A comparative study of three CT and MRI registration algorithms in nasopharyngeal carcinoma

Risk stratification of LA-NPC during chemoradiotherapy based on clinical classification and TVRR

PURPOSE

To investigate the correlation between tumor volume reduction rate (TVRR) and prognosis in patients with diverse clinical types of nasopharyngeal carcinoma (NPC) undergoing chemoradiotherapy, thereby aptly categorizing risks and directing the personalized treatment of NPC.

MATERIALS AND METHODS

A total of 605 NPC patients with varying clinical types were enrolled in this study and subsequently segregated into six subgroups based on their clinical types and TVRR. To accentuate the efficacy of grouping, Groups 1-6 underwent clustered analysis of hazard atio (HR) values pertaining to progression-free survival (PFS), forming three risk clusters denoted as low, intermediate, and high. The log-rank test was employed to discern differences, and R 4.1.1 was utilized for cluster analysis.

RESULTS

According to survival rates, we classified the first (G2 and G4), second (G1 and G6), and third (G3 and G5) risk clusters as low-, intermediate-, and high-risk, respectively. When comparing risk stratification with the 8th edition of the TNM staging system, our classification exhibited superior predictive prognostic performance. Subgroup analysis of treatments for each risk cluster revealed that the PFS in the neoadjuvant chemotherapy (NACT) + concurrent chemoradiotherapy (CCRT) group surpassed that of the CCRT group significantly (p < 0.05).

CONCLUSION

The reliance on clinical types and TVRR facilitates risk stratification of NPC during chemoradiotherapy, providing a foundation for physicians to tailor therapeutic strategies. Moreover, the risk cluster delineated for NPC patients during the mid-term of chemoradiotherapy stands as an independent prognostic factor for progression-free survival (PFS), overall survival (OS), distantmetastasis-free survival (DMFS), and local recurrence-free (LRRFS) posttreatment. Additionally, individuals in the high-risk cluster are recommended to undergo adjuvant chemotherapy after CCRT.

Factors influencing the accuracy of multimodal image fusion for oral and maxillofacial tumors: a retrospective study

BACKGROUND

Ensuring high accuracy in multimodal image fusion for oral and maxillofacial tumors is crucial before further application. The aim of this study was to explore the factors influencing the accuracy of multimodal image fusion for oral and maxillofacial tumors.

METHODS

Pairs of single-modality images were obtained from oral and maxillofacial tumor patients, and were fused using a proprietary navigation system by using three algorithms (automatic fusion, manual fusion, and registration point-based fusion). Fusion accuracy was evaluated including two aspects-overall fusion accuracy and tumor volume fusion accuracy-and were indicated by mean deviation and fusion index, respectively. Image modality, fusion algorithm, and other characteristics of multimodal images that may have potential influence on fusion accuracy were recorded. Univariate and multivariate analysis were used to identify relevant affecting factors.

RESULTS

Ninety-three multimodal images were generated by fusing 31 pairs of single-modality images. The interaction effect of image modality and fusion algorithm (P = 0.02, P = 0.003) and thinner slice thickness (P = 0.006) were shown to significantly influence the overall fusion accuracy. The tumor volume (P < 0.001), tumor location (P = 0.007), and image modality (P = 0.01) were significant influencing factors for tumor volume fusion accuracy.

CONCLUSIONS

To ensure high overall fusion accuracy, manual fusion was not preferred in CT/MRI image fusion, and neither was automatic fusion in image fusion containing PET modality. Using image sets with thinner slice thickness could increase overall fusion accuracy. CT/MRI fusion yielded higher tumor volume fusion accuracy than fusion containing PET modality. The tumor volume fusion accuracy should be taken into consideration during image fusion when the tumor volume is small and the tumor is located in the mandible.

