A Novel Method for Segmentation-Based Semiautomatic Quantitative Evaluation of Metal Artifact Reduction Algorithms:

Optimization of photon counting CT for cardiac imaging in patients with left ventricular assist devices: An in-depth assessment of metal artifacts

PURPOSE

Photon counting CT (PCCT) holds promise for mitigating metal artifacts and can produce virtual mono-energetic images (VMI), while maintaining temporal resolution, making it a valuable tool for characterizing the heart. This study aimed to evaluate and optimize PCCT for cardiac imaging in patients during left ventricular assistance device (LVAD) therapy by conducting an in-depth objective assessment of metal artifacts and visual grading.

METHODS

Various scan and reconstruction settings were tested on a phantom and further evaluated on a patient acquisition to identify the optimal protocol settings. The phantom comprised an empty thoracic cavity, supplemented with heart and lungs from a cadaveric lamb. The heart was implanted with an LVAD (HeartMate 3) and iodine contrast. Scans were performed on a PCCT (NAEOTOM Alpha, Siemens Healthcare). Metal artifacts were assessed by three objective methods: Hounsfield units (HU)/SD measurements (DiffHU and SDARTIFACT), Fourier analysis (AmplitudeLowFreq), and depicted LVAD volume in the images (BloomVol). Radiologists graded metal artifacts and the diagnostic interpretability in the LVAD lumen, cardiac tissue, lung tissue, and spinal cord using a 5-point rating scale. Regression and correlation analysis were conducted to determine the assessment method most closely associated with acquisition and reconstruction parameters, as well as the objective method demonstrating the highest correlation with visual grading.

RESULTS

Due to blooming artifacts, the LVAD volume fluctuated between 27.0 and 92.7 cm3. This variance was primarily influenced by kVp, kernel, keV, and iMAR (R2 = 0.989). Radiologists favored pacemaker iMAR, 3 mm slice thickness, and T3D keV and kernel Bv56f for minimal metal artifacts in cardiac tissue assessment, and 110 keV and Qr40f for lung tissue interpretation. The model adequacy for DiffHU SDARTIFACT, AmplitueLowFreq, and BloomVol was 0.28, 0.76, 0.29, and 0.99 respectively for phantom data, and 0.95, 0.98, 1.00, and 0.99 for in-vivo data. For in-vivo data, the correlation between visual grading (VGSUM) and DiffHU SDARTIFACT, AmplitueLowFreq, and BloomVol was -0.16, -0.01, -0.48, and -0.40 respectively.

CONCLUSION

We found that optimal scan settings for LVAD imaging involved using 120 kVp and IQ level 80. Employing T3D with pacemaker iMAR, the sharpest allowed vascular kernel (Bv56f), and VMI at 110 keV with kernel Qr40 yields images suitable for cardiac imaging during LVAD-therapy. Volumetric measurements of the LVAD for determination of the extent of blooming artifacts was shown to be the best objective method to assess metal artifacts.

Metal Artifact Reduction in Photon-Counting Detector CT: Quantitative Evaluation of Artifact Reduction Techniques

OBJECTIVES

With the introduction of clinical photon-counting detector computed tomography (PCD-CT) and its novel reconstruction techniques, a quantitative investigation of different acquisition and reconstruction settings is necessary to optimize clinical acquisition protocols for metal artifact reduction.

MATERIALS AND METHODS

A multienergy phantom was scanned on a clinical dual-source PCD-CT (NAEOTOM Alpha; Siemens Healthcare GmbH) with 4 different central inserts: water-equivalent plastic, aluminum, steel, and titanium. Acquisitions were performed at 120 kVp and 140 kVp (CTDI vol 10 mGy) and reconstructed as virtual monoenergetic images (VMIs; 110-150 keV), as T3D, and with the standard reconstruction "none" (70 keV VMI) using different reconstruction kernels (Br36, Br56) and with as well as without iterative metal artifact reduction (iMAR). Metal artifacts were quantified, calculating relative percentages of metal artifacts. Mean CT numbers of an adjacent water-equivalent insert and different tissue-equivalent inserts were evaluated, and eccentricity of metal rods was measured. Repeated-measures analysis of variance was performed for statistical analysis.

