A. Hans Vija

Pediatric 99mTc-MDP Bone SPECT with Ordered Subset Expectation Maximization Iterative Reconstruction with Isotropic 3D Resolution Recovery

PURPOSE To perform a preliminary evaluation of the image quality of pediatric technetium 99m ((99m)Tc) methylene diphosphonate (MDP) bone single photon emission computed tomography (SPECT) by using iterative reconstruction-ordered subset expectation maximization with three-dimensional resolution recovery (OSEM-3D)-and to assess whether any improvements with use of this technique could lead to a reduction in patient dose or a shortening in imaging time. MATERIALS AND METHODS Institutional advisory board approval was obtained for this investigation. Fifty (99m)Tc-MDP SPECT studies of the spine were evaluated (36 female and 14 male patients; mean age, 15.5 years). Each study was acquired by using a dual-detector camera, with each detector rotating 360°. By using filtered back projection (FBP) and OSEM-3D, images were reconstructed from data generated by both detectors. Likewise, OSEM-3D was used to reconstruct data from a single detector simulating half the administered radiopharmaceutical activity. Two nuclear medicine physicians, blinded to the patient data, reviewed the images for image quality in four different categories by using a four-point scale: artifacts (category 1), lesions (category 2), noise (category 3), and image sharpness (category 4). RESULTS Compared with FBP, images reconstructed by using OSEM-3D with one or two detectors showed significant improvement in image quality with regard to lesion detection, noise level, and image sharpness (P < .02, .01, and .001, respectively). With OSEM-3D, no significant differences were observed when either one or two detectors were used. CONCLUSION Improved image quality of skeletal SPECT with either a 50% reduction in radiation dose or a 50% reduction in acquisition time or combination of the two can be achieved by using OSEM-3D.

Tomographic performance characteristics of the IQ●SPECT system

The IQ●SPECT system was introduced by Siemens in 2010 to significantly improve the efficiency of myocardial perfusion imaging (MPI) using conventional, large field-of-view (FOV) SPECT and SPECT●CT systems. With IQ●SPECT, it is possible to perform MPI scans in one-fourth the time or using one-fourth the administered dose as compared to a standard protocol using parallel-hole collimators. This improvement is achieved by means of a proprietary multifocal collimator that rotates around the patient in a cardio-centric orbit resulting in a four-fold magnification of the heart while keeping the entire torso in the FOV. The data are reconstructed using an advanced reconstruction algorithm that incorporates measured values for gantry deflections, collimator-hole angles, and system point response function. This article explores the boundary conditions of IQ●SPECT imaging, as measured using the Data Spectrum® cardiac torso phantom with the cardiac insert. Impact on reconstructed image quality was evaluated for variations in positioning of the myocardium relative to the sweet spot, scan-arc limitations, and for low-dose imaging protocols. Reconstructed image quality was assessed visually using the INVIA 4DMSPECT and quantitatively using Siemens internal IQ assessment software. The results indicated that the IQ●SPECT system is capable of tolerating possible mispositioning of the myocardium relative to the sweet spot by the operator, and that no artifacts are introduced by the limited angle coverage. We also found from the study of multiple low dose protocols that the dwell time will need to be adjusted in order to acquire data with sufficient signal-to-noise ratio for good reconstructed image quality.

Reduction in Radiation Dose in Mercaptoacetyltriglycerine Renography with Enhanced Planar Processing

