New prospective 4D-CT for mitigating the effects of irregular respiratory motion

Data-driven gated positron emission tomography/computed tomography for radiotherapy

Purpose

Software-based data-driven gated (DDG) positron emission tomography/computed tomography (PET/CT) has replaced hardware-based 4D PET/CT. The purpose of this article was to review DDG PET/CT, which could improve the accuracy of treatment response assessment, tumor motion evaluation, and target tumor contouring with whole-body (WB) PET/CT for radiotherapy (RT).

Material and methods

This review covered the topics of 4D PET/CT with hardware gating, advancements in PET instrumentation, DDG PET, DDG CT, and DDG PET/CT based on a systematic literature review. It included a discussion of the large axial field-of-view (AFOV) PET detector and a review of the clinical results of DDG PET and DDG PET/CT.

Results

DDG PET matched or outperformed 4D PET with hardware gating. DDG CT was more compatible with DDG PET than 4D CT, which required hardware gating. DDG CT could replace 4D CT for RT. DDG PET and DDG CT for DDG PET/CT can be incorporated in a WB PET/CT of less than 15 min scan time on a PET/CT scanner of at least 25 cm AFOV PET detector.

Conclusions

DDG PET/CT could correct the misregistration and tumor motion artifacts in a WB PET/CT and provide the quantitative PET and tumor motion information of a registered PET/CT for RT.

Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images

BACKGROUND

4D CT imaging is an essential component of radiotherapy of thoracic and abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality and image information reliability.

PURPOSE

In this work, deep learning (DL)-based conditional inpainting is proposed to restore anatomically correct image information of artifact-affected areas.

METHODS

The restoration approach consists of a two-stage process: DL-based detection of common interpolation (INT) and double structure (DS) artifacts, followed by conditional inpainting applied to the artifact areas. In this context, conditional refers to a guidance of the inpainting process by patient-specific image data to ensure anatomically reliable results. The study is based on 65 in-house 4D CT images of lung cancer patients (48 with only slight artifacts, 17 with pronounced artifacts) and two publicly available 4D CT data sets that serve as independent external test sets.

RESULTS

Automated artifact detection revealed a ROC-AUC of 0.99 for INT and of 0.97 for DS artifacts (in-house data). The proposed inpainting method decreased the average root mean squared error (RMSE) by 52 % (INT) and 59 % (DS) for the in-house data. For the external test data sets, the RMSE improvement is similar (50 % and 59 %, respectively). Applied to 4D CT data with pronounced artifacts (not part of the training set), 72 % of the detectable artifacts were removed.

CONCLUSIONS

The results highlight the potential of DL-based inpainting for restoration of artifact-affected 4D CT data. Compared to recent 4D CT inpainting and restoration approaches, the proposed methodology illustrates the advantages of exploiting patient-specific prior image information.

Quality assurance of a breathing controlled four-dimensional computed tomography algorithm

•

Initial quality assurance of a novel breathing-controlled four-dimensional computed tomography algorithm.

•

Assessment of geometry, motion representation and image quality for regular and irregular breathing.

•

No clinically relevant differences in results for regular and irregular breathing.

•

Only minor differences in tumor geometry representation and image quality compared to static three-dimensional computed tomography.

•

Table flexion has no clinically relevant impact on geometry representation.

Background & purpose

Material & methods

Results

Conclusions

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal-guided 4D CT

PURPOSE

Four-dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal-guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632).

METHODS

Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four-dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten-phase reconstruction) to allow for a direct comparison of the image data.

RESULTS

Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning.

CONCLUSIONS

The present study confirms feasibility of fully automated respiratory signal-guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal-guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners.

Unforeseen Computed Tomography Resimulation for Initial Radiation Planning: Associated Factors and Clinical Impact

Purpose

Repeat computed tomography (CT) simulation is problematic because of additional expense of clinic resources, patient inconvenience, additional radiation exposure, and treatment delay. We investigated the factors and clinical impact of unplanned CT resimulations in our network.

Methods and Materials

We used the billing records of 18,170 patients treated at 5 clinics. A total of 213 patients were resimulated before their first treatment. The disease site, location, use of 4-dimensional CT (4DCT), contrast, image fusion, and cause for resimulation were recorded. Odds ratios determined statistical significance.