RESULTS

Metal artifacts were most prevalent for the steel insert (12.6% average artifacts), followed by titanium (4.2%) and aluminum (1.0%). The strongest metal artifact reduction was noted for iMAR (with iMAR: 1.4%, without iMAR: 10.5%; P < 0.001) or VMI (VMI: 110 keV 2.6% to 150 keV 3.3%, T3D: 11.0%, and none: 16.0%; P < 0.001) individually, with best results when combining iMAR and VMI at 110 keV (1.2%). Changing acquisition tube potential (120 kV: 6.6%, 140 kV: 5.2%; P = 0.33) or reconstruction kernel (Br36: 5.5%, Br56: 6.4%; P = 0.17) was less effective. Mean CT numbers and standard deviations were significantly affected by iMAR (with iMAR: -3.0 ± 21.5 HU, without iMAR: -8.5 ± 24.3 HU; P < 0.001), VMI (VMI: 110 keV -3.6 ± 21.6 HU to 150 keV -1.4 ± 21.2 HU, T3D: -11.7 ± 23.8 HU, and none: -16.9 ± 29.8 HU; P < 0.001), tube potential (120 kV: -4.7 ± 22.8 HU, 140 kV: -6.8 ± 23.0 HU; P = 0.03), and reconstruction kernel (Br36: -5.5 ± 14.2 HU, Br56: -6.8 ± 23.0 HU; P < 0.001). Both iMAR and VMI improved quantitative CT number accuracy and metal rod eccentricity for the steel rod, but iMAR was of limited effectiveness for the aluminum rod.

CONCLUSIONS

For metal artifact reduction in PCD-CT, a combination of iMAR and VMI at 110 keV demonstrated the strongest artifact reduction of the evaluated options, whereas the impact of reconstruction kernel and tube potential was limited.

Semi-automatic artifact quantification in thermal ablation probe and algorithms for the evaluation of metal artifact reduction

OBJECTIVES

To compare metal artifacts and evaluation of metal artifact reduction algorithms during probe positioning in computed tomography (CT)-guided microwave ablation (MWA), cryoablation (CRYO), and radiofrequency ablation (RFA).

MATERIALS AND METHODS

Using CT guidance, individual MWA, CRYO, and RFA ablation probes were placed into the livers of 15 pigs. CT imaging was then performed to determine the probe's position within the test subject's liver. Filtered back projection (B30f) and iterative reconstructions (I30-1) were both used with and without dedicated iterative metal artifact reduction (iMAR) to generate images from the initial data sets. Semi-automatic segmentation-based quantitative evaluation was conducted to estimate artifact percentage within the liver, while qualitative evaluation of metal artifact extent and overall image quality was performed by two observers using a 5-point Likert scale: 1-none, 2-mild, 3-moderate, 4-severe, 5-non-diagnostic.

RESULTS

Among MWA, RFA, and CRYO, compared with non-iMAR in B30f reconstruction, the largest extent of artifact volume percentages were observed for CRYO (11.5-17.9%), followed by MWA (4.7-6.6%) and lastly in RFA (5.5-6.2%). iMAR significantly reduces metal artifacts for CRYO and MWA quantitatively (p = 0.0020; p = 0.0036, respectively) and qualitatively (p = 0.0001, p = 0.0005), but not for RFA. No significant reduction in metal artifact percentage was seen after applying iterative reconstructions (p > 0.05). Noise, contrast-to-noise-ratio, or overall image quality did not differ between probe types, irrespective of the application of iterative reconstruction and iMAR.

CONCLUSION

A dedicated metal artifact algorithm may decrease metal artifacts and improves image quality significantly for MWA and CRYO probes. Their application alongside with dedicated metal artifact algorithm should be considered during CT-guided positioning.

Empirical scatter correction (ESC): CBCT scatter artifact reduction without prior information

BACKGROUND

The image quality of cone-beam CT (CBCT) scans severely suffers from scattered radiation if no countermeasures are taken. Scatter artifacts may induce cupping and streak artifacts and lead to a reduced image contrast and wrong CT values of the reconstructed volumes. Established software-based approaches for a correction of scattered radiation typically rely on prior knowledge of the CT system, scan parameters, the scanned object, or all of the aforementioned.

