1-10. (canceled)
11. An inverse treatment planning method for intensity modulated particle therapy for the treatment of a target within a biological system using at least two fields, each field comprising a plurality of Bragg peaks, the at least two fields being planned to place a defined number of beam spots j from different directions with certain weights wj within the target, characterized in that the inverse treatment planning method optimizes the weights wj of the beam spots for the at least two fields simultaneously in order to produce a prescribed biological effect within the target by minimizing an objective function based on biological effects \u03b5, the biological effects \u03b5 being treated in a linear-quadratic model which describes the biological effects in the target by two parameters \u03b1 and \u03b2, where \u03b5=\u03b1D+\u03b2D2, D denoting a dose, and wherein the two parameters \u03b1 and \u03b2 for each voxel i of the target \u03b1i and \u03b2i are calculated as dose-averaged mean values of \u03b1i,j- and \u221a{square root over (\u03b2i,j)}-components, which relate to all beam spots j contributing to a total dose Di in the voxel i.
12. The inverse treatment planning method according to claim 11, wherein an objective function is minimized, which is based on the formula
F
\u025b
\ue8a0
(
w
\u2192
)
=
\u2211
i
\u2208
T
\ue89e
(
\u025b
i
\ue8a0
(
w
\u2192
)
–
\u025b
T
)
2
with \u03b5i denoting the biological effect in the voxel i of the target, {right arrow over (w)} the weights of the beam spots and \u03b5T the prescribed biological effect of a reference radiation.
13. The inverse treatment planning method according to claim 11, wherein an objective function is minimized, which is based on the formula
F
\u025b
\ue8a0
(
w
\u2192
)
=
\u2211
i
\u2208
T
\ue89e
(
\u03b1
i
\ue8a0
(
w
\u2192
)
\ue89e
D
i
\ue8a0
(
w
\u2192
)
+
\u03b2
i
\ue8a0
(
w
\u2192
)
\ue89e
D
i
2
\ue8a0
(
w
\u2192
)
–
\u025b
T
)
2
with Di denoting the total dose of the at least two fields in voxel i and \u03b1i and \u03b2i the \u03b1- and \u03b2-values at voxel i, wherein
D
i
=
\u2211
j
=
1
N
\ue89e
w
j
\ue89e
D
i
,
j
with Di,j denoting a dose contribution of beam spot j at voxel i for unit fluence, N beam spots contributing to the dose Di at voxel i, and wj denoting a weighting factor of beam spot j and wherein the prescribed biological effect \u03b5T is given by
\u03b5T=\u03b1TXDT+\u03b2TXDT2
with \u03b1TX and \u03b2TX being X-ray response parameters of the target and DT denoting a prescribed photon dose.
14. The inverse treatment planning method according to claim 11, wherein an objective function is minimized, which includes constraints relating to minimum and maximum biological effect levels for the target and maximum effect levels for organs at risk or normal tissue within the biological system.
15. The inverse treatment planning method according to claim 11, wherein the two parameters \u03b1 and \u03b2 for each voxel i of the target (\u03b1i and \u03b2i) are calculated as dose-averaged mean values of the form
\u03b1
i
=
1
D
i
\ue89e
\u2211
j
=
1
N
\ue89e
w
j
\ue89e
D
i
,
j
\ue89e
\u03b1
i
,
j
\ue89e
\ue89e
and
\u03b2
i
=
1
D
i
\ue89e
\u2211
j
=
1
N
\ue89e
w
j
\ue89e
D
i
,
j
\ue89e
\u03b2
i
,
j
for Di>0,
D
i
=
\u2211
j
=
1
N
\ue89e
w
j
\ue89e
D
i
,
j
with N beam spots contributing to the total dose Di in voxel i.
16. The inverse treatment planning method according to claim 11, wherein the \u03b1i,j- and \u221a{square root over (\u03b2i,j)}-components are derived from a radiobiological model or from measured data for \u03b1 and \u03b2 as a function of depth in a single Bragg peak.
