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PurposeHigh blood glucose (hBG) in patients undergoing [18F]FDG PET/CT scans often results in rescheduling the examination, which may lead to clinical delay for the patient and decrease productivity for the department. The aim of this study was to evaluate whether long-axial field-of-view (LAFOV) PET/CT can minimize the effect of altered bio-distribution in hBG patients and is able to provide diagnostic image quality in hBG situations.Materials and methodsOncologic patients with elevated blood glucose (≥ 8.0 mmol/l) and normal blood glucose ( 11 mmol/l). Tracer uptake in the liver, muscle, and tumor was evaluated. Furthermore, image quality was compared between long acquisitions (ultra-high sensitivity mode, 360 s) on a LAFOV PET/CT and routine acquisitions equivalent to a short-axial field-of-view scanner (simulated (sSAFOV), obtained with high sensitivity mode, 120 s). Tumor-to-background ratio (TBR) and contrast-to-noise ratio (CNR) were used as the main image quality criteria.ResultsThirty-one hBG patients met the inclusion criteria and were matched with 31 nBG patients. Overall, liver uptake was significantly higher in hBG patients (SUVmean, 3.07 ± 0.41 vs. 2.37 ± 0.33; p = 0.03), and brain uptake was significantly lower (SUVmax, 7.58 ± 0.74 vs. 13.38 ± 3.94; p  11 mmol/l) patients examined with LAFOV PET/CT showed no statistical significant difference in CNR (19.84 ± 8.40 vs. 17.79 ± 9.3, p = 0.08).ConclusionWhile elevated blood glucose (> 11 mmol) negatively affected TBR and CNR in our cohort, the images from a LAFOV PET-scanner had comparable CNR to PET-images acquired from nBG patients using sSAFOV PET/CT. Therefore, we argue that oncologic patients with increased blood sugar levels might be imaged safely with LAFOV PET/CT when rescheduling is not feasible.

Background: Zirconium-89 (Zr-89) PET tracers have become increasingly significant in the field of nuclear medicine due to their 3-day physical half-life, allowing for the study of dynamic biological processes over relatively long timeframes. To date there has been limited publication of studies focused on optimisation of acquisition parameters for Zr-89 PET. This paper outlines a short phantom study investigating the optimal beta regularization parameter for quantitation and noise in block sequential regularised expectation maximisation (BSREM) also known as Bayesian penalized likelihood (BPL) reconstruction, for varying image noise characteristics (acquisition duration). Results: The choice of the beta regularisation parameter substantially impacts image quality and quantitation. For larger volumes, BSREM reconstruction enhanced image quality (lower noise) and maintained quantitation, whereas for smaller volumes quantitation worsened as compared to OSEM for high regularisation parameters. Conclusion: Where BSREM reconstruction is used for Zr-89 images, careful attention must be paid to the choice of weighting factor, especially for quantitative clinical studies. The effect of varying beta on several measures of image quality was characterised for the case of a phantom, with the results indicating that the value of beta for optimal Zr-89 quantitation is lower than what is generally used for optimal visualisation. This work demonstrates the need for careful attention to the reconstruction methods used for quantitative imaging studies, such as those required for theragnostic imaging. [ABSTRACT FROM AUTHOR]

Recent advances in spectral imaging have enabled snapshot acquisition, as a means to mitigate the impracticalities of spectral imaging, e.g., expert operators and cumbersome hardware. Snapshot spectral imaging, e.g., in technologies like spectral filter arrays, has also enabled higher temporal resolution at the expense of the spatio-spectral resolution, allowing for the observation of temporal events. Designing, realising, and deploying such technologies is yet challenging, particularly due to the lack of clear, user-meaningful quality criteria across diverse applications, sensor types, and workflows. Key research gaps include optimising raw image processing from snapshot spectral imagers and assessing spectral image and video quality in ways valuable to end-users, manufacturers, and developers. This paper identifies several challenges and current opportunities. It proposes considering them jointly and suggests creating a new unified snapshot spectral imaging paradigm that would combine new systems and standards, new algorithms, new cost functions, and quality indices. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction. Materials and methods: We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations. Results: The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20. Conclusion: Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds. Clinical relevance statement: The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible. Key Points: Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience. [ABSTRACT FROM AUTHOR]