PURPOSE

This study proposes a simple and effective post-processing software-based correction method of scatter artifacts in CBCT scans without specific prior knowledge.

METHODS

We propose the empirical scatter correction (ESC) which generates scatter-like basis images from each projection image by convolution operations. A linear combination of these basis images is subtracted from the original projection image. The logarithm is taken and an FDK reconstruction is performed. The coefficients needed for the linear combination are determined automatically by a downhill simplex algorithm such that the resulting reconstructed images show no scatter artifacts. We demonstrate the potential of ESC by correcting simulated volumes with Monte Carlo scatter artifacts, a head phantom scan performed on our table-top CBCT, and a pelvis scan from a Varian Edge CBCT scanner.

RESULTS

ESC is able to improve the image quality of CBCT scans which is shown on the basis of our simulations and on measured data. For a simulated head CT, the CT value difference to the scatter-free reference image was as low as -6 HU after using ESC whereas the uncorrected data deviated by more than -200 HU from the reference data. Simulations of thorax and abdomen CT scans show that although scatter artifacts are not fully removed, anatomical features which were hard to discover prior to the correction become clearly visible and better segmentable with ESC. Similar results are obtained in the phantom measurement where a comparison to a slit scan of our head phantom shows only small differences. The CT values in soft tissue are improved in this measurement, as well. In soft tissue areas with severe scatter artifacts the CT values agree well with those of the slit scan (difference to slit scan: 35 HU corrected, -289 HU uncorrected). Scatter artifacts in measured patient data can also be reduced using the proposed empirical scatter correction. The results are comparable to those achieved with designated correction algorithms installed on the Varian Edge CBCT system.

CONCLUSIONS

ESC allows to reduce artifacts caused by patient scatter solely based on the projection data. This article is protected by copyright. All rights reserved.

Photon-counting normalized metal artifact reduction (NMAR) in diagnostic CT

PURPOSE

Metal artifacts can drastically reduce the diagnostic value of computed tomography (CT) images. Even the state-of-the-art algorithms cannot remove them completely. Photon-counting CT inherently provides spectral information, similar to dual-energy CT. Many applications, such as material decomposition, are not possible when metal artifacts are present. Our aim is to develop a prior-based metal artifact reduction specifically for photon-counting CT that can correct each bin image individually or in their combinations.

METHODS

Photon-counting CT sorts incoming photons into several energy bins, producing bin and threshold images containing spectral information. We use this spectral information to obtain a better prior image for the state-of-the-art metal artifact reduction algorithm FSNMAR. First, we apply a non-linear transformation to the bin images to obtain bone-emphasized images. Subsequently, we forward-project the bin images and bone-emphasized images and multiply the resulting sinograms with each other element-wise to mimic beam hardening effects. These sinograms are reconstructed and linearly combined to produce an artifact-reduced image. The coefficients of this linear combination are automatically determined by minimizing a threshold-based cost function in the image domain. After thresholding, we obtain the prior image for FSNMAR, which is applied to the individual bin images and the lowest threshold image. We test our photon-counting normalized metal artifact reduction (PCNMAR) on forensic CT data and compare it to conventional FSNMAR, where the prior is generated via linear sinogram inpainting. For numerical analysis, we compute both the standard deviation in an ROI with metal artifacts and the CNR of soft tissue and fat.

RESULTS

PCNMAR can effectively reduce metal artifacts without sacrificing the overall image quality. Compared to FSNMAR, our method produces fewer secondary artifacts and is more consistent with the measurements. Areas that contain metal, air, and soft tissue are more accurate in PCNMAR. In some cases, the standard deviation in the artifact ROI is reduced by more than 50% relative to FSNMAR, while the CNR values are similar. If extreme artifacts are present, PCNMAR is unable to outperform FSNMAR. Using either two, four, or only the highest energy bin to produce the prior image yielded comparable results.