17. The inverse treatment planning method according to claim 11, wherein a constant value \u03b2i,j=\u03b2i is set, which depends only on a tissue type at voxel i.
18. The inverse treatment planning method according to claim 11, wherein the \u03b1i,j-components are assumed to be laterally constant and are extracted from a single curve as a function of residual range, independent of an initial beam energy.
19. Use of an inverse treatment planning method for intensity modulated particle therapy for the treatment of a target within a biological system using at least two fields, each field comprising a plurality of Bragg peaks, the at least two fields being planned to place a defined number of beam spots j from different directions with certain weights wj within the target, characterized in that the inverse treatment planning method optimizes the weights wj of the beam spots for the at least two fields simultaneously in order to produce a prescribed biological effect within the target by minimizing an objective function based on biological effects \u03b5, the biological effects \u03b5 being treated in a linear-quadratic model which describes the biological effects in the target by two parameters \u03b1 and \u03b2, where \u03b5=\u03b1D+\u03b2D2, D denoting a dose, and wherein the two parameters \u03b1 and \u03b2 for each voxel i of the target \u03b1i and \u03b2i are calculated as dose-averaged mean values of \u03b1i,j- and \u221a{square root over (\u03b2i,j)}-components, which relate to all beam spots j contributing to a total dose Di in the voxel i for planning the treatment of a tumor with an ion beam containing ions with an atomic number\u22672.
20. A radiation treatment apparatus for delivering a radiation treatment to a target within a biological system comprising means for generating at least two fields, each field comprising a plurality of Bragg peaks, the at least two fields being provided to place a defined number of beam spots j from different directions with certain weights wj within the target and characterized by means for inverse treatment planning being provided for optimizing the weights wj of the beam spots for the at least two fields simultaneously before the radiation treatment in order to produce a prescribed biological effect within the target by minimizing an objective function based on biological effects \u03b5, the biological effects \u03b5 being treated in a linear-quadratic model, which describes the biological effects in the target by two parameters \u03b1 and \u03b2, where \u03b5=\u03b1D+\u03b2D2, D denoting a dose, and wherein the two parameters \u03b1 and \u03b2 for each voxel i of the target (\u03b1i, \u03b2i) are calculated as dose-averaged mean values of \u03b1i,j- and \u221a{square root over (\u03b2i,j)}-components, which relate to all beam spots j contributing to a total dose Di in the voxel i.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A computer-implemented method of recommending a list of hyperlinks, comprising:
maintaining a database of hyperlinks associated with activities in the past of a user, said activities including passive receipt of hyperlinks;
receiving a selection of text, the selection being by the user from a current workspace in which the user is composing content;
extracting from the user’s current workspace a context of said selection of text;
searching the database for hyperlinks based on at least the context of said selection of text, wherein maintaining the database includes:
(i) throughout different workspaces of the user, capturing different activity content links, each activity content link comprising a respective hyperlink associated with a respective activity of the user, and
(ii) for each activity content link, generating a metric based on said respective activity;
from results of said searching, displaying to the user a list of one or more recommended hyperlinks based on said context and the metric of the respective activity associated with the hyperlink; and
upon user command, substituting one of the displayed recommended hyperlinks for the selection of text in the content being composed by the user.
2. The method as claimed in claim 1 wherein the hyperlinks in the database being from activity content links include:
URL history links,
links in emails to the user,
links in emails from the user,
links in instant messages to the user,
links in instant messages from the user,
links in electronic communications to the user,
links in electronic communications from the user,
links in social network content, and
links in social bookmarking content.
3. The method as claimed in claim 1 wherein the respective activity associated with the hyperlink is the user viewing content, and wherein the metric includes amount or duration of time the user spent viewing the content.
4. The method as claimed in claim 1 wherein the selection of text is user highlighted text in any of: a word processing document, a web page, an email message, a text message, an electronic presentation, an electronic communication and other electronic content.