Characterization of positioning uncertainties in PET-CT-MR trimodality solutions for radiotherapy

The purpose of this study was to evaluate the positioning uncertainties of two PET/CT‐MR imaging setups, C1 and C2. Because the PET/CT data were acquired on the same hybrid device with automatic image registration, experiments were conducted using CT‐MRI data. In C1, a transfer table was used, which allowed the patient to move from one imager to another while maintaining the same position. In C2, the patient stood up and was positioned in the same radiotherapy treatment position on each imager. The two setups provided a set of PET/CT and MR images. The accuracy of the registration software was evaluated on the CT‐MRI data of one patient using known translations and rotations of MRI data. The uncertainties on the two setups were estimated using a phantom and a cohort of 30 patients. The accuracy of the positioning uncertainties was evaluated using descriptive statistics and a t‐test to determine whether the mean shift significantly deviated from zero (p < 0.05) for each setup. The maximum registration errors were less than 0.97 mm and 0.6° for CT‐MRI registration. On the phantom, the mean total uncertainties were less than 2.74 mm and 1.68° for C1 and 1.53 mm and 0.33° for C2. For C1, the t‐test showed that the displacements along the z‐axis did not significantly deviate from zero (p = 0.093). For C2, significant deviations from zero were present for anterior‐posterior and superior‐inferior displacements. The mean total uncertainties were less than 4 mm and 0.42° for C1 and less than 1.39 mm and 0.27° for C2 in the patients. Furthermore, the t‐test showed significant deviations from zero for C1 on the anterior‐posterior and roll sides. For C2, there was a significant deviation from zero for the left‐right displacements.This study shows that transfer tables require careful evaluation before use in radiotherapy.

Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks

Background

Stereotactic radiosurgery (SRS), a validated treatment for brain tumors, requires accurate tumor contouring. This manual segmentation process is time-consuming and prone to substantial inter-practitioner variability. Artificial intelligence (AI) with deep neural networks have increasingly been proposed for use in lesion detection and segmentation but have seldom been validated in a clinical setting.

Methods

We conducted a randomized, cross-modal, multi-reader, multispecialty, multi-case study to evaluate the impact of AI assistance on brain tumor SRS. A state-of-the-art auto-contouring algorithm built on multi-modality imaging and ensemble neural networks was integrated into the clinical workflow. Nine medical professionals contoured the same case series in two reader modes (assisted or unassisted) with a memory washout period of 6 weeks between each section. The case series consisted of 10 algorithm-unseen cases, including five cases of brain metastases, three of meningiomas, and two of acoustic neuromas. Among the nine readers, three experienced experts determined the ground truths of tumor contours.

Results

With the AI assistance, the inter-reader agreement significantly increased (Dice similarity coefficient [DSC] from 0.86 to 0.90, P < 0.001). Algorithm-assisted physicians demonstrated a higher sensitivity for lesion detection than unassisted physicians (91.3% vs 82.6%, P = .030). AI assistance improved contouring accuracy, with an average increase in DSC of 0.028, especially for physicians with less SRS experience (average DSC from 0.847 to 0.865, P = .002). In addition, AI assistance improved efficiency with a median of 30.8% time-saving. Less-experienced clinicians gained prominent improvement on contouring accuracy but less benefit in reduction of working hours. By contrast, SRS specialists had a relatively minor advantage in DSC, but greater time-saving with the aid of AI.

Conclusions

Deep learning neural networks can be optimally utilized to improve accuracy and efficiency for the clinical workflow in brain tumor SRS.

Trimodality PET/CT/MRI and Radiotherapy: A Mini-Review

Computed tomography (CT) has revolutionized external radiotherapy by making it possible to visualize and segment the tumors and the organs at risk in a three-dimensional way. However, if CT is a now a standard, it presents some limitations, notably concerning tumor characterization and delineation. Its association with functional and anatomical images, that are positron emission tomography (PET) and magnetic resonance imaging (MRI), surpasses its limits. This association can be in the form of a trimodality PET/CT/MRI. The objective of this mini-review is to describe the process of performing this PET/CT/MRI trimodality for radiotherapy and its potential clinical applications. Trimodality can be performed in two ways, either a PET/MRI fused to a planning CT (possibly with a pseudo-CT generated from the MRI for the planning), or a PET/CT fused to an MRI and then registered to a planning CT (possibly the CT of PET/CT if calibrated for radiotherapy). These examinations should be performed in the treatment position, and in the second case, a patient transfer system can be used between the PET/CT and MRI to limit movement. If trimodality requires adapted equipment, notably compatible MRI equipment with high-performance dedicated coils, it allows the advantages of the three techniques to be combined with a synergistic effect while limiting their disadvantages when carried out separately. Trimodality is already possible in clinical routine and can have a high clinical impact and good inter-observer agreement, notably for head and neck cancers, brain tumor, prostate cancer, cervical cancer.

