The effect of Q.Clear reconstruction on quantification and spatial resolution of 18F-FDG PET in simultaneous PET/MR

Background Q.Clear is a block sequential regularized expectation maximization penalized-likelihood reconstruction algorithm for Positron Emission Tomography (PET). It has shown high potential in improving image reconstruction quality and quantification accuracy in PET/CT system. However, the evaluation of Q.Clear in PET/MR system, especially for clinical applications, is still rare. This study aimed to evaluate the impact of Q.Clear on the 18F-fluorodeoxyglucose (FDG) PET/MR system and to determine the optimal penalization factor β for clinical use. Methods A PET National Electrical Manufacturers Association/ International Electrotechnical Commission (NEMA/IEC) phantom was scanned on GE SIGNA PET/MR, based on NEMA NU 2-2012 standard. Metrics including contrast recovery (CR), background variability (BV), signal-to-noise ratio (SNR) and spatial resolution were evaluated for phantom data. For clinical data, lesion SNR, signal to background ratio (SBR), noise level and visual scores were evaluated. PET images reconstructed from OSEM + TOF and Q.Clear were visually compared and statistically analyzed, where OSEM + TOF adopted point spread function as default procedure, and Q.Clear used different β values of 100, 200, 300, 400, 500, 800, 1100 and 1400. Results For phantom data, as β value increased, CR and BV of all sizes of spheres decreased in general; images reconstructed from Q.Clear reached the peak SNR with β value of 400 and generally had better resolution than those from OSEM + TOF. For clinical data, compared with OSEM + TOF, Q.Clear with β value of 400 achieved 138% increment in median SNR (from 58.8 to 166.0), 59% increment in median SBR (from 4.2 to 6.8) and 38% decrement in median noise level (from 0.14 to 0.09). Based on visual assessment from two physicians, Q.Clear with β values ranging from 200 to 400 consistently achieved higher scores than OSEM + TOF, where β value of 400 was considered optimal. Conclusions The present study indicated that, on 18F-FDG PET/MR, Q.Clear reconstruction improved the image quality compared to OSEM + TOF. β value of 400 was optimal for Q.Clear reconstruction.

Photoacoustic-MR Images Registration Based on Co-Sparse Analysis Model to Compensate for Brain Shift

Brain shift is an important obstacle for the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging systems to update the image-guided surgery systems with real-time data. However, due to the innate limitations of the current imaging modalities, accurate and real-time brain shift compensation remains as a challenging problem. In this study, application of the intra-operative photoacoustic (PA) imaging and registration of the intra-operative PA images with pre-operative brain MR images is proposed to compensate brain deformation during surgery. Finding a satisfactory multimodal image registration method is a challenging problem due to complicated and unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for PA-MR image registration which can capture the interdependency of two modalities. The proposed algorithm works based on the minimization of mapping transform by using a pair of analysis operators. These operators are learned by the alternating direction method of multipliers. The method was evaluated using experimental phantom and ex-vivo data obtained from mouse brain. The results of phantom data show about 60% and 63% improvement in root mean square error (RMSE) and target registration error (TRE) in comparison with commonly used normalized mutual information registration method. In addition, the results of mouse brain and phantom data shown more accurate performance for PA versus ultrasound imaging for brain shift calculation. Finally, by using the proposed registration method, the intra-operative PA images could become a promising tool when the brain shift invalidated pre-operative MRI.

Automatic delineation and quantification of pulmonary vascular obstruction index in patients with pulmonary embolism using Perfusion SPECT-CT: a simulation study