5. The method as claimed in claim 1 wherein extracting extracts a plurality of words before and after the selected text.
6. The method as claimed in claim 1 wherein the user’s current workspace is separate from the different workspaces of the user.
7. The method as claimed in claim 6 wherein the user’s current workspace is separate in time and space from the different workspaces.
8. The method as claimed in claim 1 wherein the selection of text is indicative of or representative of a topic of the user.
9. The method as claimed in claim 1 wherein said displaying displays multiple recommended links ordered by the metric or filterable by the metric.
10. The method as claimed in claim 1 further comprising:
upon user command, searching a social networking site for hyperlinks relevant to the context, and displaying said hyperlinks as recommended hyperlinks to the user.
11. Computer apparatus recommending hyperlinks, comprising:
a processor;
a memory coupled to the processor and holding a database of hyperlinks that are associated with activities in the past of a users, said activities including passive receipt of hyperlinks;
a user interface executed by the processor and receiving a selection of text, the selection of text being by the user from a current workspace in which the user is composing content;
a recommendation engine operatively coupled to the user interface, the recommendation engine executed by the processor and extracting from the user’s current workspace a context of said selection of text, and the recommendation engine searching the database for hyperlinks based on at least the context of said selection of text, wherein a context analysis engine executed by the processor maintains the database by:
(i) throughout different workspaces of the user, capturing different activity content links, each activity content link comprising a respective hyperlink associated with a respective activity of the user, and
(ii) for each activity content link, generating a metric based on said respective activity; and
from results of said searching, the recommendation engine displaying in the user interface to the user, a recommended hyperlink based on said context and the metric of the respective activity associated with the hyperlink, and the recommendation engine, upon user command, substituting the displayed recommended hyperlink for the selection of text in the content being composed by the user.
12. The Computer apparatus as claimed in claim 11 wherein the hyperlinks in the database include:
URL history links,
links in emails to the user,
links in emails from the user,
links in instant messages to the user,
links in instant messages from the user,
links in electronic communications to the user,
links in electronic communications from the user,
links in social network content, and
links in social bookmarking content.
13. The Computer apparatus as claimed in claim 11 wherein the respective activity associated with the hyperlink is the user viewing a content, and wherein the metric includes amount or duration of time the user spent viewing the content.
14. The Computer apparatus as claimed in claim 11 wherein the selection of text is user highlighted text in any of: a word processing document, a web page, an email message, a text message, an electronic presentation, an electronic communication and other electronic content.
15. The Computer apparatus as claimed in claim 11 wherein the recommendation engine extracting extracts a plurality of words before and after the selected text.
16. The Computer apparatus as claimed in claim 11 wherein the user’s current workspace is separate in time and space from the different workspaces.
17. The Computer apparatus as claimed in claim 11 wherein the selection of text is indicative of or representative of a topic of the user.
18. The Computer apparatus as claimed in claim 11 wherein the recommendation engine displays in the user interface multiple recommended links ordered by the metric or filterable by the metric.
19. The Computer apparatus as claimed in claim 11 wherein the context analysis engine and the recommendation engine further:
upon user command, search a social networking site for hyperlinks relevant to the context, and display said hyperlinks as recommended hyperlinks to the user.
20. A computer program product recommending hyperlinks, comprising:
a non-transition computer readable storage medium having computer readable program code embodied therewith,
the computer readable program code comprising program instruction to a computer to:
capture an activity content link, wherein the activity content link comprises a hyperlink associated with an activity of a user;
generate a metric based on said activity;
from (i) the hyperlink of the captured activity content link and (ii) the generated metric, form a database of hyperlinks;
subsequently receive a user-selection of text from a current workspace of the user in which the user is composing content, the user-selection of text being indicative of a topic;
extract from the user’s current workspace a context of the topic;
search the database for hyperlinks based on the topic and the context;
from results of the search, display to the user link recommendations formed of one or more hyperlinks from the database, said display being in a manner filterable on the metric; and
upon user command, substitute one of the hyperlinks corresponding to a displayed link recommendation for the user-selection of text in the content being composed by the user.