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ObjectiveSimultaneous PET/MRIs vary in their quantitative PET performance due to inherent differences in the physical systems and differences in the image reconstruction implementation. This variability in quantitative accuracy confounds the ability to meaningfully combine and compare data across scanners. In this work, we define image reconstruction parameters that lead to comparable contrast recovery curves across simultaneous PET/MRI systems.MethodThe NEMA NU-2 image quality phantom was imaged on one GE Signa and on one Siemens mMR PET/MRI scanner. The phantom was imaged at 9.7:1 contrast with standard spheres (diameter 10, 13, 17, 22, 28, 37 mm) and with custom spheres (diameter: 8.5, 11.5, 15, 25, 32.5, 44 mm) using a standardized methodology. Analysis was performed on a 30 min listmode data acquisition and on 6 realizations of 5 min from the listmode data. Images were reconstructed with the manufacturer provided iterative image reconstruction algorithms with and without point spread function (PSF) modeling. For both scanners, a post-reconstruction Gaussian filter of 3-7 mm in steps of 1 mm was applied. Attenuation correction was provided from a scaled computed tomography (CT) image of the phantom registered to the MR-based attenuation images and verified to align on the non-attenuation corrected PET images. For each of these image reconstruction parameter sets, contrast recovery coefficients (CRCs) were determined for the SUVmean, SUVmax and SUVpeak for each sphere. A hybrid metric combining the root-mean-squared discrepancy (RMSD) and the absolute CRC values was used to simultaneously optimize for best match in CRC between the two scanners while simultaneously weighting toward higher resolution reconstructions. The image reconstruction parameter set was identified as the best candidate reconstruction for each vendor for harmonized PET image reconstruction.ResultsThe range of clinically relevant image reconstruction parameters demonstrated widely different quantitative performance across cameras. The best match of CRC curves was obtained at the lowest RMSD values with: for CRCmean, 2 iterations-7 mm filter on the GE Signa and 4 iterations-6 mm filter on the Siemens mMR, for CRCmax, 4 iterations-6 mm filter on the GE Signa, 4 iterations-5 mm filter on the Siemens mMR and for CRCpeak, 4 iterations-7 mm filter with PSF on the GE Signa and 4 iterations-7 mm filter on the Siemens mMR. Over all reconstructions, the RMSD between CRCs was 1.8%, 3.6% and 2.9% for CRC mean, max and peak, respectively. The solution of 2 iterations-3 mm on the GE Signa and 4 iterations-3 mm on Siemens mMR, both with PSF, led to simultaneous harmonization and with high CRC and low RMSD for CRC mean, max and peak with RMSD values of 2.8%, 5.8% and 3.2%, respectively.ConclusionsFor two commercially available PET/MRI scanners, user-selectable parameters that control iterative updates, image smoothing and PSF modeling provide a range of contrast recovery curves that allow harmonization in harmonization strategies of optimal match in CRC or high CRC values. This work demonstrates that nearly identical CRC curves can be obtained on different commercially available scanners by selecting appropriate image reconstruction parameters.

Lung cancer is the leading cancer cause of death in the United States. Radiotherapy is an essential component of the definitive treatment of early-stage and locally-advanced lung cancer, and the palliative treatment of metastatic lung cancer. Proton beam therapy (PBT), through its characteristic Bragg peak, has the potential to decrease the toxicity of radiotherapy, and, subsequently improve the therapeutic ratio. Herein, we provide a primer on the physics of proton beam therapy for lung cancer, present the existing data in early-stage and locally-advanced non-small cell lung cancer (NSCLC), as well as in special situations such as re-irradiation and post-operative radiation therapy. We then present the technical challenges, such as anatomic changes and motion management, and future directions for PBT in lung cancer, including pencil beam scanning. [ABSTRACT FROM AUTHOR]

For the radiation treatment of lung cancer patients, four-dimensional computed tomography (4D-CT) is a common practice used clinically to image tumor motion and subsequently determine the internal target volume (ITV) from the maximum intensity projection (MIP) images. ITV, which is derived from short pre-treatment 4D-CT scan (<6 s per couch position), may not adequately cover the extent of tumor motion during the treatment, particularly for patients that exhibit a large respiratory variability. Inaccurate tumor localization may result in under-dosage of the tumor or over-dosage of the surrounding tissues. The purpose of this study is therefore to assess the degree of tumor under-dosage in case of regular and irregular breathing for proton radiotherapy using ITV-based treatment planning. We place a spherical lesion into a modified XCAT phantom that is also capable of producing 4D images based on irregular breathing, and move the tumor according to real tumor motion data, which is acquired over multiple days by tracking gold fiducial markers implanted into the lung tumors of patients. We derive ITVs by taking the union of all tumor positions during 6 s of tumor motion in the phantom using the first day patient tumor tracking data. This is equivalent to ITVs generated clinically from cine-mode 4D-CT MIP images. The treatment plans created for different ITVs are then implemented on dynamic phantoms with tumor motion governed by real tumor tracking data from consecutive days. By comparing gross tumor volume dose distribution on days of ‘treatment’ with the ITV dose distribution, we evaluate the deviation of the actually delivered dose from the predicted dose. Our results have shown that the proton treatment planning on ITV derived from pre-treatment cine-mode 4D-CT can result in under-dosage (dose covering 95% of volume) of the tumor by up to 25.7% over 3 min of treatment for the patient with irregular respiratory motion. Tumor under-dosage is less significant for the patient with relatively regular breathing. We have demonstrated that proton therapy using the pre-treatment 4D-CT based ITV method can lead to significant under-dosage of the tumor, highlighting the need for daily customization to generate a target volume that represents tumor positions during the treatment more accurately. [ABSTRACT FROM AUTHOR]

