Cardiac computed tomography (CT) has emerged as a major imaging modality for the diagnosis and monitoring of cardiovascular diseases. High temporal resolution is essential to ensure diagnostic accuracy. Limited-angle data acquisition can reduce scan time and improve temporal resolution, but typically leads to severe image degradation and motivates for improved reconstruction techniques. In this paper, we propose a novel physics-informed score-based diffusion model (PSDM) for limited-angle reconstruction of cardiac CT. At the sampling time, we combine a data prior from a diffusion model and a model prior obtained via an iterative algorithm and Fourier fusion to further enhance the image quality. Specifically, our approach integrates the primal-dual hybrid gradient (PDHG) algorithm with score-based diffusion models, thereby enabling us to reconstruct high-quality cardiac CT images from limited-angle data. The numerical simulations and real data experiments confirm the effectiveness of our proposed approach.
Arterial endothelial dysfunction is an early event in atherosclerosis and correlates with cardiovascular disease risk factors. The most widely used noninvasive measure of endothelial function involves brachial artery (BA) diameter measurement using ultrasound imaging. The change in arterial diameter after reactive hyperemia is a measure of endothelium-mediated vasorelaxation (EMVR). High technical complexity and cost render this technique unsuitable for routine clinical use. To assess EMVR we induce artificial pulses at the superficial radial artery using a linear actuator. An ultrasonic flowmeter detects these pulses 10-30 cm proximal to the point of induction. The delay between pulse application and detection provides the pulse transit time (PTT). By measuring PTT before and after BA occlusion and ensuing reactive hyperemia, EMVR can be quantified since smooth muscle relaxation increases PTT. This method is shown to provide 37% greater sensitivity (p<0.05) to EMVR than BA diameter measurement in the eleven human subjects examined.
In this note we simplify the formulation of the Poincare-invariant eective string theory in D dimensions by adding an intrinsic metric and embedding its dynamics into the Polyakov formalism. We use this formalism to classify operators order-by-order in the inverse physical length of the string, in a fully gauge-invariant framework. We then use this classication to analyze the universality and nonuniversality of observables, up to and including the second sub-leading order in the long string expansion.
The very nature of nuclear medicine, the visual representation of injected radiopharmaceuticals, implies imaging of dynamic processes such as the uptake and wash-out of radiotracers from body organs. For years, nuclear medicine has been touted as the modality of choice for evaluating function in health and disease. This evaluation is greatly enhanced using single photon emission computed tomography (SPECT), which permits three-dimensional (3D) visualization of tracer distributions in the body. However, to fully realize the potential of the technique requires the imaging of in vivo dynamic processes of flow and metabolism. Tissue motion and deformation must also be addressed. Absolute quantification of these dynamic processes in the body has the potential to improve diagnosis. This paper presents a review of advancements toward the realization of the potential of dynamic SPECT imaging and a brief history of the development of the instrumentation. A major portion of the paper is devoted to the review of special data processing methods that have been developed for extracting kinetics from dynamic cardiac SPECT data acquired using rotating detector heads that move as radiopharmaceuticals exchange between biological compartments. Recent developments in multi-resolution spatiotemporal methods enable one to estimate kinetic parameters of compartment models of dynamic processes using data acquired from a single camera head with slow gantry rotation. The estimation of kinetic parameters directly from projection measurements improves bias and variance over the conventional method of first reconstructing 3D dynamic images, generating time-activity curves from selected regions of interest and then estimating the kinetic parameters from the generated time-activity curves. Although the potential applications of SPECT for imaging dynamic processes have not been fully realized in the clinic, it is hoped that this review illuminates the potential of SPECT for dynamic imaging, especially in light of new developments that enable measurement of dynamic processes directly from projection measurements.
The integrity of endothelial function in major arteries (EFMA) is a powerful independent predictor of heart attack and stroke. Existing ultrasound-based non-invasive assessment methods are technically challenging and suitable only for laboratory settings. EFMA, like blood pressure (BP), is both acutely and chronically affected by factors such as lifestyle and medication. Consequently, laboratory-based measurements cannot fully gauge the effects of medical interventions on EFMA. EFMA and BP have, arguably, comparable (but complementary) value in the assessment of cardiovascular health. Widespread deployment of EFMA assessment is thus a desirable clinical goal. To this end, we propose a device based on modifying the measurement protocol of a standard electronic sphygmomanometer. The protocol involves inflating the cuff to sub-diastolic levels to enable recording of the pulse waveform before and after vasodilatory stimulus. The mechanical unloading of the arterial wall provided by the cuff amplifies the distension that occurs with each pulse, which is measured as a pressure variation in the cuff. We show that the height of the rising edge of each pulse is proportional to the change in lumen area between diastole and systole. This allows the effect of vasodilatory stimuli on the artery to be measured with high sensitivity. We compare the proposed cuff flow-mediated dilation (cFMD) method to ultrasound flow-mediated dilation (uFMD). We find significant correlation (r = 0.55, p = 0.003, N = 27) between cFMD- and uFMD-based metrics obtained when the release of a 5 min cuff occlusion is employed to induce endothelial stimulus via reactive hyperemia. cFMD is approximately proportional to the square of uFMD, representing a typical increase in sensitivity to vasodilation of 300-600%. This study illustrates the potential for an individual to conveniently measure his/her EFMA by using a low-cost reprogrammed home sphygmomanometer.