Background In patients with pulmonary embolism (PE), there is a growing interest in quantifying the pulmonary vascular obtruction index (PVOI), which may be an independent risk factor for PE recurrence. Perfusion SPECT/CT is a very attractive tool to provide an accurate quantification of the PVOI. However, there is currently no reliable method to automatically delineate and quantify it. The aim of this phantom study was to assess and compare 3 segmentation methods for PVOI quantification with perfusion SPECT/CT imaging. Methods Three hundred ninety-six SPECT/CT scans, with various PE scenarios (n = 44), anterior to posterior perfusion gradients (n = 3), and lung volumes (n = 3) were simulated using Simind software. Three segmentation methods were assesssed: (1) using an intensity threshold expressed as a percentage of the maximal voxel value (MaxTh), (2) using a Z-score threshold (ZTh) after building a Z-score parametric lung map, and (3) using a relative difference threshold (RelDiffTh) after building a relative difference parametric map. Ninety randomly selected simulations were used to define the optimal threshold, and 306 simulations were used for the complete analysis. Spacial correlation between PE volumes from the phantom data and the delineated PE volumes was assessed by computing DICEPE indices. Bland-Altman statistics were used to calculate agreement for PVOI between the phantom data and the segmentation methods. Results Mean DICEPE index was higher with the RelDiffTh method (0.85 ± 0.08), as compared with the MaxTh method (0.78 ± 0.16) and the ZTh method (0.67 ± 0.15). Using the RelDiffTh method, mean DICEPE index remained high (> 0.81) regardless of the perfusion gradient and the lung volumes. Using the RelDiffTh method, mean relative difference in PVOI was − 12%, and the limits of agreement were − 40% to 16%. Values were 3% (− 75% to 81%) for MaxTh method and 0% (− 120% to 120%) for ZTh method. Graphycal analysis of the Bland-Altman graph for the RelDiffTh method showed very close estimation of the PVOI for small and medium PE, and a trend toward an underestimation of large PE. Conclusion In this phantom study, a delineation method based on a relative difference parametric map provided a good estimation of the PVOI, regardless of the extent of PE, the intensity of the anterior to posterior gradient, and the whole lung volumes.

Analisis Noise Level Hasil Citra CT-Scan Pada Phantom Kepala Dengan Variasi Tegangan Tabung Dan Ketebalan Irisan

Telah dilakukan penelitian analisis noise level hasil citra CT-scan pada phantom dengan variasi tegangan tabung dean variasi ketebalan irisan. Penelitian ini bertujuan untuk mengetahui dan menganalisis noise level hasil citra CT-scan pada phantom kepala dengan variasi tegangan tabung dan ketebalan irisan. Evaluasi noise level harus dilakukan sehingga dapat mengurangi noise pada CT-scan dan phantom kepala air. Nilai noise level diperoleh dengan variasi tegangan tabung (80-140) kV dan ketebalan irisan (1-10) mm. Hasil noise level pada variasi tegangan tabung berkisar (0,72-2,97)%. Sedangkan noise level pada variasi ketebalan irisan berkisar (0,97-2,97)%. Hasil penelitian nilai noise level hasail CT-scan dengan variasi tegangan tabung dan ketebalan irisan maka dapat disimpulkan bahwa hasil citra ct-scan terbaik diperoleh pada tegangan tabung 120 kV dan ketebalan irisan 5 mm. kata kunci : Noise, Ct-Scan, Phantom, Data Sekunder, Tegangan Tabung, Ketebalan Irisan

Cross-validation study between the HRRT and the PET component of the SIGNA PET/MRI system with focus on neuroimaging

Background The Siemens high-resolution research tomograph (HRRT - a dedicated brain PET scanner) is to this day one of the highest resolution PET scanners; thus, it can serve as useful benchmark when evaluating performance of newer scanners. Here, we report results from a cross-validation study between the HRRT and the whole-body GE SIGNA PET/MR focusing on brain imaging. Phantom data were acquired to determine recovery coefficients (RCs), % background variability (%BG), and image voxel noise (%). Cross-validation studies were performed with six healthy volunteers using [11C]DTBZ, [11C]raclopride, and [18F]FDG. Line profiles, regional time-activity curves, regional non-displaceable binding potentials (BPND) for [11C]DTBZ and [11C]raclopride scans, and radioactivity ratios for [18F]FDG scans were calculated and compared between the HRRT and the SIGNA PET/MR. Results Phantom data showed that the PET/MR images reconstructed with an ordered subset expectation maximization (OSEM) algorithm with time-of-flight (TOF) and TOF + point spread function (PSF) + filter revealed similar RCs for the hot spheres compared to those obtained on the HRRT reconstructed with an ordinary Poisson-OSEM algorithm with PSF and PSF + filter. The PET/MR TOF + PSF reconstruction revealed the highest RCs for all hot spheres. Image voxel noise of the PET/MR system was significantly lower. Line profiles revealed excellent spatial agreement between the two systems. BPND values revealed variability of less than 10% for the [11C]DTBZ scans and 19% for [11C]raclopride (based on one subject only). Mean [18F]FDG ratios to pons showed less than 12% differences. Conclusions These results demonstrated comparable performances of the two systems in terms of RCs with lower voxel-level noise (%) present in the PET/MR system. Comparison of in vivo human data confirmed the comparability of the two systems. The whole-body GE SIGNA PET/MR system is well suited for high-resolution brain imaging as no significant performance degradation was found compared to that of the reference standard HRRT.

