Potential radiation dose reduction in clinical photon-counting CT by the small pixel effect: ultra-high resolution (UHR) acquisitions reconstructed to standard resolution.

OBJECTIVE: To assess the potential dose reduction achievable with clinical photon-counting CT (PCCT) in ultra-high resolution (UHR) mode compared to acquisitions using the standard resolution detector mode (Std). MATERIALS AND METHODS: With smaller detector pixels, PCCT achieves far higher spatial resolution than energy-integrating (EI) CT systems. The reconstruction of UHR acquisitions to the lower spatial resolution of conventional systems results in an image noise and radiation dose reduction. We quantify this small pixel effect in measurements of semi-anthropomorphic abdominal phantoms of different sizes as well as in a porcine knuckle in the first clinical PCCT system by using the UHR mode (0.2 mm pixel size at isocenter) in comparison to the standard resolution mode (0.4 mm). At different slice thicknesses (0.4 up to 4 mm) and dose levels between 4 and 12 mGy, reconstructions using filtered backprojection were performed to the same target spatial resolution, i.e., same modulation transfer function, using both detector modes. Image noise and the resulting potential dose reduction was quantified as a figure of merit. RESULTS: Images acquired using the UHR mode yield lower noise in comparison to acquisitions using standard pixels at the same resolution and noise level. This holds for sharper convolution kernels at the spatial resolution limit of the standard mode, e.g., up to a factor 3.2 in noise reduction and a resulting potential dose reduction of up to almost 90%. CONCLUSION: Using sharper convolution kernels, UHR acquisitions allow for a significant dose reduction compared to acquisitions using the standard detector mode. CLINICAL RELEVANCE: Acquisitions should always be performed using the ultra-high resolution detector mode, if possible, to benefit from the intrinsic noise and dose reduction. KEY POINTS: • Ionizing radiation used in computed tomography examinations is a concern to public health. • The ultra-high resolution of novel photon-counting systems can be invested towards a noise and dose reduction if only a spatial resolution below the resolution limit of the detector is desired. • Acquisitions should always be performed in ultra-high resolution mode, if possible, to benefit from an intrinsic dose reduction.

Single-spoke binning: Reducing motion artifacts in abdominal radial stack-of-stars imaging.

PURPOSE: To increase the effectiveness of respiratory gating in radial stack-of-stars MRI, particularly when imaging at high spatial resolutions or with multiple echoes. METHODS: Free induction decay (FID) navigators were integrated into a three-dimensional gradient echo radial stack-of-stars pulse sequence. These navigators provided a motion signal with a high temporal resolution, which allowed single-spoke binning (SSB): each spoke at each phase encode step was sorted individually to the corresponding motion state of the respiratory signal. SSB was compared with spoke-angle binning (SAB), in which all phase encode steps of one projection angle were sorted without the use of additional navigator data. To illustrate the benefit of SSB over SAB, images of a motion phantom and of six free-breathing volunteers were reconstructed after motion-gating using either method. Image sharpness was quantitatively compared using image gradient entropies. RESULTS: The proposed method resulted in sharper images of the motion phantom and free-breathing volunteers. Differences in gradient entropy were statistically significant (p = 0.03) in favor of SSB. The increased accuracy of motion-gating led to a decrease of streaking artifacts in motion-gated four-dimensional reconstructions. To consistently estimate respiratory signals from the FID-navigator data, specific types of gradient spoiler waveforms were required. CONCLUSION: SSB allowed high-resolution motion-corrected MR imaging, even when acquiring multiple gradient echo signals or large acquisition matrices, without sacrificing accuracy of motion-gating. SSB thus relieves restrictions on the choice of pulse sequence parameters, enabling the use of motion-gated radial stack-of-stars MRI in a broader domain of clinical applications.

Potential of iterative reconstruction for maxillofacial cone beam CT imaging: technical note.