Background: Accurate reconstruction and quantification in the post-therapy SPECT/CT imaging of 166Ho microspheres for hepatic malignancies is crucial for treatment evaluation. This present study aimed to explore the impact of the OSEM reconstruction parameters on SPECT/CT image features for dose distribution determination, using Hybrid Recon™ (Hermes Medical Solutions AB) and full Monte Carlo (MC) collimator modeling. Methods: Image quality and activity quantification were assessed through two acquisitions of the Jaszczak phantom using a Siemens Symbia Intevo Bold SPECT/CT system. The datasets were reconstructed using the OSEM method, with variations in the number of iterations for 15 and 8 subsets, both with and without full MC collimator modeling. Contrast recovery coefficient ( Q H ), coefficient of variation (CV), contrast-to-noise ratio (CNR), calibration factor (CF), and activity recovery coefficient (ARC) were calculated and used to evaluate image quality and activity quantification. Results: Reconstructions with 5 iterations and 15 subsets, as well as 10 iterations and 8 subsets, were selected as the most suitable for 166Ho imaging, as they provided higher Q H and ARCs. Incorporating full MC collimator modeling in both reconstructions led to significant improvements in image quality and activity recovery. The CFs remained consistent for a fixed value of 15 and 8 subsets, with values of (14.9 ± 0.5) cps/MBq and (14.6 ± 0.5) cps/MBq, respectively. However, when applying full collimator modeling, the CF values decreased to a range between 10.9 and 12.1 cps/MBq. Conclusions: For 166Ho SPECT/CT imaging, OSEM (with either 5 iterations and 15 subsets or 10 iterations and 8 subsets) combined with full MC collimator modeling yielded superior image quality and quantification results. [ABSTRACT FROM AUTHOR]

Image-to-image translation can create large impact in medical imaging, as images can be synthetically transformed to other modalities, sequence types, higher resolutions or lower noise levels. To ensure patient safety, these methods should be validated by human readers, which requires a considerable amount of time and costs. Quantitative metrics can effectively complement such studies and provide reproducible and objective assessment of synthetic images. If a reference is available, the similarity of MR images is frequently evaluated by SSIM and PSNR metrics, even though these metrics are not or too sensitive regarding specific distortions. When reference images to compare with are not available, non-reference quality metrics can reliably detect specific distortions, such as blurriness. To provide an overview on distortion sensitivity, we quantitatively analyze 11 similarity (reference) and 12 quality (non-reference) metrics for assessing synthetic images. We additionally include a metric on a downstream segmentation task. We investigate the sensitivity regarding 11 kinds of distortions and typical MR artifacts, and analyze the influence of different normalization methods on each metric and distortion. Finally, we derive recommendations for effective usage of the analyzed similarity and quality metrics for evaluation of image-to-image translation models. [ABSTRACT FROM AUTHOR]

Background: A recently released digital solid-state positron emission tomography/x-ray CT (PET/CT) scanner with bismuth germanate (BGO) scintillators provides an artificial intelligence (AI) based system for automatic patient positioning. The efficacy of this digital-BGO system in patient placement at the isocenter and its impact on image quality and radiation exposure was evaluated. Method: The digital-BGO PET/CT with AI-based auto-positioning was compared (χ2, Mann–Whitney tests) to a solid-state lutetium-yttrium oxyorthosilicate (digital-LYSO) PET/CT with manual patient positioning (n = 432 and 343 studies each, respectively), with results split into groups before and after the date of a recalibration of the digital-BGO auto-positioning camera. To measure the transverse displacement of the patient center from the scanner isocenter (off-centering), CT slices were retrospectively selected and automatically analyzed using in-house software. Noise was measured as the coefficient of variation within the liver of absolute Hounsfield units referenced to air. Radiation exposure was recorded as dose-length product (DLP). Off-centering measurements were validated by a phantom study. Results: The phantom validation study gave < 1.6 mm error in 15 off-centering measurements. Patient off-centering was biased 1.92 ± 1.79 cm (mean ± standard deviation) in the posterior direction which was significantly different from the 0.22 ± 1.21 cm bias in the left lateral direction (p < 0.0001, Wilcoxon). After recalibration, 27% (38/140) of the studies had off-centering results > 2.5cm for the digital-BGO, which was significantly better than the 49% (143/292, p < 0.001) before recalibration and better than for the digital-LYSO: 54% (119/222, p < 0.001) before and 55% (66/121, p < 0.001) after. On average, CT image quality was superior for non-obese patients who were most closely aligned with the isocenter: noise increased by 3.2 ± 0.1% for every 1 cm increase in off-centering. DLP increased by 144 ± 22 Gy cm for every 1 cm increase in anterior off-centering. Conclusion: AI-based automatic patient positioning in a digital-BGO PET/CT scanner significantly reduces patient off-centering, thereby improving image quality and ensuring proper radiation exposure. [ABSTRACT FROM AUTHOR]