PURPOSE To determine the minimum dose of technetium 99m ((99m)Tc) mercaptoacetyltriglycerine (MAG3) needed to perform dynamic renal scintigraphy in the pediatric population without loss of diagnostic quality or accurate quantification of renal function and to investigate whether adaptive noise reduction could help further reduce the minimum dose required. MATERIALS AND METHODS Approval for this retrospective study was obtained from the institutional review board, with waiver of informed consent. A retrospective review was conducted in 33 pediatric patients consecutively referred for a (99m)Tc-MAG3 study. In each patient, a 20-minute dynamic study was performed after administration of 7.4 MBq/kg. Binomial subsampling was used to simulate studies performed with 50%, 30%, 20%, and 10% of the administered dose. Four nuclear medicine physicians independently reviewed the original and subsampled images, with and without noise reduction, for image quality. Two observers independently performed a quantitative analysis of renal function. Subjective rater confidence was analyzed by using a logistic regression model, and the quantitative analysis was performed by using the paired Student t test. RESULTS Reducing the administered dose to 30% did not substantially affect image quality, with or without noise reduction. When the dose was reduced to 20%, there was a slight but significant decrease (P = .0074) in image quality, which resolved with noise reduction. Reducing the dose to 10% caused a decrease in image quality (P = .0003) that was not corrected with noise reduction. However, the dose could be reduced to 10% without a substantial change in the quantitative evaluation of renal function independent of the application of noise reduction. CONCLUSION Decreasing the dose of (99m)Tc-MAG3 from 7.4 to 2.2 MBq/kg did not compromise image quality. With noise reduction, the dose can be reduced to 1.5 MBq/kg without subjective loss in image quality. The quantitative evaluation of renal function was not substantially altered, even with a theoretical dose as low as 0.74 MBq/kg.

Preprocessing of SPECT projection data: benefits and pitfalls

The Pixon method, a statistically rigorous procedure for adaptive noise suppression that avoids the generation of spurious artifacts yet preserves all the statistically justifiable image features resident in the raw counts, is applied to nuclear studies. The present work focuses on the denoising of projection data at various count levels for subsequent SPECT iterative reconstructions, where each projection is denoised independently. The pitfall of applying such preprocessing to projection images is that tomographic information could be lost, resulting in the loss of weak or small sources. The goal is to investigate the benefits and pitfalls of noise suppression of projection data on the resulting reconstruction, with the ultimate goals to (i) increase sensitivity for detection of lesions of small size and/or of small activity-to-background ratio, (ii) reduce data acquisition time, and (iii) reduce patient dose. We use simulated and measured data and human observer studies, which are analyzed using quantitative measures. An accurate reconstruction at reduced counts using view-independent, noise-reduced projection images can result in significant gain in detectability based on simple SNR measures, but only minor improvements as tested with human observers. At the same time, conservative denoising of the projections results in the loss of small and weak sources, particularly cold lesions. Further analysis and clinical feedback may be warranted, yet it seems that such an approach contains serious pitfalls, likely outweighing the benefits.

Statistically based spatially adaptive noise reduction of planar nuclear studies

The data-driven Pixon noise-reduction method is applied to nuclear studies. By using the local information content, it preserves all statistically justifiable image features without generating artifacts. Statistical measures provide the user a feedback to judge if the processing parameters are optimal. The present work focuses on planar nuclear images with known Poisson noise characteristics. Its ultimate goals are to: (a) increase sensitivity for detection of lesions of small size and/or of small activity-to-background ratio, (b) reduce data acquisition time, and (c) reduce patient dose. Data are acquired using Data Spectrum’s cylinder phantom in two configurations: (a) with hot and cold rod inserts at varying total counts and (b) with hot sphere inserts at varying activity-to-background ratios. We show that the method adapts automatically to both hot and cold lesions, concentration ratios, and different noise levels and structure dimensions. In clinical applications, slight adjustment of the parameters may be needed to adapt to the specific clinical protocols and physician preference. Visually, the processed images are comparable to raw images with ~16 times as many counts, and quantitatively the reduced noise equals that obtained with ~50 times as many counts. We also show that the Pixon method allows for identification of spheres at low concentration ratios, where raw planar imaging fails and matched filtering underperforms. Conclusion: The Pixon method significantly improves the image quality of data at either reduced count levels, or low target-to-background ratios. An analysis of clinical studies is now warranted to assess the clinical impact of the method.

Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps

We propose a data-driven method for extracting a respiratory surrogate signal from SPECT list-mode data. The approach is based on dimensionality reduction with Laplacian Eigenmaps. By setting a scale parameter adaptively and adding a series of post-processing steps to correct polarity and normalization between projections, we enable fully-automatic operation and deliver a respiratory surrogate signal for the entire SPECT acquisition. We validated the method using 67 patient scans from three acquisition types (myocardial perfusion, liver shunt diagnostic, lung inhalation/perfusion) and an Anzai pressure belt as a gold standard. The proposed method achieved a mean correlation against the Anzai of 0.81 ± 0.17 (median 0.89). In a subsequent analysis, we characterize the performance of the method with respect to count rates and describe a predictor for identifying scans with insufficient statistics. To the best of our knowledge, this is the first large validation of a data-driven respiratory signal extraction method published thus far for SPECT, and our results compare well with those reported in the literature for such techniques applied to other modalities such as MR and PET.

IQ·SPECT technology and its clinical applications using multicenter normal databases

IQ·SPECT (Siemens Medical Solutions) is a solution for high-sensitivity and short-time acquisition imaging of the heart for a variable angle general purpose gamma camera. It consists of a multi-focal collimator, a cardio-centric orbit and advanced iterative reconstruction, modeling the image formation physics accurately. The multi-focal collimator enables distance-dependent enlargement of the center region while avoiding truncation at the edges. With the specified configuration and a cardio-centric orbit it can obtain a fourfold sensitivity increase for the heart at the center of the scan orbit. Since IQ·SPECT shows characteristic distribution patterns in the myocardium, appropriate acquisition and processing conditions are required, and normal databases are convenient for quantification of both normal and abnormal perfusion images. The use of prone imaging can be a good option when X-ray computed tomography (CT) is not available for attenuation correction. CT-based attenuation correction changes count distribution significantly in the inferior wall and around the apex, hence image interpretation training and additional use of normal databases are recommended. Recent reports regarding its technology, Japanese Society of Nuclear Medicine working group activities, and clinical studies using 201Tl and 99mTc-perfusion tracers in Japan are summarized.

Data-driven respiratory signal extraction for SPECT imaging using Laplacian Eigenmaps

In Single Photon Emission-Computed Tomography (SPECT) imaging, respiratory motion may lead to artifacts and loss of quantitative accuracy. To compensate for this motion, a respiratory surrogate signal representing the patients respiratory state over time is required. In practice, this surrogate signal is obtained via sensor-based approaches, but we seek to develop a data-driven solution that requires no external hardware. In this work, we compare two such methods, one linear and one non-linear, based on dimensionality reduction: Principle Component Analysis (PCA) and Laplacian Eigenmaps (LE). Our aim is to apply both to conventional SPECT and assess the feasibility of data-driven respiratory surrogate signal extraction for this modality. We expect that LE, which is less sensitive to outliers in data, will outperform PCA at high levels of image noise. Two phantom acquisitions were performed: one in which a sphere in cold background was translated axially by a piston actuator (dynamic), and a warm background with no sphere (static). Using binomial subsampling, both datasets were combined at various Signal-to-Noise Ratios (SNRs). LE and PCA surrogate signals were computed and compared via Pearsons correlation to the truth signal obtained from the actuator. As a follow-up, LE and PCA estimates from 27 cardiac SPECT acquisitions were compared to a simultaneously acquired signal from a pressure sensor embedded in an elastic belt. In the phantom experiment, correlations between LE/PCA and truth were >0.9 for all SNR>5. For SNR<;5, PCA deteriorated rapidly, whereas LE remained stable through SNR=2.5. For the patient validation, LE and PCA yielded average correlations of 0.86±0.14 and 0.37±0.26, respectively. The phantom experiment indicated that LE outperforms PCA for low-SNR data. This conclusion was supported by the superior performance of LE for patient datasets, where noise may be high. However, the present work is limited by the simplistic motion present in the phantom experiment and the limited scope of the patient validation.

Development of a database driven statistical quality control framework for medical imaging systems

Phantom studies are typically the first tests when the performance of the imaging system needs to be verified or when a problem needs troubleshooting. It is not always clear whether the resulting image indicates a systematic problem with the system. Often perceived artifacts are only due to residual calibration errors or noise (quantum and system noise), yet the system being at nominal performance.