Validation of quantitative magnetic resonance imaging techniques in head and neck healthy structures involved in the salivary and swallowing function: Accuracy and repeatability.

Background and Purpose: Radiation-induced damage to the organs at risk (OARs) in head-and-neck cancer (HNC) patient can result in long-term complications. Quantitative magnetic resonance imaging (qMRI) techniques such as diffusion-weighted imaging (DWI), DIXON for fat fraction (FF) estimation and T2 mapping could potentially provide a spatial assessment of such damage. The goal of this study is to validate these qMRI techniques in terms of accuracy in phantoms and repeatability in-vivo across a broad selection of healthy OARs in the HN region. Materials and Methods: Scanning was performed at a 3 T diagnostic MRI scanner, including the calculation of apparent diffusion coefficient (ADC) from DWI, FF and T2 maps. Phantoms were scanned to estimate the qMRI techniques bias using Bland-Altman statistics. Twenty-six healthy subjects were scanned twice in a test-retest study to determine repeatability. Repeatability coefficients (RC) were calculated for the parotid, submandibular, sublingual and tubarial salivary glands, oral cavity, pharyngeal constrictor muscle and brainstem. Additionally, a linear mixed-effect model analysis was used to evaluate the effect of subject-specific characteristics on the qMRI values. Results: Bias was 0.009x10-3 mm2/s for ADC, -0.7 % for FF and -7.9 ms for T2. RCs ranged 0.11-0.25x10-3 mm2/s for ADC, 1.2-6.3 % for FF and 2.5-6.3 ms for T2. A significant positive linear relationship between age and the FF and T2 for some of the OARs was found. Conclusion: These qMRI techniques are feasible, accurate and repeatable, which is promising for treatment response monitoring and/or differentiating between healthy and unhealthy tissues due to radiation-induced damage in HNC patients.

Intra-fractional per-beam adaptive workflow to mitigate the need for a rotating gantry during MRI-guided proton therapy.

The integration of real-time magnetic resonance imaging (MRI) guidance and proton therapy would potentially improve the proton dose steering capability by reducing daily uncertainties due to anatomical variations. The use of a fixed beamline coupled with an axial patient couch rotation would greatly simplify the proton delivery with MRI guidance. Nonetheless, it is mandatory to assure that the plan quality is not deteriorated by the anatomical deformations due to patient rotation. In this work, an in-house tool allowing for intra-fractional per-beam adaptation of intensity-modulated proton plans (BeamAdapt) was implemented through features available in RayStation. A set of three MRIs was acquired for two healthy volunteers (V1,V2): (1) no rotation/static, (2) rotation to the right and (3) left.V1was rotated by 15°, to simulate a clinical pediatric abdominal case andV2by 45°, to simulate an extreme patient rotation case. For each volunteer, a total of four intensity-modulated pencil beam scanning plans were optimized on the static MRI using virtual abdominal targets and two-three posterior-oblique beams. Beam angles were defined according to the angulations on the rotated MRIs. With BeamAdapt, each original plan was initially converted into separate plans with one beam per plan. In an iterative order, individual beam doses were non-rigidly deformed to the rotated anatomies and re-optimized accounting for the consequent deformations and the beam doses delivered so far. For evaluation, the final accumulated dose distribution was propagated back to the static MRI. Planned and adapted dose distributions were compared by computing relative differences between dose-volume histogram metrics. Absolute target dose differences were on average below 1% and organs-at-risk mean dose differences were below 3%. With BeamAdapt, not only intra-fractional per-beam proton plan adaptation coupled with axial patient rotation is possible but also the need for a rotating gantry during MRI guidance might be mitigated.

Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours.

OBJECTIVE: Dose prediction using deep learning networks prior to radiotherapy might lead tomore efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess their clinical usage for paediatric abdominal tumours. MATERIAL AND METHODS: Data from 80 patients with neuroblastoma or Wilms' tumour was included. Pencil beam scanning (PBS) (5 mm/ 3%) and volumetric-modulated arc therapy (VMAT) plans (5 mm) were robustly optimized on the internal target volume (ITV). Separate 3-dimensional patch-based U-net networks were trained to predict PBS and VMAT dose distributions. Doses, planning-computed tomography images and relevant optimization masks (ITV, vertebra and organs-at-risk) of 60 patients were used for training with a 5-fold cross validation. The networks' performance was evaluated by computing the relative error between planned and predicted dose-volume histogram (DVH) parameters for 20 inference patients. In addition, the organs-at-risk mean dose difference between modalities was calculated using planned and predicted dose distributions (ΔDmean = DVMAT-DPBS). Two radiation oncologists performed a blind PBS/VMAT modality selection based on either planned or predicted ΔDmean. RESULTS: Average DVH differences between planned and predicted dose distributions were ≤ |6%| for both modalities. The networks classified the organs-at-risk Dmean difference as a gain (ΔDmean > 0) with 98% precision. An identical modality selection based on planned compared to predicted ΔDmean was made for 18/20 patients. CONCLUSION: Deep learning networks for accurate prediction of proton and photon dose distributions for abdominal paediatric tumours were established. These networks allowing fast dose visualisation might aid in identifying the optimal radiotherapy technique when experience and/or resources are unavailable.

