Ultrahigh resolution whole body photon counting computed tomography as a novel versatile tool for translational research from mouse to man.

X-ray computed tomography (CT) is a cardinal tool in clinical practice. It provides cross-sectional images within seconds. The recent introduction of clinical photon-counting CT allowed for an increase in spatial resolution by more than a factor of two resulting in a pixel size in the center of rotation of about 150 µm. This level of spatial resolution is in the order of dedicated preclinical micro-CT systems. However so far, the need for different dedicated clinical and preclinical systems often hinders the rapid translation of early research results to applications in men. This drawback might be overcome by ultra-high resolution (UHR) clinical photon-counting CT unifying preclinical and clinical research capabilities in a single machine. Herein, the prototype of a clinical UHR PCD CT (SOMATOM CounT, Siemens Healthineers, Forchheim, Germany) was used. The system comprises a conventional energy-integrating detector (EID) and a novel photon-counting detector (PCD). While the EID provides a pixel size of 0.6 mm in the centre of rotation, the PCD provides a pixel size of 0.25 mm. Additionally, it provides a quantification of photon energies by sorting them into up to four distinct energy bins. This acquisition of multi-energy data allows for a multitude of applications, e.g. pseudo-monochromatic imaging. In particular, we examine the relation between spatial resolution, image noise and administered radiation dose for a multitude of use-cases. These cases include ultra-high resolution and multi-energy acquisitions of mice administered with a prototype bismuth-based contrast agent (nanoPET Pharma, Berlin, Germany) as well as larger animals and actual patients. The clinical EID provides a spatial resolution of about 9 lp/cm (modulation transfer function at 10%, MTF10%) while UHR allows for the acquisition of images with up to 16 lp/cm allowing for the visualization of all relevant anatomical structures in preclinical and clinical specimen. The spectral capabilities of the system enable a variety of applications previously not available in preclinical research such as pseudo-monochromatic images. Clinical ultra-high resolution photon-counting CT has the potential to unify preclinical and clinical research on a single system enabling versatile imaging of specimens and individuals ranging from mice to man.

[Spectral computed tomography in the age of photon-counting X-ray detectors]. / Spektrale Computertomographie im Zeitalter der photonenzählenden Röntgendetektoren.

BACKGROUND: Since its introduction, spectral computed tomography has become an integral part of clinical imaging with a variety of possible applications. Over time, technical innovations have considerably improved the spatial and energy resolution. The recent introduction of computed tomographs utilizing photon-counting x­ray detectors has opened up further applications, which need to be investigated regarding their clinical utility. OBJECTIVES: This article gives an overview of the development of spectral computed tomography in general and photon-counting computed tomography in particular, with a special focus on recent technical developments and their clinical applications. CONCLUSION: Very likely, photon-counting X­ray detectors will over time prevail over conventional energy-integrating detectors. Most technical problems hindering clinical use have been overcome, so that the unquestionable advantages outweigh the remaining disadvantages. Further developments especially of detector electronics, reconstruction algorithms and software-based postprocessing will further support its clinical introduction.

Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration.

