Cardiac gated images in single photon emission computed tomography (SPECT) are known to suffer from increased noise due to low data counts. In this work, we investigate a 4D reconstruction approach based on an adaptive spatiotemporal smoothing prior, which is used to exploit the common signal component among the different cardiac gates in a sequence. In the experiments, we evaluated this approach with both simulated NCAT imaging data and two sets of clinical acquisitions. The results demonstrate that the proposed 4D approach can be more effective for improving the heart wall in terms of both noise levels and spatial resolution than motion-compensated 4D reconstruction. The proposed approach was also found to be robust for noise suppression when the imaging dose was reduced.
Dose fractionation is a popular proposed method to lower the radioactive exposure to patients undergoing myocardial perfusion imaging (MPI) SPECT/CT. Recently, we optimized reconstruction strategies employed during dose fractionation in MPI using polar maps in combination with the calculation of total perfusion deficit (TPD) scores employing hybrid defects of various sizes. Although observed to agree well with experienced observers, TPD scores have not been used to judge the impact of reduced dose SPECT imaging for standard two-headed SPECT systems. Thus, the aim of this study was to confirm that the optimized reconstruction strategies are indeed ranked accordingly by TPD in comparison to human observers reading hybrid cardiac defects studies with known truth. We setup our human observer study with 126 test cases and 44 training cases. Four observers participated by reading three different reconstruction strategies, ordered-subset expectation maximization with the original dose, (OSEM100), OSEM with only 25% of the dose (OSEM25), and filtered backprojection with the original dose (FBP100). Before commencing the reading of the actual test cases, training with feedback were done for each of the reconstruction strategies. The test cases were read in nine randomized sessions (42 test cases per session), three for each reconstruction strategy with 8 training images as a warm-up with feedback (50 image sets per reading session). The order of reading the different sets as well as the order of the test cases were different for each observer. ROCKIT (University of Chicago) software was used to analyze the observer data. Areas under the curve (AUC) values of 0.786, 0.754, and 0.643 were recorded for OSEM100, OSEM25, and FBP100 respectively. The ranking of the reconstruction strategies is the same as for the TPD scoring method, while the observers performed worse scoring FBP100.
Retrieving a set of known lesions similar to the one being evaluated might be of value for assisting radiologists to distinguish between benign and malignant clustered microcalcifications (MCs) in mammograms. In this work, we investigate how perceptually similar cases with clustered MCs may relate to one another in terms of their underlying characteristics (from disease condition to image features). We first conduct an observer study to collect similarity scores from a group of readers (five radiologists and five non-radiologists) on a set of 2,000 image pairs, which were selected from 222 cases based on their images features. We then explore the potential relationship among the different cases as revealed by their similarity ratings. We apply the multi-dimensional scaling (MDS) technique to embed all the cases in a 2-D plot, in which perceptually similar cases are placed in close vicinity of one another based on their level of similarity. Our results show that cases having different characteristics in their clustered MCs are accordingly placed in different regions in the plot. Moreover, cases of same pathology tend to be clustered together locally, and neighboring cases (which are more similar) tend to be also similar in their clustered MCs (e.g., cluster size and shape). These results indicate that subjective similarity ratings from the readers are well correlated with the image features of the underlying MCs of the cases, and that perceptually similar cases could be of diagnostic value for discriminating between malignant and benign cases.
In conventional computed tomography (CT) a single volumetric image representing the linear attenuation coefficient of an object is produced. For weakly absorbing tissues, the attenuation of the X-ray beam may not be the best description of disease-related information. In this work we present a new volumetric imaging method, called multiple-image computed tomography (MICT), that can concurrently produce several images from a set of measurements made with a single X-ray beam. MICT produces three volumetric images that represent the attenuation, refraction, ultra-small-angle scattering properties of an object. The MICT method is implemented to reconstruct images of a physical phantom and a biological object from measurement data produced by a synchroton light source. An iterative reconstruction method is employed for reconstruction of MICT images from experimental data sets that contains enhanced Poisson noise levels that are representative of future benchtop implementations of MICT. We also demonstrated that images produced by the DEI-CT method (the predecessor of MICT) can contain significant artifacts due to ultra-small-angle scattering effects while the corresponding MICT images do not.