Potential benefit of MRI-guided IMRT for flank irradiation in pediatric patients with Wilms' tumor.

PURPOSE/OBJECTIVE: Flank irradiation for Wilms' tumor (WT) is currently performed at our institute using a cone-beam computed tomography-guided volumetric modulated arc (VMATCBCT) workflow. By adding real-time magnetic resonance imaging (MRI) guidance to the treatment, safety margins could be reduced. The study purpose was to quantify the potential reduction of the planning target volume (PTV) margin and its dosimetric impact when using an MRI-guided intensity modulated radiation therapy (IMRTMRI) workflow compared to the VMATCBCT workflow. MATERIAL/METHODS: 4D-CT, MRI and CBCT scans acquired during preparation and treatment of 15 patients, were used to estimate both geometric, motion and patient set-up systematic (∑) and random (σ) errors for VMATCBCT and IMRTMRI workflows. The mean PTV (PTVmean) expansion was calculated using the van Herk formula. Treatment plans were generated using five margin scenarios (PTVmean ± 0, 1 and 2 mm). Furthermore, the IMRTMRI plans were optimized with a 1.5T transverse magnetic field turned-on to realistically model an MRI-guided treatment. Plans were evaluated using dose-volume statistics (p<.01, Wilcoxon). RESULTS: Analysis of ∑ and σ errors resulted in a PTVmean of 5 mm for the VMATCBCT and 3 mm for the IMRTMRI workflows in each orthogonal direction. Target coverage was unaffected by the margin decrease with a mean V95%=100% for all margin scenarios. For the PTVmean, an average reduction of the mean dose to the organs at risk (OARs) was achieved with IMRTMRI compared to VMATCBCT: 3.4 ± 2.4% (p<.01) for the kidney, 3.4 ± 2.1% (p<.01) for the liver, 2.8 ± 3.0% (p<.01) for the spleen and 4.9 ± 3.8% (p<.01) for the pancreas, respectively. CONCLUSIONS: Imaging data in children with WT demonstrated that the PTV margin could be reduced isotropically down to 2 mm when using the IMRTMRI compared to the VMATCBCT workflow. The former results in a dose reduction to the OARs while maintaining target coverage.

Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning.

BACKGROUND AND PURPOSE: Computed tomography (CT) imaging is the current gold standard for radiotherapy treatment planning (RTP). The establishment of a magnetic resonance imaging (MRI) only RTP workflow requires the generation of a synthetic CT (sCT) for dose calculation. This study evaluates the feasibility of using a multi-atlas sCT synthesis approach (sCTa) for head and neck and prostate patients. MATERIAL AND METHODS: The multi-atlas method was based on pairs of non-rigidly aligned MR and CT images. The sCTa was obtained by registering the MRI atlases to the patient's MRI and by fusing the mapped atlases according to morphological similarity to the patient. For comparison, a bulk density assignment approach (sCTbda) was also evaluated. The sCTbda was obtained by assigning density values to MRI tissue classes (air, bone and soft-tissue). After evaluating the synthesis accuracy of the sCTs (mean absolute error), sCT-based delineations were geometrically compared to the CT-based delineations. Clinical plans were re-calculated on both sCTs and a dose-volume histogram and a gamma analysis was performed using the CT dose as ground truth. RESULTS: Results showed that both sCTs were suitable to perform clinical dose calculations with mean dose differences less than 1% for both the planning target volume and the organs at risk. However, only the sCTa provided an accurate and automatic delineation of bone. CONCLUSIONS: Combining MR delineations with our multi-atlas CT synthesis method could enable MRI-only treatment planning and thus improve the dosimetric and geometric accuracy of the treatment, and reduce the number of imaging procedures.

Hyperbaric Oxygen Therapy Improves Glucose Homeostasis in Type 2 Diabetes Patients: A Likely Involvement of the Carotid Bodies.

The carotid bodies (CBs) are peripheral chemoreceptors that respond to hypoxia increasing minute ventilation and activating the sympathetic nervous system. Besides its role in ventilation we recently described that CB regulate peripheral insulin sensitivity. Knowing that the CB is functionally blocked by hyperoxia and that hyperbaric oxygen therapy (HBOT) improves fasting blood glucose in diabetes patients, we have investigated the effect of HBOT on glucose tolerance in type 2 diabetes patients. Volunteers with indication for HBOT were recruited at the Subaquatic and Hyperbaric Medicine Center of Portuguese Navy and divided into two groups: type 2 diabetes patients and controls. Groups were submitted to 20 sessions of HBOT. OGTT were done before the first and after the last HBOT session. Sixteen diabetic patients and 16 control individual were included. Fasting glycemia was143.5 ± 12.62 mg/dl in diabetic patients and 92.06 ± 2.99 mg/dl in controls. In diabetic patients glycemia post-OGTT was 280.25 ± 22.29 mg/dl before the first HBOT session. After 20 sessions, fasting and 2 h post-OGTT glycemia decreased significantly. In control group HBOT did not modify fasting glycemia and post-OGTT glycemia. Our results showed that HBOT ameliorates glucose tolerance in diabetic patients and suggest that HBOT could be used as a therapeutic intervention for type 2 diabetes.