Background: Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality. Methods: We enrolled a cohort of sixty patients who underwent DL-MRI, conventional MRI, and No-DL MRI examinations to evaluate image quality. Key metrics considered in the assessment included scan duration, overall image quality, quantitative assessments of Relative Signal-to-Noise Ratio (rSNR), Relative Contrast-to-Noise Ratio (rCNR), and diagnostic efficacy. Two experienced radiologists independently assessed image quality using a 5-point scale (5 indicating the highest quality). To gauge interobserver agreement for the assessed pathologies across image sets, we employed weighted kappa statistics. Additionally, the Wilcoxon signed rank test was employed to compare image quality and quantitative rSNR and rCNR measurements. Results: Scan time was significantly reduced with DL-MRI and represented an approximate 66.5% reduction. DL-MRI consistently exhibited superior image quality in both coronal T2WI and axial T2WI when compared to both conventional MRI (p < 0.01) and No-DL-MRI (p < 0.01). Interobserver agreement was robust, with kappa values exceeding 0.735. For rSNR data, coronal fat-saturated(FS) T2WI and axial FS T2WI in DL-MRI consistently outperformed No-DL-MRI, with statistical significance (p < 0.01) observed in all cases. Similarly, rCNR data revealed significant improvements (p < 0.01) in coronal FS T2WI of DL-MRI when compared to No-DL-MRI. Importantly, our findings indicated that DL-MRI demonstrated diagnostic performance comparable to conventional MRI. Conclusion: Integrating deep learning-based reconstruction methods into standard clinical workflows has the potential to the promise of accelerating image acquisition, enhancing image clarity, and increasing patient throughput, thereby optimizing diagnostic efficiency. Trial registration: Retrospectively registered. Key points: Deep learning based reconstruction Magnetic Resonance Imaging (DL-MRI) not only expedites scan times but also significantly enhances imaging quality. [ABSTRACT FROM AUTHOR]

Objective. The accuracy of material decomposition in spectral computed tomography (CT) depends on the information quality captured in image acquisition, a factor that cannot be adequately assessed using conventional image quality metrologies due to the multi-energy nature of spectral CT. This work used metrologies specific to spectral CT to evaluate the impact of acquisition conditions on the quality of spectral CT images and accuracy of material decomposition techniques. Approach. Computational phantoms were created with cylindrical shapes and variable sizes (20–40 cm), containing inserts of iodine and gadolinium (1–8 mg ml−1). The phantoms were imaged using a validated CT simulator modeling a clinical photon-counting CT scanner. The acquisitions were done at different detector energy thresholds (50–90 keV) and tube currents (25–250 mAs). The images were used to develop and train a data-driven material identification and quantification algorithm. Two spectral metrologies, multivariate contrast-to-noise ratio (CNR) and separability index, were used to characterize the impact of energy threshold, tube current, phantom size, and material concentration on signal quality. The results were interpreted in terms of figures of merit of accuracy for classification and mean absolute error (MAE) and root mean squared error (RMSE) for regression. Main results. Signal quality for iodine and gadolinium was maximized with a low energy threshold, high tube current, and small phantom size. While conventional CNR terms predicted variable image quality for two-thirds of all conditions, multivariate CNR was above 10 for half of those. Separability index showed that for a phantom size greater than 30 cm, a minimum of 75–110 mAs is required to separate 2 mg ml−1 of iodine and gadolinium. For both classification and regression tasks, a random forest model with a local statistics dataset provided the best performance. Across conditions, classification performance was 0.66–0.99 for I accuracy, 0.72–0.99 for Gd accuracy. Regression performance was 0.02–0.91 mg ml−1 I and 0.02–0.59 mg ml−1 Gd for MAE and 0.11–1.08 mg ml−1 I and 0.07-0.76 mg ml−1 Gd for RMSE. Significance. Multivariate CNR and separability index metrologies can predict material decomposition performance. Theses metrics demonstrated that the decomposition of iodine and gadolinium have higher separability when the acquisition is done at a lower energy threshold, with a higher tube current, and when the imaged object has a smaller size. Object size had the largest impact on metrics and decomposition performance. [ABSTRACT FROM AUTHOR]

