Prior Imaging Data Research Articles

Prior-image-based low-dose CT reconstruction for adaptive radiation therapy.

The study aims to reduce the imaging radiation dose in Adaptive Radiotherapy (ART) while maintaining high-quality CT images, critical for effective treatment planning and monitoring. We developed the Prior-aware Learned Primal-Dual Network (pLPD-UNet), which uses prior CT images to enhance reconstructions from low-dose scans. The network was separately trained on thorax and abdomen datasets to accommodate the unique imaging requirements of each anatomical region. The pLPD-UNet demonstrated improved reconstruction accuracy and robustness in handling sparse data compared to traditional methods. It effectively maintained image quality essential for precise organ delineation and dose calculation, while achieving a significant reduction in radiation exposure. This method offers a significant advancement in the practice of ART by integrating prior imaging data, potentially setting a new standard for balancing radiation safety with the need for high-resolution imaging in cancer treatment planning.

Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET.

Quantitative PET attenuation correction (AC) for cardiac PET/CT and PET/MR is a challenging problem. We propose and evaluate an AC approach that uses coincidences from a relatively weak and physically fixed sparse external source, in combination with that from the patient, to reconstruct μ -maps based on physics principles alone. The low 30 cm3 volume of the source makes it easy to fill and place, and the method does not use prior image data or attenuation map assumptions. Our supplemental transmission aided maximum likelihood reconstruction of attenuation and activity (sTX-MLAA) algorithm contains an attenuation map update that maximizes the likelihood of terms representing coincidences originating from tracer in the patient and a weighted expression of counts segmented from the external source alone. Both external source and patient scatter and randoms are fully corrected. We evaluated performance of sTX-MLAA compared to reference standard CT-based AC with FDG PET/CT phantom studies; including modeling a patient with myocardial inflammation. Through an ROI analysis we measured ≤ 5 % bias in activity concentrations for PET images generated with sTX-MLAA and a TX source strength ≥ 12.7 MBq, relative to CT-AC. PET background variability (from noise and sparse sampling) was substantially reduced with sTX-MLAA compared to using counts segmented from the transmission source alone for AC. Results suggest that sTX-MLAA will enable quantitative PET during cardiac PET/CT and PET/MR of human patients.

PROGRESS IN DEVELOPING A LIGHT‐STABLE 4R TAU PET IMAGING AGENT: APN‐1701 FIH

AbstractBackgroundThe need for a light stable 4R tau PET tracer for use in diverse tauopathies is widely appreciated. We aimed to assess the initial human imaging profile of [18F]APN‐1701 as such a tracer and to compare its imaging profile to the well‐established but light‐sensitive tracer [18F]‐APN‐1607 that can detect 4R aggregates as well as 3R and mixed 3R/4R aggregates. [18F]APN‐1607 has progressed to a multicenter Phase 2 clinical trial for Alzheimer’s disease (AD); therefore, prior imaging data with it was considered valuable for comparison.MethodOne cognitively normal (CN) subject (male, 73yo) and one AD subject (female, 58 yo), who had undergone prior amyloid profiling using [18F]florbetapir PET and subsequent imaging with [18F]APN‐1607 in a proof‐of‐concept study and a test‐retest study, respectively, participated in the current study. Dynamic imaging with [18F]APN‐1701 was performed over 180 min to obtain kinetic data.ResultOverall, for both subjects [18F]APN‐1701 generally matched [18F]APN‐1607 findings, i.e., peak SUV values reached similar levels. The CN subject, who was amyloid negative, had slight, uniform cortical [18F]‐APN‐1701 uptake and more substantial uptake in the pallidum, pons and midbrain. SUVr images for the AD subject showed retention in similar brain regions for both tau tracers with the same rank order of regional SUV/SUVr. Notably, for this subject [18F]APN‐1701 continued to accumulate up to the end of image acquisition.Conclusion[18F]APN‐1701 demonstrates cortical uptake generally similar to [18F]APN‐1607 but its slow kinetics are unfavorable for further development.