Iterative reconstruction has been proven to be an effective tool for low-dose computed tomography imaging. However, this technology is currently not available in commercial diagnostic maxillofacial cone beam CT. For this technical note, an iterative reconstruction technique was applied to cone beam CT raw data of two maxillofacial clinical cases to explore its potential for dose reduction and metal artifact reduction. Low-dose imaging was emulated by using only fractions of the clinical projection dataset. The reconstruction algorithms tested were filtered backprojection (FBP) as a reference method, and a total variation minimization (TV) regularized ordered subsets convex (OSC-TV) method as the iterative technique. Upon qualitative examination, the OSC-TV technique was found to conserve most diagnostic information using half the projections. Test images have also shown that at 1/4 of the projections, OSC-TV was more robust than FBP with respect to streaking and metal artifacts.

Dual-energy material decomposition for cone-beam computed tomography in image-guided radiotherapy.

Background: Dual-energy (DE) diagnostic computed tomography (CT) combines two scans of different photon energy spectra which can provide additional image information as compared to standard CT. We developed a DE material decomposition scan protocol for daily cone-beam CT (CBCT) of head-and-neck patients receiving radiotherapy and tested it in a clinical trial. Material and methods: Our DE CBCT protocol consisted of an 80 and 140 kVp scan. The material decomposition algorithm split the low and high energy scan into components of two basis materials, aluminum and acrylic. Scans of different thicknesses and overlap of the basis materials were acquired to calibrate the model which decomposed the CBCT projections into thicknesses of aluminum and acrylic on a per-pixel basis. Pseudo monochromatic projections were created from these thicknesses and the known energy dependence of the attenuation coefficient of the basis materials. A frequency selective de-noising method was further applied to the basis material projections. The DE CBCT protocol was tested on seven patients. Two DE images were chosen, one at low (50-60) keV to evaluate soft tissue image quality and one at 150 keV to assess metal artifact reduction as compared to standard CBCT. Results: The de-noising algorithm reduced noise by 41% and 69% in the 60 and 150 keV images, respectively, compared to images without the de-noising. The low keV image showed an increase in soft tissue contrast-to-noise ratio of 7-43% compared to the standard clinical CBCT for six of the seven patients. The 150 keV DE CBCT image reduced metal artifacts. Enhanced streaking from metal artifacts were observed in some of the DE CBCT images. Conclusion: Monochromatic DE images from material decomposition can improve soft tissue contrast-to-noise ratio and metal artifact reduction. Improvements are limited, however, and new artifacts were also introduced by the DE algorithm.

The value of iterative metal artifact reduction algorithms during antenna positioning for CT-guided microwave ablation.

Objectives: To compare image quality between filtered back projection (FBP) and iterative reconstruction algorithm and dedicated metal artifact reduction (iMAR) algorithms during antenna positioning for computed tomography-guided microwave ablation (MWA).Materials and methods: An MWA antenna was positioned in the liver of five pigs under CT guidance. Different exposure settings (120kVp/200mAs-120kVp/50mAs) and image reconstruction techniques (FBP, iterative reconstruction with and without iMAR) were applied. Quantitative image analysis included density measurements in six positions (e.g., liver in extension of the antenna [ANTENNA] and liver >3 cm away from the antenna [LIVER-1]). Qualitative image analysis included assessment of overall quality, image noise, artifacts at the antenna tip, artifacts in liver parenchyma bordering antenna tip and newly generated artifacts. Two independent observers performed the analyses twice and interreader agreement was compared with Bland-Altman analysis.Results: For all exposure and reconstruction settings, density measurements for ANTENNA were significantly higher for the I30-1 iMAR compared with FBP and I30-1 (e.g., 8.3-17.2HU vs. -104.5 to 155.1HU; p ≤ 0.01, respectively). In contrast, for all exposure settings, density measurements for LIVER-1 were comparable between FBP and I30-1 iMAR (e.g., 49.4-50.4HU vs. 50.1-52.5U, respectively). For all exposure and reconstruction settings, subjective image quality for LIVER-1 was better for the I30-1 iMAR algorithm compared with FBP and I30-1. Bland-Altman interobserver agreement was from -0.2 to 0.2 for FBP and iMAR, and Cohen's kappa was 0.74.Conclusion: Iterative algorithms I30-1 with iMAR algorithm improves image quality during antenna positioning and placement for CT-guided MWA and is applicable over a range of exposure settings.