A new virtue of phantom MRI data: explaining variance in human participant data

Background: Magnetic resonance imaging (MRI) is an important yet complex data acquisition technology for studying the brain. MRI signals can be affected by many factors and many sources of variance are often simply attributed to “noise”. Unexplained variance in MRI data hinders the statistical power of MRI studies and affects their reproducibility. We hypothesized that it would be possible to use phantom data as a proxy of scanner characteristics with a simplistic model of seasonal variation to explain some variance in human MRI data. Methods: We used MRI data from human participants collected in several studies, as well as phantom data collected weekly for scanner quality assurance (QA) purposes. From phantom data we identified the variables most likely to explain variance in acquired data and assessed their statistical significance by using them to model signal-to-noise ratio (SNR), a fundamental MRI QA metric. We then included phantom data SNR in the models of morphometric measures obtained from human anatomical MRI data from the same scanner. Results: Phantom SNR and seasonal variation, after multiple comparisons correction, were statistically significant predictors of the volume of gray brain matter. However, a sweep over 16 other brain matter areas and types revealed no statistically significant predictors among phantom SNR or seasonal variables after multiple comparison correction. Conclusions: Seasonal variation and phantom SNR may be important factors to account for in MRI studies. Our results show weak support that seasonal variations are primarily caused by biological human factors instead of scanner performance variation. The phantom QA metric and scanning parameters are useful for more than just QA. Using QA metrics, scanning parameters, and seasonal variation data can help account for some variance in MRI studies, thus making them more powerful and reproducible.

Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit

ABSTRACTBackgroundDiffusion kurtosis imaging (DKI) quantifies the microstructure’s non-Gaussian diffusion properties. However, it has increased fitting parameters and requires higher b-values. Evaluation of DKI reproducibility is important for clinical purposes.PurposeTo assess reproducibility in whole-brain high resolution DKI at varying b-values.Study TypeProspective.Subjects and PhantomsForty-four individuals from the test-retest Human Connectome Project (HCP) database and twelve 3D-printed tissue mimicking phantoms.Field Strength/SequenceMultiband echo-planar imaging for in vivo and phantom diffusion-weighted imaging at 3T and 9.4T respectively. MPRAGE at 3T for in vivo structural data.AssessmentFrom HCP data with b-value =1000,2000,3000 s/mm2 (dataset A), two additional datasets with b-values=1000, 3000 s/mm2 (dataset B) and b-values=1000, 2000 s/mm2 (dataset C) were extracted. Estimated DKI metrics from each dataset were used for evaluating reproducibility and fitting quality in whole-brain white matter (WM), region of interest (ROI) and gray matter (GM).Statistical TestsDKI reproducibility was assessed using the within-subject coefficient of variation (CoV), fitting residuals to evaluate DKI fitting accuracy and Pearson’s correlation to investigate presence of systematic biases.ResultsCompared to dataset C, the CoV from DKI parameters from datasets A and B were comparable, with WM and GM CoVs <20%, while differences between datasets were smaller for the DKI-derived DTI parameters. Slightly higher fitting residuals were observed in dataset C compared to A and B, but lower residuals in dataset B were detected for the WM ROIs. A similar trend was observed for the phantom data with comparable CoVs at varying fiber orientations for datasets A and B. In addition, dataset C was characterized by higher residuals across the different fiber crossings.Data ConclusionThe comparable reproducibility of DKI maps between datasets A and B observed in the in vivo and phantom data indicates that high reproducibility can still be achieved within a reasonable scan time, supporting DKI for clinical purposes.HIGHLIGHTS:Reproducibility and fitting accuracy of high resolution DKI were evaluated as function of available b-values.A DKI dataset with b-values of 1000 and 3000 s/mm2 performs equally well as the original HCP three-shell dataset, while a dataset with b-values of 1000 and 2000 s/mm2 has lower reproducibility and fitting quality.In vivo results were verified using phantoms capable of mimicking different white matter configurations.These results suggest that DKI data can be obtained within less time, without sacrificing data quality.