PROGRESS IN DEVELOPING A LIGHT‐STABLE 4R TAU PET IMAGING AGENT: APN‐1701 FIH

AbstractBackgroundThe need for a light stable 4R tau PET tracer for use in diverse tauopathies is widely appreciated. We aimed to assess the initial human imaging profile of [18F]APN‐1701 as such a tracer and to compare its imaging profile to the well‐established but light‐sensitive tracer [18F]‐APN‐1607 that can detect 4R aggregates as well as 3R and mixed 3R/4R aggregates. [18F]APN‐1607 has progressed to a multicenter Phase 2 clinical trial for Alzheimer’s disease (AD); therefore, prior imaging data with it was considered valuable for comparison.MethodOne cognitively normal (CN) subject (male, 73yo) and one AD subject (female, 58 yo), who had undergone prior amyloid profiling using [18F]florbetapir PET and subsequent imaging with [18F]APN‐1607 in a proof‐of‐concept study and a test‐retest study, respectively, participated in the current study. Dynamic imaging with [18F]APN‐1701 was performed over 180 min to obtain kinetic data.ResultOverall, for both subjects [18F]APN‐1701 generally matched [18F]APN‐1607 findings, i.e., peak SUV values reached similar levels. The CN subject, who was amyloid negative, had slight, uniform cortical [18F]‐APN‐1701 uptake and more substantial uptake in the pallidum, pons and midbrain. SUVr images for the AD subject showed retention in similar brain regions for both tau tracers with the same rank order of regional SUV/SUVr. Notably, for this subject [18F]APN‐1701 continued to accumulate up to the end of image acquisition.Conclusion[18F]APN‐1701 demonstrates cortical uptake generally similar to [18F]APN‐1607 but its slow kinetics are unfavorable for further development.

Patient-controlled sharing of medical imaging data across unaffiliated healthcare organizations

Current image sharing is carried out by manual transportation of CDs by patients or organization-coordinated sharing networks. The former places a significant burden on patients and providers. The latter faces challenges to patient privacy. To allow healthcare providers efficient access to medical imaging data acquired at other unaffiliated healthcare facilities while ensuring strong protection of patient privacy and minimizing burden on patients, providers, and the information technology infrastructure. An image sharing framework is described that involves patients as an integral part of, and with full control of, the image sharing process. Central to this framework is the Patient Controlled Access-key REgistry (PCARE) which manages the access keys issued by image source facilities. When digitally signed by patients, the access keys are used by any requesting facility to retrieve the associated imaging data from the source facility. A centralized patient portal, called a PCARE patient control portal, allows patients to manage all the access keys in PCARE. A prototype of the PCARE framework has been developed by extending open-source technology. The results for feasibility, performance, and user assessments are encouraging and demonstrate the benefits of patient-controlled image sharing. The PCARE framework is effective in many important clinical cases of image sharing and can be used to integrate organization-coordinated sharing networks. The same framework can also be used to realize a longitudinal virtual electronic health record. The PCARE framework allows prior imaging data to be shared among unaffiliated healthcare facilities while protecting patient privacy with minimal burden on patients, providers, and infrastructure. A prototype has been implemented to demonstrate the feasibility and benefits of this approach.

WE‐A‐301‐07: Using Prior Images with Registration in Penalized Likelihood Estimation for CT with Sparse Data

Purpose: We aim to develop tomographic reconstruction methods that utilize prior CT volumes to improve image quality for sparse and undersampled datasets. The ability to handle such data has applications in region‐of‐interest scanning, tomosynthesis, and intraoperative imaging based on few view angles. While compressed sensing techniques like the PICCS algorithm are attractive, there are potential drawbacks. Such approaches often rely on pre‐registered prior images and the forward models for PICCS‐type algorithms tend to be simplified and ignore the noise model. We leverage the ability of likelihood‐based techniques to incorporate both prior images and sophisticated forward models. Methods: We have developed a framework for reconstruction of sparse tomographic data based on penalized‐likelihood estimation. The objective function for this approach is composed of a likelihood term that incorporates the forward model and noise; and a parameterized penalty term that discourages differences from the prior image. The penalty uses a compressed sensing norm and is parameterized to accommodate registration of the prior scan. An iterative algorithm is used to simultaneously solve for the tomographic volume and the registration parameters. The framework is general and can be used with both rigid and deformable registrations. Results: Reconstruction of sparse data using the proposed approach is demonstrated in cases where either rigid or deformable registration is employed in the reconstruction objective. We compare reconstruction results with traditional approaches where prior image data is not utilized. We find a substantial improvement in image quality with significant reduction in artifacts that are associated with the sparse data acquisitions. Conclusions: A new class of penalized‐likelihood estimator has been developed that simultaneously refines attenuation and registration estimates. The framework is general and supports both rigid and non‐rigid transformations of the prior imagery. Image quality and handling of artifacts using the proposed estimator with sparse data is superior to traditional approaches.