Digital phantoms continue to play a significant role in modeling and characterizing medical imaging. The currently available XCAT phantom incorporates both the flexibility of mathematical phantoms and the realistic nature of voxelized phantoms. This phantom generates images based on a regular breathing pattern and can include arbitrary lung tumor trajectories. In this work, we present an algorithm that modifies the current XCAT phantom to generate 4D imaging data based on irregular breathing. First, a parameter is added to the existing XCAT phantom to include any arbitrary tumor motion. This modification introduces the desired tumor motion but, comes at the cost of decoupled diaphragm, chest wall and lung motion. To remedy this problem diaphragm and chest wall motion is first modified based on initial tumor location and then input to the XCAT phantom. This generates a phantom with synchronized respiratory motion. Mapping of tumor motion trajectories to diaphragm and chest wall motion is done by adaptively calculating a scale factor based on tumor to lung contour distance. The distance is calculated by projecting the initial tumor location to lung edge contours characterized by quadratic polynomials. Data from ten patients were used to evaluate the accuracy between actual independent tumor location and the location obtained from the modified XCAT phantom. The RMSE and standard deviations for ten patients in x, y, and z directions are: (0.29 ± 0.04, 0.54 ± 0.17, and 0.39 ± 0.06) mm. To demonstrate the utility of the phantom, we use the new phantom to simulate a 4DCT acquisition as well as a recently published method for phase sorting. The modified XCAT phantom can be used to generate more realistic imaging data for enhanced testing of algorithms for CT reconstruction, tumor tracking, and dose reconstruction. [ABSTRACT FROM AUTHOR]

Accurate understanding and modeling of respiration-induced uncertainties is essential in image-guided radiotherapy. Explicit modeling of overall lung motion and interaction among different organs promises to be a useful approach. Recently, preliminary studies on 3D fluoroscopic treatment imaging and tumor localization based on Principal Component Analysis (PCA) motion models and cost function optimization have shown encouraging results. However, the performance of this technique for varying breathing parameters and under realistic conditions remains unclear and thus warrants further investigation. In this work, we present a systematic evaluation of a 3D fluoroscopic image generation algorithm via two different approaches. In the first approach the model’s accuracy is tested for changing parameters for sinusoidal breathing. These parameters included changing respiratory motion amplitude, period, and baseline shift. The effects of setup error, imaging noise and different tumor sizes are also examined. In the second approach, we test the model for anthropomorphic images obtained from a modified XCAT phantom. This set of experiments is important as all the underlying breathing parameters are simultaneously tested, as in realistic clinical conditions. Based on our simulation results for more than 250 seconds of breathing data for 8 different lung patients, the overall tumor localization accuracy of the model in left-right (LR), anterior-posterior (AP) and superior-inferior (SI) directions are 0.1 ± 0.1 mm, 0.5 ± 0.5 mm and 0.8 ± 0.8 mm respectively. 3D tumor centroid localization accuracy is 1.0 ± 0.9 mm.

By using detectors with good depth encoding accuracy (∼2 mm), an animal PET scanner can be built with a small ring diameter and thick crystals to simultaneously obtain high spatial resolution and high sensitivity. However, there will be large wedge-shaped gaps between detector modules in such a scanner if traditional cuboid crystal arrays are used in a polygonal arrangement. The gaps can be minimized by using tapered scintillator arrays enabling the sensitivity of the scanner to be further improved. In this work, tapered lutetium oxyorthosilicate (LSO) arrays with different crystal dimensions and different combinations of inter-crystal reflector and crystal surface treatments were manufactured and their performance was evaluated. Arrays were read out from both ends by position-sensitive avalanche photodiodes (PSAPDs). In the optimal configuration, arrays consisting of 0.5 mm LSO elements could be clearly resolved and a depth of interaction resolution of 2.6 mm was obtained for a 20 mm thick array. For this tapered array, the intrinsic spatial is degraded from 0.67 to 0.75 mm compared to a standard cuboidal array with similar dimensions, while the increase in efficiency is 41%. Tapered scintillator arrays offer the prospect of improvements in sensitivity and sampling for small-bore scanners, without large increases in manufacturing complexity.