4D respiratory motion-compensated image reconstruction of free-breathing radial MR data with very high undersampling.

PURPOSE: To develop four-dimensional (4D) respiratory time-resolved MRI based on free-breathing acquisition of radial MR data with very high undersampling. METHODS: We propose the 4D joint motion-compensated high-dimensional total variation (4D joint MoCo-HDTV) algorithm, which alternates between motion-compensated image reconstruction and artifact-robust motion estimation at multiple resolution levels. The algorithm is applied to radial MR data of the thorax and upper abdomen of 12 free-breathing subjects with acquisition times between 37 and 41 s and undersampling factors of 16.8. Resulting images are compared with compressed sensing-based 4D motion-adaptive spatio-temporal regularization (MASTeR) and 4D high-dimensional total variation (HDTV) reconstructions. RESULTS: For all subjects, 4D joint MoCo-HDTV achieves higher similarity in terms of normalized mutual information and cross-correlation than 4D MASTeR and 4D HDTV when compared with reference 4D gated gridding reconstructions with 8.4 ± 1.1 times longer acquisition times. In a qualitative assessment of artifact level and image sharpness by two radiologists, 4D joint MoCo-HDTV reveals higher scores (P < 0.05) than 4D HDTV and 4D MASTeR at the same undersampling factor and the reference 4D gated gridding reconstructions, respectively. CONCLUSIONS: 4D joint MoCo-HDTV enables time-resolved image reconstruction of free-breathing radial MR data with undersampling factors of 16.8 while achieving low-streak artifact levels and high image sharpness. Magn Reson Med 77:1170-1183, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Effects of arm truncation on the appearance of the halo artifact in 68Ga-PSMA-11 (HBED-CC) PET/MRI.

PURPOSE: PSMA ligand imaging with hybrid PET/MRI scanners could be an integral part of the clinical routine in the future. However, the first study about this novel method revealed a severe photopenic artifact ("halo artifact") around the urinary bladder causing significantly reduced tumor visibility. The aim of this evaluation was to analyze the role of arm truncation on the appearance of the halo artifact in 68Ga-PSMA-11 PET/MRI hypothesizing that this influences the appearance. METHODS: Twenty-seven consecutive patients were subjected to 68Ga-PSMA-11 PET/CT (1 h p.i.) followed by PET/MRI (3 h p.i.). PET/MRI was first started with scans of the abdomen to pelvis with arms positioned up above the head. Immediately thereafter, additional scans from the pelvis to abdomen were conducted with arms positioned down beside the trunk. All investigations were first analyzed separately and then compared with respect to tumor detection and tumor uptake (SUV) as well as the presence and intensity of the halo artifact. The Wilcoxon signed rank test was used to determine statistical differences including Bonferroni correction. RESULTS: The halo was significantly reduced if the arms were elevated. Lesions inside the halo artifact (n = 16) demonstrated significantly increased SUVmean (p = 0.0007) and SUVmax (p = 0.0024) with arms positioned up. The halo appearance and intensity was not dependent on the total activity and activity concentration of the urinary bladder. CONCLUSION: Positioning the arms down was shown to be significantly associated with the appearance of the halo artifact in PET/MRI. Positioning the arms up above the head can significantly reduce the halo artifact, thereby detecting more tumor lesions.

Local recurrence of prostate cancer after radical prostatectomy is at risk to be missed in 68Ga-PSMA-11-PET of PET/CT and PET/MRI: comparison with mpMRI integrated in simultaneous PET/MRI.