Evaluation of CT-MR image registration methodologies for 3D preoperative planning of forearm surgeries

Computerized surgical planning for forearm procedures that considers both soft and bony tissue, requires alignment of preoperatively acquired computed tomography (CT) and magnetic resonance (MR) images by image registration. Normalized mutual information (NMI) registration techniques have been researched to improve efficiency and to eliminate the user dependency associated with manual alignment. While successfully applied in various medical fields, the application of NMI registration to images of the forearm, for which the relative pose of the radius and ulna likely differs between CT and MR acquisitions, is yet to be described. To enable the alignment of CT and MR forearm data, we propose an NMI-based registration pipeline, which allows manual steering of the registration algorithm to the desired image subregion and is, thus, applicable to the forearm. Successive automated registration is proposed to enable planning incorporating multiple target anatomical structures such as the radius and ulna. With respect to gold-standard manual registration, the proposed registration methodology achieved mean accuracies of 0.08 ± 0.09 mm (0.01-0.41 mm range) in comparison with 0.28 ± 0.23 mm (0.03-0.99 mm range) associated with a landmark-based registration when tested on 40 patient data sets. Application of the proposed registration pipeline required less than 10 minutes on average compared with 20 minutes required by the landmark-based registration. The clinical feasibility and relevance of the method were tested on two different clinical applications, a forearm tumor resection and radioulnar joint instability analysis, obtaining accurate and robust CT-MR image alignment for both cases.

Diagnostic efficacy of CBCT, MRI, and CBCT-MRI fused images in distinguishing articular disc calcification from loose body of temporomandibular joint

OBJECTIVES

To evaluate the diagnostic efficacy of CBCT-MRI fused images for articular disc calcification of temporomandibular joint (TMJ).

MATERIALS AND METHODS

Twenty patients (24 TMJs) whose image examinations showed dense bodies in the TMJ space were included in the study. The locations of dense bodies evaluated by the three experts were used as a reference standard. Three oral and maxillofacial radiology residents evaluated whether the dense bodies were disc calcification or not, with a five-point scale for four sets of images (CBCT alone, MRI alone, both CBCT and MRI observed at a time, and CBCT-MRI fused images) randomly and independently. Each set of images was observed at least 1 week apart. A second evaluation was performed after 4 weeks. Intraclass correlation coefficients were calculated to assess the intra- and inter-observer agreement. The areas under the ROC curves (AUCs) were compared between the four image sets using Z test.

RESULTS

Ten cases were determined as articular disc calcifications, and fourteen cases were recognized as loose bodies in the TMJ spaces. The average AUC index for the CBCT-MRI fused images was 0.95 and significantly higher than the other sets (p < 0.01). The intra- and inter-observer agreement in the CBCT-MRI fused images (0.90-0.91, 0.93) was excellent and higher than those in the other images.

CONCLUSIONS

CBCT-MRI fused images can significantly improve the observers' reliability and accuracy in determining articular disc calcification of the TMJ.

CLINICAL RELEVANCE

The multimodality image fusion is feasible in detecting articular disc calcification of the TMJ which are hard to define by CBCT or MRI alone. It can be utilized especially for inexperienced residents to shorten the learning curve and improve diagnostic accuracy.

Accuracy of multimodal image fusion for oral and maxillofacial tumors: A revised evaluation method and its application

OBJECTIVE

To develop a revised evaluation method for accuracy of multimodal image fusion for oral and maxillofacial tumors and explore its application for comparing the accuracy of three commonly used fusion algorithms, automatic fusion, manual fusion, and registration point-based fusion.

