your primary doctor. PET/CT is believed to be the most accurate imaging test available to evaluate lung cancer, colon cancer, breast cancer, melanoma, lymphoma, head and neck cancer, and esophageal cancer. In published research studies, PET has been shown to have an approximately 90% accuracy in many of these cancer types. PET is the most accurate imaging test available to determine the presence of a dementia process such as Alzheimer’s disease. PET is also the most accurate test available to evaluate patients who have had a previous heart attack and are being considered for a procedure to improve blood flow to the injured heart muscle. References Bar-Shalom, R. ; Yefremov, N. ; Guralnik, L. ; Gaitini, D. ; Frenkel, A. ; Kuten, A. ; Altman, H. ; Keidar, Z. ; Israel, O. 2003. 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