PURPOSE: The positron emission tomography (PET) tracer 68Ga-PSMA-11, targeting the prostate-specific membrane antigen (PSMA), is rapidly excreted into the urinary tract. This leads to significant radioactivity in the bladder, which may limit the PET-detection of local recurrence (LR) of prostate cancer (PC) after radical prostatectomy (RP), developing in close proximity to the bladder. Here, we analyze if there is additional value of multi-parametric magnetic resonance imaging (mpMRI) compared to the 68Ga-PSMA-11-PET-component of PET/CT or PET/MRI to detect LR. METHODS: One hundred and nineteen patients with biochemical recurrence after prior RP underwent both hybrid 68Ga-PSMA-11-PET/CTlow-dose (1 h p.i.) and -PET/MRI (2-3 h p.i.) including a mpMRI protocol of the prostatic bed. The comparison of both methods was restricted to the abdomen with focus on LR (McNemar). Bladder-LR distance and recurrence size were measured in axial T2w-TSE. A logistic regression was performed to determine the influence of these variables on detectability in 68Ga-PSMA-11-PET. Standardized-uptake-value (SUVmean) quantification of LR was performed. RESULTS: There were 93/119 patients that had at least one pathologic finding. In addition, 18/119 Patients (15.1%) were diagnosed with a LR in mpMRI of PET/MRI but only nine were PET-positive in PET/CT and PET/MRI. This mismatch was statistically significant (p = 0.004). Detection of LR using the PET-component was significantly influenced by proximity to the bladder (p = 0.028). The PET-pattern of LR-uptake was classified into three types (1): separated from bladder; (2): fuses with bladder, and (3): obliterated by bladder). The size of LRs did not affect PET-detectability (p = 0.84), mean size was 1.7 ± 0.69 cm long axis, 1.2 ± 0.46 cm short-axis. SUVmean in nine men was 8.7 ± 3.7 (PET/CT) and 7.0 ± 4.2 (PET/MRI) but could not be quantified in the remaining nine cases (obliterated by bladder). CONCLUSION: The present study demonstrates additional value of hybrid 68Ga-PSMA-11-PET/MRI by gaining complementary diagnostic information compared to the 68Ga-PSMA-11-PET/CTlow-dose for patients with LR of PC.

Recent developments of dual-energy CT in oncology.

UNLABELLED: Dual-energy computed tomography (DECT) can amply contribute to support oncological imaging: the DECT technique offers promising clinical applications in oncological imaging for tumour detection and characterisation while concurrently reducing the radiation dose. Fast image acquisition at two different X-ray energies enables the determination of tissue- or material-specific features, the calculation of virtual unenhanced images and the quantification of contrast medium uptake; thus, tissue can be characterised and subsequently monitored for any changes during treatment. DECT is already widely used, but its potential in the context of oncological imaging has not been fully exploited yet. The technology is the subject of ongoing innovation and increasingly with respect to its clinical potential, particularly in oncology. This review highlights recent state-of-the-art DECT techniques with a strong emphasis on ongoing DECT developments relevant to oncologic imaging, and then focuses on clinical DECT applications, especially its prospective uses in areas of oncological imaging. KEY POINTS: • Dual-energy CT (DECT) offers fast, robust, quantitative and functional whole-body imaging. • DECT provides improved tumour detection and more detailed tissue differentiation and characterisation. • DECT affords therapy monitoring with complementary information and reduced radiation dose. • The use of DECT in oncology is of increasing clinical importance. • The potential of DECT in oncology has not been fully exploited yet.

Raw data consistent deep learning-based field of view extension for dual-source dual-energy CT.

BACKGROUND: Due to technical constraints, dual-source dual-energy CT scans may lack spectral information in the periphery of the patient. PURPOSE: Here, we propose a deep learning-based iterative reconstruction to recover the missing spectral information outside the field of measurement (FOM) of the second source-detector pair. METHODS: In today's Siemens dual-source CT systems, one source-detector pair (referred to as A) typically has a FOM of about 50 cm, while the FOM of the other pair (referred to as B) is limited by technical constraints to a diameter of about 35 cm. As a result, dual-energy applications are currently only available within the small FOM, limiting their use for larger patients. To derive a reconstruction at B's energy for the entire patient cross-section, we propose a deep learning-based iterative reconstruction. Starting with A's reconstruction as initial estimate, it employs a neural network in each iteration to refine the current estimate according to a raw data fidelity measure. Here, the corresponding mapping is trained using simulated chest, abdomen, and pelvis scans based on a data set containing 70 full body CT scans. Finally, the proposed approach is tested on simulated and measured dual-source dual-energy scans and compared against existing reference approaches. RESULTS: For all test cases, the proposed approach was able to provide artifact-free CT reconstructions of B for the entire patient cross-section. Considering simulated data, the remaining error of the reconstructions is between 10 and 17 HU on average, which is about half as low as the reference approaches. A similar performance with an average error of 8 HU could be achieved for real phantom measurements. CONCLUSIONS: The proposed approach is able to recover missing dual-energy information for patients exceeding the small 35 cm FOM of dual-source CT systems. Therefore, it potentially allows to extend dual-energy applications to the entire-patient cross section.