Background: Evaluation of cardiothoracic pathologies is a common indication for computed tomography (CT) in infants. However, CT is fraught with challenges specific to the patient population, such as increased sensitivity to radiation and inability to remain stationary during imaging. Objective: This study investigates potential advantages of a high-pitch helical CT protocol for infants with cardiothoracic pathologies. Namely, we evaluate whether a high-pitch helical CT protocol can minimize radiation exposure without compromising image quality. Materials and methods: This retrospective study included 98 consecutive cardiac protocol CT examinations of infants (56 males, 42 females; mean age 3.3 ± 2.8 months) performed at a tube voltage of 80 kV between 2016 and 2022. Forty-seven examinations were acquired with a non-gated conventional helical protocol on a multi-detector CT scanner (control group) and 51 were acquired with a non-gated high-pitch helical protocol on a dual-source CT scanner (high-pitch (HP) group). Patient characteristics, radiation exposure parameters, and imaging datasets were extracted from the picture archiving and communication system (PACS). Image quality was assessed subjectively by two radiologists who independently assigned ratings, and objectively through attenuation measurements. Results: Radiation exposure was approximately 75% lower in the HP group (0.54 mSv vs. 2.46 mSv, P <.001). HP examinations demonstrated comparable, or better, image quality across all metrics in both subjective and objective analyses. In the subjective analysis, the HP group achieved superior ratings for visualization of the aorta (P =.04). In the objective analysis, the HP group achieved superior signal-to-noise ratio (SNR) in the left atrium (P <.001), left ventricle (P =.04), and aorta (P =.003), and superior contrast-to-noise ratio (CNR) in the left atrium (P =.003) and aorta (P =.009). Conclusion: Our findings suggest that employing high-pitch helical CT protocols for evaluation of cardiothoracic pathologies in infants decreases radiation exposure while achieving similar to slightly better image quality compared to conventional helical CT protocols. [ABSTRACT FROM AUTHOR]

Purpose: To assess the trends in administered 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) doses, computed tomography (CT) radiation doses, and image quality over the last 15 years in children with drug-resistant epilepsy (DRE) undergoing hybrid positron emission tomography (PET) brain scans. Methods: We retrospectively analyzed data from children with DRE who had [18F]FDG-PET/CT or magnetic resonance scans for presurgical evaluation between 2005 and 2021. We evaluated changes in injected [18F]FDG doses, administered activity per body weight, CT dose index volume (CTDIvol), and dose length product (DLP). PET image quality was assessed visually by four trained raters. Conversely, CT image quality was measured using region-of-interest analysis, normalized by signal-to-noise (SNR) and contrast-to-noise ratio (CNR). Results: We included 55 children (30 male, mean age: 9 ± 6 years) who underwent 61 [18F]FDG-PET scans (71% as PET/CT). Annually, the injected [18F]FDG dose decreased by ~ 1% (95% CI: 0.92%-0.98%, p < 0.001), with no significant changes in administered activity per body weight (p = 0.51). CTDIvol and DLP decreased annually by 16% (95% CI: 9%-23%) and 15% (95% CI: 8%-21%, both p < 0.001), respectively. PET image quality improved by 9% year-over-year (95% CI: 6%-13%, p < 0.001), while CT-associated SNR and CNR decreased annually by 7% (95% CI: 3%-11%, p = 0.001) and 6% (95% CI: 2%-10%, p = 0.008), respectively. Conclusion: Our findings indicate stability in [18F]FDG administered activity per body weight alongside improvements in PET image quality. Conversely, CT-associated radiation doses reduced. These results reaffirm [18F]FDG-PET as an increasingly safer and higher-resolution auxiliary imaging modality for children with DRE. These improvements, driven by technological advancements, may enhance the diagnostic precision and patient outcomes in pediatric epilepsy surgery. [ABSTRACT FROM AUTHOR]