MATERIALS AND METHODS

Image sets of patients with oral and maxillofacial tumor were fused using the iPlan 3.0 navigation system. Fusion accuracy included two aspects: (1) overall fusion accuracy: represented by the mean value of the coordinate differences along the x-, y-, and z- axes (Δx, Δy, and Δz), mean deviation (MD), and root mean square (RMS) of six pairs of landmarks on the two image sets; (2) tumor volume fusion accuracy: represented by Fusion Index (FI), which was calculated based on the volume of tumor delineated on the two image sets.

RESULTS

Eighteen pairs of image sets of 17 patients were enrolled in this study. The Δx and Δy values for the three algorithms were less than 1.5 mm. The Δz values for automatic fusion, manual fusion and registration point-based fusion was 1.049 mm, 1.864 mm and 1.254 mm. The MD for automatic fusion, manual fusion and registration point-based fusion was 1.978 mm, 2.788 mm and 1.926 mm. Significant differences existed in Δz for manual fusion and that for automatic fusion (P = 0.058), in MD for manual fusion and that for automatic fusion (P = 0.087), and in MD for manual fusion and that for registration point-based fusion (P = 0.069). The FI for automatic fusion, manual fusion, and registration point-based fusion was 0.594, 0.520, and 0.549; the inter-algorithm differences were not significant (P = 0.290).

CONCLUSION

The automatic fusion and the registration point-based fusion were more accurate than manual fusion, and therefore were recommended to be used in multimodal image fusion for oral and maxillofacial tumors.

"Après Mois, Le Déluge": Preparing for the Coming Data Flood in the MRI-Guided Radiotherapy Era

Magnetic resonance imaging provides a sea of quantitative and semi-quantitative data. While radiation oncologists already navigate a pool of clinical (semantic) and imaging data, the tide will swell with the advent of hybrid MRI/linear accelerator devices and increasing interest in MRI-guided radiotherapy (MRIgRT), including adaptive MRIgRT. The variety of MR sequences (of greater complexity than the single parameter Hounsfield unit of CT scanning routinely used in radiotherapy), the workflow of adaptive fractionation, and the sheer quantity of daily images acquired are challenges for scaling this technology. Biomedical informatics, which is the science of information in biomedicine, can provide helpful insights for this looming transition. Funneling MRIgRT data into clinically meaningful information streams requires committing to the flow of inter-institutional data accessibility and interoperability initiatives, standardizing MRIgRT dosimetry methods, streamlining MR linear accelerator workflow, and standardizing MRI acquisition and post-processing. This review will attempt to conceptually ford these topics using clinical informatics approaches as a theoretical bridge.

Prospective quantitative quality assurance and deformation estimation of MRI-CT image registration in simulation of head and neck radiotherapy patients

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MRI-CT deformable image registration was not superior to rigid registration.

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Dice similarity coefficients were 0.65, 0.62, and 0.63 for deformable registrations.

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Dice similarity coefficient was 0.63 for rigid registration.

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Registration quality was superior in muscle and gland compared to bone and vessel.

Background

MRI-guided radiotherapy planning (MRIgRT) may be superior to CT-guided planning in some instances owing to its improved soft tissue contrast. However, MR images do not communicate tissue electron density information necessary for dose calculation and therefore must either be co-registered to CT or algorithmically converted to synthetic CT. No robust quality assessment of commercially available MR-CT registration algorithms is yet available; thus we sought to quantify MR-CT registration formally.

Methods

Head and neck non-contrast CT and T2 MRI scans acquired with standard treatment immobilization techniques were prospectively acquired from 15 patients. Per scan, 35 anatomic regions of interest (ROIs) were manually segmented. MRIs were registered to CT rigidly (RIR) and by three commercially available deformable registration algorithms (DIR). Dice similarity coefficient (DSC), Hausdorff distance mean (HD mean) and Hausdorff distance max (HD max) metrics were calculated to assess concordance between MRI and CT segmentations. Each DIR algorithm was compared to DIR using the nonparametric Steel test with control for individual ROIs (n = 105 tests) and for all ROIs in aggregate (n = 3 tests). The influence of tissue type on registration fidelity was assessed using nonparametric Wilcoxon pairwise tests between ROIs grouped by tissue type (n = 12 tests). Bonferroni corrections were applied for multiple comparisons.