Training of a deep learning based digital subtraction angiography method using synthetic data.

BACKGROUND: Digital subtraction angiography (DSA) is a fluoroscopy method primarily used for the diagnosis of cardiovascular diseases (CVDs). Deep learning-based DSA (DDSA) is developed to extract DSA-like images directly from fluoroscopic images, which helps in saving dose while improving image quality. It can also be applied where C-arm or patient motion is present and conventional DSA cannot be applied. However, due to the lack of clinical training data and unavoidable artifacts in DSA targets, current DDSA models still cannot satisfactorily display specific structures, nor can they predict noise-free images. PURPOSE: In this study, we propose a strategy for producing abundant synthetic DSA image pairs in which synthetic DSA targets are free of typical artifacts and noise commonly found in conventional DSA targets for DDSA model training. METHODS: More than 7,000 forward-projected computed tomography (CT) images and more than 25,000 synthetic vascular projection images were employed to create contrast-enhanced fluoroscopic images and corresponding DSA images, which were utilized as DSA image pairs for training of the DDSA networks. The CT projection images and vascular projection images were generated from eight whole-body CT scans and 1,584 3D vascular skeletons, respectively. All vessel skeletons were generated with stochastic Lindenmayer systems. We trained DDSA models on this synthetic dataset and compared them to the trainings on a clinical DSA dataset, which contains nearly 4,000 fluoroscopic x-ray images obtained from different models of C-arms. RESULTS: We evaluated DDSA models on clinical fluoroscopic data of different anatomies, including the leg, abdomen, and heart. The results on leg data showed for different methods that training on synthetic data performed similarly and sometimes outperformed training on clinical data. The results on abdomen and cardiac data demonstrated that models trained on synthetic data were able to extract clearer DSA-like images than conventional DSA and models trained on clinical data. The models trained on synthetic data consistently outperformed their clinical data counterparts, achieving higher scores in the quantitative evaluation of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics for DDSA images, as well as accuracy, precision, and Dice scores for segmentation of the DDSA images. CONCLUSIONS: We proposed an approach to train DDSA networks with synthetic DSA image pairs and extract DSA-like images from contrast-enhanced x-ray images directly. This is a potential tool to aid in diagnosis.

Reducing windmill artifacts in clinical spiral CT using a deep learning-based projection raw data upsampling: Method and robustness evaluation.