Intraoperative 3D imaging with cone-beam CT (CBCT) improves assessment of implant position and reduces complications in spine surgery. It is also used for image-guided surgical techniques, resulting in improved quality of care. However, in some cases, metal artifacts can reduce image quality and make it difficult to assess pedicle screw position and reduction. The objective of this study was to investigate whether a change in CBCT acquisition trajectory in relation to pedicle screw position during dorsal instrumentation can reduce metal artifacts and consequently improve image quality and clinical assessability. Experimental cadaver study. A human cadaver was instrumented with pedicle screws in the thoracic and lumbar spine region (Th11 to L5). Then, the acquisition trajectory of the CBCT (Cios Spin, Siemens, Germany) to the pedicle screws was systematically changed in 5° steps in angulation (−30° to +30°) and swivel (−25° to +25°). Subsequently, radiological evaluation was performed by 3 blinded, qualified raters on image quality using 9 questions (including anatomical structures, implant position, appearance of artifacts) with a score (1–5 points). For statistical evaluation, the image quality of the different acquisition trajectories was compared to the standard acquisition trajectory and checked for significant differences. The angulated acquisition trajectory significantly increased the score for subjective image quality (p<.001) as well as the clinical assessability of pedicle screw position (p<.001) with particularly strong effects on subjective image quality in the vertebral pedicle region (d=1.61). Swivel of the acquisition trajectory significantly improved all queried domains of subjective image quality (p<.001) as well as clinical assessability of pedicle screw position (p<.001). In this cadaver study, the angulation as well as the swivel of the acquisition trajectory led to a significantly improved image quality in intraoperative 3D imaging (CBCT) with a constant isocenter. The data show that maximizing the angulation/swivel angle towards 30°/25° provides the best tested subjective image quality and enhances clinical assessability. Therefore, a correct adjustment of the acquisition trajectory can help to make intraoperative revision decisions more reliably. The knowledge of enhanced image quality by changing the acquisition trajectory in intraoperative 3D imaging can be used for the assessment of critical screw positions in spine surgery. The implementation of this knowledge requires only a minor change of the current intraoperative imaging workflow without additional technical equipment and could further reduce the need for revision surgery. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the study was to investigate the value of SwiftScan Step-and-Shoot Continuous (SSC) scanning mode in enhancing image quality and to explore appropriate scanning parameters for reducing scan time. Methods: This study was composed of a phantom study and two clinical tests. The differences in visual image quality scores, coefficient of variance (COV) of the background, image signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and recovery coefficient (RC) of the sphere were compared between SSC mode and traditional Step-and-Shoot (SS) mode in the phantom study. Various "shoot" acquisition times (5s, 10s, 15s) and "step" angles (3-degree, 6-degree, 9-degree) were evaluated and verified. In the clinical tests, bone tomography and parathyroid tomography were performed on 30 patients each. Differences in visual image quality scores, background COV, image SNR, CNR, and standardized uptake value (SUV) of lesions were compared between the two modes. Results: In the phantom study, SSC mode demonstrated higher visual scores and significantly reduced background COV (P < 0.05), and significantly increased SNR and CNR (P < 0.05) compared to SS mode. No significant alteration in RC was observed (P > 0.05). In the clinical tests, no significant differences were found between the optimal SSC scan combination (10s "shoot" and 6-degree "step")/ (10s "shoot" and 3-degree "step") and the traditional SS scan combination (15s "shoot" and 6-degree "step")/ (15s "shoot" and 3-degree "step") in visual image quality scores, background COV, image SNR, CNR, and SUV of bone and parathyroid high uptake lesions (P > 0.05). Conclusion: The SwiftScan SSC mode can reduce acquisition time by 33% while maintaining similar image quality and quantification accuracy compared to SS mode. An SSC scanning protocol with a 10s "shoot" acquisition and 6-degree "step" or with a 10s "shoot" acquisition and 3-degree "step" over a 360-degree rotation, is recommended for clinical use. [ABSTRACT FROM AUTHOR]