Results

No DIR algorithm improved the segmentation quality over RIR for any ROI nor all ROIs in aggregate (all p values >0.05). Muscle and gland ROIs were significantly more concordant than vessel and bone, but DIR remained non-different from RIR.

Conclusions

For MR-CT co-registration, our results question the utility and applicability of commercially available DIR over RIR alone. The poor overall performance also questions the feasibility of translating tissue electron density information to MRI by CT registration, rather than addressing this need with synthetic CT generation or bulk-density assignment.

Optimisation of three-dimensional lower jaw resection margin planning using a novel Black Bone magnetic resonance imaging protocol

BACKGROUND

MRI is the optimal method for sensitive detection of tumour tissue and pre-operative staging in oral cancer. When jawbone resections are necessary, the current standard of care for oral tumour surgery in our hospital is 3D virtual planning from CT data. 3D printed jawbone cutting guides are designed from the CT data. The tumour margins are difficult to visualise on CT, whereas they are clearly visible on MRI scans. The aim of this study was to change the conventional CT-based workflow by developing a method for 3D MRI-based lower jaw models. The MRI-based visualisation of the tumour aids in planning bone resection margins.

MATERIALS AND FINDINGS

A workflow for MRI-based 3D surgical planning with bone cutting guides was developed using a four-step approach. Key MRI parameters were defined (phase 1), followed by an application of selected Black Bone MRI sequences on healthy volunteers (phase 2). Three Black Bone MRI sequences were chosen for phase 3: standard, fat saturated, and an out of phase sequence. These protocols were validated by applying them on patients (n = 10) and comparison to corresponding CT data. The mean deviation values between the MRI- and the CT-based models were 0.63, 0.59 and 0.80 mm for the three evaluated Black Bone MRI sequences. Phase 4 entailed examination of the clinical value during surgery, using excellently fitting printed bone cutting guides designed from MRI-based lower jaw models, in two patients with oral cancer. The mean deviation of the resection planes was 2.3 mm, 3.8 mm for the fibula segments, and the mean axis deviation was the fibula segments of 1.9°.

CONCLUSIONS

This study offers a method for 3D virtual resection planning and surgery using cutting guides based solely on MRI imaging. Therefore, no additional CT data are required for 3D virtual planning in oral cancer surgery.

Nonsubsampled rotated complex wavelet transform (NSRCxWT) for medical image fusion related to clinical aspects in neurocysticercosis

Neurocysticercosis (NCC) is a parasite infection caused by the tapeworm Taenia solium in its larvae stage which affects the central nervous system of the human body (a definite host). It results in the formation of multiple lesions in the brain at different locations during its various stages. During diagnosis of such symptomatic patients, these lesions can be better visualized using a feature based fusion of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). This paper presents a novel approach to Multimodality Medical Image Fusion (MMIF) used for the analysis of the lesions for the diagnostic purpose and post treatment review of NCC. The MMIF presented here is a technique of combining CT and MRI data of the same patient into a new slice using a Nonsubsampled Rotated Complex Wavelet Transform (NSRCxWT). The forward NSRCxWT is applied on both the source modalities separately to extract the complementary and the edge related features. These features are then combined to form a composite spectral plane using average and maximum value selection fusion rules. The inverse transformation on this composite plane results into a new, visually better, and enriched fused image. The proposed technique is tested on the pilot study data sets of patients infected with NCC. The quality of these fused images is measured using objective and subjective evaluation metrics. Objective evaluation is performed by estimating the fusion parameters like entropy, fusion factor, image quality index, edge quality measure, mean structural similarity index measure, etc. The fused images are also evaluated for their visual quality using subjective analysis with the help of three expert radiologists. The experimental results on 43 image data sets of 17 patients are promising and superior when compared with the state of the art wavelet based fusion algorithms. The proposed algorithm can be a part of computer-aided detection and diagnosis (CADD) system which assists the radiologists in clinical practices.