BACKGROUND: Multislice spiral computed tomography (MSCT) requires an interpolation between adjacent detector rows during backprojection. Not satisfying the Nyquist sampling condition along the z-axis results in aliasing effects, also known as windmill artifacts. These image distortions are characterized by bright streaks diverging from high contrast structures. PURPOSE: The z-flying focal spot (zFFS) is a well-established hardware-based solution that aims to double the sampling rate in longitudinal direction and therefore reduce aliasing artifacts. However, given the technical complexity of the zFFS, this work proposes a deep learning-based approach as an alternative solution. METHODS: We propose a supervised learning approach to perform a mapping between input projections and the corresponding rows required for double sampling in the z-direction. We present a comprehensive evaluation using both a clinical dataset obtained using raw data from 40 real patient scans acquired with zFFS and a synthetic dataset consisting of 100 simulated spiral scans using a phantom specifically designed for our problem. For the clinical dataset, we utilized 32 scans as training set and 8 scans as validation set, whereas for the synthetic dataset, we used 80 scans for training and 20 scans for validation purposes. Both qualitative and quantitative assessments are conducted on a test set consisting of nine real patient scans and six phantom measurements to validate the performance of our approach. A simulation study was performed to investigate the robustness against different scan configurations in terms of detector collimation and pitch value. RESULTS: In the quantitative comparison based on clinical patient scans from the test set, all network configurations show an improvement in the root mean square error (RMSE) of approximately 20% compared to neglecting the doubled longitudinal sampling by the zFFS. The results of the qualitative analysis indicate that both clinical and synthetic training data can reduce windmill artifacts through the application of a correspondingly trained network. Together with the qualitative results from the test set phantom measurements it is emphasized that a training of our method with synthetic data resulted in superior performance in windmill artifact reduction. CONCLUSIONS: Deep learning-based raw data interpolation has the potential to enhance the sampling in z-direction and thus minimize aliasing effects, as it is the case with the zFFS. Especially a training with synthetic data showed promising results. While it may not outperform zFFS, our method represents a beneficial solution for CT scanners lacking the necessary hardware components for zFFS.

Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization.

To facilitate a prospective estimation of the effective dose of an CT scan prior to the actual scanning in order to use sophisticated patient risk minimizing methods, a prospective spatial dose estimation and the known anatomical structures are required. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e. by using the topograms, with anatomical structures as correct as possible. In this work, an optimized CT reconstruction model based on a generative adversarial network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an anterior-posterior and a lateral CT projection. To enhance anatomical structures, a pre-trained organ segmentation network and the 3D perceptual loss are applied during the training phase, so that the model can then generate both organ-enhanced CT volume and organ segmentation masks. The proposed method can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of 0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of the anatomical structure, the proposed method effectively enhances the organ shapes and boundaries and allows for a straight-forward identification of the relevant anatomical structures. We note that conventional reconstruction metrics fail to indicate the enhancement of anatomical structures. In addition to such metrics, the evaluation is expanded with assessing the organ segmentation performance. The average organ dice of the proposed method is 0.71 compared with 0.63 for the baseline model, indicating the enhancement of anatomical structures.

Dental imaging in clinical photon-counting CT at a quarter of DVT dose.

OBJECTIVE: To investigate the image quality of a low-dose dental imaging protocol in the first clinical photon-counting computed tomography (PCCT) system in comparison to a normal-dose acquisition in a digital volume tomography (DVT) system. MATERIALS AND METHODS: Clinical PCCT systems offer an increased spatial resolution compared to previous generations of clinical systems. Their spatial resolution is in the order of dental DVT systems. Resolution-matched acquisitions of ten porcine jaws were performed in a PCCT (Naeotom Alpha, Siemens Healthineers) and in a DVT (Orthophos XL, Dentsply Sirona). PCCT images were acquired with 90 kV at a dose of 1 mGy CTDI16 cm. DVT used 85 kV at 4 mGy. Image reconstruction was performed using the standard algorithms of each system to a voxel size of 160 × 160 × 200 µm. The dose-normalized contrast-to-noise ratio (CNRD) was measured between dentine and enamel and dentine and bone. Two readers evaluated overall diagnostic quality of images and quality of relevant structures such as root channels and dentine. RESULTS: CNRD is higher in all PCCT acquisitions. CNRD is 37 % higher for the contrast dentine-enamel and 31 % higher for the dentine-bone contrast (p < 0.05). Overall diagnostic image quality was higher for PCCT over DVT (p < 0.02 and p < 0.04 for readers 1 and 2). Quality scores for anatomical structures were higher in PCCT compared to DVT (all p < 0.05). Inter- and intrareader reproducibility were acceptable (all ICC>0.64). CONCLUSIONS: PCCT provides an increased image quality over DVT even at a lower dose level and might enable complex dental imaging protocols in the future. CLINICAL SIGNIFICANCE: The evolution of photon-counting technology and it's optimization will increasingly move dental imaging towards standardized 3D visualizations providing both minimal radiation exposure and high diagnostic accuracy.