OBJECTIVES: The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI). MATERIALS AND METHODS: The biparametric (T2-weighted and diffusion-weighted) portion of clinical multiparametric prostate MRI from consecutive men included between November 2019 and September 2020 was fully automatically and individually analyzed by a CNN briefly after image acquisition (pseudoprospective design). Radiology residents performed 2 research Prostate Imaging Reporting and Data System (PI-RADS) assessments of the multiparametric dataset independent from clinical reporting (paraclinical design) before and after review of the CNN results and completed a survey. Presence of clinically significant PC was determined by the presence of an International Society of Urological Pathology grade 2 or higher PC on combined targeted and extended systematic transperineal MRI/transrectal ultrasound fusion biopsy. Sensitivities and specificities on a patient and prostate sextant basis were compared using the McNemar test and compared with the receiver operating characteristic (ROC) curve of CNN. Survey results were summarized as absolute counts and percentages. RESULTS: A total of 201 men were included. The CNN achieved an ROC area under the curve of 0.77 on a patient basis. Using PI-RADS ≥3-emulating probability threshold (c3), CNN had a patient-based sensitivity of 81.8% and specificity of 54.8%, not statistically different from the current clinical routine PI-RADS ≥4 assessment at 90.9% and 54.8%, respectively ( P = 0.30/ P = 1.0). In general, residents achieved similar sensitivity and specificity before and after CNN review. On a prostate sextant basis, clinical assessment possessed the highest ROC area under the curve of 0.82, higher than CNN (AUC = 0.76, P = 0.21) and significantly higher than resident performance before and after CNN review (AUC = 0.76 / 0.76, P ≤ 0.03). The resident survey indicated CNN to be helpful and clinically useful. CONCLUSIONS: Pseudoprospective paraclinical integration of fully automated CNN-based detection of suspicious lesions on prostate multiparametric MRI was demonstrated and showed good acceptance among residents, whereas no significant improvement in resident performance was found. General CNN performance was preserved despite an observed shift in CNN calibration, identifying the requirement for continuous quality control and recalibration.

Toward molecular imaging using spectral photon-counting computed tomography?

Molecular imaging is a valuable tool in drug discovery and development, early screening and diagnosis of diseases, and therapy assessment among others. Although many different imaging modalities are in use today, molecular imaging with computed tomography (CT) is still challenging owing to its low sensitivity and soft tissue contrast compared with other modalities. Recent technical advances, particularly the introduction of spectral photon-counting detectors, might allow overcoming these challenges. Herein, the fundamentals and recent advances in CT relevant to molecular imaging are reviewed and potential future preclinical and clinical applications are highlighted. The review concludes with a discussion of potential future advancements of CT for molecular imaging.

Potential of contrast agents based on high-Z elements for contrast-enhanced photon-counting computed tomography.

PURPOSE: In clinics, only iodine- and barium-based contrast agents are currently used for contrast-enhanced x-ray computed tomography (CT). Recently, the introduction of new photon-counting (PC) detectors increased the interest in developing new contrast agents based on heavier elements. These elements may provide more contrast and spectral information compared to iodine and barium thanks to their k-edges at higher energies. In this paper, the potential of high-Z elements in contrast-enhanced CT was evaluated for different patient sizes and x-ray spectra using a PC detector. METHODS: An adult liver phantom with five high-Z element solutions (iodine, gadolinium, ytterbium, tungsten, and bismuth) was scanned with a whole-body photon-counting computed tomography (PCCT) prototype. For each element, the contrast-to-noise ratio at unit concentration and at unit dose (CNRCD) was evaluated in low threshold images ( T 0 = 20 keV ) as function of the tube voltage (80, 100, 120, and 140 kV) and in bin images (tube voltage = 120 kV) as function of the higher threshold ( T 0 = 20 keV and T 1 ∈ [ 50 , 90 ] keV ). Simulations were performed for validation with measurements and to investigate more elements (cerium and gold), different patient sizes (infant, adult, and obese), and spectrum filtration (with and without 0.4-mm tin filter). The dose reductions associated with the CNRCD improvements over iodine were quantified as well. RESULTS: CNRCD improvements and dose reductions depend on the investigated scenario. For the infant phantom, dose reductions around 30% were reached using cerium or gadolinium in combination with the tin filter. For the adult and obese phantom, reductions around 50% were provided by gadolinium or ytterbium in combination with the tin filter. Independently of the high-Z element, the CNRCD of two optimally combined bin images was higher than the CNRCD of the low threshold image. Good agreement was found between measurements and simulations. CONCLUSIONS: Between the investigated elements, gadolinium resulted to have the highest potential as novel contrast agent in PCCT, providing significant dose reductions for all patient sizes. Compared to the other elements, the implementation of gadolinium as CT contrast agent may be facilitated since it is already deployed as contrast agents for magnetic resonance imaging.