CONCLUSIONS

PCNMAR is an effective method of reducing metal artifacts in photon-counting CT. The spectral information available in photon-counting CT is highly beneficial for metal artifact reduction, especially the high-energy bin, which inherently contains fewer artifacts. While scanning with four instead of two bins does not provide a better artifact reduction, it allows for more freedom in the selection of energy thresholds.

A semi-automated quantitative comparison of metal artifact reduction in photon-counting computed tomography by energy-selective thresholding

An evaluation of energy thresholding and acquisition mode for metal artifact reduction in Photon-counting detector CT (PCD-CT) compared to conventional energy-integrating detector CT (EID-CT) was performed. Images of a hip prosthesis phantom placed in a water bath were acquired on a scanner with PCD-CT and EID-CT (tube potentials: 100, 120 and 140 kV_p) and energy thresholds (above 55–75 keV) in Macro and Chess mode. Only high-energy threshold images (HTI) were used. Metal artifacts were quantified by a semi-automated segmentation algorithm, calculating artifact volumes, means and standard deviations of CT numbers. Images of a human cadaver with hip prosthesis were acquired on the PCD-CT in Macro mode as proof-of-concept. Images at 140 kV_p showed less metal artifacts than 120 kV_p or 100 kV_p. HTI (70, 75 keV) had fewer artifacts than low energy thresholds (55, 60, 65 keV). Fewer artifacts were observed in the Macro-HTI (8.9–13.3%) for cortical bone compared to Chess-HTI (9.4–19.1%) and EID-CT (10.7–19.0%) whereas in bone marrow Chess-HTI (19.9–45.1%) showed less artifacts compared to Macro-HTI (21.9–38.3%) and EID-CT (36.4–54.9%). Noise for PCD-CT (56–81 HU) was higher than EID-CT (33–36 HU) irrespective of tube potential. High-energy thresholding could be used for metal artifact reduction in PCD-CT, but further investigation of acquisition modes depending on target structure is required.

Virtual monochromatic spectral imaging versus linearly blended dual-energy and single-energy imaging during CT-guided biopsy needle positioning: Optimization of keV settings and impact on image quality

Objectives

To compare image quality and metal artifact reduction between virtual monochromatic spectral imaging (VMSI), linearly blended dual-energy (DE) and single-energy (SE) images, each with and without dedicated iterative metal artifact reduction (iMAR) for CT-guided biopsy.

Materials and methods

A biopsy trocar was positioned in the liver of six pigs. DE (Sn140/100kV_p) and SE (120kV_p/200mAs) acquisitions were performed with equivalent dose. From dual-energy datasets DE Q30-3 images and VMSI between 40–180 keV in steps of 20 keV were generated. From SE datasets I30-3 images were reconstructed. All images were reconstructed with and without iMAR. Objective image quality was analyzed applying density measurements at standardized positions (e.g. trocar tip and liver parenchyma adjacent to the trocar tip) and semi-automated threshold based segmentation. Subjective image quality was performed using semi-quantitative scores. Analyses were performed by two observers.

Results

At the trocar tip quantitative image analysis revealed significant difference in CT numbers between reconstructions with iMAR compared to reconstructions without iMAR for VMSI at lower keV levels (80 and 100 keV; p = 0.03) and DE (p = 0.03). For liver parenchyma CT numbers were significantly higher in VMSI at high keV compared to low keV (p≤0.01). VMSI at high keV also showed higher CT numbers compared to DE and SE images, though not the level of statistical significance. The best signal-to-noise ratio for VMSI was at 80 keV and comparable to DE and SE. Noise was lowest at 80 keV and lower than in DE and SE. Subjective image quality was best with VMSI at 80 keV regardless of the application of iMAR. iMAR significantly improved image quality at levels of 140 keV and 160 keV. Interreader-agreement was good for quantitative and qualitative analysis.

Conclusion

iMAR improved image quality in all settings. VMSI with iMAR provided metal artifact reduction and better image quality at 80 keV and thus could improve the accurate positioning in CT-guided needle biopsy. In comparison, DE imaging did not improve image quality compared to SE.