The quality assurance of particle therapy treatment is a fundamental issue that can be addressed by developing reliable monitoring techniques and indicators of the treatment plan correctness. Among the available imaging techniques, positron emission tomography (PET) has long been investigated and then clinically applied to proton and carbon beams. In 2013, the Innovative Solutions for Dosimetry in Hadrontherapy (INSIDE) collaboration proposed an innovative bimodal imaging concept that combines an in-beam PET scanner with a tracking system for charged particle imaging. This paper presents the general architecture of the INSIDE project but focuses on the in-beam PET scanner that has been designed to reconstruct the particles range with millimetric resolution within a fraction of the dose delivered in a treatment of head and neck tumors. The in-beam PET scanner has been recently installed at the Italian National Center of Oncologic Hadrontherapy (CNAO) in Pavia, Italy, and the commissioning phase has just started. The results of the first beam test with clinical proton beams on phantoms clearly show the capability of the in-beam PET to operate during the irradiation delivery and to reconstruct on-line the beam-induced activity map. The accuracy in the activity distal fall-off determination is millimetric for therapeutic doses.
The Channeler Ant Model (CAM) is an algorithm based on virtual ant colonies, conceived for the segmentation
of complex structures with different shapes and intensity in a 3D environment. It exploits the natural capabilities
of virtual ant colonies to modify the environment and communicate with each other by pheromone deposition.
When applied to lung CTs, the CAM can be turned into a Computer Aided Detection (CAD) method for the
identification of pulmonary nodules and the support to radiologists in the identification of early-stage pathological
objects. The CAM has been validated with the segmentation of 3D artificial objects and it has already been
successfully applied to the lung nodules detection in Computed Tomography images within the ANODE09
challenge. The model improvements for the segmentation of nodules attached to the pleura and to the vessel
tree are discussed, as well as a method to enhance the detection of low-intensity nodules. The results on five
datasets annotated with different criteria show that the analytical modules (i.e. up to the filtering stage) provide
a sensitivity in the 80 - 90% range with a number of FP/scan of the order of 20. The classification module,
although not yet optimised, keeps the sensitivity in the 70 - 85% range at about 10 FP/scan, in spite of the
fact that the annotation criteria for the training and the validation samples are different.
Computer-Aided Detection (CAD) systems are becoming widespread supporting tools to radiologists' diagnosis,
especially in screening contexts. However, a large amount of false positive (FP) alarms would inevitably lead
both to an undesired possible increase in time for diagnosis, and to a reduction in radiologists' confidence in
CAD as a useful tool. Most CAD systems implement as final step of the analysis a classifier which assigns a
score to each entry of a list of findings; by thresholding this score it is possible to define the system performance
on an annotated validation dataset in terms of a FROC curve (sensitivity vs. FP per scan). To use a CAD as
a supportive tool for most clinical activities, an operative point has to be chosen on the system FROC curve,
according to the obvious criterion of keeping the sensitivity as high as possible, while maintaining the number
of FP alarms still acceptable. The strategy proposed in this study is to choose an operative point with high
sensitivity on the CAD FROC curve, then to implement in cascade a further classification step, constituted by
a smarter classifier. The key issue of this approach is that the smarter classifier is actually a meta-classifier of
more then one decision system, each specialized in rejecting a particular type of FP findings generated by the
The application of this approach to a dataset of 16 lung CT scans previously processed by the <sup>VBNA</sup>CAD
system is presented. The lung CT <sup>VBNA</sup>CAD performance of 87.1% sensitivity to juxtapleural nodules with 18.5
FP per scan is improved up to 10.1 FP per scan while maintaining the same value of sensitivity. This work has
been carried out in the framework of the MAGIC-V collaboration.
The computer-aided detection (CAD) system we applied on the ANODE09 dataset is devoted to identify pulmonary
nodules in low-dose and thin-slice computed tomography (CT) images: we developed two different
systems for internal (CAD<sub>I</sub>) and juxtapleural nodules (CAD<sub>JP</sub>) in the framework of the italian MAGIC-5 collaboration.
The basic modules of CADI subsystem are: a 3D dot-enhancement algorithm for nodule candidate
identification and an original approach, we referred as Voxel-Based Neural Approach (VBNA), to reduce the
amount of false-positive findings based on a neural classifier working at the voxel level. To detect juxtapleural
nodules we developed the CADJP subsystem based on a procedure enhancing regions where many pleura surface
normals intersect, followed by a VBNA classification. We present both the FROC curves we obtained on the 5
annotated ANODE09 example dataset, and on all the ANODE09 50 test cases.