Results

Our total rate of resimulation was 1.2%. Anal/colorectal (P < .001) and head and neck (P < .001) disease sites had higher rates of resimulation. Brain (P = .001) and lung/thorax (P = .008) had lower rates of resimulation. The most common causes for resimulation were setup change (11.7%), change in patient anatomy (9.8%), and rectal filling (8.5%). The resimulation rate for 4DCTs was 3.03% compared with 1.0% for non-4DCTs (P < .001). Median time between simulations was 7 days.

Conclusions

The most common sites for resimulation were anal/colorectal and head and neck, largely because of change in setup or changes in anatomy. The 4DCT technique correlated with higher resimulation rates. The resimulation rate was 1.2%, and median treatment delay was 7 days. Further studies are warranted to limit the rate of resimulation.

Effects of respiratory motion on volumetric and positional difference of GTV in lung cancer based on 3DCT and 4DCT scanning

Differences in gross target volume (GTV) and central point positions among moving lung cancer models constructed by CT scanning at different frequencies were compared, in order to explore the effect of different respiratory frequencies on the GTV constructions in moving lung tumors. Eight models in different shapes and sizes were established to stimulate lung tumors. The three-dimensional computed tomography (3DCT) and four-dimensional computed tomography (4DCT) scanning were performed at 10, 15 and 20 times/min in different models. Differences in GTV volumes and central point positions at different motion frequencies were compared by means of GTV3Ds (GTV_3D-10, GTV_3D-15, GTV_3D-20) and IGTV4Ds (IGTV_4D-10, IGTV_4D-15, IGTV_4D-20). Volumes of GTV_3D-10, GTV_3D-15, GTV_3D-20 were 12.41±14.26, 10.38±11.18 and 12.50±15.23 cm³ respectively (P=0.687). Central point coordinates in the x-axis direction were −8.16±96.21, −8.57±96.08 and −8.56±95.73 respectively (P=0.968). Central point coordinates in the y-axis direction were 108.22±25.03, 110.41±22.47 and 109.04±24.24 (P=0.028). Central point coordinates in the z-axis direction were 65.19±13.68, 65.43±13.40 and 65.38±13.17 (P=0.902). The difference was significant in the y-axis direction (P=0.028). Volumes of IGTV_4D-10, IGTV_4D-15, IGTV_4D-20 were 17.78±19.42, 17.43±19.56 and 17.44±18.80 cm³ (P=0.417). Central point coordinates in the x-axis direction were −7.73±95.93, −7.86±95.56 and −7.92±95.14 (P=0.325). Central point coordinates in the y-axis direction were 109.41±24.54, 109.60±24.13 and 109.16±24.28 (P=0.525). Central point coordinates in the z-axis direction were 65.52±13.31, 65.59±13.39 and 65.51±13.34 (P=0.093). However, the central point position of GTV in the head and foot direction by 3DCT scanning was severely affected by the respiratory frequency.

A prospective gating method to acquire a diverse set of free-breathing CT images for model-based 4DCT

Breathing motion modeling requires observation of tissues at sufficiently distinct respiratory states for proper 4D characterization. This work proposes a method to improve sampling of the breathing cycle with limited imaging dose. We designed and tested a prospective free-breathing acquisition protocol with a simulation using datasets from five patients imaged with a model-based 4DCT technique. Each dataset contained 25 free-breathing fast helical CT scans with simultaneous breathing surrogate measurements. Tissue displacements were measured using deformable image registration. A correspondence model related tissue displacement to the surrogate. Model residual was computed by comparing predicted displacements to image registration results. To determine a stopping criteria for the prospective protocol, i.e. when the breathing cycle had been sufficiently sampled, subsets of N scans where 5  ⩽  N  ⩽  9 were used to fit reduced models for each patient. A previously published metric was employed to describe the phase coverage, or 'spread', of the respiratory trajectories of each subset. Minimum phase coverage necessary to achieve mean model residual within 0.5 mm of the full 25-scan model was determined and used as the stopping criteria. Using the patient breathing traces, a prospective acquisition protocol was simulated. In all patients, phase coverage greater than the threshold necessary for model accuracy within 0.5 mm of the 25 scan model was achieved in six or fewer scans. The prospectively selected respiratory trajectories ranked in the (97.5  ±  4.2)th percentile among subsets of the originally sampled scans on average. Simulation results suggest that the proposed prospective method provides an effective means to sample the breathing cycle with limited free-breathing scans. One application of the method is to reduce the imaging dose of a previously published model-based 4DCT protocol to 25% of its original value while achieving mean model residual within 0.5 mm.