Real-time X-ray-based 4D image guidance of minimally invasive interventions.

OBJECTIVE: A new technology is introduced that enables real-time 4D (three spatial dimensions plus time) X-ray guidance for vascular catheter interventions with acceptable levels of ionising radiation. METHODS: The enabling technology is a combination of low-dose tomographic data acquisition with novel compressed sensing reconstruction and use of prior image information. It was implemented in a prototype set-up consisting of a gantry-based flat detector system. In pigs (n = 5) angiographic interventions were simulated. Radiation dosage on a per time base was compared with the "gold standard" of X-ray projection imaging. RESULTS: Contrary to current image guidance methods that lack permanent 4D updates, the spatial position of interventional instruments could be resolved in continuous, spatial 4D guidance; the movement of the guide wire as well as the expansion of stents could be precisely tracked in 3D angiographic road maps. Dose rate was 23.8 µGy/s, similar to biplane standard angiographic fluoroscopy, which has a dose rate of 20.6 µGy/s. CONCLUSION: Real-time 4D X-ray image-guidance with acceptable levels of radiation has great potential to significantly influence the field of minimally invasive medicine by allowing faster and safer interventions and by enabling novel, much more complex procedures for vascular and oncological minimally invasive therapy. KEY POINTS: • Real-time 4D (three spatial dimensions plus time) angiographic intervention guidance is realistic. • Low-dose tomographic data acquisition with special compressed sensing-based algorithms is enabled. • Compared with 4D CT fluoroscopy, this method reduces radiation to acceptable levels. • Once implemented, vascular interventions may become safer and faster. • More complex intervention approaches may be developed.

[Risk-minimizing tube current modulation for computed tomography]. / Risikominimierende Röhrenstrommodulation in der Computertomographie.

AIM/PROBLEM: Every computed tomography (CT) examination is accompanied by radiation exposure. The aim is to reduce this as much as possible without compromising image quality by using a tube current modulation technique. STANDARD PROCEDURE: CT tube current modulation (TCM), which has been in use for about two decades, adjusts the tube current to the patient's attenuation (in the angular and z­directions) in a way that minimizes the mAs product (tube current-time product) of the scan without compromising image quality. This mAsTCM, present in all CT devices, is associated with a significant dose reduction in those anatomical areas that have high attenuation differences between anterior-posterior (a.p.) and lateral, particularly the shoulder and pelvis. Radiation risk of individual organs or of the patient is not considered in mAsTCM. METHODOLOGICAL INNOVATION: Recently, a TCM method was proposed that directly minimizes the patient's radiation risk by predicting organ dose levels and taking them into account when choosing tube current. It is shown that this so-called riskTCM is significantly superior to mAsTCM in all body regions. To be able to use riskTCM in clinical routine, only a software adaptation of the CT system would be necessary. CONCLUSIONS: With riskTCM, significant dose reductions can be achieved compared to the standard procedure, typically around 10%-30%. This is especially true in those body regions where the standard procedure shows only moderate advantages over a scan without any tube current modulation at all. It is now up to the CT vendors to take action and implement riskTCM.

Computed Tomography 2.0: New Detector Technology, AI, and Other Developments.

ABSTRACT: Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

Real-time 3D reconstruction of guidewires and stents using two update X-ray projections in a rotating imaging setup.