Locally Advanced Rectal Cancer: Added Value of Diffusion-weighted MR Imaging in the Evaluation of Tumor Response to Neoadjuvant Chemo- and Radiation Therapy

To investigate the added value of diffusion-weighted (DW) magnetic resonance (MR) imaging in the evaluation of complete response (CR) to neoadjuvant combined chemotherapy and radiation therapy (CRT) in patients with locally advanced rectal cancer. Institutional review board approval was obtained for this retrospective study, and the patient informed consent requirement was waived. Forty consecutive patients with locally advanced rectal cancer (>or=T3 or lymph node positive) who underwent CRT and subsequent surgery were enrolled in this study. All patients underwent pre- and post-CRT 1.5-T rectal MR imaging and post-CRT DW MR imaging. For qualitative analysis, two radiologists who were blinded to pathologic staging and prior imaging data retrospectively and independently reviewed conventional MR images and the combined set of MR images and DW MR images over a 2-week interval and recorded their confidence level with respect to the CR to neoadjuvant CRT. Diagnostic accuracy was calculated for each reviewer with receiver operating characteristic (ROC) curve analysis. For quantitative analysis, a third radiologist measured the apparent diffusion coefficient (ADC) of the region of interest three times. Mean ADCs in the CR group were compared with those in the non-CR group. Pathology reports served as the reference standard. Diagnostic accuracy (area under the ROC curve [A(z)]) in the evaluation of CR was significantly improved after additional review of DW MR images for both reviewers: For reviewer 1, A(z) improved from 0.676 to 0.876 (P = .005), whereas for reviewer 2, A(z) improved from 0.658 to 0.815 (P = .036). Mean ADC ([1.62 +/- 0.36] x 10(-3) mm(2)/sec) (standard deviation) of the CR group (n = 11) was significantly higher than that ([1.04 +/- 0.24] x 10(-3) mm(2)/sec) of the non-CR group (n = 29) (P < .0001). Adding DW MR imaging to conventional MR imaging yields better diagnostic accuracy than use of conventional MR imaging alone in the evaluation of CR to neoadjuvant CRT in patients with locally advanced rectal cancer.

White Matter Structural Integrity in Healthy Aging Adults and Patients With Alzheimer Disease

Imaging and postmortem studies suggest that frontal lobe white matter (FLWM) volume expands until about the age of 44.6 years and then declines. Postmortem evidence indicates that the structural integrity of myelin sheaths deteriorates during normal aging, especially in late myelinating regions such as the frontal lobes. To assess the integrity of FLWM by magnetic resonance imaging and, thus, to provide an important index of brain aging and its relationship to Alzheimer disease (AD). Cross-sectional study. Two metropolitan university hospitals and AD research centers. Two hundred fifty-two healthy adults (127 men and 125 women), aged 19 to 82 years, and 34 subjects with AD (16 men and 18 women), aged 59 to 85 years. Calculated transverse relaxation rate (R( 2)) of the FLWM (an indirect measure of the structural integrity of white matter). As expected from prior imaging data on FLWM volume, the quadratic function best represented the relationship between age and the FLWM R(2) (P<.001). In healthy individuals, the FLWM R(2) increased until the age of 38 years and then declined markedly with age. The R( 2) of subjects with AD was significantly lower than that of a group of healthy control subjects who were of similar age and sex (P<.001). The R(2) changes in white matter suggest that the healthy adult brain is in a constant state of change, roughly defined as periods of maturation continuing into middle age followed by progressive loss of myelin integrity. Clinically diagnosed AD is associated with more severe myelin breakdown. Noninvasive measures, such as the determination of the R(2), may have the potential to track prospectively the trajectory of deteriorating white matter integrity during normal aging and the development of AD and, thus, may be a useful marker for medication development aimed at the prevention of AD.

On the Relationship Between Feature‐Recognizing MRI and MRI Encoded by Singular Value Decomposition

This paper describes the similarity between two methods of non-Fourier MRI: feature-recognizing MRI (FR MRI) and MRI with encoding by singular value decomposition (SVD MRI). Both methods represented images as truncated expansions of non-Fourier basis functions; these basis images were derived from prior image data by using closely-related mathematical techniques: the Karhunen-Loeve decomposition (or principal components analysis) and singular value decomposition, respectively. We demonstrate that FR and SVD MRI are equivalent in the following sense: given the same prior image data, they lead to exactly the same basis functions. FR MRI utilized prior images of the same body part in many "training" subjects, thought to be similar to the "unknown" subject to be imaged. SVD MRI utilized a single prior image of one subject in order to perform dynamic imaging of that subject. We demonstrate that the basis function expansion derived from a single prior image may not be capable of representing new features (features not found in the prior image). Therefore, the SVD basis functions may be inappropriate for dynamic imaging.