Ultra-high resolution images are desirable in photon counting CT (PCCT), but resolution is physically limited by interactions such as charge sharing. Deep learning is a possible method for super-resolution (SR), but sourcing paired training data that adequately models the target task is difficult. Additionally, SR algorithms can distort noise texture, which is an important in many clinical diagnostic scenarios. Here, we train conditional denoising diffusion probabilistic models (DDPMs) for PCCT super-resolution, with the objective to retain textural characteristics of local noise. PCCT simulation methods are used to synthesize realistic resolution degradation. To preserve noise texture, we explore decoupling the noise and signal image inputs and outputs via deep denoisers, explicitly mapping to each during the SR process. Our experimental results indicate that our DDPM trained on simulated data can improve sharpness in real PCCT images. Additionally, the disentanglement of noise from the original image allows our model more faithfully preserve noise texture.
Purpose: In image‐guided pretreatment patient position adjustment methods, image registration is used to determine alignment parameters. Since most positioning hardware lacks the full six degrees of freedom (DsOF), accuracy is compromised. We present an algorithm for determining optimal realizable adjustments and demonstrate that such compromises are often unnecessary when planned treatment beams are modelled as part of the adjustment calculation. Method: Our beam shape model is based on the polygonal intersection of each beam segment with the plane in pretreatment image volume that passes through machine isocenter, perpendicular to the central axis of the beam. Under a virtual 6‐DOF correction, ideal positions of these polygon vertices are computed. The proposed method determines the couch, gantry and collimator adjustments that minimize the total mismatch of all vertices over all segments with respect to their ideal positions. Using this geometric error metric as a function of the number of available DsOF, the user may select the most desirable correction regime. Results: For a simulated plan consisting of three equally weighted coplanar fixed beams, we achieve a 7% residual geometric error (with respect to the ideal correction, considered 0% error) by applying gantry rotation as well as translation and isocentric rotation of the couch. For a clinical head‐and‐neck IMRT plan with 7 beams and 5 segments per beam, the corresponding error is 6%. Correction involving only couch translation (typical clinical practice) leads to a much larger 18% mismatch. After adjustment, dose volume histograms demonstrate clinically significance of geometric error reduction. Conclusion: Suggested algorithm improves delivery accuracy using standard delivery hardware without significantly increasing total treatment session duration. It encourages parsimonious utilization of all available DsOF. Finally, in certain cases, it obviates the need of a 6‐DOF robotic couch for pre‐treatment position adjusment. Conflict of Interest: Research sponsored by Siemens Medical Solutions.
Purpose: The purpose of this work is to devise an efficient, effective and routine imaging modality to guide lung radiotherapy. Current methods involve acquiring a 4D-CBCT and comparing the digitally reconstructed radiographs (DRRs) for multiple breathing phases with online fluoroscopic images. A major shortcoming of DRRs and fluoroscopy is that unwanted structures such as bones occlude the target. Furthermore 4D-CBCT requires long acquisition times and large patient dose, making it impractical for routine use. Method and Materials: We propose a partial arc cone beam acquisition, which we call “cone beam digital tomosynthesis” (CBDT), to obtain cross-sectional images of a slab just thick enough to enclose the soft tissue target. Projections through this slab make “digitally reconstructed tomograms” (DRTs). Similar to DRRs, DRTs correct for beam divergence. However, different from DRRs, DRTs do not contain irrelevant structures outside the slab, making the target far more conspicuous. By gating this acquisition, dynamic cross sections and DRTs are obtained at multiple respiratory phases. These dynamic images are registered with those obtained from the planning 4D-CT dataset to guide treatment. Results: The DRRs constructed from multiple phases of a 4D-CT emphasize bony anatomy and other irrelevant structures overlaying the target, making the edges of the target difficult to find. We have demonstrated that this difficulty is overcome by the use of cross-sectional images from CBDT and DRTs, which are obtained with an image acquisition time that is significantly shorter than full-volume 4D-CBCT. Matching DRTs were obtained from the planning 4D-CT for each phase. 2D-2D registrations were performed to obtain the phase-varying offset. Conclusion: A new imaging technique has been introduced for image-guided lung treatment. With this approach, images are acquired faster and the appearance of the tumor is significantly enhanced by eliminating many extraneous structures. Conflict of Interest: This work is supported by Siemens.