BACKGROUND: Vascular diseases are often treated minimally invasively. The interventional material (stents, guidewires, etc.) used during such percutaneous interventions are visualized by some form of image guidance. Today, this image guidance is usually provided by 2D X-ray fluoroscopy, that is, a live 2D image. 3D X-ray fluoroscopy, that is, a live 3D image, could accelerate existing and enable new interventions. However, existing algorithms for the 3D reconstruction of interventional material require either too many X-ray projections and therefore dose, or are only capable of reconstructing single, curvilinear structures. PURPOSE: Using only two new X-ray projections per 3D reconstruction, we aim to reconstruct more complex arrangements of interventional material than was previously possible. METHODS: This is achieved by improving a previously presented deep learning-based reconstruction pipeline, which assumes that the X-ray images are acquired by a continuously rotating biplane system, in two ways: (a) separation of the reconstruction of different object types, (b) motion compensation using spatial transformer networks. RESULTS: Our pipeline achieves submillimeter accuracy on measured data of a stent and two guidewires inside an anthropomorphic phantom with respiratory motion. In an ablation study, we find that the aforementioned algorithmic changes improve our two figures of merit by 75 % (1.76 mm → 0.44 mm) and 59 % (1.15 mm → 0.47 mm) respectively. A comparison of our measured dose area product (DAP) rate to DAP rates of 2D fluoroscopy indicates a roughly similar dose burden. CONCLUSIONS: This dose efficiency combined with the ability to reconstruct complex arrangements of interventional material makes the presented algorithm a promising candidate to enable 3D fluoroscopy.

Dual-contrast photon-counting micro-CT using iodine and a novel bismuth-based contrast agent.

Objectives.To characterize for the first timein vivoa novel bismuth-based nanoparticular contrast agent developed for preclinical applications. Then, to design and testin vivoa multi-contrast protocol for functional cardiac imaging using the new bismuth nanoparticles and a well-established iodine-based contrast agent.Approach.A micro-computed tomography scanner was assembled and equipped with a photon-counting detector. Five mice were administered with the bismuth-based contrast agent and systematically scanned over 5 h to quantify the contrast enhancement in relevant organs of interest. Subsequently, the multi-contrast agent protocol was tested on three mice. Material decomposition was performed on the acquired spectral data to quantify the concentration of bismuth and iodine in multiple structures, e.g. the myocardium and vasculature.Main results.In the vasculature, the bismuth agent provides a peak enhancement of 1100 HU and a half-life of about 260 min. After the injection, it accumulates in the liver, spleen and intestinal wall reaching a CT value of 440 HU about 5 h post injection. Phantom measurements showed that the bismuth provides more contrast enhancement than iodine for a variety of tube voltages. The multi-contrast protocol for cardiac imaging successfully allowed the simultaneous decomposition of the vasculature, the brown adipose tissue and the myocardium.Significance.The new bismuth-based contrast agent was proven to have a long circulation time suitable for preclinical applications and to provide more contrast than iodine agents. The proposed multi-contrast protocol resulted in a new tool for cardiac functional imaging. Furthermore, thanks to the contrast enhancement provided in the intestinal wall, the novel contrast agent may be used to develop further multi contrast agent protocols for abdominal and oncological imaging.

Towards real-time EPID-based 3D in vivo dosimetry for IMRT with Deep Neural Networks: A feasibility study.

We investigate the potential of the Deep Dose Estimate (DDE) neural network to predict 3D dose distributions inside patients with Monte Carlo (MC) accuracy, based on transmitted EPID signals and patient CTs. The network was trained using as input patient CTs and first-order dose approximations (FOD). Accurate dose distributions (ADD) simulated with MC were given as training targets. 83 pelvic CTs were used to simulate ADDs and respective EPID signals for subfields of prostate IMRT plans (gantry at 0∘). FODs were produced as backprojections from the EPID signals. 581 ADD-FOD sets were produced and divided into training and test sets. An additional dataset simulated with gantry at 90∘ (lateral set) was used for evaluating the performance of the DDE at different beam directions. The quality of the FODs and DDE-predicted dose distributions (DDEP) with respect to ADDs, from the test and lateral sets, was evaluated with gamma analysis (3%,2 mm). The passing rates between FODs and ADDs were as low as 46%, while for DDEPs the passing rates were above 97% for the test set. Meaningful improvements were also observed for the lateral set. The high passing rates for DDEPs indicate that the DDE is able to convert FODs into ADDs. Moreover, the trained DDE predicts the dose inside a patient CT within 0.6 s/subfield (GPU), in contrast to 14 h needed for MC (CPU-cluster). 3D in vivo dose distributions due to clinical patient irradiation can be obtained within seconds, with MC-like accuracy, potentially paving the way towards real-time EPID-based in vivo dosimetry.