The variability of cognitive status and clinical progression in AD patient populations poses a major confound in clinical trials. While regional cerebral glucose metabolism (rCMglc) has been found to correlate with and predict clinical worsening in AD, the complexity of multi-region involvement and lack of consistent correlation across accepted cognitive metrics have limited its use as a biomarker. We show that a multivariate classifier approach can provide a single, interpretable biomarker of rCMglc that allows baseline stratification and prediction of subsequent cognitive deterioration. The FDG-PET scans, MMSE, CDR sum of box (CDR-sb), and ADAS-11 scores of 55 AD patients from the ADNI database (76+7 yrs, 33M/22F) having 24 months of data were analyzed to assess relationships between baseline status and longitudinal progression. FDG-PET scans were sampled using automated regions of interest (ROI) for parietal cortex, posterior cingulate/precuneus, medial temporal cortex, hippocampal subregion (HIP), prefrontal cortex, lateral temporal cortex (LTL), and occipital cortex. Additionally, we applied an FDG-PET multivariate canonical variates (CV) classifier developed to characterize the progression from Normal to AD as a single pattern-based score (Strother et al, OHBM, Quebec, 2011), and evaluated the relationship between CV scores, ROI rCMglc, and MMSE, CDR-sb, and ADAS-11 performance. AD subjects worsened on average over 24 months by 4 points (SD=5) on MMSE, 3 points (SD=3) on CDR-sb and 8 points (SD=7.6) on ADAS-11. Baseline rCMglc CV score was significantly correlated with baseline MMSE (P<0.011), CDR-sb (P<0.0004), and ADAS-11 (P<.0002) and predicted 24 month change (24mchg) in MMSE (P<.005), CDR-sb (P<.010), and ADAS-11 (P<.001). Additionally, 12mchg in CV scores predicted 24mchg in MMSE (P<0.02), CDR-sb (P<0.004), and ADAS-11 (P<0.001). The 24mchg in CV scores was significantly correlated with 24mchg in MMSE (P<.005), CDR-sb (P<.010), and ADAS-11 (P<.0001). On a regional basis, significant correlations were found between LTL and MMSE baseline and 24mchg scores (both P<0.0001), and between HIP and ADAS-11 baseline (P<0.010), ADAS-11 24mchg (P<0.072), CDR-sb baseline (P<0.003), and CDR-sb 24mchg (P<0.051). Measurement of glucose metabolism using a multivariate classifier approach, and with ROI measures, provides a valuable biomarker to predict cognitive status and subsequent longitudinal outcome. Change in the CV1 pattern-based classifier score over 24 months vs. change in ADAS-11 score over 24 months. Decreasing CV1 scores correspond to increasing disease severity.
Purpose Pigment dispersion syndrome (PDS) is considered to be rare among blacks, although the inability to detect iris transillumination defects (ITDs) among very darkly pigmented irides could diminish the clinician's commitment toward the PDS diagnosis due to uncertainty brought on by the lack of this clinical sign. The goal of this study was to investigate the potential utility of a new infrared (IR) imaging technique to demonstrate ITDs among a group of blacks whose initial PDS diagnosis had to be based on pigment dispersal signs other than iris transillumination. Methods A previously described digital camera system, modified to detect visible and IR light, was used to image the irides of 10 blacks (20 eyes, 8 females, 2 males; age range=51 to 67 y) considered to have PDS on the basis of the clinical signs not including the presence of ITDs as detected with traditional slit lamp examination. Only 1 eye of 2 different subjects had ITDs that were detected with slit lamp examination, but these consisted of a very small, isolated ITD of questionable importance in each of the eyes. Normal control eyes that were matched according to age, race, sex, and refractive error were also photographed, and 2 glaucoma specialists independently reviewed PDS/control eye pairs in a masked fashion. They were instructed to select the eye more likely to be the PDS eye without the benefit of clinical information other than the digital transillumination characteristics. Results Observer no. 1 correctly selected the PDS eye among 19 of 20 (95%) PDS-normal eye pairs, and observer no. 2 correctly selected the PDS eye among 15 of 20 (75%) matched pairs. On the basis of these results, it was unlikely that observer no. 1 (Fisher exact test, P<0.0001) or observer no. 2 (P=0.06) selected the PDS eye IR image due to chance alone. It was also unlikely that selection agreement between the 2 observers was due to chance alone (κ coefficient=0.58). Conclusions Digital IR iris photography may help demonstrate abnormal ITDs among the darkly pigmented irides of blacks who have signs of pigment dispersal but who do not have detectable ITDs with traditional slit lamp examination. Infrared iris examination with newer methods should be studied further relative to blacks and others because useful clinical and/or research oriented information could be gained.
In this work we present a motion analysis for cardiac-gated tagged MRI data of the left ventricle (LV) of the heart. The dense field motion analysis is based on a deformable mesh model (DMM) that incorporates a heart surface model. The motion field is derived using intensity based image matching. DMM is well suited for motion tracking and analysis of organs undergoing non-rigid deformation. Previously, we successfully utilized DMM for heart motion tracking in cardiac SPECT image reconstruction. Preliminary results presented here for tagged MRI demonstrate further potential of this approach.
In previous studies convolutional neural networks (CNN) have been demonstrated to be effective for suppressing the elevated imaging noise in low-dose single-photon emission computed tomography (SPECT). In this study, we investigate a spatiotemporal CNN model (ST-CNN) to exploit the signal redundancy in both spatial and temporal domain among the gate frames in a cardiac-gated sequence. In the experiments, we demonstrated the proposed ST-CNN model on a set of 119 clinical acquisitions with imaging dose reduced by four times. The quantitative results show that ST-CNN can lead to further improvement in the reconstructed myocardium in terms of the overall error level and the spatial resolution of the left ventricular (LV) wall. Compared to a spatial-only CNN, ST-CNN decreased the mean-squared-error of the reconstructed myocardium by 21.1% and the full-width at half-maximum of the LV wall by 5.3%.
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