No abstract

Dual-Modality Imaging of Cancer with SPECT/CTwww.tcrt.org Dual-modality imaging is an in vivo diagnostic technique that obtains structural and functional information directly from patient studies in a way that cannot be achieved with separate imaging systems alone. Dual-modality imaging systems are configured by combining computed tomography (CT) with radionuclide imaging (using positron emission tomography (PET) or single-photon emission computed tomography (SPECT)) on a single gantry which allows both functional and structural imaging to be performed during a single imaging session without having the patient leave the imaging system. A SPECT/CT system developed at UCSF is being used in a study to determine if dual-modality imaging offers advantages for assessment of patients with prostate cancer using 111 In-ProstaScint ® , a radiolabeled antibody for the prostate-specific membrane antigen. 111 In-ProstaScint ® images are reconstructed using an iterative maximum-likelihood expectation-maximization (ML-EM) algorithm with correction for photon attenuation using a patient-specific map of attenuation coefficients derived from CT. The ML-EM algorithm accounts for the dual-photon nature of the 111 In-labeled radionuclide, and incorporates correction for the geometric response of the radionuclide collimator. The radionuclide image then can be coregistered and overlaid in color on a grayscale CT image for improved localization of the functional information from SPECT. Radionuclide images obtained with SPECT/CT and reconstructed using ML-EM with correction for photon attenuation and collimator response improve image quality in comparison to conventional radionuclide images obtained with filtered backprojection reconstruction. These results illustrate the potential advantages of dual-modality imaging for improving the quality and the localization of radionuclide uptake for staging disease, planning treatment, and monitoring therapeutic response in patients with cancer.

The current climate of rapid technological evolution is reflected in newer and better methods to modulate and direct radiation beams for cancer therapy. This Vision 20/20 paper focuses on part of this evolution, locating and targeting moving tumors. The two processes are somewhat independent and in principle different implementations of the locating and targeting processes can be interchanged. Advanced localization and targeting methods have an impact on treatment planning and also present new challenges for quality assurance (QA), that of verifying real-time delivery. Some methods to locate and target moving tumors with radiation beams are currently FDA approved for clinical use-and this availability and implementation will increase with time. Extensions of current capabilities will be the integration of higher order dimensionality, such as rotation and deformation in addition to translation, into the estimate of the patient pose and real-time reoptimization and adaption of delivery to the dynamically changing anatomy of cancer patients.

Blast-related traumatic brain injury (bTBI) and post-traumatic stress disorder (PTSD) have been of particular relevance to the military and civilian health care sectors since the onset of the Global War on Terror, and TBI has been called the ''signature injury'' of this war. Currently there are many questions about the fundamental nature, diagnosis, and long-term consequences of bTBI and its relationship to PTSD. This workshop was organized to consider these questions and focus on how brain imaging techniques may be used to enhance current diagnosis, research, and treatment of bTBI. The general conclusion was that although the study of blast physics in nonbiological systems is mature, few data are presently available on key topics such as blast exposure in combat scenarios, the pathological characteristics of human bTBI, and imaging signatures of bTBI. Addressing these gaps is critical to the success of bTBI research. Foremost among our recommendations is that human autopsy and pathoanatomical data from bTBI patients need to be obtained and disseminated to the military and civilian research communities, and advanced neuroimaging used in studies of acute, subacute, and chronic cases, to determine whether there is a distinct pathoanatomical signature that correlates with long-term functional impairment, including PTSD. These data are also critical for the development of animal models to illuminate fundamental mechanisms of bTBI and provide leads for new treatment approaches. Brain imaging will need to play an increasingly important role as gaps in the scientific knowledge of bTBI and PTSD are addressed through increased coordination, cooperation, and data sharing among the academic and military biomedical research communities.

Respiratory motion is a significant and challenging problem for radiation medicine. Without adequate compensation for respiratory motion, it is impossible to deliver highly conformal doses to tumors in the thorax and abdomen. The CyberKnife frameless stereotactic radiosurgery system with Synchrony provides respiratory motion adaptation by monitoring skin motion and dynamically steering the beam to follow the moving tumor. This study quantitatively evaluated this beam steering technology using optical tracking of both the linear accelerator and a ball-cube target. Respiratory motion of the target was simulated using a robotic motion platform and movement patterns recorded from previous CyberKnife patients. Our results show that Synchrony respiratory tracking can achieve sub-millimeter precision when following a moving object.

Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.

Nuclear medicine tracers using 111In as a radiolabel are increasing in their use, especially in the domain of oncologic imaging. In these applications, it often is critical to have the capability of quantifying radionuclide uptake and being able to relate it to the biological properties of the tumor. However, images from single photon emission computed tomography (SPECT) can be degraded by photon attenuation, photon scattering, and collimator blurring; without compensation for these effects, image quality can be degraded, and accurate and precise quantification is impossible. Although attenuation correction for SPECT is becoming more common, most implementations can only model single energy radionuclides such as 99mTc and 123I. Thus, attenuation correction for 111In is challenging because it emits two photons (171 and 245 keV) at nearly equal rates (90.2% and 94% emission probabilities). In this paper, we present a method of calculating a single "effective" attenuation coefficient for the dual-energy emissions of 111In, and that can be used to correct for photon attenuation in radionuclide images acquired with this radionuclide. Using this methodology, we can derive an effective linear attenuation coefficient Micro(eff) and an effective photon energy E(eff) based on the emission probabilities and linear attenuation coefficients of the 111In photons. This approach allows us to treat the emissions from 111In as a single photon with an effective energy of 210 keV. We obtained emission projection data from a tank filled with a uniform solution of 111In. The projection data were reconstructed using an iterative maximum-likelihood algorithm with no attenuation correction, and with attenuation correction assuming photon energies of 171, 245, and 210 keV (the derived E(eff)). The reconstructed tomographic images demonstrate that the use of no attenuation correction, or correction assuming photon energies of 171 or 245 keV introduces inaccuracies into the reconstructed radioactivity distribution when compared against the effective energy method. In summary, this work provides both a theoretical framework and experimental methodology of attenuation correction for the dual-energy emissions from 111In. Although these results are specific to 111In, the foundation could easily be extended to other multiple-energy isotopes.

Dual-modality imaging is a technique where computed tomography or magnetic resonance imaging is combined with positron emission tomography or single-photon computed tomography to acquire structural and functional images with an integrated system. The data are acquired during a single procedure with the patient on a table viewed by both detectors to facilitate correlation between the structural and function images. The resulting data can be useful for localization for more specific diagnosis of disease. In addition, the anatomical information can be used to compensate the correlated radionuclide data for physical perturbations such as photon attenuation, scatter radiation, and partial volume errors. Thus, dual-modality imaging provides a priori information that can be used to improve both the visual quality and the quantitative accuracy of the radionuclide images. Dual-modality imaging systems also are being developed for biological research that involves small animals. The small-animal dual-modality systems offer advantages for measurements that currently are performed invasively using autoradiography and tissue sampling. By acquiring the required data noninvasively, dual-modality imaging has the potential to allow serial studies in a single animal, to perform measurements with fewer animals, and to improve the statistical quality of the data.