MRI and CBCT image registration of temporomandibular joint: a systematic review

Purpose

The purpose of the present review is to systematically and critically analyze the available literature regarding the importance, applicability, and practicality of (MRI), computerized tomography (CT) or cone-beam CT (CBCT) image registration for TMJ anatomy and assessment.

Data sources

A systematic search of 4 databases; MEDLINE, EMBASE, EBM reviews and Scopus, was conducted by 2 reviewers. An additional manual search of the bibliography was performed.

Inclusion criteria

All articles discussing the magnetic resonance imaging MRI and CT or CBCT image registration for temporomandibular joint (TMJ) visualization or assessment were included.

Results and included articles’ characteristics

Only 3 articles satisfied the inclusion criteria. All included articles were published within the last 7 years. Two articles described MRI to CT multimodality image registration as a complementary tool to visualize TMJ. Both articles used images of one patient only to introduce the complementary concept of MRI-CT fused image. One article assessed the reliability of using MRI-CBCT registration to evaluate the TMJ disc position and osseous pathology for 10 temporomandibular disorder (TMD) patients.

Conclusion

There are very limited studies of MRI-CT/CBCT registration to reach a conclusion regarding its accuracy or clinical use in the temporomandibular joints.

Electronic supplementary material

The online version of this article (doi:10.1186/s40463-016-0144-4) contains supplementary material, which is available to authorized users.

Commissioning of a new wide-bore MRI scanner for radiotherapy planning of head and neck cancer

OBJECTIVE

A combination of CT and MRI is recommended for radiotherapy planning of head and neck cancers, and optimal spatial co-registration is achieved by imaging in the treatment position using the necessary immobilisation devices on both occasions, something which requires wide-bore scanners. Quality assurance experiments were carried out to commission a newly installed 1.5-T wide-bore MRI scanner and a dedicated, flexible six-channel phased array head and neck coil.

METHODS

Signal-to-noise ratio (SNR) and spatial signal uniformity were quantified using a homogeneous aqueous phantom, and geometric distortion was quantified using a phantom with water-filled fiducials in a grid pattern. Volunteer scans were also used to determine the in vivo image quality. Clinically relevant T1 weighted and T2 weighted fat-suppressed sequences were assessed in multiple scan planes (both sequences fast spin echo based). The performance of two online signal uniformity correction schemes, one utilising low-resolution reference scans and the other not utilising low-resolution reference scans, was compared.

RESULTS

Geometric distortions, for a ±35-kHz bandwidth, were <1 mm for locations within 10 cm of the isocentre rising to 1.8 mm at 18 cm away. SNR was above 50, and uniformity in the axial plane was 71% and 95% before and after uniformity correction, respectively.

CONCLUSION

The combined performance of the wide-bore scanner and the dedicated coil was adjudged adequate, although superior-inferior spatial coverage was slightly limited in the lower neck.

ADVANCES IN KNOWLEDGE

These results will be of interest to the increasing number of oncology centres that are seeking to incorporate MRI into planning practice using dedicated equipment.

[Stereotactic and diagnostic imaging in radiosurgery]

Constant progress in medical imaging and particularly magnetic resonance imaging has profound impact in planning for stereotactic radiosurgery and radiotherapy. The purpose of this paper is to discuss the integration of medical imaging modalities in the planning process. Principles of generic algorithms to calculate stereotactic coordinates are treated for tomographic imaging and digital substraction angiography, and their accuracies are analyzed in a review of the literature. The algorithmic foundations and performance of automatic intermodality co-registration methods are developed. Finally, the MRI sequences useful in planning and follow-up are discussed and the role of MR angiographic sequences compared to conventional X-ray angiography in the particular case of the arteriovenous malformation planning.